ci: update version string in docs

feat: implement generalized Freivalds' algorithm for arbitrary einsum (#990 )
--------- Co-authored-by: DoHoon Kim <59155248+DoHoonKim8@users.noreply.github.com> Co-authored-by: therealyingtong <yingtong.lai@gmail.com> Co-authored-by: DoHoonKim <dohoon1097819@gmail.com>
2026-01-13 16:27:59 -05:00 · 2025-10-08 11:34:48 +00:00 · 2025-10-08 07:34:32 -04:00 · 2025-08-15 23:58:04 -04:00 · 2025-08-14 18:21:06 -04:00 · 2025-08-10 23:03:45 +01:00
151 changed files with 49107 additions and 15621 deletions
--- a/.github/workflows/benchmarks.yml
+++ b/.github/workflows/benchmarks.yml
@@ -15,7 +15,7 @@ jobs:
      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
        with:
          persist-credentials: false
-      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
+      - uses: actions-rust-lang/setup-rust-toolchain@fb51252c7ba57d633bc668f941da052e410add48  #v1.0.6
        with:
          toolchain: nightly-2023-06-27
          override: true
@@ -32,7 +32,7 @@ jobs:
      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
        with:
          persist-credentials: false
-      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
+      - uses: actions-rust-lang/setup-rust-toolchain@fb51252c7ba57d633bc668f941da052e410add48  #v1.0.6
        with:
          toolchain: nightly-2023-06-27
          override: true
@@ -49,7 +49,7 @@ jobs:
      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
        with:
          persist-credentials: false
-      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
+      - uses: actions-rust-lang/setup-rust-toolchain@fb51252c7ba57d633bc668f941da052e410add48  #v1.0.6
        with:
          toolchain: nightly-2023-06-27
          override: true
@@ -66,7 +66,7 @@ jobs:
      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
        with:
          persist-credentials: false
-      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
+      - uses: actions-rust-lang/setup-rust-toolchain@fb51252c7ba57d633bc668f941da052e410add48  #v1.0.6
        with:
          toolchain: nightly-2023-06-27
          override: true
@@ -83,7 +83,7 @@ jobs:
      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
        with:
          persist-credentials: false
-      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
+      - uses: actions-rust-lang/setup-rust-toolchain@fb51252c7ba57d633bc668f941da052e410add48  #v1.0.6
        with:
          toolchain: nightly-2023-06-27
          override: true
@@ -100,7 +100,7 @@ jobs:
      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
        with:
          persist-credentials: false
-      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
+      - uses: actions-rust-lang/setup-rust-toolchain@fb51252c7ba57d633bc668f941da052e410add48  #v1.0.6
        with:
          toolchain: nightly-2023-06-27
          override: true
@@ -117,7 +117,7 @@ jobs:
      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
        with:
          persist-credentials: false
-      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
+      - uses: actions-rust-lang/setup-rust-toolchain@fb51252c7ba57d633bc668f941da052e410add48  #v1.0.6
        with:
          toolchain: nightly-2023-06-27
          override: true
@@ -134,7 +134,7 @@ jobs:
      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
        with:
          persist-credentials: false
-      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
+      - uses: actions-rust-lang/setup-rust-toolchain@fb51252c7ba57d633bc668f941da052e410add48  #v1.0.6
        with:
          toolchain: nightly-2023-06-27
          override: true
@@ -151,7 +151,7 @@ jobs:
      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
        with:
          persist-credentials: false
-      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
+      - uses: actions-rust-lang/setup-rust-toolchain@fb51252c7ba57d633bc668f941da052e410add48  #v1.0.6
        with:
          toolchain: nightly-2023-06-27
          override: true
@@ -168,7 +168,7 @@ jobs:
      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
        with:
          persist-credentials: false
-      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
+      - uses: actions-rust-lang/setup-rust-toolchain@fb51252c7ba57d633bc668f941da052e410add48  #v1.0.6
        with:
          toolchain: nightly-2023-06-27
          override: true
@@ -185,7 +185,7 @@ jobs:
      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
        with:
          persist-credentials: false
-      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
+      - uses: actions-rust-lang/setup-rust-toolchain@fb51252c7ba57d633bc668f941da052e410add48  #v1.0.6
        with:
          toolchain: nightly-2023-06-27
          override: true
--- a/.github/workflows/engine.yml
+++ b/.github/workflows/engine.yml
@@ -18,29 +18,32 @@ jobs:
    permissions:
      contents: read
      packages: write
+      id-token: write # Required for provenance
    name: publish-wasm-bindings
    env:
      RELEASE_TAG: ${{ github.ref_name }}
+      RUSTFLAGS: "-C target-feature=+atomics,+bulk-memory"
    runs-on: ubuntu-latest
    if: startsWith(github.ref, 'refs/tags/')
    steps:
-      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 #v4.2.2
        with:
          persist-credentials: false
-      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
+      - uses: actions-rust-lang/setup-rust-toolchain@fb51252c7ba57d633bc668f941da052e410add48 #v1.0.6
        with:
-          toolchain: nightly-2025-02-17
+          toolchain: nightly-2025-05-01
          override: true
          components: rustfmt, clippy
-      - uses: jetli/wasm-pack-action@0d096b08b4e5a7de8c28de67e11e945404e9eefa  #v0.4.0
+          cache: false
+      - uses: jetli/wasm-pack-action@0d096b08b4e5a7de8c28de67e11e945404e9eefa #v0.4.0
        with:
          # Pin to version 0.12.1
-          version: 'v0.12.1'
+          version: "v0.12.1"
      - name: Add wasm32-unknown-unknown target
        run: rustup target add wasm32-unknown-unknown

      - name: Add rust-src
-        run: rustup component add rust-src --toolchain nightly-2025-02-17-x86_64-unknown-linux-gnu
+        run: rustup component add rust-src --toolchain nightly-2025-05-01-x86_64-unknown-linux-gnu
      - name: Install binaryen
        run: |
          set -e
@@ -49,45 +52,45 @@ jobs:
          wasm-opt --version
      - name: Build wasm files for both web and nodejs compilation targets
        run: |
-            wasm-pack build --release --target nodejs --out-dir ./pkg/nodejs . -- -Z build-std="panic_abort,std"
-            wasm-pack build --release --target web --out-dir ./pkg/web . -- -Z build-std="panic_abort,std" --features web
+          wasm-pack build --release --target nodejs --out-dir ./pkg/nodejs . -- -Z build-std="panic_abort,std"
+          wasm-pack build --release --target web --out-dir ./pkg/web . -- -Z build-std="panic_abort,std" --features web
      - name: Create package.json in pkg folder
        shell: bash
        run: |
-            cat > pkg/package.json << EOF
-            {
-              "name": "@ezkljs/engine",
-              "version": "$RELEASE_TAG",
-              "dependencies": {
-                "@types/json-bigint": "^1.0.1",
-                "json-bigint": "^1.0.0"
-              },
-              "files": [
-                "nodejs/ezkl_bg.wasm",
-                "nodejs/ezkl.js",
-                "nodejs/ezkl.d.ts",
-                "nodejs/package.json",
-                "nodejs/utils.js",
-                "web/ezkl_bg.wasm",
-                "web/ezkl.js",
-                "web/ezkl.d.ts",
-                "web/snippets/**/*",
-                "web/package.json",
-                "web/utils.js",
-                "ezkl.d.ts"
-              ],
-              "main": "nodejs/ezkl.js",
-              "module": "web/ezkl.js",
-              "types": "nodejs/ezkl.d.ts",
-              "sideEffects": [
-                "web/snippets/*"
-              ]
-            }
-            EOF
+          cat > pkg/package.json << EOF
+          {
+            "name": "@ezkljs/engine",
+            "version": "$RELEASE_TAG",
+            "dependencies": {
+              "@types/json-bigint": "^1.0.1",
+              "json-bigint": "^1.0.0"
+            },
+            "files": [
+              "nodejs/ezkl_bg.wasm",
+              "nodejs/ezkl.js",
+              "nodejs/ezkl.d.ts",
+              "nodejs/package.json",
+              "nodejs/utils.js",
+              "web/ezkl_bg.wasm",
+              "web/ezkl.js",
+              "web/ezkl.d.ts",
+              "web/snippets/**/*",
+              "web/package.json",
+              "web/utils.js",
+              "ezkl.d.ts"
+            ],
+            "main": "nodejs/ezkl.js",
+            "module": "web/ezkl.js",
+            "types": "nodejs/ezkl.d.ts",
+            "sideEffects": [
+              "web/snippets/*"
+            ]
+          }
+          EOF

      - name: Replace memory definition in nodejs
        run: |
-          sed -i "3s|.*|imports['env'] = {memory: new WebAssembly.Memory({initial:20,maximum:65536,shared:true})}|" pkg/nodejs/ezkl.js
+          sed -i "3s|.*|imports['env'] = {memory: new WebAssembly.Memory({initial:21,maximum:65536,shared:true})}|" pkg/nodejs/ezkl.js

      - name: Replace `import.meta.url` with `import.meta.resolve` definition in workerHelpers.js
        run: |
@@ -175,76 +178,17 @@ jobs:
        run: |
          curl -s "https://raw.githubusercontent.com/zkonduit/ezkljs-engine/main/README.md" > ./pkg/README.md

+      # zizmor: ignore cache-poisoning
      - name: Set up Node.js
-        uses: actions/setup-node@1a4442cacd436585916779262731d5b162bc6ec7  #v3.8.2
+        uses: actions/setup-node@1a4442cacd436585916779262731d5b162bc6ec7 #v3.8.2
        with:
          node-version: "18.12.1"
          registry-url: "https://registry.npmjs.org"
-      - name: Publish to npm
+          package-manager-cache: false
+
+      - name: Publish to npm with provenance
        run: |
          cd pkg
-          npm install
-          npm ci
-          npm publish
-        env:
-          NODE_AUTH_TOKEN: ${{ secrets.NPM_TOKEN }}
-
-
-  in-browser-evm-ver-publish:
-    permissions:
-      contents: read
-      packages: write
-    name: publish-in-browser-evm-verifier-package
-    needs: [publish-wasm-bindings]
-    runs-on: ubuntu-latest
-    env:
-        RELEASE_TAG: ${{ github.ref_name }}
-    if: startsWith(github.ref, 'refs/tags/')
-    steps:
-      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
-        with:
-          persist-credentials: false
-      - name: Update version in package.json
-        shell: bash
-        run: |
-          sed -i "s|\"version\": \".*\"|\"version\": \"$RELEASE_TAG\"|" in-browser-evm-verifier/package.json
-      - name: Prepare tag and fetch package integrity
-        run: |
-          CLEANED_TAG=${RELEASE_TAG} # Get the tag from ref_name
-          CLEANED_TAG="${CLEANED_TAG#v}" # Remove leading 'v'
-          echo "CLEANED_TAG=${CLEANED_TAG}" >> $GITHUB_ENV # Set it as an environment variable for later steps
-          ENGINE_INTEGRITY=$(npm view @ezkljs/engine@$CLEANED_TAG dist.integrity)
-          echo "ENGINE_INTEGRITY=$ENGINE_INTEGRITY" >> $GITHUB_ENV
-      - name: Update @ezkljs/engine version in package.json
-        shell: bash
-        env:
-          RELEASE_TAG: ${{ github.ref_name }}
-        run: |
-          sed -i "s|\"@ezkljs/engine\": \".*\"|\"@ezkljs/engine\": \"$CLEANED_TAG\"|" in-browser-evm-verifier/package.json
-      - name: Update the engine import in in-browser-evm-verifier to use @ezkljs/engine package instead of the local one;
-        run: |
-          sed -i "s|import { encodeVerifierCalldata } from '../nodejs/ezkl';|import { encodeVerifierCalldata } from '@ezkljs/engine';|" in-browser-evm-verifier/src/index.ts
-      - name: Update pnpm-lock.yaml versions and integrity
-        run: |
-          awk -v integrity="$ENGINE_INTEGRITY" -v tag="$CLEANED_TAG" '
-          NR==30{$0="    specifier: \"" tag "\""}
-          NR==31{$0="    version: \"" tag "\""}
-          NR==400{$0="  /@ezkljs/engine@" tag ":"}
-          NR==401{$0="    resolution: {integrity: \"" integrity "\"}"} 1' in-browser-evm-verifier/pnpm-lock.yaml > temp.yaml && mv temp.yaml in-browser-evm-verifier/pnpm-lock.yaml
-      - name: Use pnpm 8
-        uses: pnpm/action-setup@eae0cfeb286e66ffb5155f1a79b90583a127a68b  #v2.4.1
-        with:
-          version: 8
-      - name: Set up Node.js
-        uses: actions/setup-node@1a4442cacd436585916779262731d5b162bc6ec7  #v3.8.2
-        with:
-          node-version: "18.12.1"
-          registry-url: "https://registry.npmjs.org"
-      - name: Publish to npm
-        run: |
-          cd in-browser-evm-verifier
-          pnpm install --frozen-lockfile
-          pnpm run build
-          pnpm publish --no-git-checks
+          npm publish --provenance --access public
        env:
          NODE_AUTH_TOKEN: ${{ secrets.NPM_TOKEN }}
--- a/.github/workflows/large-tests.yml
+++ b/.github/workflows/large-tests.yml
@@ -13,9 +13,9 @@ jobs:
      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
        with:
          persist-credentials: false
-      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
+      - uses: actions-rust-lang/setup-rust-toolchain@fb51252c7ba57d633bc668f941da052e410add48  #v1.0.6
        with:
-          toolchain: nightly-2025-02-17
+          toolchain: nightly-2025-05-01
          override: true
          components: rustfmt, clippy
      - name: nanoGPT Mock
--- a/.github/workflows/pypi-gpu.yml
+++ b/.github/workflows/pypi-gpu.yml
@@ -27,6 +27,8 @@ jobs:
        target: [x86_64]
    env:
      RELEASE_TAG: ${{ github.ref_name }}
+      RUSTFLAGS: "-C linker=gcc"
+      OPENSSL_NO_VENDOR: 1
    steps:
      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
        with:
@@ -36,6 +38,16 @@ jobs:
          python-version: 3.12
          architecture: x64

+
+
+      - name: Install build dependencies
+        run: |
+          sudo apt-get update
+          sudo apt-get install -y build-essential g++ gcc cmake libclang-dev llvm-dev libstdc++-12-dev libc6 libc6-dev libssl-dev pkg-config
+
+      - name: Force rebuild icicle dependencies
+        run: cargo clean -p icicle-runtime -p icicle-core -p icicle-hash -p icicle-bn254
+
      - name: Set pyproject.toml version to match github tag and rename ezkl to ezkl-gpu
        shell: bash
        run: |
@@ -43,11 +55,12 @@ jobs:
            sed "s/ezkl/ezkl-gpu/" pyproject.toml.orig > pyproject.toml.tmp
            sed "s/0\\.0\\.0/${RELEASE_TAG//v}/" pyproject.toml.tmp > pyproject.toml

-      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
+      - uses: actions-rust-lang/setup-rust-toolchain@fb51252c7ba57d633bc668f941da052e410add48  #v1.0.6
        with:
          toolchain: nightly-2023-06-27
          override: true
          components: rustfmt, clippy
+          cache: false

      - name: Set Cargo.toml version to match github tag and rename ezkl to ezkl-gpu
        shell: bash
@@ -70,7 +83,7 @@ jobs:
          target: ${{ matrix.target }}
          manylinux: auto
          container: off
-          args: --release --out dist --features python-bindings,icicle
+          args: --release --out dist --features python-bindings,gpu-accelerated

      - name: Install built wheel
        if: matrix.target == 'x86_64'
--- a/.github/workflows/pypi.yml
+++ b/.github/workflows/pypi.yml
@@ -48,11 +48,12 @@ jobs:
          mv Cargo.lock Cargo.lock.orig
          sed "s/0\\.0\\.0/${RELEASE_TAG//v}/" Cargo.lock.orig >Cargo.lock

-      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
+      - uses: actions-rust-lang/setup-rust-toolchain@fb51252c7ba57d633bc668f941da052e410add48  #v1.0.6
        with:
-          toolchain: nightly-2025-02-17
+          toolchain: nightly-2025-05-01
          override: true
          components: rustfmt, clippy
+          cache: false

      - name: Build wheels
        if: matrix.target == 'universal2-apple-darwin'
@@ -113,11 +114,12 @@ jobs:
          mv Cargo.lock Cargo.lock.orig
          sed "s/0\\.0\\.0/${RELEASE_TAG//v}/" Cargo.lock.orig >Cargo.lock

-      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
+      - uses: actions-rust-lang/setup-rust-toolchain@fb51252c7ba57d633bc668f941da052e410add48  #v1.0.6
        with:
-          toolchain: nightly-2025-02-17
+          toolchain: nightly-2025-05-01
          override: true
          components: rustfmt, clippy
+          cache: false

      - name: Build wheels
        uses: PyO3/maturin-action@5f8a1b3b0aad13193f46c9131f9b9e663def8ce5  #v1.46.0
@@ -258,7 +260,7 @@ jobs:

      - name: Install built wheel
        if: matrix.target == 'x86_64-unknown-linux-musl'
-        uses: addnab/docker-run-action@v3
+        uses: addnab/docker-run-action@3e77f186b7a929ef010f183a9e24c0f9955ea609
        with:
          image: alpine:latest
          options: -v ${{ github.workspace }}:/io -w /io
@@ -380,7 +382,7 @@ jobs:
        with:
          persist-credentials: false
      - name: Trigger RTDs build
-        uses: dfm/rtds-action@v1
+        uses: dfm/rtds-action@618148c547f4b56cdf4fa4dcf3a94c91ce025f2d
        with:
          webhook_url: ${{ secrets.RTDS_WEBHOOK_URL }}
          webhook_token: ${{ secrets.RTDS_WEBHOOK_TOKEN }}
--- a/.github/workflows/release.yml
+++ b/.github/workflows/release.yml
@@ -26,7 +26,6 @@ jobs:
        shell: bash
        run: |
          echo "EZKL_VERSION=${GITHUB_REF#refs/*/}" >> $GITHUB_ENV
-          echo "version is: ${{ env.EZKL_VERSION }}"

      - name: Create Github Release
        id: create-release
@@ -48,23 +47,32 @@ jobs:
      TARGET_DIR: ./target
      RUST_BACKTRACE: 1
      PCRE2_SYS_STATIC: 1
+      RUSTFLAGS: "-C linker=gcc"
+      OPENSSL_NO_VENDOR: 1
    steps:
-      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
+      - uses: actions-rust-lang/setup-rust-toolchain@fb51252c7ba57d633bc668f941da052e410add48   #v1.0.6
        with:
-          toolchain: nightly-2025-02-17
+          toolchain: nightly-2025-05-01
          override: true
          components: rustfmt, clippy
+          cache: false
      - name: Checkout repo
        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
        with:
          persist-credentials: false

+      - name: Install build dependencies
+        run: |
+          sudo apt-get update
+          sudo apt-get install -y build-essential g++ gcc cmake libclang-dev llvm-dev libstdc++-12-dev libc6 libc6-dev libssl-dev pkg-config
+
+      - name: Force rebuild icicle dependencies
+        run: cargo clean -p icicle-runtime -p icicle-core -p icicle-hash -p icicle-bn254

      - name: Get release version from tag
        shell: bash
        run: |
          echo "EZKL_VERSION=${GITHUB_REF#refs/*/}" >> $GITHUB_ENV
-          echo "version is: ${{ env.EZKL_VERSION }}"

      - name: Set Cargo.toml version to match github tag
        shell: bash
@@ -80,7 +88,7 @@ jobs:
          sudo apt-get update

      - name: Build release binary
-        run: cargo build --release -Z sparse-registry --features icicle
+        run: cargo build --release -Z sparse-registry --features gpu-accelerated

      - name: Build archive
        shell: bash
@@ -119,27 +127,27 @@ jobs:
        include:
          - build: windows-msvc
            os: windows-latest
-            rust: nightly-2025-02-17
+            rust: nightly-2025-05-01
            target: x86_64-pc-windows-msvc
          - build: macos
            os: macos-13
-            rust: nightly-2025-02-17
+            rust: nightly-2025-05-01
            target: x86_64-apple-darwin
          - build: macos-aarch64
            os: macos-13
-            rust: nightly-2025-02-17
+            rust: nightly-2025-05-01
            target: aarch64-apple-darwin
          - build: linux-musl
            os: ubuntu-22.04
-            rust: nightly-2025-02-17
+            rust: nightly-2025-05-01
            target: x86_64-unknown-linux-musl
          - build: linux-gnu
            os: ubuntu-22.04
-            rust: nightly-2025-02-17
+            rust: nightly-2025-05-01
            target: x86_64-unknown-linux-gnu
          - build: linux-aarch64
            os: ubuntu-22.04
-            rust: nightly-2025-02-17
+            rust: nightly-2025-05-01
            target: aarch64-unknown-linux-gnu

    steps:
@@ -152,7 +160,6 @@ jobs:
        shell: bash
        run: |
          echo "EZKL_VERSION=${GITHUB_REF#refs/*/}" >> $GITHUB_ENV
-          echo "version is: ${{ env.EZKL_VERSION }}"

      - name: Set Cargo.toml version to match github tag
        shell: bash
@@ -198,15 +205,15 @@ jobs:

      - name: Build release binary (no asm or metal)
        if: matrix.build != 'linux-gnu' && matrix.build != 'macos-aarch64'
-        run: ${{ env.CARGO }} build --release ${{ env.TARGET_FLAGS }} -Z sparse-registry
+        run: ${{ env.CARGO }} build --release ${{ env.TARGET_FLAGS }} -Z sparse-registry --features mimalloc

      - name: Build release binary (asm)
        if: matrix.build == 'linux-gnu'
-        run: ${{ env.CARGO }} build --release ${{ env.TARGET_FLAGS }} -Z sparse-registry --features asm
+        run: ${{ env.CARGO }} build --release ${{ env.TARGET_FLAGS }} -Z sparse-registry --features mimalloc

      - name: Build release binary (metal)
        if: matrix.build == 'macos-aarch64'
-        run: ${{ env.CARGO }} build --release ${{ env.TARGET_FLAGS }} -Z sparse-registry --features macos-metal
+        run: ${{ env.CARGO }} build --release ${{ env.TARGET_FLAGS }} -Z sparse-registry --features macos-metal,mimalloc

      - name: Strip release binary
        if: matrix.build != 'windows-msvc' && matrix.build != 'linux-aarch64'
--- a/.github/workflows/rust.yml
+++ b/.github/workflows/rust.yml
--- a/.github/workflows/static-analysis.yml
+++ b/.github/workflows/static-analysis.yml
@@ -15,9 +15,9 @@ jobs:
      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683  #v4.2.2
        with:
          persist-credentials: false
-      - uses: actions-rs/toolchain@b2417cde72dcf67f306c0ae8e0828a81bf0b189f  #v1.0.6
+      - uses: actions-rust-lang/setup-rust-toolchain@fb51252c7ba57d633bc668f941da052e410add48  #v1.0.6
        with:
-          toolchain: nightly-2025-02-17
+          toolchain: nightly-2025-05-01
          override: true
          components: rustfmt, clippy

--- a/.gitignore
+++ b/.gitignore
@@ -52,3 +52,5 @@ docs/python/build
 !tests/assets/vk_aggr.key
 cache
 out
+!tests/assets/wasm.code
+!tests/assets/wasm.sol
--- a/Cargo.lock
+++ b/Cargo.lock
--- a/Cargo.toml
+++ b/Cargo.toml
@@ -3,7 +3,7 @@ cargo-features = ["profile-rustflags"]
 [package]
 name = "ezkl"
 version = "0.0.0"
-edition = "2024"
+edition = "2021"
 default-run = "ezkl"

 # See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
@@ -16,12 +16,12 @@ crate-type = ["cdylib", "rlib", "staticlib"]


 [dependencies]
-halo2_gadgets = { git = "https://github.com/zkonduit/halo2" }
+halo2_gadgets = { git = "https://github.com/zkonduit/halo2", branch= "ac/conditional-compilation-icicle2" }
 halo2curves = { git = "https://github.com/privacy-scaling-explorations/halo2curves", rev = "b753a832e92d5c86c5c997327a9cf9de86a18851", features = [
    "derive_serde",
 ] }
-halo2_proofs = { git = "https://github.com/zkonduit/halo2", package = "halo2_proofs", features = [
-    "circuit-params",
+halo2_proofs = { git = "https://github.com/zkonduit/halo2", package = "halo2_proofs", branch= "ac/conditional-compilation-icicle2", features = [
+    "circuit-params", "mv-lookup"
 ] }
 rand = { version = "0.8", default-features = false }
 itertools = { version = "0.10.3", default-features = false }
@@ -33,9 +33,11 @@ thiserror = { version = "1.0.38", default-features = false }
 hex = { version = "0.4.3", default-features = false }
 halo2_wrong_ecc = { git = "https://github.com/zkonduit/halo2wrong", branch = "ac/chunked-mv-lookup", package = "ecc" }
 snark-verifier = { git = "https://github.com/zkonduit/snark-verifier", branch = "ac/chunked-mv-lookup", features = [
-    "derive_serde",
+    "derive_serde", "mv-lookup"
+] }
+halo2_solidity_verifier = { git = "https://github.com/zkonduit/ezkl-verifier", branch = "main", optional = true, features = [
+    "evm", "mv-lookup",
 ] }
-halo2_solidity_verifier = { git = "https://github.com/alexander-camuto/halo2-solidity-verifier", optional = true }
 maybe-rayon = { version = "0.1.1", default-features = false }
 bincode = { version = "1.3.3", default-features = false }
 unzip-n = "0.1.2"
@@ -43,10 +45,12 @@ num = "0.4.1"
 tosubcommand = { git = "https://github.com/zkonduit/enum_to_subcommand", package = "tosubcommand", optional = true }
 semver = { version = "1.0.22", optional = true }

+
 [target.'cfg(not(target_arch = "wasm32"))'.dependencies]
-serde_json = { version = "1.0.97", features = ["float_roundtrip", "raw_value"] }

 # evm related deps
+serde_json = { version = "1.0.97", features = ["float_roundtrip", "raw_value"] }
+
 alloy = { git = "https://github.com/alloy-rs/alloy", version = "0.1.0", rev = "5fbf57bac99edef9d8475190109a7ea9fb7e5e83", features = [
    "provider-http",
    "signers",
@@ -56,6 +60,7 @@ alloy = { git = "https://github.com/alloy-rs/alloy", version = "0.1.0", rev = "5
    "node-bindings",

 ], optional = true }
+
 foundry-compilers = { version = "0.4.1", features = [
    "svm-solc",
 ], optional = true }
@@ -69,20 +74,18 @@ reqwest = { version = "0.12.4", default-features = false, features = [
    "stream",
 ], optional = true }
 openssl = { version = "0.10.55", features = ["vendored"], optional = true }
-tokio-postgres = { version = "0.7.10", optional = true }
-pg_bigdecimal = { version = "0.1.5", optional = true }
 lazy_static = { version = "1.4.0", optional = true }
 colored_json = { version = "3.0.1", default-features = false, optional = true }
 tokio = { version = "1.35.0", default-features = false, features = [
    "macros",
    "rt-multi-thread",
 ], optional = true }
-pyo3 = { version = "0.23.2", features = [
+pyo3 = { version = "0.24.2", features = [
    "extension-module",
    "abi3-py37",
    "macros",
 ], default-features = false, optional = true }
-pyo3-async-runtimes = { git = "https://github.com/PyO3/pyo3-async-runtimes", version = "0.23.0", features = [
+pyo3-async-runtimes = { git = "https://github.com/PyO3/pyo3-async-runtimes", version = "0.24.0", features = [
    "attributes",
    "tokio-runtime",
 ], default-features = false, optional = true }
@@ -90,9 +93,9 @@ pyo3-log = { version = "0.12.0", default-features = false, optional = true }
 tract-onnx = { git = "https://github.com/sonos/tract/", rev = "37132e0397d0a73e5bd3a8615d932dabe44f6736", default-features = false, optional = true }
 tabled = { version = "0.12.0", optional = true }
 objc = { version = "0.2.4", optional = true }
-mimalloc = { version = "0.1", optional = true }
 pyo3-stub-gen = { version = "0.6.0", optional = true }
-
+jemallocator = { version = "0.5", optional = true }
+mimalloc = { version = "0.1", optional = true }
 # universal bindings
 uniffi = { version = "=0.28.0", optional = true }
 getrandom = { version = "0.2.8", optional = true }
@@ -100,6 +103,10 @@ uniffi_bindgen = { version = "=0.28.0", optional = true }
 camino = { version = "^1.1", optional = true }
 uuid = { version = "1.10.0", features = ["v4"], optional = true }

+# GPU / device related things (optional - only enabled with gpu-accelerated feature)
+icicle-runtime = { git = "https://github.com/ingonyama-zk/icicle", branch="emir/gate_eval_2", package="icicle-runtime", optional = true }
+
+
 [target.'cfg(not(all(target_arch = "wasm32", target_os = "unknown")))'.dependencies]
 colored = { version = "2.0.0", default-features = false, optional = true }
 env_logger = { version = "0.10.0", default-features = false, optional = true }
@@ -219,77 +226,86 @@ required-features = ["python-bindings"]
 [features]
 web = ["wasm-bindgen-rayon"]
 default = [
+    "eth",
+    "dep:halo2_solidity_verifier",
    "ezkl",
-    "mv-lookup",
    "precompute-coset",
    "no-banner",
    "parallel-poly-read",
+    "reusable-verifier",
 ]
 onnx = ["dep:tract-onnx"]
 python-bindings = ["pyo3", "pyo3-log", "pyo3-async-runtimes", "pyo3-stub-gen"]
-ios-bindings = ["mv-lookup", "precompute-coset", "parallel-poly-read", "uniffi"]
+universal-bindings = [
+    "uniffi",
+    "mv-lookup",
+    "precompute-coset",
+    "parallel-poly-read",
+    "dep:halo2_solidity_verifier"
+]
+logging = ["dep:colored", "dep:env_logger", "dep:chrono"]
+ios-bindings = ["universal-bindings"]
 ios-bindings-test = ["ios-bindings", "uniffi/bindgen-tests"]
 ezkl = [
    "onnx",
-    "dep:colored",
-    "dep:env_logger",
    "tabled/color",
    "serde_json/std",
    "colored_json",
-    "dep:alloy",
-    "dep:foundry-compilers",
    "dep:ethabi",
    "dep:indicatif",
    "dep:gag",
    "dep:reqwest",
-    "dep:tokio-postgres",
-    "dep:pg_bigdecimal",
    "dep:lazy_static",
    "dep:tokio",
    "dep:openssl",
-    "dep:mimalloc",
    "dep:chrono",
    "dep:sha256",
    "dep:clap_complete",
-    "dep:halo2_solidity_verifier",
    "dep:semver",
    "dep:clap",
    "dep:tosubcommand",
+    "logging",
 ]
+eth = ["dep:alloy", "dep:foundry-compilers", "dep:ethabi"]
 parallel-poly-read = [
    "halo2_proofs/circuit-params",
    "halo2_proofs/parallel-poly-read",
 ]
-mv-lookup = [
-    "halo2_proofs/mv-lookup",
-    "snark-verifier/mv-lookup",
-    "halo2_solidity_verifier/mv-lookup",
-]
+mv-lookup = ["halo2_proofs/mv-lookup", "snark-verifier/mv-lookup"]
 asm = ["halo2curves/asm", "halo2_proofs/asm"]
 precompute-coset = ["halo2_proofs/precompute-coset"]
 det-prove = []
-icicle = ["halo2_proofs/icicle_gpu"]
+gpu-accelerated = ["halo2_proofs/gpu-accelerated", "dep:icicle-runtime"]
 empty-cmd = []
 no-banner = []
 no-update = []
 macos-metal = ["halo2_proofs/macos"]
 ios-metal = ["halo2_proofs/ios"]
+jemalloc = ["dep:jemallocator"]
+mimalloc = ["dep:mimalloc"]
+reusable-verifier = []
+

 [patch.'https://github.com/zkonduit/halo2']
-halo2_proofs = { git = "https://github.com/zkonduit/halo2#f441c920be45f8f05d2c06a173d82e8885a5ed4d", package = "halo2_proofs" }
+halo2_proofs = { git = "https://github.com/zkonduit/halo2#1dd2090741f006fd031a07da7f3c9dfce5e0015e", package = "halo2_proofs", branch= "ac/conditional-compilation-icicle2", features = [
+    "circuit-params", "mv-lookup"
+] }

-[patch.'https://github.com/zkonduit/halo2#0654e92bdf725fd44d849bfef3643870a8c7d50b']
-halo2_proofs = { git = "https://github.com/zkonduit/halo2#f441c920be45f8f05d2c06a173d82e8885a5ed4d", package = "halo2_proofs" }
+[patch.'https://github.com/zkonduit/halo2#f441c920be45f8f05d2c06a173d82e8885a5ed4d']
+halo2_proofs = { git = "https://github.com/zkonduit/halo2#1dd2090741f006fd031a07da7f3c9dfce5e0015e", package = "halo2_proofs", branch= "ac/conditional-compilation-icicle2", features = [
+    "circuit-params", "mv-lookup"
+] }


 [patch.crates-io]
 uniffi_testing = { git = "https://github.com/ElusAegis/uniffi-rs", branch = "feat/testing-feature-build-fix" }

 [profile.release]
+# debug = true
 rustflags = ["-C", "relocation-model=pic"]
 lto = "fat"
 codegen-units = 1
-#panic = "abort"
+# panic = "abort"


 [profile.test-runs]
@@ -298,3 +314,5 @@ opt-level = 3

 [package.metadata.wasm-pack.profile.release]
 wasm-opt = ["-O4", "--flexible-inline-max-function-size", "4294967295"]
+
+
--- a/README.md
+++ b/README.md
@@ -76,11 +76,6 @@ For more details visit the [docs](https://docs.ezkl.xyz). The CLI is faster than

 Build the auto-generated rust documentation and open the docs in your browser locally. `cargo doc --open`

-#### In-browser EVM Verifier
-
-As an alternative to running the native Halo2 verifier as a WASM binding in the browser, you can use the in-browser EVM verifier. The source code of which you can find in the `in-browser-evm-verifier` directory and a README with instructions on how to use it.
-
-
 ### Building the Project 🔨

 #### Rust CLI
--- a/abis/DataAttestation.json
+++ b/abis/DataAttestation.json
@@ -1,312 +0,0 @@
-[
-    {
-        "inputs": [
-            {
-                "internalType": "address",
-                "name": "_contractAddresses",
-                "type": "address"
-            },
-            {
-                "internalType": "bytes",
-                "name": "_callData",
-                "type": "bytes"
-            },
-            {
-                "internalType": "uint256[]",
-                "name": "_decimals",
-                "type": "uint256[]"
-            },
-            {
-                "internalType": "uint256[]",
-                "name": "_bits",
-                "type": "uint256[]"
-            },
-            {
-                "internalType": "uint8",
-                "name": "_instanceOffset",
-                "type": "uint8"
-            }
-        ],
-        "stateMutability": "nonpayable",
-        "type": "constructor"
-    },
-    {
-        "inputs": [],
-        "name": "HALF_ORDER",
-        "outputs": [
-            {
-                "internalType": "uint256",
-                "name": "",
-                "type": "uint256"
-            }
-        ],
-        "stateMutability": "view",
-        "type": "function"
-    },
-    {
-        "inputs": [],
-        "name": "ORDER",
-        "outputs": [
-            {
-                "internalType": "uint256",
-                "name": "",
-                "type": "uint256"
-            }
-        ],
-        "stateMutability": "view",
-        "type": "function"
-    },
-    {
-        "inputs": [
-            {
-                "internalType": "uint256[]",
-                "name": "instances",
-                "type": "uint256[]"
-            }
-        ],
-        "name": "attestData",
-        "outputs": [],
-        "stateMutability": "view",
-        "type": "function"
-    },
-    {
-        "inputs": [],
-        "name": "callData",
-        "outputs": [
-            {
-                "internalType": "bytes",
-                "name": "",
-                "type": "bytes"
-            }
-        ],
-        "stateMutability": "view",
-        "type": "function"
-    },
-    {
-        "inputs": [],
-        "name": "contractAddress",
-        "outputs": [
-            {
-                "internalType": "address",
-                "name": "",
-                "type": "address"
-            }
-        ],
-        "stateMutability": "view",
-        "type": "function"
-    },
-    {
-        "inputs": [
-            {
-                "internalType": "bytes",
-                "name": "encoded",
-                "type": "bytes"
-            }
-        ],
-        "name": "getInstancesCalldata",
-        "outputs": [
-            {
-                "internalType": "uint256[]",
-                "name": "instances",
-                "type": "uint256[]"
-            }
-        ],
-        "stateMutability": "pure",
-        "type": "function"
-    },
-    {
-        "inputs": [
-            {
-                "internalType": "bytes",
-                "name": "encoded",
-                "type": "bytes"
-            }
-        ],
-        "name": "getInstancesMemory",
-        "outputs": [
-            {
-                "internalType": "uint256[]",
-                "name": "instances",
-                "type": "uint256[]"
-            }
-        ],
-        "stateMutability": "pure",
-        "type": "function"
-    },
-    {
-        "inputs": [
-            {
-                "internalType": "uint256",
-                "name": "index",
-                "type": "uint256"
-            }
-        ],
-        "name": "getScalars",
-        "outputs": [
-            {
-                "components": [
-                    {
-                        "internalType": "uint256",
-                        "name": "decimals",
-                        "type": "uint256"
-                    },
-                    {
-                        "internalType": "uint256",
-                        "name": "bits",
-                        "type": "uint256"
-                    }
-                ],
-                "internalType": "struct DataAttestation.Scalars",
-                "name": "",
-                "type": "tuple"
-            }
-        ],
-        "stateMutability": "view",
-        "type": "function"
-    },
-    {
-        "inputs": [],
-        "name": "instanceOffset",
-        "outputs": [
-            {
-                "internalType": "uint8",
-                "name": "",
-                "type": "uint8"
-            }
-        ],
-        "stateMutability": "view",
-        "type": "function"
-    },
-    {
-        "inputs": [
-            {
-                "internalType": "uint256",
-                "name": "x",
-                "type": "uint256"
-            },
-            {
-                "internalType": "uint256",
-                "name": "y",
-                "type": "uint256"
-            },
-            {
-                "internalType": "uint256",
-                "name": "denominator",
-                "type": "uint256"
-            }
-        ],
-        "name": "mulDiv",
-        "outputs": [
-            {
-                "internalType": "uint256",
-                "name": "result",
-                "type": "uint256"
-            }
-        ],
-        "stateMutability": "pure",
-        "type": "function"
-    },
-    {
-        "inputs": [
-            {
-                "internalType": "int256",
-                "name": "x",
-                "type": "int256"
-            },
-            {
-                "components": [
-                    {
-                        "internalType": "uint256",
-                        "name": "decimals",
-                        "type": "uint256"
-                    },
-                    {
-                        "internalType": "uint256",
-                        "name": "bits",
-                        "type": "uint256"
-                    }
-                ],
-                "internalType": "struct DataAttestation.Scalars",
-                "name": "_scalars",
-                "type": "tuple"
-            }
-        ],
-        "name": "quantizeData",
-        "outputs": [
-            {
-                "internalType": "int256",
-                "name": "quantized_data",
-                "type": "int256"
-            }
-        ],
-        "stateMutability": "pure",
-        "type": "function"
-    },
-    {
-        "inputs": [
-            {
-                "internalType": "address",
-                "name": "target",
-                "type": "address"
-            },
-            {
-                "internalType": "bytes",
-                "name": "data",
-                "type": "bytes"
-            }
-        ],
-        "name": "staticCall",
-        "outputs": [
-            {
-                "internalType": "bytes",
-                "name": "",
-                "type": "bytes"
-            }
-        ],
-        "stateMutability": "view",
-        "type": "function"
-    },
-    {
-        "inputs": [
-            {
-                "internalType": "int256",
-                "name": "x",
-                "type": "int256"
-            }
-        ],
-        "name": "toFieldElement",
-        "outputs": [
-            {
-                "internalType": "uint256",
-                "name": "field_element",
-                "type": "uint256"
-            }
-        ],
-        "stateMutability": "pure",
-        "type": "function"
-    },
-    {
-        "inputs": [
-            {
-                "internalType": "address",
-                "name": "verifier",
-                "type": "address"
-            },
-            {
-                "internalType": "bytes",
-                "name": "encoded",
-                "type": "bytes"
-            }
-        ],
-        "name": "verifyWithDataAttestation",
-        "outputs": [
-            {
-                "internalType": "bool",
-                "name": "",
-                "type": "bool"
-            }
-        ],
-        "stateMutability": "view",
-        "type": "function"
-    }
-]
--- a/abis/QuantizeData.json
+++ b/abis/QuantizeData.json
@@ -1,98 +0,0 @@
-[
-	{
-		"inputs": [
-			{
-				"internalType": "int256[]",
-				"name": "quantized_data",
-				"type": "int256[]"
-			}
-		],
-		"name": "check_is_valid_field_element",
-		"outputs": [
-			{
-				"internalType": "uint256[]",
-				"name": "output",
-				"type": "uint256[]"
-			}
-		],
-		"stateMutability": "pure",
-		"type": "function"
-	},
-	{
-		"inputs": [
-			{
-				"internalType": "bytes[]",
-				"name": "data",
-				"type": "bytes[]"
-			},
-			{
-				"internalType": "uint256[]",
-				"name": "decimals",
-				"type": "uint256[]"
-			},
-			{
-				"internalType": "uint256[]",
-				"name": "scales",
-				"type": "uint256[]"
-			}
-		],
-		"name": "quantize_data_multi",
-		"outputs": [
-			{
-				"internalType": "int256[]",
-				"name": "quantized_data",
-				"type": "int256[]"
-			}
-		],
-		"stateMutability": "pure",
-		"type": "function"
-	},
-	{
-		"inputs": [
-			{
-				"internalType": "bytes",
-				"name": "data",
-				"type": "bytes"
-			},
-			{
-				"internalType": "uint256",
-				"name": "decimals",
-				"type": "uint256"
-			},
-			{
-				"internalType": "uint256[]",
-				"name": "scales",
-				"type": "uint256[]"
-			}
-		],
-		"name": "quantize_data_single",
-		"outputs": [
-			{
-				"internalType": "int256[]",
-				"name": "quantized_data",
-				"type": "int256[]"
-			}
-		],
-		"stateMutability": "pure",
-		"type": "function"
-	},
-	{
-		"inputs": [
-			{
-				"internalType": "int64[]",
-				"name": "quantized_data",
-				"type": "int64[]"
-			}
-		],
-		"name": "to_field_element",
-		"outputs": [
-			{
-				"internalType": "uint256[]",
-				"name": "output",
-				"type": "uint256[]"
-			}
-		],
-		"stateMutability": "pure",
-		"type": "function"
-	}
-]
--- a/abis/TestReads.json
+++ b/abis/TestReads.json
@@ -1,32 +0,0 @@
-[
-	{
-		"inputs": [
-			{
-				"internalType": "int256[]",
-				"name": "_numbers",
-				"type": "int256[]"
-			}
-		],
-		"stateMutability": "nonpayable",
-		"type": "constructor"
-	},
-	{
-		"inputs": [
-			{
-				"internalType": "uint256",
-				"name": "",
-				"type": "uint256"
-			}
-		],
-		"name": "arr",
-		"outputs": [
-			{
-				"internalType": "int256",
-				"name": "",
-				"type": "int256"
-			}
-		],
-		"stateMutability": "view",
-		"type": "function"
-	}
-]
--- a/benches/accum_conv.rs
+++ b/benches/accum_conv.rs
@@ -68,7 +68,7 @@ impl Circuit<Fr> for MyCircuit {
                config
                    .layout(
                        &mut region,
-                        &[self.image.clone(), self.kernel.clone(), self.bias.clone()],
+                        &[&self.image, &self.kernel, &self.bias],
                        Box::new(PolyOp::Conv {
                            padding: vec![(0, 0)],
                            stride: vec![1; 2],
--- a/benches/accum_dot.rs
+++ b/benches/accum_dot.rs
@@ -15,6 +15,7 @@ use halo2_proofs::{
    plonk::{Circuit, ConstraintSystem, Error},
 };
 use halo2curves::bn256::{Bn256, Fr};
+use itertools::Itertools;
 use rand::rngs::OsRng;
 use snark_verifier::system::halo2::transcript::evm::EvmTranscript;
 use std::marker::PhantomData;
@@ -59,7 +60,7 @@ impl Circuit<Fr> for MyCircuit {
                config
                    .layout(
                        &mut region,
-                        &self.inputs,
+                        &self.inputs.iter().collect_vec(),
                        Box::new(PolyOp::Einsum {
                            equation: "i,i->".to_string(),
                        }),
--- a/benches/accum_einsum_matmul.rs
+++ b/benches/accum_einsum_matmul.rs
@@ -1,52 +1,78 @@
-use criterion::{criterion_group, criterion_main, BenchmarkId, Criterion, Throughput};
+use criterion::{
+    criterion_group, criterion_main, AxisScale, BenchmarkId, Criterion, PlotConfiguration,
+    Throughput,
+};
+use ezkl::circuit::einsum::analysis::analyze_einsum_usage;
+use ezkl::circuit::einsum::circuit_params::SingleEinsumParams;
 use ezkl::circuit::poly::PolyOp;
 use ezkl::circuit::*;
-use ezkl::pfsys::create_proof_circuit;
-use ezkl::pfsys::TranscriptType;
-use ezkl::pfsys::{create_keys, srs::gen_srs};
+use ezkl::pfsys::srs::gen_srs;
+use ezkl::pfsys::{create_keys, create_proof_circuit, TranscriptType};
 use ezkl::tensor::*;
+use halo2_proofs::circuit::floor_planner::V1;
 use halo2_proofs::poly::kzg::commitment::KZGCommitmentScheme;
-use halo2_proofs::poly::kzg::multiopen::ProverSHPLONK;
-use halo2_proofs::poly::kzg::multiopen::VerifierSHPLONK;
+use halo2_proofs::poly::kzg::multiopen::{ProverSHPLONK, VerifierSHPLONK};
 use halo2_proofs::poly::kzg::strategy::SingleStrategy;
 use halo2_proofs::{
    arithmetic::Field,
-    circuit::{Layouter, SimpleFloorPlanner, Value},
+    circuit::{Layouter, Value},
    plonk::{Circuit, ConstraintSystem, Error},
 };
 use halo2curves::bn256::{Bn256, Fr};
+use halo2curves::ff::PrimeField;
+use itertools::Itertools;
 use rand::rngs::OsRng;
 use snark_verifier::system::halo2::transcript::evm::EvmTranscript;
-use std::marker::PhantomData;
+use std::collections::HashMap;

 static mut LEN: usize = 4;
-const K: usize = 16;
+static mut K: usize = 15;

 #[derive(Clone)]
-struct MyCircuit {
-    inputs: [ValTensor<Fr>; 2],
-    _marker: PhantomData<Fr>,
+struct MyCircuit<F: PrimeField + TensorType + PartialOrd> {
+    inputs: [ValTensor<F>; 2],
+    einsum_params: SingleEinsumParams<F>,
 }

-impl Circuit<Fr> for MyCircuit {
+impl Circuit<Fr> for MyCircuit<Fr> {
    type Config = BaseConfig<Fr>;
-    type FloorPlanner = SimpleFloorPlanner;
-    type Params = ();
+    type FloorPlanner = V1;
+    type Params = SingleEinsumParams<Fr>;

    fn without_witnesses(&self) -> Self {
        self.clone()
    }

-    fn configure(cs: &mut ConstraintSystem<Fr>) -> Self::Config {
-        let len = unsafe { LEN };
+    fn configure_with_params(cs: &mut ConstraintSystem<Fr>, params: Self::Params) -> Self::Config {
+        let mut config = Self::Config::default();

-        let a = VarTensor::new_advice(cs, K, 1, len * len);
+        let mut equations = HashMap::new();
+        equations.insert((0, params.equation), params.input_axes_to_dims);
+        let analysis = analyze_einsum_usage(&equations).unwrap();
+        let num_einsum_inner_cols = 1;
+        unsafe {
+            config
+                .configure_einsums(cs, &analysis, num_einsum_inner_cols, K)
+                .unwrap();
+            let _constant = VarTensor::constant_cols(cs, K, 2, false);
+        }

-        let b = VarTensor::new_advice(cs, K, 1, len * len);
+        config
+    }

-        let output = VarTensor::new_advice(cs, K, 1, (len + 1) * len);
+    fn params(&self) -> Self::Params {
+        SingleEinsumParams::<Fr>::new(
+            &self.einsum_params.equation,
+            &[
+                &self.inputs[0].get_inner().unwrap(),
+                &self.inputs[1].get_inner().unwrap(),
+            ],
+        )
+        .unwrap()
+    }

-        Self::Config::configure(cs, &[a, b], &output, CheckMode::UNSAFE)
+    fn configure(_cs: &mut ConstraintSystem<Fr>) -> Self::Config {
+        unimplemented!("call configure_with_params instead")
    }

    fn synthesize(
@@ -54,16 +80,33 @@ impl Circuit<Fr> for MyCircuit {
        mut config: Self::Config,
        mut layouter: impl Layouter<Fr>,
    ) -> Result<(), Error> {
+        let challenges = config
+            .einsums
+            .as_ref()
+            .ok_or(Error::Synthesis)?
+            .challenges()
+            .unwrap()
+            .iter()
+            .map(|c| layouter.get_challenge(*c))
+            .collect_vec();
+
        layouter.assign_region(
            || "",
            |region| {
-                let mut region = region::RegionCtx::new(region, 0, 1, 1024, 2);
+                let mut region = region::RegionCtx::new_with_challenges(
+                    region,
+                    0,
+                    1,
+                    1024,
+                    2,
+                    challenges.clone(),
+                );
                config
                    .layout(
                        &mut region,
-                        &self.inputs,
+                        &self.inputs.iter().collect_vec(),
                        Box::new(PolyOp::Einsum {
-                            equation: "ab,bc->ac".to_string(),
+                            equation: self.einsum_params.equation.clone(),
                        }),
                    )
                    .unwrap();
@@ -76,41 +119,49 @@ impl Circuit<Fr> for MyCircuit {

 fn runmatmul(c: &mut Criterion) {
    let mut group = c.benchmark_group("accum_einsum_matmul");
-    let params = gen_srs::<KZGCommitmentScheme<_>>(17);
-    for &len in [4, 32].iter() {
-        unsafe {
-            LEN = len;
+    group.plot_config(PlotConfiguration::default().summary_scale(AxisScale::Linear));
+    group.sampling_mode(criterion::SamplingMode::Flat);
+    group.sample_size(10);
+    let len = 128;
+    unsafe {
+        LEN = len;
+    }
+    for k in 16..17 {
+        let params = unsafe {
+            K = k;
+            gen_srs::<KZGCommitmentScheme<_>>(K as u32)
        };

        let mut a = Tensor::from((0..len * len).map(|_| Value::known(Fr::random(OsRng))));
        a.reshape(&[len, len]).unwrap();

-        // parameters
        let mut b = Tensor::from((0..len * len).map(|_| Value::known(Fr::random(OsRng))));
        b.reshape(&[len, len]).unwrap();

+        let einsum_params = SingleEinsumParams::<Fr>::new("ij,jk->ik", &[&a, &b]).unwrap();
+
        let circuit = MyCircuit {
            inputs: [ValTensor::from(a), ValTensor::from(b)],
-            _marker: PhantomData,
+            einsum_params,
        };

        group.throughput(Throughput::Elements(len as u64));
-        group.bench_with_input(BenchmarkId::new("pk", len), &len, |b, &_| {
+        group.bench_with_input(BenchmarkId::new("pk", k), &k, |b, &_| {
            b.iter(|| {
-                create_keys::<KZGCommitmentScheme<Bn256>, MyCircuit>(&circuit, &params, true)
+                create_keys::<KZGCommitmentScheme<Bn256>, MyCircuit<Fr>>(&circuit, &params, true)
                    .unwrap();
            });
        });

-        let pk =
-            create_keys::<KZGCommitmentScheme<Bn256>, MyCircuit>(&circuit, &params, true).unwrap();
+        let pk = create_keys::<KZGCommitmentScheme<Bn256>, MyCircuit<Fr>>(&circuit, &params, false)
+            .unwrap();

        group.throughput(Throughput::Elements(len as u64));
-        group.bench_with_input(BenchmarkId::new("prove", len), &len, |b, &_| {
+        group.bench_with_input(BenchmarkId::new("prove", k), &k, |b, &_| {
            b.iter(|| {
                let prover = create_proof_circuit::<
                    KZGCommitmentScheme<_>,
-                    MyCircuit,
+                    MyCircuit<Fr>,
                    ProverSHPLONK<_>,
                    VerifierSHPLONK<_>,
                    SingleStrategy<_>,
--- a/benches/accum_matmul_sigmoid.rs
+++ b/benches/accum_matmul_sigmoid.rs
@@ -17,6 +17,7 @@ use halo2_proofs::{
    plonk::{Circuit, ConstraintSystem, Error},
 };
 use halo2curves::bn256::{Bn256, Fr};
+use itertools::Itertools;
 use snark_verifier::system::halo2::transcript::evm::EvmTranscript;
 use std::marker::PhantomData;

@@ -86,13 +87,13 @@ impl Circuit<Fr> for MyCircuit {
                let mut region = region::RegionCtx::new(region, 0, 1, 1024, 2);
                let output = config
                    .base_config
-                    .layout(&mut region, &self.inputs, Box::new(op))
+                    .layout(&mut region, &self.inputs.iter().collect_vec(), Box::new(op))
                    .unwrap();
                let _output = config
                    .base_config
                    .layout(
                        &mut region,
-                        &[output.unwrap()],
+                        &[&output.unwrap()],
                        Box::new(LookupOp::Sigmoid { scale: 1.0.into() }),
                    )
                    .unwrap();
--- a/benches/accum_matmul_sigmoid_overflow.rs
+++ b/benches/accum_matmul_sigmoid_overflow.rs
@@ -17,6 +17,7 @@ use halo2_proofs::{
    plonk::{Circuit, ConstraintSystem, Error},
 };
 use halo2curves::bn256::{Bn256, Fr};
+use itertools::Itertools;
 use snark_verifier::system::halo2::transcript::evm::EvmTranscript;
 use std::marker::PhantomData;

@@ -87,13 +88,13 @@ impl Circuit<Fr> for MyCircuit {
                let mut region = region::RegionCtx::new(region, 0, 1, 1024, 2);
                let output = config
                    .base_config
-                    .layout(&mut region, &self.inputs, Box::new(op))
+                    .layout(&mut region, &self.inputs.iter().collect_vec(), Box::new(op))
                    .unwrap();
                let _output = config
                    .base_config
                    .layout(
                        &mut region,
-                        &[output.unwrap()],
+                        &[&output.unwrap()],
                        Box::new(LookupOp::Sigmoid { scale: 1.0.into() }),
                    )
                    .unwrap();
--- a/benches/accum_sum.rs
+++ b/benches/accum_sum.rs
@@ -15,6 +15,7 @@ use halo2_proofs::{
    plonk::{Circuit, ConstraintSystem, Error},
 };
 use halo2curves::bn256::{Bn256, Fr};
+use itertools::Itertools;
 use rand::rngs::OsRng;
 use snark_verifier::system::halo2::transcript::evm::EvmTranscript;
 use std::marker::PhantomData;
@@ -59,7 +60,7 @@ impl Circuit<Fr> for MyCircuit {
                config
                    .layout(
                        &mut region,
-                        &self.inputs,
+                        &self.inputs.iter().collect_vec(),
                        Box::new(PolyOp::Sum { axes: vec![0] }),
                    )
                    .unwrap();
--- a/benches/accum_sumpool.rs
+++ b/benches/accum_sumpool.rs
@@ -63,7 +63,7 @@ impl Circuit<Fr> for MyCircuit {
                config
                    .layout(
                        &mut region,
-                        &[self.image.clone()],
+                        &[&self.image],
                        Box::new(HybridOp::SumPool {
                            padding: vec![(0, 0); 2],
                            stride: vec![1, 1],
--- a/benches/pairwise_add.rs
+++ b/benches/pairwise_add.rs
@@ -15,6 +15,7 @@ use halo2_proofs::{
    plonk::{Circuit, ConstraintSystem, Error},
 };
 use halo2curves::bn256::{Bn256, Fr};
+use itertools::Itertools;
 use rand::rngs::OsRng;
 use snark_verifier::system::halo2::transcript::evm::EvmTranscript;
 use std::marker::PhantomData;
@@ -57,7 +58,11 @@ impl Circuit<Fr> for MyCircuit {
            |region| {
                let mut region = region::RegionCtx::new(region, 0, 1, 1024, 2);
                config
-                    .layout(&mut region, &self.inputs, Box::new(PolyOp::Add))
+                    .layout(
+                        &mut region,
+                        &self.inputs.iter().collect_vec(),
+                        Box::new(PolyOp::Add),
+                    )
                    .unwrap();
                Ok(())
            },
--- a/benches/pairwise_pow.rs
+++ b/benches/pairwise_pow.rs
@@ -16,6 +16,7 @@ use halo2_proofs::{
    plonk::{Circuit, ConstraintSystem, Error},
 };
 use halo2curves::bn256::{Bn256, Fr};
+use itertools::Itertools;
 use rand::rngs::OsRng;
 use snark_verifier::system::halo2::transcript::evm::EvmTranscript;
 use std::marker::PhantomData;
@@ -58,7 +59,11 @@ impl Circuit<Fr> for MyCircuit {
            |region| {
                let mut region = RegionCtx::new(region, 0, 1, 1024, 2);
                config
-                    .layout(&mut region, &self.inputs, Box::new(PolyOp::Pow(4)))
+                    .layout(
+                        &mut region,
+                        &self.inputs.iter().collect_vec(),
+                        Box::new(PolyOp::Pow(4)),
+                    )
                    .unwrap();
                Ok(())
            },
--- a/benches/relu_lookupless.rs
+++ b/benches/relu_lookupless.rs
@@ -70,7 +70,7 @@ impl Circuit<Fr> for NLCircuit {
                config
                    .layout(
                        &mut region,
-                        &[self.input.clone()],
+                        &[&self.input],
                        Box::new(PolyOp::LeakyReLU {
                            slope: 0.0.into(),
                            scale: 1,
--- a/benches/sigmoid.rs
+++ b/benches/sigmoid.rs
@@ -67,7 +67,7 @@ impl Circuit<Fr> for NLCircuit {
                config
                    .layout(
                        &mut region,
-                        &[self.input.clone()],
+                        &[&self.input],
                        Box::new(LookupOp::Sigmoid { scale: 1.0.into() }),
                    )
                    .unwrap();
--- a/contracts/AttestData.sol
+++ b/contracts/AttestData.sol
@@ -1,397 +0,0 @@
-// SPDX-License-Identifier: MIT
-pragma solidity ^0.8.20;
-contract LoadInstances {
-    /**
-     * @dev Parse the instances array from the Halo2Verifier encoded calldata.
-     * @notice must pass encoded bytes from memory
-     * @param encoded - verifier calldata
-     */
-    function getInstancesMemory(
-        bytes memory encoded
-    ) public pure returns (uint256[] memory instances) {
-        bytes4 funcSig;
-        uint256 instances_offset;
-        uint256 instances_length;
-        assembly {
-            // fetch function sig. Either `verifyProof(bytes,uint256[])` or `verifyProof(address,bytes,uint256[])`
-            funcSig := mload(add(encoded, 0x20))
-        }
-        if (funcSig == 0xaf83a18d) {
-            instances_offset = 0x64;
-        } else if (funcSig == 0x1e8e1e13) {
-            instances_offset = 0x44;
-        } else {
-            revert("Invalid function signature");
-        }
-        assembly {
-            // Fetch instances offset which is 4 + 32 + 32 bytes away from
-            // start of encoded for `verifyProof(bytes,uint256[])`,
-            // and 4 + 32 + 32 +32 away for `verifyProof(address,bytes,uint256[])`
-
-            instances_offset := mload(add(encoded, instances_offset))
-
-            instances_length := mload(add(add(encoded, 0x24), instances_offset))
-        }
-        instances = new uint256[](instances_length); // Allocate memory for the instances array.
-        assembly {
-            // Now instances points to the start of the array data
-            // (right after the length field).
-            for {
-                let i := 0x20
-            } lt(i, add(mul(instances_length, 0x20), 0x20)) {
-                i := add(i, 0x20)
-            } {
-                mstore(
-                    add(instances, i),
-                    mload(add(add(encoded, add(i, 0x24)), instances_offset))
-                )
-            }
-        }
-        require(
-            funcSig == 0xaf83a18d || funcSig == 0x1e8e1e13,
-            "Invalid function signature"
-        );
-    }
-    /**
-     * @dev Parse the instances array from the Halo2Verifier encoded calldata.
-     * @notice must pass encoded bytes from calldata
-     * @param encoded - verifier calldata
-     */
-    function getInstancesCalldata(
-        bytes calldata encoded
-    ) public pure returns (uint256[] memory instances) {
-        bytes4 funcSig;
-        uint256 instances_offset;
-        uint256 instances_length;
-        assembly {
-            // fetch function sig. Either `verifyProof(bytes,uint256[])` or `verifyProof(address,bytes,uint256[])`
-            funcSig := calldataload(encoded.offset)
-        }
-        if (funcSig == 0xaf83a18d) {
-            instances_offset = 0x44;
-        } else if (funcSig == 0x1e8e1e13) {
-            instances_offset = 0x24;
-        } else {
-            revert("Invalid function signature");
-        }
-        // We need to create a new assembly block in order for solidity
-        // to cast the funcSig to a bytes4 type. Otherwise it will load the entire first 32 bytes of the calldata
-        // within the block
-        assembly {
-            // Fetch instances offset which is 4 + 32 + 32 bytes away from
-            // start of encoded for `verifyProof(bytes,uint256[])`,
-            // and 4 + 32 + 32 +32 away for `verifyProof(address,bytes,uint256[])`
-
-            instances_offset := calldataload(
-                add(encoded.offset, instances_offset)
-            )
-
-            instances_length := calldataload(
-                add(add(encoded.offset, 0x04), instances_offset)
-            )
-        }
-        instances = new uint256[](instances_length); // Allocate memory for the instances array.
-        assembly {
-            // Now instances points to the start of the array data
-            // (right after the length field).
-
-            for {
-                let i := 0x20
-            } lt(i, add(mul(instances_length, 0x20), 0x20)) {
-                i := add(i, 0x20)
-            } {
-                mstore(
-                    add(instances, i),
-                    calldataload(
-                        add(add(encoded.offset, add(i, 0x04)), instances_offset)
-                    )
-                )
-            }
-        }
-    }
-}
-
-// The kzg commitments of a given model, all aggregated into a single bytes array.
-// At solidity generation time, the commitments are hardcoded into the contract via the COMMITMENT_KZG constant.
-// It will be used to check that the proof commitments match the expected commitments.
-bytes constant COMMITMENT_KZG = hex"1234";
-
-contract SwapProofCommitments {
-    /**
-     * @dev Swap the proof commitments
-     * @notice must pass encoded bytes from memory
-     * @param encoded - verifier calldata
-     */
-    function checkKzgCommits(
-        bytes calldata encoded
-    ) internal pure returns (bool equal) {
-        bytes4 funcSig;
-        uint256 proof_offset;
-        uint256 proof_length;
-        assembly {
-            // fetch function sig. Either `verifyProof(bytes,uint256[])` or `verifyProof(address,bytes,uint256[])`
-            funcSig := calldataload(encoded.offset)
-        }
-        if (funcSig == 0xaf83a18d) {
-            proof_offset = 0x24;
-        } else if (funcSig == 0x1e8e1e13) {
-            proof_offset = 0x04;
-        } else {
-            revert("Invalid function signature");
-        }
-        assembly {
-            // Fetch proof offset which is 4 + 32 bytes away from
-            // start of encoded for `verifyProof(bytes,uint256[])`,
-            // and 4 + 32 + 32 away for `verifyProof(address,bytes,uint256[])`
-
-            proof_offset := calldataload(add(encoded.offset, proof_offset))
-
-            proof_length := calldataload(
-                add(add(encoded.offset, 0x04), proof_offset)
-            )
-        }
-        // Check the length of the commitment against the proof bytes
-        if (proof_length < COMMITMENT_KZG.length) {
-            return false;
-        }
-
-        // Load COMMITMENT_KZG into memory
-        bytes memory commitment = COMMITMENT_KZG;
-
-        // Compare the first N bytes of the proof with COMMITMENT_KZG
-        uint words = (commitment.length + 31) / 32; // Calculate the number of 32-byte words
-
-        assembly {
-            // Now we compare the commitment with the proof,
-            // ensuring that the commitments divided up into 32 byte words are all equal.
-            for {
-                let i := 0x20
-            } lt(i, add(mul(words, 0x20), 0x20)) {
-                i := add(i, 0x20)
-            } {
-                let wordProof := calldataload(
-                    add(add(encoded.offset, add(i, 0x04)), proof_offset)
-                )
-                let wordCommitment := mload(add(commitment, i))
-                equal := eq(wordProof, wordCommitment)
-                if eq(equal, 0) {
-                    break
-                }
-            }
-        }
-
-        return equal; // Return true if the commitment comparison passed
-    } /// end checkKzgCommits
-}
-
-contract DataAttestation is LoadInstances, SwapProofCommitments {
-    // the address of the account to make calls to
-    address public immutable contractAddress;
-
-    // the abi encoded function calls to make to the `contractAddress` that returns the attested to data
-    bytes public callData;
-
-    struct Scalars {
-        // The number of base 10 decimals to scale the data by.
-        // For most ERC20 tokens this is 1e18
-        uint256 decimals;
-        // The number of fractional bits of the fixed point EZKL data points.
-        uint256 bits;
-    }
-
-    Scalars[] private scalars;
-
-    function getScalars(uint256 index) public view returns (Scalars memory) {
-        return scalars[index];
-    }
-
-    /**
-     * @notice EZKL P value
-     * @dev In order to prevent the verifier from accepting two version of the same pubInput, n and the quantity (n + P),  where n + P <= 2^256, we require that all instances are stricly less than P. a
-     * @dev The reason for this is that the assmebly code of the verifier performs all arithmetic operations modulo P and as a consequence can't distinguish between n and n + P.
-     */
-    uint256 public constant ORDER =
-        uint256(
-            0x30644e72e131a029b85045b68181585d2833e84879b9709143e1f593f0000001
-        );
-
-    uint256 public constant HALF_ORDER = ORDER >> 1;
-
-    uint8 public instanceOffset;
-
-    /**
-     * @dev Initialize the contract with account calls the EZKL model will read from.
-     * @param _contractAddresses - The calls to all the contracts EZKL reads storage from.
-     * @param _callData - The abi encoded function calls to make to the `contractAddress` that EZKL reads storage from.
-     */
-    constructor(
-        address _contractAddresses,
-        bytes memory _callData,
-        uint256[] memory _decimals,
-        uint[] memory _bits,
-        uint8 _instanceOffset
-    ) {
-        require(
-            _bits.length == _decimals.length,
-            "Invalid scalar array lengths"
-        );
-        for (uint i; i < _bits.length; i++) {
-            scalars.push(Scalars(10 ** _decimals[i], 1 << _bits[i]));
-        }
-        contractAddress = _contractAddresses;
-        callData = _callData;
-        instanceOffset = _instanceOffset;
-    }
-
-    function mulDiv(
-        uint256 x,
-        uint256 y,
-        uint256 denominator
-    ) public pure returns (uint256 result) {
-        unchecked {
-            uint256 prod0;
-            uint256 prod1;
-            assembly {
-                let mm := mulmod(x, y, not(0))
-                prod0 := mul(x, y)
-                prod1 := sub(sub(mm, prod0), lt(mm, prod0))
-            }
-
-            if (prod1 == 0) {
-                return prod0 / denominator;
-            }
-
-            require(denominator > prod1, "Math: mulDiv overflow");
-
-            uint256 remainder;
-            assembly {
-                remainder := mulmod(x, y, denominator)
-                prod1 := sub(prod1, gt(remainder, prod0))
-                prod0 := sub(prod0, remainder)
-            }
-
-            uint256 twos = denominator & (~denominator + 1);
-            assembly {
-                denominator := div(denominator, twos)
-                prod0 := div(prod0, twos)
-                twos := add(div(sub(0, twos), twos), 1)
-            }
-
-            prod0 |= prod1 * twos;
-
-            uint256 inverse = (3 * denominator) ^ 2;
-
-            inverse *= 2 - denominator * inverse;
-            inverse *= 2 - denominator * inverse;
-            inverse *= 2 - denominator * inverse;
-            inverse *= 2 - denominator * inverse;
-            inverse *= 2 - denominator * inverse;
-            inverse *= 2 - denominator * inverse;
-
-            result = prod0 * inverse;
-            return result;
-        }
-    }
-    /**
-     * @dev Quantize the data returned from the account calls to the scale used by the EZKL model.
-     * @param x - One of the elements of the data returned from the account calls
-     * @param _scalars - The scaling factors for the data returned from the account calls.
-     *
-     */
-    function quantizeData(
-        int x,
-        Scalars memory _scalars
-    ) public pure returns (int256 quantized_data) {
-        if (_scalars.bits == 1 && _scalars.decimals == 1) {
-            return x;
-        }
-        bool neg = x < 0;
-        if (neg) x = -x;
-        uint output = mulDiv(uint256(x), _scalars.bits, _scalars.decimals);
-        if (
-            mulmod(uint256(x), _scalars.bits, _scalars.decimals) * 2 >=
-            _scalars.decimals
-        ) {
-            output += 1;
-        }
-        if (output > HALF_ORDER) {
-            revert("Overflow field modulus");
-        }
-        quantized_data = neg ? -int256(output) : int256(output);
-    }
-    /**
-     * @dev Make a static call to the account to fetch the data that EZKL reads from.
-     * @param target - The address of the account to make calls to.
-     * @param data  - The abi encoded function calls to make to the `contractAddress` that EZKL reads storage from.
-     * @return The data returned from the account calls. (Must come from either a view or pure function. Will throw an error otherwise)
-     */
-    function staticCall(
-        address target,
-        bytes memory data
-    ) public view returns (bytes memory) {
-        (bool success, bytes memory returndata) = target.staticcall(data);
-        if (success) {
-            if (returndata.length == 0) {
-                require(
-                    target.code.length > 0,
-                    "Address: call to non-contract"
-                );
-            }
-            return returndata;
-        } else {
-            revert("Address: low-level call failed");
-        }
-    }
-    /**
-     * @dev Convert the fixed point quantized data into a field element.
-     * @param x - The quantized data.
-     * @return field_element - The field element.
-     */
-    function toFieldElement(
-        int256 x
-    ) public pure returns (uint256 field_element) {
-        // The casting down to uint256 is safe because the order is about 2^254, and the value
-        // of x ranges of -2^127 to 2^127, so x + int(ORDER) is always positive.
-        return uint256(x + int(ORDER)) % ORDER;
-    }
-
-    /**
-     * @dev Make the account calls to fetch the data that EZKL reads from and attest to the data.
-     * @param instances - The public instances to the proof (the data in the proof that publicly accessible to the verifier).
-     */
-    function attestData(uint256[] memory instances) public view {
-        bytes memory returnData = staticCall(contractAddress, callData);
-        int256[] memory x = abi.decode(returnData, (int256[]));
-        int output;
-        uint fieldElement;
-        for (uint i = 0; i < x.length; i++) {
-            output = quantizeData(x[i], scalars[i]);
-            fieldElement = toFieldElement(output);
-            if (fieldElement != instances[i]) {
-                revert("Public input does not match");
-            }
-        }
-    }
-
-    /**
-     * @dev Verify the proof with the data attestation.
-     * @param verifier - The address of the verifier contract.
-     * @param encoded - The verifier calldata.
-     */
-    function verifyWithDataAttestation(
-        address verifier,
-        bytes calldata encoded
-    ) public view returns (bool) {
-        require(verifier.code.length > 0, "Address: call to non-contract");
-        attestData(getInstancesCalldata(encoded));
-        require(checkKzgCommits(encoded), "Invalid KZG commitments");
-        // static call the verifier contract to verify the proof
-        (bool success, bytes memory returndata) = verifier.staticcall(encoded);
-
-        if (success) {
-            return abi.decode(returndata, (bool));
-        } else {
-            revert("low-level call to verifier failed");
-        }
-    }
-}
--- a/docs/python/src/conf.py
+++ b/docs/python/src/conf.py
@@ -1,7 +1,7 @@
 import ezkl

 project = 'ezkl'
-release = '21.0.2'
+release = '22.3.0'
 version = release


--- a/examples/accum_einsum_matmul.rs
+++ b/examples/accum_einsum_matmul.rs
@@ -0,0 +1,171 @@
+use ezkl::circuit::einsum::analysis::analyze_einsum_usage;
+use ezkl::circuit::poly::PolyOp;
+use ezkl::circuit::*;
+use ezkl::tensor::*;
+use halo2_proofs::circuit::floor_planner::V1;
+use halo2_proofs::dev::MockProver;
+use halo2_proofs::{
+    arithmetic::Field,
+    circuit::{Layouter, Value},
+    plonk::{Circuit, ConstraintSystem, Error},
+};
+use halo2curves::bn256::Fr;
+use halo2curves::ff::PrimeField;
+use itertools::Itertools;
+use rand::rngs::OsRng;
+use std::collections::HashMap;
+use std::marker::PhantomData;
+
+const K: usize = 13;
+
+#[derive(Clone)]
+struct MyCircuit<F: PrimeField + TensorType + PartialOrd> {
+    inputs: [ValTensor<F>; 2],
+    einsum: Einsum<F>,
+}
+
+#[derive(Clone, Default)]
+struct Einsum<F: PrimeField + TensorType + PartialOrd> {
+    equation: String,
+    input_axes_to_dims: HashMap<char, usize>,
+    _marker: PhantomData<F>,
+}
+
+impl<F: PrimeField + TensorType + PartialOrd> Einsum<F> {
+    pub fn new(equation: &str, inputs: &[&Tensor<Value<F>>]) -> Result<Self, CircuitError> {
+        let mut eq = equation.split("->");
+        let inputs_eq = eq.next().ok_or(CircuitError::InvalidEinsum)?;
+        let inputs_eq = inputs_eq.split(',').collect::<Vec<_>>();
+
+        // Check that the number of inputs matches the number of inputs in the equation
+        if inputs.len() != inputs_eq.len() {
+            return Err(TensorError::DimMismatch("einsum".to_string()).into());
+        }
+
+        let mut input_axes_to_dims = HashMap::new();
+        for (i, input) in inputs.iter().enumerate() {
+            for j in 0..inputs_eq[i].len() {
+                let c = inputs_eq[i]
+                    .chars()
+                    .nth(j)
+                    .ok_or(CircuitError::InvalidEinsum)?;
+                if let std::collections::hash_map::Entry::Vacant(e) = input_axes_to_dims.entry(c) {
+                    e.insert(input.dims()[j]);
+                } else if input_axes_to_dims[&c] != input.dims()[j] {
+                    return Err(TensorError::DimMismatch("einsum".to_string()).into());
+                }
+            }
+        }
+
+        Ok(Self {
+            equation: equation.to_owned(),
+            input_axes_to_dims,
+            _marker: PhantomData,
+        })
+    }
+}
+
+impl Circuit<Fr> for MyCircuit<Fr> {
+    type Config = BaseConfig<Fr>;
+    type FloorPlanner = V1;
+    type Params = Einsum<Fr>;
+
+    fn without_witnesses(&self) -> Self {
+        self.clone()
+    }
+
+    fn configure_with_params(cs: &mut ConstraintSystem<Fr>, params: Self::Params) -> Self::Config {
+        let mut config = Self::Config::default();
+
+        let mut equations = HashMap::new();
+        equations.insert((0, params.equation), params.input_axes_to_dims);
+        let analysis = analyze_einsum_usage(&equations).unwrap();
+        let num_einsum_inner_cols = 1;
+        config
+            .configure_einsums(cs, &analysis, num_einsum_inner_cols, K)
+            .unwrap();
+
+        config
+    }
+
+    fn params(&self) -> Self::Params {
+        Einsum::<Fr>::new(
+            &self.einsum.equation,
+            &[
+                &self.inputs[0].get_inner().unwrap(),
+                &self.inputs[1].get_inner().unwrap(),
+            ],
+        )
+        .unwrap()
+    }
+
+    fn configure(_cs: &mut ConstraintSystem<Fr>) -> Self::Config {
+        unimplemented!("call configure_with_params instead")
+    }
+
+    fn synthesize(
+        &self,
+        mut config: Self::Config,
+        mut layouter: impl Layouter<Fr>,
+    ) -> Result<(), Error> {
+        let challenges = config
+            .einsums
+            .as_ref()
+            .ok_or(Error::Synthesis)?
+            .challenges()
+            .unwrap()
+            .iter()
+            .map(|c| layouter.get_challenge(*c))
+            .collect_vec();
+
+        layouter.assign_region(
+            || "",
+            |region| {
+                let mut region = region::RegionCtx::new_with_challenges(
+                    region,
+                    0,
+                    1,
+                    1024,
+                    2,
+                    challenges.clone(),
+                );
+                config
+                    .layout(
+                        &mut region,
+                        &self.inputs.iter().collect_vec(),
+                        Box::new(PolyOp::Einsum {
+                            equation: self.einsum.equation.clone(),
+                        }),
+                    )
+                    .unwrap();
+                Ok(())
+            },
+        )?;
+        Ok(())
+    }
+}
+
+fn runmatmul() {
+    let len = 64;
+
+    let mut a = Tensor::from((0..len * len).map(|_| Value::known(Fr::random(OsRng))));
+    a.reshape(&[len, len]).unwrap();
+
+    // parameters
+    let mut b = Tensor::from((0..len * len).map(|_| Value::known(Fr::random(OsRng))));
+    b.reshape(&[len, len]).unwrap();
+
+    let einsum = Einsum::<Fr>::new("ij,jk->ik", &[&a, &b]).unwrap();
+
+    let circuit = MyCircuit {
+        inputs: [ValTensor::from(a), ValTensor::from(b)],
+        einsum,
+    };
+
+    let mock_prover = MockProver::run(K as u32, &circuit, vec![]).unwrap();
+    mock_prover.assert_satisfied();
+}
+
+pub fn main() {
+    runmatmul()
+}
--- a/examples/batch_mat_mul.rs
+++ b/examples/batch_mat_mul.rs
@@ -0,0 +1,179 @@
+use ezkl::circuit::einsum::analysis::analyze_einsum_usage;
+use ezkl::circuit::poly::PolyOp;
+use ezkl::circuit::*;
+use ezkl::tensor::*;
+use halo2_proofs::circuit::floor_planner::V1;
+use halo2_proofs::dev::MockProver;
+use halo2_proofs::{
+    arithmetic::Field,
+    circuit::{Layouter, Value},
+    plonk::{Circuit, ConstraintSystem, Error},
+};
+use halo2curves::bn256::Fr;
+use halo2curves::ff::PrimeField;
+use itertools::Itertools;
+use rand::rngs::OsRng;
+use std::collections::HashMap;
+use std::marker::PhantomData;
+
+static mut LEN: usize = 4;
+const K: usize = 11;
+
+#[derive(Clone)]
+struct MyCircuit<F: PrimeField + TensorType + PartialOrd> {
+    inputs: [ValTensor<F>; 2],
+    einsum: Einsum<F>,
+}
+
+#[derive(Clone, Default)]
+struct Einsum<F: PrimeField + TensorType + PartialOrd> {
+    equation: String,
+    input_axes_to_dims: HashMap<char, usize>,
+    _marker: PhantomData<F>,
+}
+
+impl<F: PrimeField + TensorType + PartialOrd> Einsum<F> {
+    pub fn new(equation: &str, inputs: &[&Tensor<Value<F>>]) -> Result<Self, CircuitError> {
+        let mut eq = equation.split("->");
+        let inputs_eq = eq.next().ok_or(CircuitError::InvalidEinsum)?;
+        let inputs_eq = inputs_eq.split(',').collect::<Vec<_>>();
+
+        // Check that the number of inputs matches the number of inputs in the equation
+        if inputs.len() != inputs_eq.len() {
+            return Err(TensorError::DimMismatch("einsum".to_string()).into());
+        }
+
+        let mut input_axes_to_dims = HashMap::new();
+        for (i, input) in inputs.iter().enumerate() {
+            for j in 0..inputs_eq[i].len() {
+                let c = inputs_eq[i]
+                    .chars()
+                    .nth(j)
+                    .ok_or(CircuitError::InvalidEinsum)?;
+                if let std::collections::hash_map::Entry::Vacant(e) = input_axes_to_dims.entry(c) {
+                    e.insert(input.dims()[j]);
+                } else if input_axes_to_dims[&c] != input.dims()[j] {
+                    return Err(TensorError::DimMismatch("einsum".to_string()).into());
+                }
+            }
+        }
+
+        Ok(Self {
+            equation: equation.to_owned(),
+            input_axes_to_dims,
+            _marker: PhantomData,
+        })
+    }
+}
+
+impl Circuit<Fr> for MyCircuit<Fr> {
+    type Config = BaseConfig<Fr>;
+    type FloorPlanner = V1;
+    type Params = Einsum<Fr>;
+
+    fn without_witnesses(&self) -> Self {
+        self.clone()
+    }
+
+    fn configure_with_params(cs: &mut ConstraintSystem<Fr>, params: Self::Params) -> Self::Config {
+        let len = unsafe { LEN };
+
+        let a = VarTensor::new_advice(cs, K, 1, len);
+        let b = VarTensor::new_advice(cs, K, 1, len);
+        let output = VarTensor::new_advice(cs, K, 1, len);
+
+        let mut config = Self::Config::configure(cs, &[a, b], &output, CheckMode::UNSAFE);
+
+        let mut equations = HashMap::new();
+        equations.insert((0, params.equation), params.input_axes_to_dims);
+        let analysis = analyze_einsum_usage(&equations).unwrap();
+        let num_einsum_inner_cols = 1;
+        config
+            .configure_einsums(cs, &analysis, num_einsum_inner_cols, K)
+            .unwrap();
+
+        config
+    }
+
+    fn params(&self) -> Self::Params {
+        Einsum::<Fr>::new(
+            &self.einsum.equation,
+            &[
+                &self.inputs[0].get_inner().unwrap(),
+                &self.inputs[1].get_inner().unwrap(),
+            ],
+        )
+        .unwrap()
+    }
+
+    fn configure(_cs: &mut ConstraintSystem<Fr>) -> Self::Config {
+        unimplemented!("call configure_with_params instead")
+    }
+
+    fn synthesize(
+        &self,
+        mut config: Self::Config,
+        mut layouter: impl Layouter<Fr>,
+    ) -> Result<(), Error> {
+        let challenges = config
+            .einsums
+            .as_ref()
+            .ok_or(Error::Synthesis)?
+            .challenges()
+            .unwrap()
+            .iter()
+            .map(|c| layouter.get_challenge(*c))
+            .collect_vec();
+
+        layouter.assign_region(
+            || "",
+            |region| {
+                let mut region = region::RegionCtx::new_with_challenges(
+                    region,
+                    0,
+                    1,
+                    1024,
+                    2,
+                    challenges.clone(),
+                );
+                config
+                    .layout(
+                        &mut region,
+                        &self.inputs.iter().collect_vec(),
+                        Box::new(PolyOp::Einsum {
+                            equation: self.einsum.equation.clone(),
+                        }),
+                    )
+                    .unwrap();
+                Ok(())
+            },
+        )?;
+        Ok(())
+    }
+}
+
+fn runbatchmatmul() {
+    let batch_size = 5;
+    let len = 12;
+
+    let mut a = Tensor::from((0..batch_size * len * len).map(|_| Value::known(Fr::random(OsRng))));
+    a.reshape(&[batch_size, len, len]).unwrap();
+
+    // parameters
+    let mut b = Tensor::from((0..batch_size * len * len).map(|_| Value::known(Fr::random(OsRng))));
+    b.reshape(&[batch_size, len, len]).unwrap();
+
+    let einsum = Einsum::<Fr>::new("ijk,ikl->ijl", &[&a, &b]).unwrap();
+
+    let circuit = MyCircuit {
+        inputs: [ValTensor::from(a), ValTensor::from(b)],
+        einsum,
+    };
+
+    let mock_prover = MockProver::run(K as u32, &circuit, vec![]).unwrap();
+    mock_prover.assert_satisfied();
+}
+
+pub fn main() {
+    runbatchmatmul()
+}
--- a/examples/conv2d_mnist/main.rs
+++ b/examples/conv2d_mnist/main.rs
@@ -32,7 +32,6 @@ use mnist::*;
 use rand::rngs::OsRng;
 use std::marker::PhantomData;

-
 mod params;

 const K: usize = 20;
@@ -216,11 +215,7 @@ where
                        .layer_config
                        .layout(
                            &mut region,
-                            &[
-                                self.input.clone(),
-                                self.l0_params[0].clone(),
-                                self.l0_params[1].clone(),
-                            ],
+                            &[&self.input, &self.l0_params[0], &self.l0_params[1]],
                            Box::new(op),
                        )
                        .unwrap();
@@ -229,7 +224,7 @@ where
                        .layer_config
                        .layout(
                            &mut region,
-                            &[x.unwrap()],
+                            &[&x.unwrap()],
                            Box::new(PolyOp::LeakyReLU {
                                slope: 0.0.into(),
                                scale: 1,
@@ -241,7 +236,7 @@ where
                        .layer_config
                        .layout(
                            &mut region,
-                            &[x.unwrap()],
+                            &[&x.unwrap()],
                            Box::new(LookupOp::Div { denom: 32.0.into() }),
                        )
                        .unwrap()
@@ -253,7 +248,7 @@ where
                        .layer_config
                        .layout(
                            &mut region,
-                            &[self.l2_params[0].clone(), x],
+                            &[&self.l2_params[0], &x],
                            Box::new(PolyOp::Einsum {
                                equation: "ij,j->ik".to_string(),
                            }),
@@ -265,7 +260,7 @@ where
                        .layer_config
                        .layout(
                            &mut region,
-                            &[x, self.l2_params[1].clone()],
+                            &[&x, &self.l2_params[1]],
                            Box::new(PolyOp::Add),
                        )
                        .unwrap()
--- a/examples/mlp_4d_einsum.rs
+++ b/examples/mlp_4d_einsum.rs
@@ -117,10 +117,7 @@ impl<const LEN: usize, const LOOKUP_MIN: IntegerRep, const LOOKUP_MAX: IntegerRe
                        .layer_config
                        .layout(
                            &mut region,
-                            &[
-                                self.l0_params[0].clone().try_into().unwrap(),
-                                self.input.clone(),
-                            ],
+                            &[&self.l0_params[0].clone().try_into().unwrap(), &self.input],
                            Box::new(PolyOp::Einsum {
                                equation: "ab,bc->ac".to_string(),
                            }),
@@ -135,7 +132,7 @@ impl<const LEN: usize, const LOOKUP_MIN: IntegerRep, const LOOKUP_MAX: IntegerRe
                        .layer_config
                        .layout(
                            &mut region,
-                            &[x, self.l0_params[1].clone().try_into().unwrap()],
+                            &[&x, &self.l0_params[1].clone().try_into().unwrap()],
                            Box::new(PolyOp::Add),
                        )
                        .unwrap()
@@ -147,7 +144,7 @@ impl<const LEN: usize, const LOOKUP_MIN: IntegerRep, const LOOKUP_MAX: IntegerRe
                        .layer_config
                        .layout(
                            &mut region,
-                            &[x],
+                            &[&x],
                            Box::new(PolyOp::LeakyReLU {
                                scale: 1,
                                slope: 0.0.into(),
@@ -163,7 +160,7 @@ impl<const LEN: usize, const LOOKUP_MIN: IntegerRep, const LOOKUP_MAX: IntegerRe
                        .layer_config
                        .layout(
                            &mut region,
-                            &[self.l2_params[0].clone().try_into().unwrap(), x],
+                            &[&self.l2_params[0].clone().try_into().unwrap(), &x],
                            Box::new(PolyOp::Einsum {
                                equation: "ab,bc->ac".to_string(),
                            }),
@@ -178,7 +175,7 @@ impl<const LEN: usize, const LOOKUP_MIN: IntegerRep, const LOOKUP_MAX: IntegerRe
                        .layer_config
                        .layout(
                            &mut region,
-                            &[x, self.l2_params[1].clone().try_into().unwrap()],
+                            &[&x, &self.l2_params[1].clone().try_into().unwrap()],
                            Box::new(PolyOp::Add),
                        )
                        .unwrap()
@@ -190,7 +187,7 @@ impl<const LEN: usize, const LOOKUP_MIN: IntegerRep, const LOOKUP_MAX: IntegerRe
                        .layer_config
                        .layout(
                            &mut region,
-                            &[x],
+                            &[&x],
                            Box::new(PolyOp::LeakyReLU {
                                scale: 1,
                                slope: 0.0.into(),
@@ -203,7 +200,7 @@ impl<const LEN: usize, const LOOKUP_MIN: IntegerRep, const LOOKUP_MAX: IntegerRe
                        .layer_config
                        .layout(
                            &mut region,
-                            &[x.unwrap()],
+                            &[&x.unwrap()],
                            Box::new(LookupOp::Div {
                                denom: ezkl::circuit::utils::F32::from(128.),
                            }),
--- a/examples/notebooks/cat_and_dog.ipynb
+++ b/examples/notebooks/cat_and_dog.ipynb
@@ -866,7 +866,7 @@
        },
        {
            "cell_type": "code",
-            "execution_count": 98,
+            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
@@ -879,6 +879,7 @@
                "run_args.input_visibility = \"private\"\n",
                "run_args.param_visibility = \"fixed\"\n",
                "run_args.output_visibility = \"public\"\n",
+                "run_args.disable_freivalds = True\n",
                "run_args.variables = [(\"batch_size\", 1)]\n",
                "\n",
                "\n",
@@ -904,7 +905,7 @@
            "outputs": [],
            "source": [
                "\n",
-                "res = await ezkl.calibrate_settings(\"input.json\", target=\"resources\", scales = [4])\n",
+                "res = ezkl.calibrate_settings(\"input.json\", target=\"resources\", scales = [4])\n",
                "assert res == True\n",
                "print(\"verified\")\n"
            ]
@@ -954,7 +955,7 @@
            "source": [
                "\n",
                "\n",
-                "res = await ezkl.gen_witness()\n"
+                "res = ezkl.gen_witness()\n"
            ]
        },
        {
@@ -1088,7 +1089,7 @@
                "\n",
                "res = await ezkl.deploy_evm(\n",
                "    address_path,\n",
-                "    rpc_url='http://127.0.0.1:3030'\n",
+                "    'http://127.0.0.1:3030'\n",
                ")\n",
                "\n",
                "assert res == True\n",
--- a/examples/notebooks/data_attest.ipynb
+++ b/examples/notebooks/data_attest.ipynb
@@ -1,589 +0,0 @@
-{
-    "cells": [
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# data-attest-ezkl\n",
-                "\n",
-                "Here's an example leveraging EZKL whereby the inputs to the model are read and attested to from an on-chain source.\n",
-                "\n",
-                "In this setup:\n",
-                "- the inputs and outputs are publicly known to the prover and verifier\n",
-                "- the on chain inputs will be fetched and then fed directly into the circuit\n",
-                "- the quantization of the on-chain inputs happens within the evm and is replicated at proving time \n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "First we import the necessary dependencies and set up logging to be as informative as possible. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# check if notebook is in colab\n",
-                "try:\n",
-                "    # install ezkl\n",
-                "    import google.colab\n",
-                "    import subprocess\n",
-                "    import sys\n",
-                "    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"ezkl\"])\n",
-                "    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"onnx\"])\n",
-                "\n",
-                "# rely on local installation of ezkl if the notebook is not in colab\n",
-                "except:\n",
-                "    pass\n",
-                "\n",
-                "\n",
-                "from torch import nn\n",
-                "import ezkl\n",
-                "import os\n",
-                "import json\n",
-                "import logging\n",
-                "\n",
-                "# uncomment for more descriptive logging \n",
-                "FORMAT = '%(levelname)s %(name)s %(asctime)-15s %(filename)s:%(lineno)d %(message)s'\n",
-                "logging.basicConfig(format=FORMAT)\n",
-                "logging.getLogger().setLevel(logging.DEBUG)\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Now we define our model. It is a very simple PyTorch model that has just one layer, an average pooling 2D layer. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import torch\n",
-                "# Defines the model\n",
-                "\n",
-                "class MyModel(nn.Module):\n",
-                "    def __init__(self):\n",
-                "        super(MyModel, self).__init__()\n",
-                "        self.layer = nn.AvgPool2d(2, 1, (1, 1))\n",
-                "\n",
-                "    def forward(self, x):\n",
-                "        return self.layer(x)[0]\n",
-                "\n",
-                "\n",
-                "circuit = MyModel()\n",
-                "\n",
-                "# this is where you'd train your model"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We omit training for purposes of this demonstration. We've marked where training would happen in the cell above. \n",
-                "Now we export the model to onnx and create a corresponding (randomly generated) input. This input data will eventually be stored on chain and read from according to the call_data field in the graph input.\n",
-                "\n",
-                "You can replace the random `x` with real data if you so wish. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "x = 0.1*torch.rand(1,*[3, 2, 2], requires_grad=True)\n",
-                "\n",
-                "# Flips the neural net into inference mode\n",
-                "circuit.eval()\n",
-                "\n",
-                "    # Export the model\n",
-                "torch.onnx.export(circuit,               # model being run\n",
-                "                      x,                   # model input (or a tuple for multiple inputs)\n",
-                "                      \"network.onnx\",            # where to save the model (can be a file or file-like object)\n",
-                "                      export_params=True,        # store the trained parameter weights inside the model file\n",
-                "                      opset_version=10,          # the ONNX version to export the model to\n",
-                "                      do_constant_folding=True,  # whether to execute constant folding for optimization\n",
-                "                      input_names = ['input'],   # the model's input names\n",
-                "                      output_names = ['output'], # the model's output names\n",
-                "                      dynamic_axes={'input' : {0 : 'batch_size'},    # variable length axes\n",
-                "                                    'output' : {0 : 'batch_size'}})\n",
-                "\n",
-                "data_array = ((x).detach().numpy()).reshape([-1]).tolist()\n",
-                "\n",
-                "data = dict(input_data = [data_array])\n",
-                "\n",
-                "    # Serialize data into file:\n",
-                "json.dump(data, open(\"input.json\", 'w' ))\n",
-                "\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We now define a function that will create a new anvil instance which we will deploy our test contract too. This contract will contain in its storage the data that we will read from and attest to. In production you would not need to set up a local anvil instance. Instead you would replace RPC_URL with the actual RPC endpoint of the chain you are deploying your verifiers too, reading from the data on said chain."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import subprocess\n",
-                "import time\n",
-                "import threading\n",
-                "\n",
-                "# make sure anvil is running locally\n",
-                "# $ anvil -p 3030\n",
-                "\n",
-                "RPC_URL = \"http://localhost:3030\"\n",
-                "\n",
-                "# Save process globally\n",
-                "anvil_process = None\n",
-                "\n",
-                "def start_anvil():\n",
-                "    global anvil_process\n",
-                "    if anvil_process is None:\n",
-                "        anvil_process = subprocess.Popen([\"anvil\", \"-p\", \"3030\", \"--code-size-limit=41943040\"])\n",
-                "        if anvil_process.returncode is not None:\n",
-                "            raise Exception(\"failed to start anvil process\")\n",
-                "        time.sleep(3)\n",
-                "\n",
-                "def stop_anvil():\n",
-                "    global anvil_process\n",
-                "    if anvil_process is not None:\n",
-                "        anvil_process.terminate()\n",
-                "        anvil_process = None\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We define our `PyRunArgs` objects which contains the visibility parameters for out model. \n",
-                "- `input_visibility` defines the visibility of the model inputs\n",
-                "- `param_visibility` defines the visibility of the model weights and constants and parameters \n",
-                "- `output_visibility` defines the visibility of the model outputs\n",
-                "\n",
-                "Here we create the following setup:\n",
-                "- `input_visibility`: \"public\"\n",
-                "- `param_visibility`: \"private\"\n",
-                "- `output_visibility`: public\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import ezkl\n",
-                "\n",
-                "model_path = os.path.join('network.onnx')\n",
-                "compiled_model_path = os.path.join('network.compiled')\n",
-                "pk_path = os.path.join('test.pk')\n",
-                "vk_path = os.path.join('test.vk')\n",
-                "settings_path = os.path.join('settings.json')\n",
-                "srs_path = os.path.join('kzg.srs')\n",
-                "data_path = os.path.join('input.json')\n",
-                "\n",
-                "run_args = ezkl.PyRunArgs()\n",
-                "run_args.input_visibility = \"public\"\n",
-                "run_args.param_visibility = \"private\"\n",
-                "run_args.output_visibility = \"public\"\n",
-                "run_args.num_inner_cols = 1\n",
-                "run_args.variables = [(\"batch_size\", 1)]\n",
-                "\n",
-                "\n",
-                "\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Now we generate a settings file. This file basically instantiates a bunch of parameters that determine their circuit shape, size etc... Because of the way we represent nonlinearities in the circuit (using Halo2's [lookup tables](https://zcash.github.io/halo2/design/proving-system/lookup.html)), it is often best to _calibrate_ this settings file as some data can fall out of range of these lookups.\n",
-                "\n",
-                "You can pass a dataset for calibration that will be representative of real inputs you might find if and when you deploy the prover. Here we create a dummy calibration dataset for demonstration purposes. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "!RUST_LOG=trace\n",
-                "# TODO: Dictionary outputs\n",
-                "res = ezkl.gen_settings(model_path, settings_path, py_run_args=run_args)\n",
-                "assert res == True"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# generate a bunch of dummy calibration data\n",
-                "cal_data = {\n",
-                "    \"input_data\": [(0.1*torch.rand(2, *[3, 2, 2])).flatten().tolist()],\n",
-                "}\n",
-                "\n",
-                "cal_path = os.path.join('val_data.json')\n",
-                "# save as json file\n",
-                "with open(cal_path, \"w\") as f:\n",
-                "    json.dump(cal_data, f)\n",
-                "\n",
-                "res = await ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "res = ezkl.compile_circuit(model_path, compiled_model_path, settings_path)\n",
-                "assert res == True"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "The graph input for on chain data sources is formatted completely differently compared to file based data sources.\n",
-                "\n",
-                "- For file data sources, the raw floating point values that eventually get quantized, converted into field elements and stored in `witness.json` to be consumed by the circuit are stored. The output data contains the expected floating point values returned as outputs from running your vanilla pytorch model on the given inputs.\n",
-                "- For on chain data sources, the input_data field contains all the data necessary to read and format the on chain data into something digestable by EZKL (aka field elements :-D). \n",
-                "Here is what the schema for an on-chain data source graph input file should look like for a single call data source:\n",
-                "    \n",
-                "```json\n",
-                "{\n",
-                "    \"input_data\": {\n",
-                "        \"rpc\": \"http://localhost:3030\", // The rpc endpoint of the chain you are deploying your verifier to\n",
-                "        \"calls\": {\n",
-                "            \"call_data\": \"1f3be514000000000000000000000000c6962004f452be9203591991d15f6b388e09e8d00000000000000000000000000000000000000000000000000000000000000040000000000000000000000000000000000000000000000000000000000000000c000000000000000000000000000000000000000000000000000000000000000b000000000000000000000000000000000000000000000000000000000000000a0000000000000000000000000000000000000000000000000000000000000009000000000000000000000000000000000000000000000000000000000000000800000000000000000000000000000000000000000000000000000000000000070000000000000000000000000000000000000000000000000000000000000006000000000000000000000000000000000000000000000000000000000000000500000000000000000000000000000000000000000000000000000000000000040000000000000000000000000000000000000000000000000000000000000003000000000000000000000000000000000000000000000000000000000000000200000000000000000000000000000000000000000000000000000000000000010000000000000000000000000000000000000000000000000000000000000000\", // The abi encoded call data to a view function that returns an array of on-chain data points we are attesting to. \n",
-                "            \"decimals\": 0, // The number of decimal places of the large uint256 value. This is our way of representing large wei values as floating points on chain, since the evm only natively supports integer values.\n",
-                "            \"address\": \"9A213F53334279C128C37DA962E5472eCD90554f\", // The address of the contract that we are calling to get the data. \n",
-                "            \"len\": 12 // The number of data points returned by the view function (the length of the array)\n",
-                "        }\n",
-                "    }\n",
-                "}\n",
-                "```"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "await ezkl.setup_test_evm_data(\n",
-                "    data_path,\n",
-                "    compiled_model_path,\n",
-                "    # we write the call data to the same file as the input data\n",
-                "    data_path,\n",
-                "    input_source=ezkl.PyTestDataSource.OnChain,\n",
-                "    output_source=ezkl.PyTestDataSource.File,\n",
-                "    rpc_url=RPC_URL)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "As we use Halo2 with KZG-commitments we need an SRS string from (preferably) a multi-party trusted setup ceremony. For an overview of the procedures for such a ceremony check out [this page](https://blog.ethereum.org/2023/01/16/announcing-kzg-ceremony). The `get_srs` command retrieves a correctly sized SRS given the calibrated settings file from [here](https://github.com/han0110/halo2-kzg-srs). \n",
-                "\n",
-                "These SRS were generated with [this](https://github.com/privacy-scaling-explorations/perpetualpowersoftau) ceremony. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "res = await ezkl.get_srs( settings_path)\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We now need to generate the circuit witness. These are the model outputs (and any hashes) that are generated when feeding the previously generated `input.json` through the circuit / model. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "!export RUST_BACKTRACE=1\n",
-                "\n",
-                "witness_path = \"witness.json\"\n",
-                "\n",
-                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Here we setup verifying and proving keys for the circuit. As the name suggests the proving key is needed for ... proving and the verifying key is needed for ... verifying. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# HERE WE SETUP THE CIRCUIT PARAMS\n",
-                "# WE GOT KEYS\n",
-                "# WE GOT CIRCUIT PARAMETERS\n",
-                "# EVERYTHING ANYONE HAS EVER NEEDED FOR ZK\n",
-                "res = ezkl.setup(\n",
-                "        compiled_model_path,\n",
-                "        vk_path,\n",
-                "        pk_path,\n",
-                "        \n",
-                "    )\n",
-                "\n",
-                "assert res == True\n",
-                "assert os.path.isfile(vk_path)\n",
-                "assert os.path.isfile(pk_path)\n",
-                "assert os.path.isfile(settings_path)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Now we generate a full proof. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# GENERATE A PROOF\n",
-                "\n",
-                "proof_path = os.path.join('test.pf')\n",
-                "\n",
-                "res = ezkl.prove(\n",
-                "        witness_path,\n",
-                "        compiled_model_path,\n",
-                "        pk_path,\n",
-                "        proof_path,\n",
-                "        \n",
-                "        \"single\",\n",
-                "    )\n",
-                "\n",
-                "print(res)\n",
-                "assert os.path.isfile(proof_path)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "And verify it as a sanity check. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# VERIFY IT\n",
-                "\n",
-                "res = ezkl.verify(\n",
-                "        proof_path,\n",
-                "        settings_path,\n",
-                "        vk_path,\n",
-                "        \n",
-                "    )\n",
-                "\n",
-                "assert res == True\n",
-                "print(\"verified\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We can now create and then deploy a vanilla evm verifier."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "abi_path = 'test.abi'\n",
-                "sol_code_path = 'test.sol'\n",
-                "\n",
-                "res = await ezkl.create_evm_verifier(\n",
-                "        vk_path,\n",
-                "        \n",
-                "        settings_path,\n",
-                "        sol_code_path,\n",
-                "        abi_path,\n",
-                "    )\n",
-                "assert res == True"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import json\n",
-                "\n",
-                "addr_path_verifier = \"addr_verifier.txt\"\n",
-                "\n",
-                "res = await ezkl.deploy_evm(\n",
-                "    addr_path_verifier,\n",
-                "    sol_code_path,\n",
-                "    'http://127.0.0.1:3030'\n",
-                ")\n",
-                "\n",
-                "assert res == True"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "With the vanilla verifier deployed, we can now create the data attestation contract, which will read in the instances from the calldata to the verifier, attest to them, call the verifier and then return the result. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "\n",
-                "abi_path = 'test.abi'\n",
-                "sol_code_path = 'test.sol'\n",
-                "input_path = 'input.json'\n",
-                "\n",
-                "res = await ezkl.create_evm_data_attestation(\n",
-                "        input_path,\n",
-                "        settings_path,\n",
-                "        sol_code_path,\n",
-                "        abi_path,\n",
-                "    )"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Now we can deploy the data attest verifier contract. For security reasons, this binding will only deploy to a local anvil instance, using accounts generated by anvil. \n",
-                "So should only be used for testing purposes."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "addr_path_da = \"addr_da.txt\"\n",
-                "\n",
-                "res = await ezkl.deploy_da_evm(\n",
-                "        addr_path_da,\n",
-                "        input_path,\n",
-                "        settings_path,\n",
-                "        sol_code_path,\n",
-                "        RPC_URL,\n",
-                "    )\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Call the view only verify method on the contract to verify the proof. Since it is a view function this is safe to use in production since you don't have to pass your private key."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# read the verifier address\n",
-                "addr_verifier = None\n",
-                "with open(addr_path_verifier, 'r') as f:\n",
-                "    addr = f.read()\n",
-                "#read the data attestation address\n",
-                "addr_da = None\n",
-                "with open(addr_path_da, 'r') as f:\n",
-                "    addr_da = f.read()\n",
-                "\n",
-                "res = await ezkl.verify_evm(\n",
-                "    addr,\n",
-                "    proof_path,\n",
-                "    RPC_URL,\n",
-                "    addr_da,\n",
-                ")"
-            ]
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": "ezkl",
-            "language": "python",
-            "name": "python3"
-        },
-        "language_info": {
-            "codemirror_mode": {
-                "name": "ipython",
-                "version": 3
-            },
-            "file_extension": ".py",
-            "mimetype": "text/x-python",
-            "name": "python",
-            "nbconvert_exporter": "python",
-            "pygments_lexer": "ipython3",
-            "version": "3.12.5"
-        },
-        "orig_nbformat": 4
-    },
-    "nbformat": 4,
-    "nbformat_minor": 2
-}
--- a/examples/notebooks/data_attest_hashed.ipynb
+++ b/examples/notebooks/data_attest_hashed.ipynb
@@ -1,660 +0,0 @@
-{
-    "cells": [
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# data-attest-ezkl hashed\n",
-                "\n",
-                "Here's an example leveraging EZKL whereby the hashes of the outputs to the model are read and attested to from an on-chain source.\n",
-                "\n",
-                "In this setup:\n",
-                "- the hashes of outputs are publicly known to the prover and verifier\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "First we import the necessary dependencies and set up logging to be as informative as possible. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# check if notebook is in colab\n",
-                "try:\n",
-                "    # install ezkl\n",
-                "    import google.colab\n",
-                "    import subprocess\n",
-                "    import sys\n",
-                "    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"ezkl\"])\n",
-                "    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"onnx\"])\n",
-                "\n",
-                "# rely on local installation of ezkl if the notebook is not in colab\n",
-                "except:\n",
-                "    pass\n",
-                "\n",
-                "\n",
-                "from torch import nn\n",
-                "import ezkl\n",
-                "import os\n",
-                "import json\n",
-                "import logging\n",
-                "\n",
-                "# uncomment for more descriptive logging \n",
-                "# FORMAT = '%(levelname)s %(name)s %(asctime)-15s %(filename)s:%(lineno)d %(message)s'\n",
-                "# logging.basicConfig(format=FORMAT)\n",
-                "# logging.getLogger().setLevel(logging.DEBUG)\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Now we define our model. It is a very simple PyTorch model that has just one layer, an average pooling 2D layer. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import torch\n",
-                "# Defines the model\n",
-                "\n",
-                "class MyModel(nn.Module):\n",
-                "    def __init__(self):\n",
-                "        super(MyModel, self).__init__()\n",
-                "        self.layer = nn.AvgPool2d(2, 1, (1, 1))\n",
-                "\n",
-                "    def forward(self, x):\n",
-                "        return self.layer(x)[0]\n",
-                "\n",
-                "\n",
-                "circuit = MyModel()\n",
-                "\n",
-                "# this is where you'd train your model\n",
-                "\n",
-                "\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We omit training for purposes of this demonstration. We've marked where training would happen in the cell above. \n",
-                "Now we export the model to onnx and create a corresponding (randomly generated) input. This input data will eventually be stored on chain and read from according to the call_data field in the graph input.\n",
-                "\n",
-                "You can replace the random `x` with real data if you so wish. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "x = 0.1*torch.rand(1,*[3, 2, 2], requires_grad=True)\n",
-                "\n",
-                "# Flips the neural net into inference mode\n",
-                "circuit.eval()\n",
-                "\n",
-                "    # Export the model\n",
-                "torch.onnx.export(circuit,               # model being run\n",
-                "                      x,                   # model input (or a tuple for multiple inputs)\n",
-                "                      \"network.onnx\",            # where to save the model (can be a file or file-like object)\n",
-                "                      export_params=True,        # store the trained parameter weights inside the model file\n",
-                "                      opset_version=10,          # the ONNX version to export the model to\n",
-                "                      do_constant_folding=True,  # whether to execute constant folding for optimization\n",
-                "                      input_names = ['input'],   # the model's input names\n",
-                "                      output_names = ['output'], # the model's output names\n",
-                "                      dynamic_axes={'input' : {0 : 'batch_size'},    # variable length axes\n",
-                "                                    'output' : {0 : 'batch_size'}})\n",
-                "\n",
-                "data_array = ((x).detach().numpy()).reshape([-1]).tolist()\n",
-                "\n",
-                "data = dict(input_data = [data_array])\n",
-                "\n",
-                "    # Serialize data into file:\n",
-                "json.dump(data, open(\"input.json\", 'w' ))\n",
-                "\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We now define a function that will create a new anvil instance which we will deploy our test contract too. This contract will contain in its storage the data that we will read from and attest to. In production you would not need to set up a local anvil instance. Instead you would replace RPC_URL with the actual RPC endpoint of the chain you are deploying your verifiers too, reading from the data on said chain."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import subprocess\n",
-                "import time\n",
-                "import threading\n",
-                "\n",
-                "# make sure anvil is running locally\n",
-                "# $ anvil -p 3030\n",
-                "\n",
-                "RPC_URL = \"http://localhost:3030\"\n",
-                "\n",
-                "# Save process globally\n",
-                "anvil_process = None\n",
-                "\n",
-                "def start_anvil():\n",
-                "    global anvil_process\n",
-                "    if anvil_process is None:\n",
-                "        anvil_process = subprocess.Popen([\"anvil\", \"-p\", \"3030\", \"--code-size-limit=41943040\"])\n",
-                "        if anvil_process.returncode is not None:\n",
-                "            raise Exception(\"failed to start anvil process\")\n",
-                "        time.sleep(3)\n",
-                "\n",
-                "def stop_anvil():\n",
-                "    global anvil_process\n",
-                "    if anvil_process is not None:\n",
-                "        anvil_process.terminate()\n",
-                "        anvil_process = None\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We define our `PyRunArgs` objects which contains the visibility parameters for out model. \n",
-                "- `input_visibility` defines the visibility of the model inputs\n",
-                "- `param_visibility` defines the visibility of the model weights and constants and parameters \n",
-                "- `output_visibility` defines the visibility of the model outputs\n",
-                "\n",
-                "Here we create the following setup:\n",
-                "- `input_visibility`: \"private\"\n",
-                "- `param_visibility`: \"private\"\n",
-                "- `output_visibility`: hashed\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import ezkl\n",
-                "\n",
-                "model_path = os.path.join('network.onnx')\n",
-                "compiled_model_path = os.path.join('network.compiled')\n",
-                "pk_path = os.path.join('test.pk')\n",
-                "vk_path = os.path.join('test.vk')\n",
-                "settings_path = os.path.join('settings.json')\n",
-                "srs_path = os.path.join('kzg.srs')\n",
-                "data_path = os.path.join('input.json')\n",
-                "\n",
-                "run_args = ezkl.PyRunArgs()\n",
-                "run_args.input_visibility = \"private\"\n",
-                "run_args.param_visibility = \"private\"\n",
-                "run_args.output_visibility = \"hashed\"\n",
-                "run_args.variables = [(\"batch_size\", 1)]\n",
-                "\n",
-                "\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Now we generate a settings file. This file basically instantiates a bunch of parameters that determine their circuit shape, size etc... Because of the way we represent nonlinearities in the circuit (using Halo2's [lookup tables](https://zcash.github.io/halo2/design/proving-system/lookup.html)), it is often best to _calibrate_ this settings file as some data can fall out of range of these lookups.\n",
-                "\n",
-                "You can pass a dataset for calibration that will be representative of real inputs you might find if and when you deploy the prover. Here we create a dummy calibration dataset for demonstration purposes. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "!RUST_LOG=trace\n",
-                "# TODO: Dictionary outputs\n",
-                "res = ezkl.gen_settings(model_path, settings_path, py_run_args=run_args)\n",
-                "assert res == True"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# generate a bunch of dummy calibration data\n",
-                "cal_data = {\n",
-                "    \"input_data\": [(0.1*torch.rand(2, *[3, 2, 2])).flatten().tolist()],\n",
-                "}\n",
-                "\n",
-                "cal_path = os.path.join('val_data.json')\n",
-                "# save as json file\n",
-                "with open(cal_path, \"w\") as f:\n",
-                "    json.dump(cal_data, f)\n",
-                "\n",
-                "res = await ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "res = ezkl.compile_circuit(model_path, compiled_model_path, settings_path)\n",
-                "assert res == True"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "As we use Halo2 with KZG-commitments we need an SRS string from (preferably) a multi-party trusted setup ceremony. For an overview of the procedures for such a ceremony check out [this page](https://blog.ethereum.org/2023/01/16/announcing-kzg-ceremony). The `get_srs` command retrieves a correctly sized SRS given the calibrated settings file from [here](https://github.com/han0110/halo2-kzg-srs). \n",
-                "\n",
-                "These SRS were generated with [this](https://github.com/privacy-scaling-explorations/perpetualpowersoftau) ceremony. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "res = await ezkl.get_srs( settings_path)\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We now need to generate the circuit witness. These are the model outputs (and any hashes) that are generated when feeding the previously generated `input.json` through the circuit / model. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "!export RUST_BACKTRACE=1\n",
-                "\n",
-                "witness_path = \"witness.json\"\n",
-                "\n",
-                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
-                "\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "print(ezkl.felt_to_big_endian(res['processed_outputs']['poseidon_hash'][0]))"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We now post the hashes of the outputs to the chain. This is the data that will be read from and attested to."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "from web3 import Web3, HTTPProvider\n",
-                "from solcx import compile_standard\n",
-                "from decimal import Decimal\n",
-                "import json\n",
-                "import os\n",
-                "import torch\n",
-                "\n",
-                "\n",
-                "# setup web3 instance\n",
-                "w3 = Web3(HTTPProvider(RPC_URL))\n",
-                "\n",
-                "def test_on_chain_data(res):\n",
-                "    print(f'poseidon_hash: {res[\"processed_outputs\"][\"poseidon_hash\"]}')\n",
-                "    # Step 0: Convert the tensor to a flat list\n",
-                "    data = [int(ezkl.felt_to_big_endian(res['processed_outputs']['poseidon_hash'][0]), 0)]\n",
-                "\n",
-                "    # Step 1: Prepare the data\n",
-                "    # Step 2: Prepare and compile the contract.\n",
-                "    # We are using a test contract here but in production you would\n",
-                "    # use whatever contract you are fetching data from.\n",
-                "    contract_source_code = '''\n",
-                "    // SPDX-License-Identifier: UNLICENSED\n",
-                "    pragma solidity ^0.8.17;\n",
-                "\n",
-                "    contract TestReads {\n",
-                "\n",
-                "        uint[] public arr;\n",
-                "        constructor(uint256[] memory _numbers) {\n",
-                "            for(uint256 i = 0; i < _numbers.length; i++) {\n",
-                "                arr.push(_numbers[i]);\n",
-                "            }\n",
-                "        }\n",
-                "        function getArr() public view returns (uint[] memory) {\n",
-                "            return arr;\n",
-                "        }\n",
-                "    }\n",
-                "    '''\n",
-                "\n",
-                "    compiled_sol = compile_standard({\n",
-                "        \"language\": \"Solidity\",\n",
-                "        \"sources\": {\"testreads.sol\": {\"content\": contract_source_code}},\n",
-                "        \"settings\": {\"outputSelection\": {\"*\": {\"*\": [\"metadata\", \"evm.bytecode\", \"abi\"]}}}\n",
-                "    })\n",
-                "\n",
-                "    # Get bytecode\n",
-                "    bytecode = compiled_sol['contracts']['testreads.sol']['TestReads']['evm']['bytecode']['object']\n",
-                "\n",
-                "    # Get ABI\n",
-                "    # In production if you are reading from really large contracts you can just use\n",
-                "    # a stripped down version of the ABI of the contract you are calling, containing only the view functions you will fetch data from.\n",
-                "    abi = json.loads(compiled_sol['contracts']['testreads.sol']['TestReads']['metadata'])['output']['abi']\n",
-                "\n",
-                "    # Step 3: Deploy the contract\n",
-                "    TestReads = w3.eth.contract(abi=abi, bytecode=bytecode)\n",
-                "    tx_hash = TestReads.constructor(data).transact()\n",
-                "    tx_receipt = w3.eth.wait_for_transaction_receipt(tx_hash)\n",
-                "    # If you are deploying to production you can skip the 3 lines of code above and just instantiate the contract like this,\n",
-                "    # passing the address and abi of the contract you are fetching data from.\n",
-                "    contract = w3.eth.contract(address=tx_receipt['contractAddress'], abi=abi)\n",
-                "\n",
-                "    # Step 4: Interact with the contract\n",
-                "    calldata = contract.functions.getArr().build_transaction()['data'][2:]\n",
-                "\n",
-                "    # Prepare the calls_to_account object\n",
-                "    # If you were calling view functions across multiple contracts,\n",
-                "    # you would have multiple entries in the calls_to_account array,\n",
-                "    # one for each contract.\n",
-                "    decimals = [0] * len(data)\n",
-                "    call_to_account = {\n",
-                "        'call_data': calldata,\n",
-                "        'decimals': decimals,\n",
-                "        'address': contract.address[2:], # remove the '0x' prefix\n",
-                "    }\n",
-                "\n",
-                "    print(f'call_to_account: {call_to_account}')\n",
-                "\n",
-                "    return call_to_account\n",
-                "\n",
-                "# Now let's start the Anvil process. You don't need to do this if you are deploying to a non-local chain.\n",
-                "start_anvil()\n",
-                "\n",
-                "# Now let's call our function, passing in the same input tensor we used to export the model 2 cells above.\n",
-                "call_to_account = test_on_chain_data(res)\n",
-                "\n",
-                "data = dict(input_data = [data_array], output_data =  {'rpc': RPC_URL, 'call': call_to_account })\n",
-                "\n",
-                "# Serialize on-chain data into file:\n",
-                "json.dump(data, open(\"input.json\", 'w'))\n",
-                "\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Here we setup verifying and proving keys for the circuit. As the name suggests the proving key is needed for ... proving and the verifying key is needed for ... verifying. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# HERE WE SETUP THE CIRCUIT PARAMS\n",
-                "# WE GOT KEYS\n",
-                "# WE GOT CIRCUIT PARAMETERS\n",
-                "# EVERYTHING ANYONE HAS EVER NEEDED FOR ZK\n",
-                "res = ezkl.setup(\n",
-                "        compiled_model_path,\n",
-                "        vk_path,\n",
-                "        pk_path,\n",
-                "        \n",
-                "    )\n",
-                "\n",
-                "assert res == True\n",
-                "assert os.path.isfile(vk_path)\n",
-                "assert os.path.isfile(pk_path)\n",
-                "assert os.path.isfile(settings_path)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Now we generate a full proof. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# GENERATE A PROOF\n",
-                "\n",
-                "proof_path = os.path.join('test.pf')\n",
-                "\n",
-                "res = ezkl.prove(\n",
-                "        witness_path,\n",
-                "        compiled_model_path,\n",
-                "        pk_path,\n",
-                "        proof_path,\n",
-                "        \n",
-                "        \"single\",\n",
-                "    )\n",
-                "\n",
-                "print(res)\n",
-                "assert os.path.isfile(proof_path)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "And verify it as a sanity check. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# VERIFY IT\n",
-                "\n",
-                "res = ezkl.verify(\n",
-                "        proof_path,\n",
-                "        settings_path,\n",
-                "        vk_path,\n",
-                "        \n",
-                "    )\n",
-                "\n",
-                "assert res == True\n",
-                "print(\"verified\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We can now create and then deploy a vanilla evm verifier."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "abi_path = 'test.abi'\n",
-                "sol_code_path = 'test.sol'\n",
-                "\n",
-                "res = await ezkl.create_evm_verifier(\n",
-                "        vk_path,\n",
-                "        \n",
-                "        settings_path,\n",
-                "        sol_code_path,\n",
-                "        abi_path,\n",
-                "    )\n",
-                "assert res == True"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import json\n",
-                "\n",
-                "addr_path_verifier = \"addr_verifier.txt\"\n",
-                "\n",
-                "res = await ezkl.deploy_evm(\n",
-                "    addr_path_verifier,\n",
-                "    sol_code_path,\n",
-                "    'http://127.0.0.1:3030'\n",
-                ")\n",
-                "\n",
-                "assert res == True"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "With the vanilla verifier deployed, we can now create the data attestation contract, which will read in the instances from the calldata to the verifier, attest to them, call the verifier and then return the result. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "\n",
-                "abi_path = 'test.abi'\n",
-                "sol_code_path = 'test.sol'\n",
-                "input_path = 'input.json'\n",
-                "\n",
-                "res = await ezkl.create_evm_data_attestation(\n",
-                "        input_path,\n",
-                "        settings_path,\n",
-                "        sol_code_path,\n",
-                "        abi_path,\n",
-                "    )"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Now we can deploy the data attest verifier contract. For security reasons, this binding will only deploy to a local anvil instance, using accounts generated by anvil. \n",
-                "So should only be used for testing purposes."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "addr_path_da = \"addr_da.txt\"\n",
-                "\n",
-                "res = await ezkl.deploy_da_evm(\n",
-                "        addr_path_da,\n",
-                "        input_path,\n",
-                "        settings_path,\n",
-                "        sol_code_path,\n",
-                "        RPC_URL,\n",
-                "    )\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Call the view only verify method on the contract to verify the proof. Since it is a view function this is safe to use in production since you don't have to pass your private key."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# read the verifier address\n",
-                "addr_verifier = None\n",
-                "with open(addr_path_verifier, 'r') as f:\n",
-                "    addr = f.read()\n",
-                "#read the data attestation address\n",
-                "addr_da = None\n",
-                "with open(addr_path_da, 'r') as f:\n",
-                "    addr_da = f.read()\n",
-                "\n",
-                "res = await ezkl.verify_evm(\n",
-                "    addr,\n",
-                "    proof_path,\n",
-                "    RPC_URL,\n",
-                "    addr_da,\n",
-                ")"
-            ]
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": ".env",
-            "language": "python",
-            "name": "python3"
-        },
-        "language_info": {
-            "codemirror_mode": {
-                "name": "ipython",
-                "version": 3
-            },
-            "file_extension": ".py",
-            "mimetype": "text/x-python",
-            "name": "python",
-            "nbconvert_exporter": "python",
-            "pygments_lexer": "ipython3",
-            "version": "3.11.5"
-        },
-        "orig_nbformat": 4
-    },
-    "nbformat": 4,
-    "nbformat_minor": 2
-}
--- a/examples/notebooks/data_attest_kzg_vis.ipynb
+++ b/examples/notebooks/data_attest_kzg_vis.ipynb
@@ -1,592 +0,0 @@
-{
-    "cells": [
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# data-attest-kzg-vis\n",
-                "\n",
-                "Here's an example leveraging EZKL whereby the inputs to the model are read and attested to from an on-chain source and the params and outputs are committed to using kzg-commitments. \n",
-                "\n",
-                "In this setup:\n",
-                "- the inputs and outputs are publicly known to the prover and verifier\n",
-                "- the on chain inputs will be fetched and then fed directly into the circuit\n",
-                "- the quantization of the on-chain inputs happens within the evm and is replicated at proving time \n",
-                "- The kzg commitment to the params and inputs will be read from the proof and checked to make sure it matches the expected commitment stored on-chain.\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "First we import the necessary dependencies and set up logging to be as informative as possible. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# check if notebook is in colab\n",
-                "try:\n",
-                "    # install ezkl\n",
-                "    import google.colab\n",
-                "    import subprocess\n",
-                "    import sys\n",
-                "    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"ezkl\"])\n",
-                "    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"onnx\"])\n",
-                "\n",
-                "# rely on local installation of ezkl if the notebook is not in colab\n",
-                "except:\n",
-                "    pass\n",
-                "\n",
-                "\n",
-                "from torch import nn\n",
-                "import ezkl\n",
-                "import os\n",
-                "import json\n",
-                "import logging\n",
-                "\n",
-                "# uncomment for more descriptive logging \n",
-                "FORMAT = '%(levelname)s %(name)s %(asctime)-15s %(filename)s:%(lineno)d %(message)s'\n",
-                "logging.basicConfig(format=FORMAT)\n",
-                "logging.getLogger().setLevel(logging.DEBUG)\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Now we define our model. It is a very simple PyTorch model that has just one layer, an average pooling 2D layer. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import torch\n",
-                "# Defines the model\n",
-                "\n",
-                "class MyModel(nn.Module):\n",
-                "    def __init__(self):\n",
-                "        super(MyModel, self).__init__()\n",
-                "        self.layer = nn.AvgPool2d(2, 1, (1, 1))\n",
-                "\n",
-                "    def forward(self, x):\n",
-                "        return self.layer(x)[0]\n",
-                "\n",
-                "\n",
-                "circuit = MyModel()\n",
-                "\n",
-                "# this is where you'd train your model"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We omit training for purposes of this demonstration. We've marked where training would happen in the cell above. \n",
-                "Now we export the model to onnx and create a corresponding (randomly generated) input. This input data will eventually be stored on chain and read from according to the call_data field in the graph input.\n",
-                "\n",
-                "You can replace the random `x` with real data if you so wish. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "x = 0.1*torch.rand(1,*[3, 2, 2], requires_grad=True)\n",
-                "\n",
-                "# Flips the neural net into inference mode\n",
-                "circuit.eval()\n",
-                "\n",
-                "    # Export the model\n",
-                "torch.onnx.export(circuit,               # model being run\n",
-                "                      x,                   # model input (or a tuple for multiple inputs)\n",
-                "                      \"network.onnx\",            # where to save the model (can be a file or file-like object)\n",
-                "                      export_params=True,        # store the trained parameter weights inside the model file\n",
-                "                      opset_version=10,          # the ONNX version to export the model to\n",
-                "                      do_constant_folding=True,  # whether to execute constant folding for optimization\n",
-                "                      input_names = ['input'],   # the model's input names\n",
-                "                      output_names = ['output'], # the model's output names\n",
-                "                      dynamic_axes={'input' : {0 : 'batch_size'},    # variable length axes\n",
-                "                                    'output' : {0 : 'batch_size'}})\n",
-                "\n",
-                "data_array = ((x).detach().numpy()).reshape([-1]).tolist()\n",
-                "\n",
-                "data = dict(input_data = [data_array])\n",
-                "\n",
-                "    # Serialize data into file:\n",
-                "json.dump(data, open(\"input.json\", 'w' ))\n",
-                "\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We now define a function that will create a new anvil instance which we will deploy our test contract too. This contract will contain in its storage the data that we will read from and attest to. In production you would not need to set up a local anvil instance. Instead you would replace RPC_URL with the actual RPC endpoint of the chain you are deploying your verifiers too, reading from the data on said chain."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import subprocess\n",
-                "import time\n",
-                "import threading\n",
-                "\n",
-                "# make sure anvil is running locally\n",
-                "# $ anvil -p 3030\n",
-                "\n",
-                "RPC_URL = \"http://localhost:3030\"\n",
-                "\n",
-                "# Save process globally\n",
-                "anvil_process = None\n",
-                "\n",
-                "def start_anvil():\n",
-                "    global anvil_process\n",
-                "    if anvil_process is None:\n",
-                "        anvil_process = subprocess.Popen([\"anvil\", \"-p\", \"3030\", \"--code-size-limit=41943040\"])\n",
-                "        if anvil_process.returncode is not None:\n",
-                "            raise Exception(\"failed to start anvil process\")\n",
-                "        time.sleep(3)\n",
-                "\n",
-                "def stop_anvil():\n",
-                "    global anvil_process\n",
-                "    if anvil_process is not None:\n",
-                "        anvil_process.terminate()\n",
-                "        anvil_process = None\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We define our `PyRunArgs` objects which contains the visibility parameters for out model. \n",
-                "- `input_visibility` defines the visibility of the model inputs\n",
-                "- `param_visibility` defines the visibility of the model weights and constants and parameters \n",
-                "- `output_visibility` defines the visibility of the model outputs\n",
-                "\n",
-                "Here we create the following setup:\n",
-                "- `input_visibility`: \"public\"\n",
-                "- `param_visibility`: \"polycommitment\" \n",
-                "- `output_visibility`: \"polycommitment\"\n",
-                "\n",
-                "**Note**:\n",
-                "When we set this to polycommitment, we are saying that the model parameters are committed to using a polynomial commitment scheme. This commitment will be stored on chain as a constant stored in the DA contract, and the proof will contain the commitment to the parameters. The DA verification will then check that the commitment in the proof matches the commitment stored on chain. \n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import ezkl\n",
-                "\n",
-                "model_path = os.path.join('network.onnx')\n",
-                "compiled_model_path = os.path.join('network.compiled')\n",
-                "pk_path = os.path.join('test.pk')\n",
-                "vk_path = os.path.join('test.vk')\n",
-                "settings_path = os.path.join('settings.json')\n",
-                "srs_path = os.path.join('kzg.srs')\n",
-                "data_path = os.path.join('input.json')\n",
-                "\n",
-                "run_args = ezkl.PyRunArgs()\n",
-                "run_args.input_visibility = \"public\"\n",
-                "run_args.param_visibility = \"polycommit\"\n",
-                "run_args.output_visibility = \"polycommit\"\n",
-                "run_args.num_inner_cols = 1\n",
-                "run_args.variables = [(\"batch_size\", 1)]\n",
-                "\n",
-                "\n",
-                "\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Now we generate a settings file. This file basically instantiates a bunch of parameters that determine their circuit shape, size etc... Because of the way we represent nonlinearities in the circuit (using Halo2's [lookup tables](https://zcash.github.io/halo2/design/proving-system/lookup.html)), it is often best to _calibrate_ this settings file as some data can fall out of range of these lookups.\n",
-                "\n",
-                "You can pass a dataset for calibration that will be representative of real inputs you might find if and when you deploy the prover. Here we create a dummy calibration dataset for demonstration purposes. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "!RUST_LOG=trace\n",
-                "# TODO: Dictionary outputs\n",
-                "res = ezkl.gen_settings(model_path, settings_path, py_run_args=run_args)\n",
-                "assert res == True"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# generate a bunch of dummy calibration data\n",
-                "cal_data = {\n",
-                "    \"input_data\": [(0.1*torch.rand(2, *[3, 2, 2])).flatten().tolist()],\n",
-                "}\n",
-                "\n",
-                "cal_path = os.path.join('val_data.json')\n",
-                "# save as json file\n",
-                "with open(cal_path, \"w\") as f:\n",
-                "    json.dump(cal_data, f)\n",
-                "\n",
-                "res = await ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "res = ezkl.compile_circuit(model_path, compiled_model_path, settings_path)\n",
-                "assert res == True"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "The graph input for on chain data sources is formatted completely differently compared to file based data sources.\n",
-                "\n",
-                "- For file data sources, the raw floating point values that eventually get quantized, converted into field elements and stored in `witness.json` to be consumed by the circuit are stored. The output data contains the expected floating point values returned as outputs from running your vanilla pytorch model on the given inputs.\n",
-                "- For on chain data sources, the input_data field contains all the data necessary to read and format the on chain data into something digestable by EZKL (aka field elements :-D). \n",
-                "Here is what the schema for an on-chain data source graph input file should look like for a single call data source:\n",
-                "    \n",
-                "```json\n",
-                "{\n",
-                "    \"input_data\": {\n",
-                "        \"rpc\": \"http://localhost:3030\", // The rpc endpoint of the chain you are deploying your verifier to\n",
-                "        \"calls\": {\n",
-                "            \"call_data\": \"1f3be514000000000000000000000000c6962004f452be9203591991d15f6b388e09e8d00000000000000000000000000000000000000000000000000000000000000040000000000000000000000000000000000000000000000000000000000000000c000000000000000000000000000000000000000000000000000000000000000b000000000000000000000000000000000000000000000000000000000000000a0000000000000000000000000000000000000000000000000000000000000009000000000000000000000000000000000000000000000000000000000000000800000000000000000000000000000000000000000000000000000000000000070000000000000000000000000000000000000000000000000000000000000006000000000000000000000000000000000000000000000000000000000000000500000000000000000000000000000000000000000000000000000000000000040000000000000000000000000000000000000000000000000000000000000003000000000000000000000000000000000000000000000000000000000000000200000000000000000000000000000000000000000000000000000000000000010000000000000000000000000000000000000000000000000000000000000000\", // The abi encoded call data to a view function that returns an array of on-chain data points we are attesting to. \n",
-                "            \"decimals\": 0, // The number of decimal places of the large uint256 value. This is our way of representing large wei values as floating points on chain, since the evm only natively supports integer values.\n",
-                "            \"address\": \"9A213F53334279C128C37DA962E5472eCD90554f\", // The address of the contract that we are calling to get the data. \n",
-                "            \"len\": 3 // The number of data points returned by the view function (the length of the array)\n",
-                "        }\n",
-                "    }\n",
-                "}\n",
-                "```"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "await ezkl.setup_test_evm_data(\n",
-                "    data_path,\n",
-                "    compiled_model_path,\n",
-                "    # we write the call data to the same file as the input data\n",
-                "    data_path,\n",
-                "    input_source=ezkl.PyTestDataSource.OnChain,\n",
-                "    output_source=ezkl.PyTestDataSource.File,\n",
-                "    rpc_url=RPC_URL)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "As we use Halo2 with KZG-commitments we need an SRS string from (preferably) a multi-party trusted setup ceremony. For an overview of the procedures for such a ceremony check out [this page](https://blog.ethereum.org/2023/01/16/announcing-kzg-ceremony). The `get_srs` command retrieves a correctly sized SRS given the calibrated settings file from [here](https://github.com/han0110/halo2-kzg-srs). \n",
-                "\n",
-                "These SRS were generated with [this](https://github.com/privacy-scaling-explorations/perpetualpowersoftau) ceremony. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "res = await ezkl.get_srs( settings_path)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We now need to generate the circuit witness. These are the model outputs (and any hashes) that are generated when feeding the previously generated `input.json` through the circuit / model. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# HERE WE SETUP THE CIRCUIT PARAMS\n",
-                "# WE GOT KEYS\n",
-                "# WE GOT CIRCUIT PARAMETERS\n",
-                "# EVERYTHING ANYONE HAS EVER NEEDED FOR ZK\n",
-                "res = ezkl.setup(\n",
-                "        compiled_model_path,\n",
-                "        vk_path,\n",
-                "        pk_path,\n",
-                "    )\n",
-                "\n",
-                "assert res == True\n",
-                "assert os.path.isfile(vk_path)\n",
-                "assert os.path.isfile(pk_path)\n",
-                "assert os.path.isfile(settings_path)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "!export RUST_BACKTRACE=1\n",
-                "\n",
-                "witness_path = \"witness.json\"\n",
-                "\n",
-                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path, vk_path)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Here we setup verifying and proving keys for the circuit. As the name suggests the proving key is needed for ... proving and the verifying key is needed for ... verifying. "
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Now we generate a full proof. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# GENERATE A PROOF\n",
-                "\n",
-                "proof_path = os.path.join('test.pf')\n",
-                "\n",
-                "res = ezkl.prove(\n",
-                "        witness_path,\n",
-                "        compiled_model_path,\n",
-                "        pk_path,\n",
-                "        proof_path,\n",
-                "        \n",
-                "        \"single\",\n",
-                "    )\n",
-                "\n",
-                "print(res)\n",
-                "assert os.path.isfile(proof_path)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "And verify it as a sanity check. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# VERIFY IT\n",
-                "\n",
-                "res = ezkl.verify(\n",
-                "        proof_path,\n",
-                "        settings_path,\n",
-                "        vk_path,\n",
-                "        \n",
-                "    )\n",
-                "\n",
-                "assert res == True\n",
-                "print(\"verified\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We can now create and then deploy a vanilla evm verifier."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "abi_path = 'test.abi'\n",
-                "sol_code_path = 'test.sol'\n",
-                "\n",
-                "res = await ezkl.create_evm_verifier(\n",
-                "        vk_path,\n",
-                "        \n",
-                "        settings_path,\n",
-                "        sol_code_path,\n",
-                "        abi_path,\n",
-                "    )\n",
-                "assert res == True"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "\n",
-                "addr_path_verifier = \"addr_verifier.txt\"\n",
-                "\n",
-                "res = await ezkl.deploy_evm(\n",
-                "    addr_path_verifier,\n",
-                "    sol_code_path,\n",
-                "    'http://127.0.0.1:3030'\n",
-                ")\n",
-                "\n",
-                "assert res == True"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "When deploying a DA with kzg commitments, we need to make sure to also pass a witness file that contains the commitments to the parameters and inputs. This is because the verifier will need to check that the commitments in the proof match the commitments stored on chain."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "\n",
-                "abi_path = 'test.abi'\n",
-                "sol_code_path = 'test.sol'\n",
-                "input_path = 'input.json'\n",
-                "\n",
-                "res = await ezkl.create_evm_data_attestation(\n",
-                "        input_path,\n",
-                "        settings_path,\n",
-                "        sol_code_path,\n",
-                "        abi_path,\n",
-                "        witness_path = witness_path,\n",
-                "    )"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Now we can deploy the data attest verifier contract. For security reasons, this binding will only deploy to a local anvil instance, using accounts generated by anvil. \n",
-                "So should only be used for testing purposes."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "addr_path_da = \"addr_da.txt\"\n",
-                "\n",
-                "res = await ezkl.deploy_da_evm(\n",
-                "        addr_path_da,\n",
-                "        input_path,\n",
-                "        settings_path,\n",
-                "        sol_code_path,\n",
-                "        RPC_URL,\n",
-                "    )\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Call the view only verify method on the contract to verify the proof. Since it is a view function this is safe to use in production since you don't have to pass your private key."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# read the verifier address\n",
-                "addr_verifier = None\n",
-                "with open(addr_path_verifier, 'r') as f:\n",
-                "    addr = f.read()\n",
-                "#read the data attestation address\n",
-                "addr_da = None\n",
-                "with open(addr_path_da, 'r') as f:\n",
-                "    addr_da = f.read()\n",
-                "\n",
-                "res = await ezkl.verify_evm(\n",
-                "    addr,\n",
-                "    proof_path,\n",
-                "    RPC_URL,\n",
-                "    addr_da,\n",
-                ")"
-            ]
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": ".env",
-            "language": "python",
-            "name": "python3"
-        },
-        "language_info": {
-            "codemirror_mode": {
-                "name": "ipython",
-                "version": 3
-            },
-            "file_extension": ".py",
-            "mimetype": "text/x-python",
-            "name": "python",
-            "nbconvert_exporter": "python",
-            "pygments_lexer": "ipython3",
-            "version": "3.11.5"
-        },
-        "orig_nbformat": 4
-    },
-    "nbformat": 4,
-    "nbformat_minor": 2
-}
--- a/examples/notebooks/decision_tree.ipynb
+++ b/examples/notebooks/decision_tree.ipynb
@@ -150,7 +150,7 @@
                "res = ezkl.gen_settings(model_path, settings_path)\n",
                "assert res == True\n",
                "\n",
-                "res = await ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
+                "res = ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
                "assert res == True"
            ]
        },
@@ -170,7 +170,7 @@
                "with open(cal_path, \"w\") as f:\n",
                "    json.dump(cal_data, f)\n",
                "\n",
-                "res = await ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
+                "res = ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
            ]
        },
        {
@@ -204,7 +204,7 @@
            "source": [
                "# now generate the witness file \n",
                "\n",
-                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
                "assert os.path.isfile(witness_path)"
            ]
        },
--- a/examples/notebooks/ezkl_demo.ipynb
+++ b/examples/notebooks/ezkl_demo.ipynb
@@ -437,7 +437,7 @@
        "\n",
        "# Optimize for resources, we cap logrows at 12 to reduce setup and proving time, at the expense of accuracy\n",
        "# You may want to increase the max logrows if accuracy is a concern\n",
-        "res = await ezkl.calibrate_settings(target = \"resources\", max_logrows = 12, scales = [2])"
+        "res = ezkl.calibrate_settings(target = \"resources\", max_logrows = 12, scales = [2])"
      ]
    },
    {
@@ -526,7 +526,7 @@
        "# now generate the witness file\n",
        "witness_path = os.path.join('witness.json')\n",
        "\n",
-        "res = await ezkl.gen_witness()\n",
+        "res = ezkl.gen_witness()\n",
        "assert os.path.isfile(witness_path)"
      ]
    },
@@ -736,4 +736,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 0
-}
+}
--- a/examples/notebooks/ezkl_demo_batch.ipynb
+++ b/examples/notebooks/ezkl_demo_batch.ipynb
--- a/examples/notebooks/felt_conversion_test.ipynb
+++ b/examples/notebooks/felt_conversion_test.ipynb
--- a/examples/notebooks/gcn.ipynb
+++ b/examples/notebooks/gcn.ipynb
@@ -467,7 +467,7 @@
      "outputs": [],
      "source": [
        "\n",
-        "res = await ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
+        "res = ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
        "assert res == True"
      ]
    },
@@ -508,7 +508,7 @@
      "source": [
        "# now generate the witness file\n",
        "\n",
-        "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+        "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
        "assert os.path.isfile(witness_path)"
      ]
    },
--- a/examples/notebooks/generalized_inverse.ipynb
+++ b/examples/notebooks/generalized_inverse.ipynb
@@ -196,7 +196,7 @@
        "json.dump(data, open(cal_path, 'w'))\n",
        "\n",
        "\n",
-        "res = await ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
+        "res = ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
        "assert res == True\n"
      ]
    },
@@ -237,7 +237,7 @@
      "source": [
        "# now generate the witness file\n",
        "\n",
-        "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+        "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
        "assert os.path.isfile(witness_path)"
      ]
    },
@@ -341,4 +341,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}
--- a/examples/notebooks/gradient_boosted_trees.ipynb
+++ b/examples/notebooks/gradient_boosted_trees.ipynb
@@ -179,7 +179,7 @@
                "json.dump(data, open(cal_path, 'w'))\n",
                "\n",
                "\n",
-                "res = await ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
+                "res = ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
                "assert res == True\n"
            ]
        },
@@ -214,7 +214,7 @@
            "source": [
                "# now generate the witness file \n",
                "\n",
-                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
                "assert os.path.isfile(witness_path)"
            ]
        },
--- a/examples/notebooks/hashed_vis.ipynb
+++ b/examples/notebooks/hashed_vis.ipynb
@@ -241,7 +241,7 @@
                "with open(cal_path, \"w\") as f:\n",
                "    json.dump(cal_data, f)\n",
                "\n",
-                "res = await ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
+                "res = ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
            ]
        },
        {
@@ -291,7 +291,7 @@
                "\n",
                "witness_path = \"witness.json\"\n",
                "\n",
-                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)"
+                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)"
            ]
        },
        {
@@ -453,8 +453,8 @@
                "\n",
                "res = await ezkl.deploy_evm(\n",
                "    address_path,\n",
+                "    'http://127.0.0.1:3030',\n",
                "    sol_code_path,\n",
-                "    'http://127.0.0.1:3030'\n",
                ")\n",
                "\n",
                "assert res == True\n",
@@ -474,8 +474,8 @@
                "\n",
                "res = await ezkl.verify_evm(\n",
                "    addr,\n",
+                "    \"http://127.0.0.1:3030\",\n",
                "    proof_path,\n",
-                "    \"http://127.0.0.1:3030\"\n",
                ")\n",
                "assert res == True"
            ]
--- a/examples/notebooks/keras_simple_demo.ipynb
+++ b/examples/notebooks/keras_simple_demo.ipynb
@@ -152,7 +152,7 @@
                "json.dump(data, open(cal_path, 'w'))\n",
                "\n",
                "\n",
-                "res = await ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
+                "res = ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
                "assert res == True\n"
            ]
        },
@@ -188,7 +188,7 @@
                "# now generate the witness file \n",
                "witness_path = \"witness.json\"\n",
                "\n",
-                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
                "assert os.path.isfile(witness_path)"
            ]
        },
--- a/examples/notebooks/kmeans.ipynb
+++ b/examples/notebooks/kmeans.ipynb
@@ -155,7 +155,7 @@
                "json.dump(data, open(cal_path, 'w'))\n",
                "\n",
                "\n",
-                "res = await ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
+                "res = ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
                "assert res == True\n"
            ]
        },
@@ -190,7 +190,7 @@
            "source": [
                "# now generate the witness file \n",
                "\n",
-                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
                "assert os.path.isfile(witness_path)"
            ]
        },
--- a/examples/notebooks/kzg_vis.ipynb
+++ b/examples/notebooks/kzg_vis.ipynb
@@ -233,7 +233,7 @@
                "with open(cal_path, \"w\") as f:\n",
                "    json.dump(cal_data, f)\n",
                "\n",
-                "res = await ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
+                "res = ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
            ]
        },
        {
@@ -315,7 +315,7 @@
                "\n",
                "witness_path = \"witness.json\"\n",
                "\n",
-                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path, vk_path)\n"
+                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path, vk_path)\n"
            ]
        },
        {
@@ -462,8 +462,8 @@
                "\n",
                "res = await ezkl.deploy_evm(\n",
                "    address_path,\n",
+                "    'http://127.0.0.1:3030',\n",
                "    sol_code_path,\n",
-                "    'http://127.0.0.1:3030'\n",
                ")\n",
                "\n",
                "assert res == True\n",
@@ -483,8 +483,8 @@
                "\n",
                "res = await ezkl.verify_evm(\n",
                "    addr,\n",
+                "    \"http://127.0.0.1:3030\",\n",
                "    proof_path,\n",
-                "    \"http://127.0.0.1:3030\"\n",
                ")\n",
                "assert res == True"
            ]
--- a/examples/notebooks/lightgbm.ipynb
+++ b/examples/notebooks/lightgbm.ipynb
@@ -193,7 +193,7 @@
                "json.dump(data, open(cal_path, 'w'))\n",
                "\n",
                "\n",
-                "res = await ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
+                "res = ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
                "assert res == True\n"
            ]
        },
@@ -228,7 +228,7 @@
            "source": [
                "# now generate the witness file \n",
                "\n",
-                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
                "assert os.path.isfile(witness_path)"
            ]
        },
--- a/examples/notebooks/linear_regression.ipynb
+++ b/examples/notebooks/linear_regression.ipynb
@@ -1,284 +1,284 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "cf69bb3f-94e6-4dba-92cd-ce08df117d67",
-   "metadata": {},
-   "source": [
-    "## Linear Regression\n",
-    "\n",
-    "\n",
-    "Sklearn based models are slightly finicky to get into a suitable onnx format. \n",
-    "This notebook showcases how to do so using the `hummingbird-ml` python package ! "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "95613ee9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# check if notebook is in colab\n",
-    "try:\n",
-    "    # install ezkl\n",
-    "    import google.colab\n",
-    "    import subprocess\n",
-    "    import sys\n",
-    "    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"ezkl\"])\n",
-    "    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"onnx\"])\n",
-    "    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"hummingbird-ml\"])\n",
-    "\n",
-    "# rely on local installation of ezkl if the notebook is not in colab\n",
-    "except:\n",
-    "    pass\n",
-    "\n",
-    "import os\n",
-    "import torch\n",
-    "import ezkl\n",
-    "import json\n",
-    "from hummingbird.ml import convert\n",
-    "\n",
-    "\n",
-    "# here we create and (potentially train a model)\n",
-    "\n",
-    "# make sure you have the dependencies required here already installed\n",
-    "import numpy as np\n",
-    "from sklearn.linear_model import LinearRegression\n",
-    "X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])\n",
-    "# y = 1 * x_0 + 2 * x_1 + 3\n",
-    "y = np.dot(X, np.array([1, 2])) + 3\n",
-    "reg = LinearRegression().fit(X, y)\n",
-    "reg.score(X, y)\n",
-    "\n",
-    "circuit = convert(reg, \"torch\", X[:1]).model\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b37637c4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model_path = os.path.join('network.onnx')\n",
-    "compiled_model_path = os.path.join('network.compiled')\n",
-    "pk_path = os.path.join('test.pk')\n",
-    "vk_path = os.path.join('test.vk')\n",
-    "settings_path = os.path.join('settings.json')\n",
-    "\n",
-    "witness_path = os.path.join('witness.json')\n",
-    "data_path = os.path.join('input.json')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "82db373a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "\n",
-    "# export to onnx format\n",
-    "# !!!!!!!!!!!!!!!!! This will flash a warning but it is fine !!!!!!!!!!!!!!!!!!!!!\n",
-    "\n",
-    "# Input to the model\n",
-    "shape = X.shape[1:]\n",
-    "x = torch.rand(1, *shape, requires_grad=True)\n",
-    "torch_out = circuit(x)\n",
-    "# Export the model\n",
-    "torch.onnx.export(circuit,               # model being run\n",
-    "                  # model input (or a tuple for multiple inputs)\n",
-    "                  x,\n",
-    "                  # where to save the model (can be a file or file-like object)\n",
-    "                  \"network.onnx\",\n",
-    "                  export_params=True,        # store the trained parameter weights inside the model file\n",
-    "                  opset_version=10,          # the ONNX version to export the model to\n",
-    "                  do_constant_folding=True,  # whether to execute constant folding for optimization\n",
-    "                  input_names=['input'],   # the model's input names\n",
-    "                  output_names=['output'],  # the model's output names\n",
-    "                  dynamic_axes={'input': {0: 'batch_size'},    # variable length axes\n",
-    "                                'output': {0: 'batch_size'}})\n",
-    "\n",
-    "d = ((x).detach().numpy()).reshape([-1]).tolist()\n",
-    "\n",
-    "data = dict(input_shapes=[shape],\n",
-    "            input_data=[d],\n",
-    "            output_data=[((o).detach().numpy()).reshape([-1]).tolist() for o in torch_out])\n",
-    "\n",
-    "# Serialize data into file:\n",
-    "json.dump(data, open(\"input.json\", 'w'))\n",
-    "\n",
-    "\n",
-    "# note that you can also call the following function to generate random data for the model\n",
-    "# it is functionally equivalent to the code above\n",
-    "ezkl.gen_random_data()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d5e374a2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!RUST_LOG=trace\n",
-    "# TODO: Dictionary outputs\n",
-    "res = ezkl.gen_settings(model_path, settings_path)\n",
-    "assert res == True\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cal_path = os.path.join(\"calibration.json\")\n",
-    "\n",
-    "data_array = (torch.randn(20, *shape).detach().numpy()).reshape([-1]).tolist()\n",
-    "\n",
-    "data = dict(input_data = [data_array])\n",
-    "\n",
-    "# Serialize data into file:\n",
-    "json.dump(data, open(cal_path, 'w'))\n",
-    "\n",
-    "res = await ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
-    "assert res == True\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3aa4f090",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "res = ezkl.compile_circuit(model_path, compiled_model_path, settings_path)\n",
-    "assert res == True"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8b74dcee",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# srs path\n",
-    "res = await ezkl.get_srs( settings_path)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "18c8b7c7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# now generate the witness file \n",
-    "\n",
-    "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
-    "assert os.path.isfile(witness_path)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b1c561a8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "# HERE WE SETUP THE CIRCUIT PARAMS\n",
-    "# WE GOT KEYS\n",
-    "# WE GOT CIRCUIT PARAMETERS\n",
-    "# EVERYTHING ANYONE HAS EVER NEEDED FOR ZK\n",
-    "\n",
-    "\n",
-    "\n",
-    "res = ezkl.setup(\n",
-    "        compiled_model_path,\n",
-    "        vk_path,\n",
-    "        pk_path,\n",
-    "        \n",
-    "    )\n",
-    "\n",
-    "assert res == True\n",
-    "assert os.path.isfile(vk_path)\n",
-    "assert os.path.isfile(pk_path)\n",
-    "assert os.path.isfile(settings_path)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c384cbc8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# GENERATE A PROOF\n",
-    "\n",
-    "\n",
-    "proof_path = os.path.join('test.pf')\n",
-    "\n",
-    "res = ezkl.prove(\n",
-    "        witness_path,\n",
-    "        compiled_model_path,\n",
-    "        pk_path,\n",
-    "        proof_path,\n",
-    "        \n",
-    "        \"single\",\n",
-    "    )\n",
-    "\n",
-    "print(res)\n",
-    "assert os.path.isfile(proof_path)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "76f00d41",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# VERIFY IT\n",
-    "\n",
-    "res = ezkl.verify(\n",
-    "        proof_path,\n",
-    "        settings_path,\n",
-    "        vk_path,\n",
-    "        \n",
-    "    )\n",
-    "\n",
-    "assert res == True\n",
-    "print(\"verified\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.15"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "cf69bb3f-94e6-4dba-92cd-ce08df117d67",
+            "metadata": {},
+            "source": [
+                "## Linear Regression\n",
+                "\n",
+                "\n",
+                "Sklearn based models are slightly finicky to get into a suitable onnx format. \n",
+                "This notebook showcases how to do so using the `hummingbird-ml` python package ! "
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "id": "95613ee9",
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "# check if notebook is in colab\n",
+                "try:\n",
+                "    # install ezkl\n",
+                "    import google.colab\n",
+                "    import subprocess\n",
+                "    import sys\n",
+                "    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"ezkl\"])\n",
+                "    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"onnx\"])\n",
+                "    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"hummingbird-ml\"])\n",
+                "\n",
+                "# rely on local installation of ezkl if the notebook is not in colab\n",
+                "except:\n",
+                "    pass\n",
+                "\n",
+                "import os\n",
+                "import torch\n",
+                "import ezkl\n",
+                "import json\n",
+                "from hummingbird.ml import convert\n",
+                "\n",
+                "\n",
+                "# here we create and (potentially train a model)\n",
+                "\n",
+                "# make sure you have the dependencies required here already installed\n",
+                "import numpy as np\n",
+                "from sklearn.linear_model import LinearRegression\n",
+                "X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])\n",
+                "# y = 1 * x_0 + 2 * x_1 + 3\n",
+                "y = np.dot(X, np.array([1, 2])) + 3\n",
+                "reg = LinearRegression().fit(X, y)\n",
+                "reg.score(X, y)\n",
+                "\n",
+                "circuit = convert(reg, \"torch\", X[:1]).model\n",
+                "\n",
+                "\n",
+                "\n"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "id": "b37637c4",
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "model_path = os.path.join('network.onnx')\n",
+                "compiled_model_path = os.path.join('network.compiled')\n",
+                "pk_path = os.path.join('test.pk')\n",
+                "vk_path = os.path.join('test.vk')\n",
+                "settings_path = os.path.join('settings.json')\n",
+                "\n",
+                "witness_path = os.path.join('witness.json')\n",
+                "data_path = os.path.join('input.json')"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "id": "82db373a",
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "\n",
+                "\n",
+                "# export to onnx format\n",
+                "# !!!!!!!!!!!!!!!!! This will flash a warning but it is fine !!!!!!!!!!!!!!!!!!!!!\n",
+                "\n",
+                "# Input to the model\n",
+                "shape = X.shape[1:]\n",
+                "x = torch.rand(1, *shape, requires_grad=True)\n",
+                "torch_out = circuit(x)\n",
+                "# Export the model\n",
+                "torch.onnx.export(circuit,               # model being run\n",
+                "                  # model input (or a tuple for multiple inputs)\n",
+                "                  x,\n",
+                "                  # where to save the model (can be a file or file-like object)\n",
+                "                  \"network.onnx\",\n",
+                "                  export_params=True,        # store the trained parameter weights inside the model file\n",
+                "                  opset_version=10,          # the ONNX version to export the model to\n",
+                "                  do_constant_folding=True,  # whether to execute constant folding for optimization\n",
+                "                  input_names=['input'],   # the model's input names\n",
+                "                  output_names=['output'],  # the model's output names\n",
+                "                  dynamic_axes={'input': {0: 'batch_size'},    # variable length axes\n",
+                "                                'output': {0: 'batch_size'}})\n",
+                "\n",
+                "d = ((x).detach().numpy()).reshape([-1]).tolist()\n",
+                "\n",
+                "data = dict(input_shapes=[shape],\n",
+                "            input_data=[d],\n",
+                "            output_data=[((o).detach().numpy()).reshape([-1]).tolist() for o in torch_out])\n",
+                "\n",
+                "# Serialize data into file:\n",
+                "json.dump(data, open(\"input.json\", 'w'))\n",
+                "\n",
+                "\n",
+                "# note that you can also call the following function to generate random data for the model\n",
+                "# it is functionally equivalent to the code above\n",
+                "ezkl.gen_random_data()\n"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "id": "d5e374a2",
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "!RUST_LOG=trace\n",
+                "# TODO: Dictionary outputs\n",
+                "res = ezkl.gen_settings(model_path, settings_path)\n",
+                "assert res == True\n"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "cal_path = os.path.join(\"calibration.json\")\n",
+                "\n",
+                "data_array = (torch.randn(20, *shape).detach().numpy()).reshape([-1]).tolist()\n",
+                "\n",
+                "data = dict(input_data = [data_array])\n",
+                "\n",
+                "# Serialize data into file:\n",
+                "json.dump(data, open(cal_path, 'w'))\n",
+                "\n",
+                "res = ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
+                "assert res == True\n"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "id": "3aa4f090",
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "res = ezkl.compile_circuit(model_path, compiled_model_path, settings_path)\n",
+                "assert res == True"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "id": "8b74dcee",
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "# srs path\n",
+                "res = await ezkl.get_srs( settings_path)"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "id": "18c8b7c7",
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "# now generate the witness file \n",
+                "\n",
+                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+                "assert os.path.isfile(witness_path)"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "id": "b1c561a8",
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "\n",
+                "# HERE WE SETUP THE CIRCUIT PARAMS\n",
+                "# WE GOT KEYS\n",
+                "# WE GOT CIRCUIT PARAMETERS\n",
+                "# EVERYTHING ANYONE HAS EVER NEEDED FOR ZK\n",
+                "\n",
+                "\n",
+                "\n",
+                "res = ezkl.setup(\n",
+                "        compiled_model_path,\n",
+                "        vk_path,\n",
+                "        pk_path,\n",
+                "        \n",
+                "    )\n",
+                "\n",
+                "assert res == True\n",
+                "assert os.path.isfile(vk_path)\n",
+                "assert os.path.isfile(pk_path)\n",
+                "assert os.path.isfile(settings_path)"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "id": "c384cbc8",
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "# GENERATE A PROOF\n",
+                "\n",
+                "\n",
+                "proof_path = os.path.join('test.pf')\n",
+                "\n",
+                "res = ezkl.prove(\n",
+                "        witness_path,\n",
+                "        compiled_model_path,\n",
+                "        pk_path,\n",
+                "        proof_path,\n",
+                "        \n",
+                "        \"single\",\n",
+                "    )\n",
+                "\n",
+                "print(res)\n",
+                "assert os.path.isfile(proof_path)"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "id": "76f00d41",
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "# VERIFY IT\n",
+                "\n",
+                "res = ezkl.verify(\n",
+                "        proof_path,\n",
+                "        settings_path,\n",
+                "        vk_path,\n",
+                "        \n",
+                "    )\n",
+                "\n",
+                "assert res == True\n",
+                "print(\"verified\")"
+            ]
+        }
+    ],
+    "metadata": {
+        "kernelspec": {
+            "display_name": "Python 3 (ipykernel)",
+            "language": "python",
+            "name": "python3"
+        },
+        "language_info": {
+            "codemirror_mode": {
+                "name": "ipython",
+                "version": 3
+            },
+            "file_extension": ".py",
+            "mimetype": "text/x-python",
+            "name": "python",
+            "nbconvert_exporter": "python",
+            "pygments_lexer": "ipython3",
+            "version": "3.9.15"
+        }
+    },
+    "nbformat": 4,
+    "nbformat_minor": 5
+}
--- a/examples/notebooks/little_transformer.ipynb
+++ b/examples/notebooks/little_transformer.ipynb
@@ -347,7 +347,7 @@
                "# Serialize data into file:\n",
                "json.dump(data, open(cal_path, 'w'))\n",
                "\n",
-                "res = await ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
+                "res = ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
                "assert res == True\n"
            ]
        },
@@ -383,7 +383,7 @@
                "# now generate the witness file \n",
                "witness_path = \"gan_witness.json\"\n",
                "\n",
-                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
                "assert os.path.isfile(witness_path)"
            ]
        },
--- a/examples/notebooks/logistic_regression.ipynb
+++ b/examples/notebooks/logistic_regression.ipynb
@@ -142,7 +142,7 @@
                "# Serialize data into file:\n",
                "json.dump(data, open(cal_path, 'w'))\n",
                "\n",
-                "res = await ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
+                "res = ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
                "assert res == True\n"
            ]
        },
@@ -177,7 +177,7 @@
            "source": [
                "# now generate the witness file \n",
                "\n",
-                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
                "assert os.path.isfile(witness_path)"
            ]
        },
@@ -276,4 +276,4 @@
    },
    "nbformat": 4,
    "nbformat_minor": 5
-}
+}
--- a/examples/notebooks/lstm.ipynb
+++ b/examples/notebooks/lstm.ipynb
@@ -139,7 +139,7 @@
                "res = ezkl.gen_settings(model_path, settings_path, py_run_args=run_args)\n",
                "assert res == True\n",
                "\n",
-                "res = await ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
+                "res = ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
                "assert res == True\n"
            ]
        },
@@ -193,7 +193,7 @@
                "# now generate the witness file \n",
                "witness_path = \"lstmwitness.json\"\n",
                "\n",
-                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
                "assert os.path.isfile(witness_path)"
            ]
        },
--- a/examples/notebooks/mean_postgres.ipynb
+++ b/examples/notebooks/mean_postgres.ipynb
@@ -1,462 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Mean of ERC20 transfer amounts\n",
-    "\n",
-    "This notebook shows how to calculate the mean of ERC20 transfer amounts, pulling data in from a Postgres database. First we install and get the necessary libraries running. \n",
-    "The first of which is [shovel](https://indexsupply.com/shovel/docs/#getting-started), which is a library that allows us to pull data from the Ethereum blockchain into a Postgres database.\n",
-    "\n",
-    "Make sure you install postgres if needed https://indexsupply.com/shovel/docs/#getting-started. \n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import getpass\n",
-    "import json\n",
-    "import time\n",
-    "import subprocess\n",
-    "\n",
-    "# swap out for the relevant linux/amd64, darwin/arm64, darwin/amd64, windows/amd64\n",
-    "os.system(\"curl -LO https://indexsupply.net/bin/1.0/linux/amd64/shovel\")\n",
-    "os.system(\"chmod +x shovel\")\n",
-    "\n",
-    "\n",
-    "os.environ[\"PG_URL\"] = \"postgres://\" + getpass.getuser() + \":@localhost:5432/shovel\"\n",
-    "\n",
-    "# create a config.json file with the following contents\n",
-    "config = {\n",
-    "  \"pg_url\": \"$PG_URL\",\n",
-    "  \"eth_sources\": [\n",
-    "    {\"name\": \"mainnet\", \"chain_id\": 1, \"url\": \"https://ethereum-rpc.publicnode.com\"},\n",
-    "    {\"name\": \"base\", \"chain_id\": 8453, \"url\": \"https://base-rpc.publicnode.com\"}\n",
-    "  ],\n",
-    "  \"integrations\": [{\n",
-    "    \"name\": \"usdc_transfer\",\n",
-    "    \"enabled\": True,\n",
-    "    \"sources\": [{\"name\": \"mainnet\"}, {\"name\": \"base\"}],\n",
-    "    \"table\": {\n",
-    "      \"name\": \"usdc\",\n",
-    "      \"columns\": [\n",
-    "        {\"name\": \"log_addr\",  \"type\": \"bytea\"},\n",
-    "        {\"name\": \"block_num\", \"type\": \"numeric\"},\n",
-    "        {\"name\": \"f\",         \"type\": \"bytea\"},\n",
-    "        {\"name\": \"t\",         \"type\": \"bytea\"},\n",
-    "        {\"name\": \"v\",         \"type\": \"numeric\"}\n",
-    "      ]\n",
-    "    },\n",
-    "    \"block\": [\n",
-    "      {\"name\": \"block_num\", \"column\": \"block_num\"},\n",
-    "      {\n",
-    "        \"name\": \"log_addr\",\n",
-    "        \"column\": \"log_addr\",\n",
-    "        \"filter_op\": \"contains\",\n",
-    "        \"filter_arg\": [\n",
-    "          \"a0b86991c6218b36c1d19d4a2e9eb0ce3606eb48\",\n",
-    "          \"833589fCD6eDb6E08f4c7C32D4f71b54bdA02913\"\n",
-    "        ]\n",
-    "      }\n",
-    "    ],\n",
-    "    \"event\": {\n",
-    "      \"name\": \"Transfer\",\n",
-    "      \"type\": \"event\",\n",
-    "      \"anonymous\": False,\n",
-    "      \"inputs\": [\n",
-    "        {\"indexed\": True,  \"name\": \"from\",  \"type\": \"address\", \"column\": \"f\"},\n",
-    "        {\"indexed\": True,  \"name\": \"to\",    \"type\": \"address\", \"column\": \"t\"},\n",
-    "        {\"indexed\": False, \"name\": \"value\", \"type\": \"uint256\", \"column\": \"v\"}\n",
-    "      ]\n",
-    "    }\n",
-    "  }]\n",
-    "}\n",
-    "\n",
-    "# write the config to a file\n",
-    "with open(\"config.json\", \"w\") as f:\n",
-    "  f.write(json.dumps(config))\n",
-    "\n",
-    "\n",
-    "# print the two env variables\n",
-    "os.system(\"echo $PG_URL\")\n",
-    "\n",
-    "os.system(\"createdb -h localhost -p 5432 shovel\")\n",
-    "\n",
-    "os.system(\"echo shovel is now installed. starting:\")\n",
-    "\n",
-    "command = [\"./shovel\", \"-config\", \"config.json\"]\n",
-    "proc = subprocess.Popen(command)\n",
-    "\n",
-    "os.system(\"echo shovel started.\")\n",
-    "\n",
-    "time.sleep(10)\n",
-    "\n",
-    "# after we've fetched some data -- kill the process\n",
-    "proc.terminate()\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "2wIAHwqH2_mo"
-   },
-   "source": [
-    "**Import Dependencies**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "9Byiv2Nc2MsK"
-   },
-   "outputs": [],
-   "source": [
-    "# check if notebook is in colab\n",
-    "try:\n",
-    "    # install ezkl\n",
-    "    import google.colab\n",
-    "    import subprocess\n",
-    "    import sys\n",
-    "    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"ezkl\"])\n",
-    "    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"onnx\"])\n",
-    "\n",
-    "# rely on local installation of ezkl if the notebook is not in colab\n",
-    "except:\n",
-    "    pass\n",
-    "\n",
-    "import ezkl\n",
-    "import torch\n",
-    "import datetime\n",
-    "import pandas as pd\n",
-    "import requests\n",
-    "import json\n",
-    "import os\n",
-    "\n",
-    "import logging\n",
-    "# # uncomment for more descriptive logging \n",
-    "FORMAT = '%(levelname)s %(name)s %(asctime)-15s %(filename)s:%(lineno)d %(message)s'\n",
-    "logging.basicConfig(format=FORMAT)\n",
-    "logging.getLogger().setLevel(logging.DEBUG)\n",
-    "\n",
-    "print(\"ezkl version: \", ezkl.__version__)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "osjj-0Ta3E8O"
-   },
-   "source": [
-    "**Create Computational Graph**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "x1vl9ZXF3EEW",
-    "outputId": "bda21d02-fe5f-4fb2-8106-f51a8e2e67aa"
-   },
-   "outputs": [],
-   "source": [
-    "from torch import nn\n",
-    "import torch\n",
-    "\n",
-    "\n",
-    "class Model(nn.Module):\n",
-    "    def __init__(self):\n",
-    "        super(Model, self).__init__()\n",
-    "\n",
-    "    # x is a time series \n",
-    "    def forward(self, x):\n",
-    "        return [torch.mean(x)]\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "circuit = Model()\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "x = 0.1*torch.rand(1,*[1,5], requires_grad=True)\n",
-    "\n",
-    "# # print(torch.__version__)\n",
-    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
-    "\n",
-    "print(device)\n",
-    "\n",
-    "circuit.to(device)\n",
-    "\n",
-    "# Flips the neural net into inference mode\n",
-    "circuit.eval()\n",
-    "\n",
-    "# Export the model\n",
-    "torch.onnx.export(circuit,               # model being run\n",
-    "                      x,                   # model input (or a tuple for multiple inputs)\n",
-    "                      \"lol.onnx\",            # where to save the model (can be a file or file-like object)\n",
-    "                      export_params=True,        # store the trained parameter weights inside the model file\n",
-    "                      opset_version=11,          # the ONNX version to export the model to\n",
-    "                      do_constant_folding=True,  # whether to execute constant folding for optimization\n",
-    "                      input_names = ['input'],   # the model's input names\n",
-    "                      output_names = ['output'], # the model's output names\n",
-    "                      dynamic_axes={'input' : {0 : 'batch_size'},    # variable length axes\n",
-    "                                    'output' : {0 : 'batch_size'}})\n",
-    "\n",
-    "# export(circuit, input_shape=[1, 20])\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "E3qCeX-X5xqd"
-   },
-   "source": [
-    "**Set Data Source and Get Data**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "6RAMplxk5xPk",
-    "outputId": "bd2158fe-0c00-44fd-e632-6a3f70cdb7c9"
-   },
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "# make an input.json file from the df above\n",
-    "input_filename = os.path.join('input.json')\n",
-    "\n",
-    "pg_input_file = dict(input_data = {\n",
-    "  \"host\": \"localhost\",\n",
-    "  # make sure you replace this with your own username\n",
-    "  \"user\": getpass.getuser(),\n",
-    "  \"dbname\": \"shovel\",\n",
-    "  \"password\": \"\",\n",
-    "  \"query\": \"SELECT v FROM usdc ORDER BY block_num DESC LIMIT 5\",\n",
-    "  \"port\": \"5432\",\n",
-    "})\n",
-    "\n",
-    "json_formatted_str = json.dumps(pg_input_file, indent=2)\n",
-    "print(json_formatted_str)\n",
-    "\n",
-    "\n",
-    "    # Serialize data into file:\n",
-    "json.dump(pg_input_file, open(input_filename, 'w' ))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# this corresponds to 4 batches\n",
-    "calibration_filename = os.path.join('calibration.json')\n",
-    "\n",
-    "pg_cal_file = dict(input_data = {\n",
-    "  \"host\": \"localhost\",\n",
-    "  # make sure you replace this with your own username\n",
-    "  \"user\": getpass.getuser(),\n",
-    "  \"dbname\": \"shovel\",\n",
-    "  \"password\": \"\",\n",
-    "  \"query\": \"SELECT v FROM usdc ORDER BY block_num DESC LIMIT 20\",\n",
-    "  \"port\": \"5432\",\n",
-    "})\n",
-    "\n",
-    "    # Serialize data into file:\n",
-    "json.dump( pg_cal_file, open(calibration_filename, 'w' ))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "eLJ7oirQ_HQR"
-   },
-   "source": [
-    "**EZKL Workflow**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "rNw0C9QL6W88"
-   },
-   "outputs": [],
-   "source": [
-    "import subprocess\n",
-    "import os\n",
-    "\n",
-    "onnx_filename = os.path.join('lol.onnx')\n",
-    "compiled_filename = os.path.join('lol.compiled')\n",
-    "settings_filename = os.path.join('settings.json')\n",
-    "\n",
-    "run_args = ezkl.PyRunArgs()\n",
-    "run_args.decomp_legs = 4\n",
-    "\n",
-    "# Generate settings using ezkl\n",
-    "res = ezkl.gen_settings(onnx_filename, settings_filename, py_run_args=run_args)\n",
-    "\n",
-    "assert res == True\n",
-    "\n",
-    "res = await ezkl.calibrate_settings(input_filename, onnx_filename, settings_filename, \"resources\")\n",
-    "\n",
-    "assert res == True\n",
-    "\n",
-    "await ezkl.get_srs(settings_filename)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "ezkl.compile_circuit(onnx_filename, compiled_filename, settings_filename)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "4MmE9SX66_Il",
-    "outputId": "16403639-66a4-4280-ac7f-6966b75de5a3"
-   },
-   "outputs": [],
-   "source": [
-    "# generate settings\n",
-    "\n",
-    "\n",
-    "# show the settings.json\n",
-    "with open(\"settings.json\") as f:\n",
-    "  data = json.load(f)\n",
-    "  json_formatted_str = json.dumps(data, indent=2)\n",
-    "\n",
-    "  print(json_formatted_str)\n",
-    "\n",
-    "assert os.path.exists(\"settings.json\")\n",
-    "assert os.path.exists(\"input.json\")\n",
-    "assert os.path.exists(\"lol.onnx\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "fULvvnK7_CMb"
-   },
-   "outputs": [],
-   "source": [
-    "pk_path = os.path.join('test.pk')\n",
-    "vk_path = os.path.join('test.vk')\n",
-    "\n",
-    "\n",
-    "# setup the proof\n",
-    "res = ezkl.setup(\n",
-    "        compiled_filename,\n",
-    "        vk_path,\n",
-    "        pk_path\n",
-    "    )\n",
-    "\n",
-    "assert res == True\n",
-    "assert os.path.isfile(vk_path)\n",
-    "assert os.path.isfile(pk_path)\n",
-    "assert os.path.isfile(settings_filename)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "witness_path = \"witness.json\"\n",
-    "\n",
-    "# generate the witness\n",
-    "res = await ezkl.gen_witness(\n",
-    "        input_filename,\n",
-    "        compiled_filename,\n",
-    "        witness_path\n",
-    "    )\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "Oog3j6Kd-Wed",
-    "outputId": "5839d0c1-5b43-476e-c2f8-6707de562260"
-   },
-   "outputs": [],
-   "source": [
-    "# prove the zk circuit\n",
-    "# GENERATE A PROOF\n",
-    "proof_path = os.path.join('test.pf')\n",
-    "\n",
-    "\n",
-    "proof = ezkl.prove(\n",
-    "        witness_path,\n",
-    "        compiled_filename,\n",
-    "        pk_path,\n",
-    "        proof_path,\n",
-    "        \"single\"\n",
-    "    )\n",
-    "\n",
-    "\n",
-    "print(\"proved\")\n",
-    "\n",
-    "assert os.path.isfile(proof_path)\n",
-    "\n"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": ".env",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
--- a/examples/notebooks/mnist_classifier.ipynb
+++ b/examples/notebooks/mnist_classifier.ipynb
@@ -323,7 +323,7 @@
        "res = ezkl.gen_settings(model_path, settings_path, py_run_args=run_args)\n",
        "assert res == True\n",
        "\n",
-        "res = await ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\", scales=[2,7])\n",
+        "res = ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\", scales=[2,7])\n",
        "assert res == True"
      ]
    },
@@ -362,7 +362,7 @@
        "# now generate the witness file\n",
        "witness_path = \"witness.json\"\n",
        "\n",
-        "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+        "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
        "assert os.path.isfile(witness_path)"
      ]
    },
@@ -504,8 +504,8 @@
        "\n",
        "res = await ezkl.deploy_evm(\n",
        "    address_path,\n",
+        "    'http://127.0.0.1:3030',\n",
        "    sol_code_path,\n",
-        "    'http://127.0.0.1:3030'\n",
        ")\n",
        "\n",
        "assert res == True\n",
@@ -527,8 +527,8 @@
        "\n",
        "res = await ezkl.verify_evm(\n",
        "    addr,\n",
-        "    proof_path,\n",
-        "    \"http://127.0.0.1:3030\"\n",
+        "    \"http://127.0.0.1:3030\",\n",
+        "    proof_path\n",
        ")\n",
        "assert res == True"
      ]
--- a/examples/notebooks/mnist_gan.ipynb
+++ b/examples/notebooks/mnist_gan.ipynb
@@ -289,7 +289,7 @@
                "json.dump(data, open(cal_path, 'w'))\n",
                "\n",
                "\n",
-                "await ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\", scales=[0,6])"
+                "ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\", scales=[0,6])"
            ]
        },
        {
@@ -321,7 +321,7 @@
                "# now generate the witness file \n",
                "witness_path = \"gan_witness.json\"\n",
                "\n",
-                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
                "assert os.path.isfile(witness_path)"
            ]
        },
@@ -425,4 +425,4 @@
    },
    "nbformat": 4,
    "nbformat_minor": 2
-}
+}
--- a/examples/notebooks/mnist_gan_proof_splitting.ipynb
+++ b/examples/notebooks/mnist_gan_proof_splitting.ipynb
@@ -341,7 +341,7 @@
                "\n",
                "    # generate settings for the current model\n",
                "    res = ezkl.gen_settings(model_path, settings_path, py_run_args=run_args)\n",
-                "    res = await ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\", scales=[run_args.input_scale], max_logrows=run_args.logrows)\n",
+                "    res = ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\", scales=[run_args.input_scale], max_logrows=run_args.logrows)\n",
                "    assert res == True\n",
                "\n",
                "    # load settings and print them to the console\n",
@@ -361,7 +361,7 @@
                "    assert res == True\n",
                "    assert os.path.isfile(vk_path)\n",
                "    assert os.path.isfile(pk_path)\n",
-                "    res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path, vk_path)\n",
+                "    res = ezkl.gen_witness(data_path, compiled_model_path, witness_path, vk_path)\n",
                "    run_args.input_scale = settings[\"model_output_scales\"][0]\n",
                "\n",
                "for i in range(3):\n",
@@ -484,4 +484,4 @@
    },
    "nbformat": 4,
    "nbformat_minor": 2
-}
+}
--- a/examples/notebooks/mnist_vae.ipynb
+++ b/examples/notebooks/mnist_vae.ipynb
@@ -215,7 +215,7 @@
                "json.dump(data, open(cal_path, 'w'))\n",
                "\n",
                "\n",
-                "await ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
+                "ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
            ]
        },
        {
@@ -247,7 +247,7 @@
                "# now generate the witness file\n",
                "witness_path = \"ae_witness.json\"\n",
                "\n",
-                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
                "assert os.path.isfile(witness_path)"
            ]
        },
@@ -451,7 +451,7 @@
                "res = ezkl.gen_settings(model_path, settings_path)\n",
                "assert res == True\n",
                "\n",
-                "res = await ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
+                "res = ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
                "assert res == True\n",
                "print(\"verified\")"
            ]
@@ -485,7 +485,7 @@
                "# now generate the witness file \n",
                "witness_path = \"vae_witness.json\"\n",
                "\n",
-                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
                "assert os.path.isfile(witness_path)"
            ]
        },
@@ -590,4 +590,4 @@
    },
    "nbformat": 4,
    "nbformat_minor": 2
-}
+}
--- a/examples/notebooks/nbeats_timeseries_forecasting.ipynb
+++ b/examples/notebooks/nbeats_timeseries_forecasting.ipynb
@@ -845,7 +845,7 @@
        "res = ezkl.gen_settings(model_path, settings_path)\n",
        "assert res == True\n",
        "\n",
-        "res = await ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\", max_logrows = 20, scales = [3])\n",
+        "res = ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\", max_logrows = 20, scales = [3])\n",
        "assert res == True"
      ]
    },
@@ -881,7 +881,7 @@
      },
      "outputs": [],
      "source": [
-        "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+        "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
        "assert os.path.isfile(witness_path)"
      ]
    },
--- a/examples/notebooks/neural_bow.ipynb
+++ b/examples/notebooks/neural_bow.ipynb
--- a/examples/notebooks/proof_splitting.ipynb
+++ b/examples/notebooks/proof_splitting.ipynb
@@ -282,7 +282,7 @@
                "\n",
                "    # generate settings for the current model\n",
                "    res = ezkl.gen_settings(model_path, settings_path, py_run_args=run_args)\n",
-                "    res = await ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\", scales=[run_args.input_scale], max_logrows=run_args.logrows)\n",
+                "    res = ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\", scales=[run_args.input_scale], max_logrows=run_args.logrows)\n",
                "    assert res == True\n",
                "\n",
                "    # load settings and print them to the console\n",
@@ -303,7 +303,7 @@
                "    assert os.path.isfile(vk_path)\n",
                "    assert os.path.isfile(pk_path)\n",
                "\n",
-                "    res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path, vk_path)\n",
+                "    res = ezkl.gen_witness(data_path, compiled_model_path, witness_path, vk_path)\n",
                "    run_args.input_scale = settings[\"model_output_scales\"][0]\n",
                "\n",
                "for i in range(2):\n",
@@ -472,4 +472,4 @@
    },
    "nbformat": 4,
    "nbformat_minor": 2
-}
+}
--- a/examples/notebooks/random_forest.ipynb
+++ b/examples/notebooks/random_forest.ipynb
@@ -176,7 +176,7 @@
                "json.dump(data, open(cal_path, 'w'))\n",
                "\n",
                "\n",
-                "await ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
+                "ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
            ]
        },
        {
@@ -210,7 +210,7 @@
            "source": [
                "# now generate the witness file \n",
                "\n",
-                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
                "assert os.path.isfile(witness_path)"
            ]
        },
@@ -309,4 +309,4 @@
    },
    "nbformat": 4,
    "nbformat_minor": 5
-}
+}
--- a/examples/notebooks/reusable_verifier.ipynb
+++ b/examples/notebooks/reusable_verifier.ipynb
@@ -1,330 +1,336 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Reusable Verifiers \n",
-    "\n",
-    "This notebook demonstrates how to create and reuse the same set of separated verifiers for different models. Specifically, we will use the same verifier for the following four models:\n",
-    "\n",
-    "- `1l_mlp sigmoid`\n",
-    "- `1l_mlp relu`\n",
-    "- `1l_conv sigmoid`\n",
-    "- `1l_conv relu`\n",
-    "\n",
-    "When deploying EZKL verifiers on the blockchain, each associated model typically requires its own unique verifier, leading to increased on-chain state usage. \n",
-    "However, with the reusable verifier, we can deploy a single verifier that can be used to verify proofs for any valid H2 circuit. This notebook shows how to do so. \n",
-    "\n",
-    "By reusing the same verifier across multiple models, we significantly reduce the amount of state bloat on the blockchain. Instead of deploying a unique verifier for each model, we deploy a unique and much smaller verifying key artifact (VKA) contract for each model while sharing a common separated verifier. The VKA contains the VK for the model as well circuit specific metadata that was otherwise hardcoded into the stack of the original non-reusable verifier."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "import torch.nn as nn\n",
-    "import torch.onnx\n",
-    "\n",
-    "# Define the models\n",
-    "class MLP_Sigmoid(nn.Module):\n",
-    "    def __init__(self):\n",
-    "        super(MLP_Sigmoid, self).__init__()\n",
-    "        self.fc = nn.Linear(3, 3)\n",
-    "        self.sigmoid = nn.Sigmoid()\n",
-    "\n",
-    "    def forward(self, x):\n",
-    "        x = self.fc(x)\n",
-    "        x = self.sigmoid(x)\n",
-    "        return x\n",
-    "\n",
-    "class MLP_Relu(nn.Module):\n",
-    "    def __init__(self):\n",
-    "        super(MLP_Relu, self).__init__()\n",
-    "        self.fc = nn.Linear(3, 3)\n",
-    "        self.relu = nn.ReLU()\n",
-    "\n",
-    "    def forward(self, x):\n",
-    "        x = self.fc(x)\n",
-    "        x = self.relu(x)\n",
-    "        return x\n",
-    "\n",
-    "class Conv_Sigmoid(nn.Module):\n",
-    "    def __init__(self):\n",
-    "        super(Conv_Sigmoid, self).__init__()\n",
-    "        self.conv = nn.Conv1d(1, 1, kernel_size=3, stride=1)\n",
-    "        self.sigmoid = nn.Sigmoid()\n",
-    "\n",
-    "    def forward(self, x):\n",
-    "        x = self.conv(x)\n",
-    "        x = self.sigmoid(x)\n",
-    "        return x\n",
-    "\n",
-    "class Conv_Relu(nn.Module):\n",
-    "    def __init__(self):\n",
-    "        super(Conv_Relu, self).__init__()\n",
-    "        self.conv = nn.Conv1d(1, 1, kernel_size=3, stride=1)\n",
-    "        self.relu = nn.ReLU()\n",
-    "\n",
-    "    def forward(self, x):\n",
-    "        x = self.conv(x)\n",
-    "        x = self.relu(x)\n",
-    "        return x\n",
-    "\n",
-    "# Instantiate the models\n",
-    "mlp_sigmoid = MLP_Sigmoid()\n",
-    "mlp_relu = MLP_Relu()\n",
-    "conv_sigmoid = Conv_Sigmoid()\n",
-    "conv_relu = Conv_Relu()\n",
-    "\n",
-    "# Dummy input tensor for mlp\n",
-    "dummy_input_mlp = torch.tensor([[-1.5737053155899048, -1.708398461341858, 0.19544155895709991]])\n",
-    "input_mlp_path = 'mlp_input.json'\n",
-    "\n",
-    "# Dummy input tensor for conv\n",
-    "dummy_input_conv = torch.tensor([[[1.4124163389205933, 0.6938204169273376, 1.0664031505584717]]])\n",
-    "input_conv_path = 'conv_input.json'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "names = ['mlp_sigmoid', 'mlp_relu', 'conv_sigmoid', 'conv_relu']\n",
-    "models = [mlp_sigmoid, mlp_relu, conv_sigmoid, conv_relu]\n",
-    "inputs = [dummy_input_mlp, dummy_input_mlp, dummy_input_conv, dummy_input_conv]\n",
-    "input_paths = [input_mlp_path, input_mlp_path, input_conv_path, input_conv_path]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import json\n",
-    "import torch\n",
-    "import ezkl\n",
-    "\n",
-    "for name, model, x, input_path in zip(names, models, inputs, input_paths):\n",
-    "    # Create a new directory for the model if it doesn't exist\n",
-    "    if not os.path.exists(name):\n",
-    "        os.mkdir(name)\n",
-    "    # Store the paths in each of their respective directories\n",
-    "    model_path = os.path.join(name, \"network.onnx\")\n",
-    "    compiled_model_path = os.path.join(name, \"network.compiled\")\n",
-    "    pk_path = os.path.join(name, \"test.pk\")\n",
-    "    vk_path = os.path.join(name, \"test.vk\")\n",
-    "    settings_path = os.path.join(name, \"settings.json\")\n",
-    "\n",
-    "    witness_path = os.path.join(name, \"witness.json\")\n",
-    "    sol_code_path = os.path.join(name, 'test.sol')\n",
-    "    sol_key_code_path = os.path.join(name, 'test_key.sol')\n",
-    "    abi_path = os.path.join(name, 'test.abi')\n",
-    "    proof_path = os.path.join(name, \"proof.json\")\n",
-    "\n",
-    "    # Flips the neural net into inference mode\n",
-    "    model.eval()\n",
-    "\n",
-    "    # Export the model\n",
-    "    torch.onnx.export(model, x, model_path, export_params=True, opset_version=10,\n",
-    "                      do_constant_folding=True, input_names=['input'],\n",
-    "                      output_names=['output'], dynamic_axes={'input': {0: 'batch_size'},\n",
-    "                                                              'output': {0: 'batch_size'}})\n",
-    "\n",
-    "    data_array = ((x).detach().numpy()).reshape([-1]).tolist()\n",
-    "    data = dict(input_data=[data_array])\n",
-    "    json.dump(data, open(input_path, 'w'))\n",
-    "\n",
-    "    py_run_args = ezkl.PyRunArgs()\n",
-    "    py_run_args.input_visibility = \"private\"\n",
-    "    py_run_args.output_visibility = \"public\"\n",
-    "    py_run_args.param_visibility = \"fixed\"  # private by default\n",
-    "\n",
-    "    res = ezkl.gen_settings(model_path, settings_path, py_run_args=py_run_args)\n",
-    "    assert res == True\n",
-    "\n",
-    "    await ezkl.calibrate_settings(input_path, model_path, settings_path, \"resources\")\n",
-    "\n",
-    "    res = ezkl.compile_circuit(model_path, compiled_model_path, settings_path)\n",
-    "    assert res == True\n",
-    "\n",
-    "    res = await ezkl.get_srs(settings_path)\n",
-    "    assert res == True\n",
-    "\n",
-    "    # now generate the witness file\n",
-    "    res = await ezkl.gen_witness(input_path, compiled_model_path, witness_path)\n",
-    "    assert os.path.isfile(witness_path) == True\n",
-    "\n",
-    "    # SETUP \n",
-    "    # We recommend disabling selector compression for the setup as it decreases the size of the VK artifact\n",
-    "    res = ezkl.setup(compiled_model_path, vk_path, pk_path, disable_selector_compression=True)\n",
-    "    assert res == True\n",
-    "    assert os.path.isfile(vk_path)\n",
-    "    assert os.path.isfile(pk_path)\n",
-    "    assert os.path.isfile(settings_path)\n",
-    "\n",
-    "    # GENERATE A PROOF\n",
-    "    res = ezkl.prove(witness_path, compiled_model_path, pk_path, proof_path, \"single\")\n",
-    "    assert os.path.isfile(proof_path)\n",
-    "\n",
-    "    res = await ezkl.create_evm_verifier(vk_path, settings_path, sol_code_path, abi_path, reusable=True)\n",
-    "    assert res == True\n",
-    "\n",
-    "    res = await ezkl.create_evm_vka(vk_path, settings_path, sol_key_code_path, abi_path)\n",
-    "    assert res == True\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import subprocess\n",
-    "import time\n",
-    "\n",
-    "# make sure anvil is running locally\n",
-    "# $ anvil -p 3030\n",
-    "\n",
-    "RPC_URL = \"http://localhost:3030\"\n",
-    "\n",
-    "# Save process globally\n",
-    "anvil_process = None\n",
-    "\n",
-    "def start_anvil():\n",
-    "    global anvil_process\n",
-    "    if anvil_process is None:\n",
-    "        anvil_process = subprocess.Popen([\"anvil\", \"-p\", \"3030\", \"--code-size-limit=41943040\"])\n",
-    "        if anvil_process.returncode is not None:\n",
-    "            raise Exception(\"failed to start anvil process\")\n",
-    "        time.sleep(3)\n",
-    "\n",
-    "def stop_anvil():\n",
-    "    global anvil_process\n",
-    "    if anvil_process is not None:\n",
-    "        anvil_process.terminate()\n",
-    "        anvil_process = None\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Check that the generated verifiers are identical for all models."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import filecmp\n",
-    "\n",
-    "def compare_files(file1, file2):\n",
-    "    return filecmp.cmp(file1, file2, shallow=False)\n",
-    "\n",
-    "sol_code_path_0 = os.path.join(\"mlp_sigmoid\", 'test.sol')\n",
-    "sol_code_path_1 = os.path.join(\"mlp_relu\", 'test.sol')\n",
-    "\n",
-    "sol_code_path_2 = os.path.join(\"conv_sigmoid\", 'test.sol')\n",
-    "sol_code_path_3 = os.path.join(\"conv_relu\", 'test.sol')\n",
-    "\n",
-    "\n",
-    "assert compare_files(sol_code_path_0, sol_code_path_1) == True\n",
-    "assert compare_files(sol_code_path_2, sol_code_path_3) == True"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here we deploy separate verifier that will be shared by the four models. We picked the `1l_mlp sigmoid` model as an example but you could have used any of the generated verifiers since they are all identical. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os \n",
-    "addr_path_verifier = \"addr_verifier.txt\"\n",
-    "sol_code_path = os.path.join(\"mlp_sigmoid\", 'test.sol')\n",
-    "\n",
-    "res = await ezkl.deploy_evm(\n",
-    "    addr_path_verifier,\n",
-    "    sol_code_path,\n",
-    "    'http://127.0.0.1:3030',\n",
-    "    \"verifier/reusable\"\n",
-    ")\n",
-    "\n",
-    "assert res == True\n",
-    "\n",
-    "with open(addr_path_verifier, 'r') as file:\n",
-    "    addr = file.read().rstrip()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Finally we deploy each of the unique VK-artifacts and verify them using the shared verifier deployed in the previous step."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for name in names:\n",
-    "    addr_path_vk = \"addr_vk.txt\"\n",
-    "    sol_key_code_path = os.path.join(name, 'test_key.sol')\n",
-    "    res = await ezkl.deploy_evm(addr_path_vk, sol_key_code_path, 'http://127.0.0.1:3030', \"vka\")\n",
-    "    assert res == True\n",
-    "\n",
-    "    with open(addr_path_vk, 'r') as file:\n",
-    "        addr_vk = file.read().rstrip()\n",
-    "        \n",
-    "    proof_path = os.path.join(name, \"proof.json\")\n",
-    "    sol_code_path = os.path.join(name, 'vk.sol')\n",
-    "    res = await ezkl.verify_evm(\n",
-    "        addr,\n",
-    "        proof_path,\n",
-    "        \"http://127.0.0.1:3030\",\n",
-    "        addr_vk = addr_vk\n",
-    "    )\n",
-    "    assert res == True"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".env",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "# Reusable Verifiers \n",
+                "\n",
+                "TODO: Update the reusable verifier solidity contract name.. Make it less generic to H2 and more bespoke to us.\n",
+                "\n",
+                "This notebook demonstrates how to create and reuse the same set of separated verifiers for different models. Specifically, we will use the same verifier for the following four models:\n",
+                "\n",
+                "- `1l_mlp sigmoid`\n",
+                "- `1l_mlp relu`\n",
+                "- `1l_conv sigmoid`\n",
+                "- `1l_conv relu`\n",
+                "\n",
+                "When deploying EZKL verifiers on the blockchain, each associated model typically requires its own unique verifier, leading to increased on-chain state usage. \n",
+                "However, with the reusable verifier, we can deploy a single verifier that can be used to verify proofs for any valid H2 circuit. This notebook shows how to do so. \n",
+                "\n",
+                "By reusing the same verifier across multiple models, we significantly reduce the amount of state bloat on the blockchain. Instead of deploying a unique verifier for each model, we register a unique and much smaller verifying key artifact (VKA) on the reusable verifier contract for each model while sharing a common separated verifier. The VKA contains the VK for the model as well circuit specific metadata that was otherwise hardcoded into the stack of the original non-reusable verifier. The VKA is passed as a parameter to the verifyProof method. This VKA calldata needs to be d with the reusable verifier before it can start verifying proofs by calling the registerVKA method. "
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "import torch\n",
+                "import torch.nn as nn\n",
+                "import torch.onnx\n",
+                "\n",
+                "# Define the models\n",
+                "class MLP_Sigmoid(nn.Module):\n",
+                "    def __init__(self):\n",
+                "        super(MLP_Sigmoid, self).__init__()\n",
+                "        self.fc = nn.Linear(3, 3)\n",
+                "        self.sigmoid = nn.Sigmoid()\n",
+                "\n",
+                "    def forward(self, x):\n",
+                "        x = self.fc(x)\n",
+                "        x = self.sigmoid(x)\n",
+                "        return x\n",
+                "\n",
+                "class MLP_Relu(nn.Module):\n",
+                "    def __init__(self):\n",
+                "        super(MLP_Relu, self).__init__()\n",
+                "        self.fc = nn.Linear(3, 3)\n",
+                "        self.relu = nn.ReLU()\n",
+                "\n",
+                "    def forward(self, x):\n",
+                "        x = self.fc(x)\n",
+                "        x = self.relu(x)\n",
+                "        return x\n",
+                "\n",
+                "class Conv_Sigmoid(nn.Module):\n",
+                "    def __init__(self):\n",
+                "        super(Conv_Sigmoid, self).__init__()\n",
+                "        self.conv = nn.Conv1d(1, 1, kernel_size=3, stride=1)\n",
+                "        self.sigmoid = nn.Sigmoid()\n",
+                "\n",
+                "    def forward(self, x):\n",
+                "        x = self.conv(x)\n",
+                "        x = self.sigmoid(x)\n",
+                "        return x\n",
+                "\n",
+                "class Conv_Relu(nn.Module):\n",
+                "    def __init__(self):\n",
+                "        super(Conv_Relu, self).__init__()\n",
+                "        self.conv = nn.Conv1d(1, 1, kernel_size=3, stride=1)\n",
+                "        self.relu = nn.ReLU()\n",
+                "\n",
+                "    def forward(self, x):\n",
+                "        x = self.conv(x)\n",
+                "        x = self.relu(x)\n",
+                "        return x\n",
+                "\n",
+                "# Instantiate the models\n",
+                "mlp_sigmoid = MLP_Sigmoid()\n",
+                "mlp_relu = MLP_Relu()\n",
+                "conv_sigmoid = Conv_Sigmoid()\n",
+                "conv_relu = Conv_Relu()\n",
+                "\n",
+                "# Dummy input tensor for mlp\n",
+                "dummy_input_mlp = torch.tensor([[-1.5737053155899048, -1.708398461341858, 0.19544155895709991]])\n",
+                "input_mlp_path = 'mlp_input.json'\n",
+                "\n",
+                "# Dummy input tensor for conv\n",
+                "dummy_input_conv = torch.tensor([[[1.4124163389205933, 0.6938204169273376, 1.0664031505584717]]])\n",
+                "input_conv_path = 'conv_input.json'"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 5,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "names = ['mlp_sigmoid', 'mlp_relu', 'conv_sigmoid', 'conv_relu']\n",
+                "models = [mlp_sigmoid, mlp_relu, conv_sigmoid, conv_relu]\n",
+                "inputs = [dummy_input_mlp, dummy_input_mlp, dummy_input_conv, dummy_input_conv]\n",
+                "input_paths = [input_mlp_path, input_mlp_path, input_conv_path, input_conv_path]"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "import os\n",
+                "import json\n",
+                "import torch\n",
+                "import ezkl\n",
+                "\n",
+                "for name, model, x, input_path in zip(names, models, inputs, input_paths):\n",
+                "    # Create a new directory for the model if it doesn't exist\n",
+                "    if not os.path.exists(name):\n",
+                "        os.mkdir(name)\n",
+                "    # Store the paths in each of their respective directories\n",
+                "    model_path = os.path.join(name, \"network.onnx\")\n",
+                "    compiled_model_path = os.path.join(name, \"network.compiled\")\n",
+                "    pk_path = os.path.join(name, \"test.pk\")\n",
+                "    vk_path = os.path.join(name, \"test.vk\")\n",
+                "    settings_path = os.path.join(name, \"settings.json\")\n",
+                "\n",
+                "    witness_path = os.path.join(name, \"witness.json\")\n",
+                "    sol_code_path = os.path.join(name, 'test.sol')\n",
+                "    vka_path = os.path.join(name, 'vka.bytes')\n",
+                "    abi_path = os.path.join(name, 'test.abi')\n",
+                "    proof_path = os.path.join(name, \"proof.json\")\n",
+                "\n",
+                "    # Flips the neural net into inference mode\n",
+                "    model.eval()\n",
+                "\n",
+                "    # Export the model\n",
+                "    torch.onnx.export(model, x, model_path, export_params=True, opset_version=10,\n",
+                "                      do_constant_folding=True, input_names=['input'],\n",
+                "                      output_names=['output'], dynamic_axes={'input': {0: 'batch_size'},\n",
+                "                                                              'output': {0: 'batch_size'}})\n",
+                "\n",
+                "    data_array = ((x).detach().numpy()).reshape([-1]).tolist()\n",
+                "    data = dict(input_data=[data_array])\n",
+                "    json.dump(data, open(input_path, 'w'))\n",
+                "\n",
+                "    py_run_args = ezkl.PyRunArgs()\n",
+                "    py_run_args.input_visibility = \"private\"\n",
+                "    py_run_args.output_visibility = \"public\"\n",
+                "    py_run_args.param_visibility = \"fixed\"  # private by default\n",
+                "\n",
+                "    res = ezkl.gen_settings(model_path, settings_path, py_run_args=py_run_args)\n",
+                "    assert res == True\n",
+                "\n",
+                "    ezkl.calibrate_settings(input_path, model_path, settings_path, \"resources\")\n",
+                "\n",
+                "    res = ezkl.compile_circuit(model_path, compiled_model_path, settings_path)\n",
+                "    assert res == True\n",
+                "\n",
+                "    res = await ezkl.get_srs(settings_path)\n",
+                "    assert res == True\n",
+                "\n",
+                "    # now generate the witness file\n",
+                "    res = ezkl.gen_witness(input_path, compiled_model_path, witness_path)\n",
+                "    assert os.path.isfile(witness_path) == True\n",
+                "\n",
+                "    # SETUP \n",
+                "    # We recommend disabling selector compression for the setup as it decreases the size of the VK artifact\n",
+                "    res = ezkl.setup(compiled_model_path, vk_path, pk_path, disable_selector_compression=True)\n",
+                "    assert res == True\n",
+                "    assert os.path.isfile(vk_path)\n",
+                "    assert os.path.isfile(pk_path)\n",
+                "    assert os.path.isfile(settings_path)\n",
+                "\n",
+                "    # GENERATE A PROOF\n",
+                "    res = ezkl.prove(witness_path, compiled_model_path, pk_path, proof_path, \"single\")\n",
+                "    assert os.path.isfile(proof_path)\n",
+                "\n",
+                "    res = await ezkl.create_evm_verifier(vk_path, settings_path, sol_code_path, abi_path, reusable=True)\n",
+                "    # TODO: Add a flag force equals true to in the deprication process to preserve OG single purpose verifier?\n",
+                "    assert res == True\n",
+                "\n",
+                "    # TODO: \n",
+                "    res = await ezkl.create_evm_vka(vk_path, settings_path, vka_path, decimals=18)\n",
+                "    assert res == True\n"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 7,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "import subprocess\n",
+                "import time\n",
+                "\n",
+                "# make sure anvil is running locally\n",
+                "# $ anvil -p 3030\n",
+                "\n",
+                "RPC_URL = \"http://localhost:3030\"\n",
+                "\n",
+                "# Save process globally\n",
+                "anvil_process = None\n",
+                "\n",
+                "def start_anvil():\n",
+                "    global anvil_process\n",
+                "    if anvil_process is None:\n",
+                "        anvil_process = subprocess.Popen([\"anvil\", \"-p\", \"3030\", \"--code-size-limit=41943040\"])\n",
+                "        if anvil_process.returncode is not None:\n",
+                "            raise Exception(\"failed to start anvil process\")\n",
+                "        time.sleep(3)\n",
+                "\n",
+                "def stop_anvil():\n",
+                "    global anvil_process\n",
+                "    if anvil_process is not None:\n",
+                "        anvil_process.terminate()\n",
+                "        anvil_process = None\n"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "Check that the generated verifiers are identical for all models."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 9,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "import filecmp\n",
+                "\n",
+                "def compare_files(file1, file2):\n",
+                "    return filecmp.cmp(file1, file2, shallow=False)\n",
+                "\n",
+                "sol_code_path_0 = os.path.join(\"mlp_sigmoid\", 'test.sol')\n",
+                "sol_code_path_1 = os.path.join(\"mlp_relu\", 'test.sol')\n",
+                "\n",
+                "sol_code_path_2 = os.path.join(\"conv_sigmoid\", 'test.sol')\n",
+                "sol_code_path_3 = os.path.join(\"conv_relu\", 'test.sol')\n",
+                "\n",
+                "\n",
+                "assert compare_files(sol_code_path_0, sol_code_path_1) == True\n",
+                "assert compare_files(sol_code_path_2, sol_code_path_3) == True"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "Here we deploy reusable verifier that will be shared by the four models. We picked the `1l_mlp sigmoid` model as an example but you could have used any of the generated verifiers since they are all identical. "
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 10,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "import os \n",
+                "addr_path_verifier = \"addr_verifier.txt\"\n",
+                "sol_code_path = os.path.join(\"mlp_sigmoid\", 'test.sol')\n",
+                "\n",
+                "res = await ezkl.deploy_evm(\n",
+                "    addr_path_verifier,\n",
+                "    'http://127.0.0.1:3030',\n",
+                "    sol_code_path,\n",
+                "    \"verifier/reusable\" # TODO deprecate this option for selecting the type of verifier you want to deploy. \n",
+                "    # verifier, verifier/reusable, vka\n",
+                ")\n",
+                "\n",
+                "assert res == True\n",
+                "\n",
+                "with open(addr_path_verifier, 'r') as file:\n",
+                "    addr = file.read().rstrip()"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "Finally we deploy each of the unique VK-artifacts and verify them using the shared verifier deployed in the previous step."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "for name in names:\n",
+                "    addr_path_vk = \"addr_vk.txt\"\n",
+                "    vka_path = os.path.join(name, 'vka.bytes')\n",
+                "    res = await ezkl.register_vka(\n",
+                "        addr, # address of the reusable verifier. TODO: If we deploy the RV across all chains to a single canoncial address, we can hardcode that address and remove this param.\n",
+                "        'http://127.0.0.1:3030',\n",
+                "        vka_path=vka_path, # TODO: Pass in private key and potentially create new command that both creates and registers the vka. Simplify testing pipeline for us and other folks. \n",
+                "    )\n",
+                "    assert res == True\n",
+                "        \n",
+                "    proof_path = os.path.join(name, \"proof.json\")\n",
+                "    res = await ezkl.verify_evm(\n",
+                "        addr,\n",
+                "        \"http://127.0.0.1:3030\",\n",
+                "        proof_path,\n",
+                "        vka_path = vka_path # TODO: Turn this from optional to required if we deprecate the orignal verifier. \n",
+                "        # TODO: Make it where the use only needs to deply a vka.  \n",
+                "    )\n",
+                "    assert res == True"
+            ]
+        }
+    ],
+    "metadata": {
+        "kernelspec": {
+            "display_name": ".env",
+            "language": "python",
+            "name": "python3"
+        },
+        "language_info": {
+            "codemirror_mode": {
+                "name": "ipython",
+                "version": 3
+            },
+            "file_extension": ".py",
+            "mimetype": "text/x-python",
+            "name": "python",
+            "nbconvert_exporter": "python",
+            "pygments_lexer": "ipython3",
+            "version": "3.11.5"
+        }
+    },
+    "nbformat": 4,
+    "nbformat_minor": 2
 }
--- a/examples/notebooks/set_membership.ipynb
+++ b/examples/notebooks/set_membership.ipynb
@@ -231,7 +231,7 @@
      "source": [
        "# now generate the witness file\n",
        "\n",
-        "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+        "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
        "assert os.path.isfile(witness_path)"
      ]
    },
@@ -267,7 +267,7 @@
        "    # Serialize data into file:\n",
        "json.dump( data, open(data_path_faulty, 'w' ))\n",
        "\n",
-        "res = await ezkl.gen_witness(data_path_faulty, compiled_model_path, witness_path_faulty)\n",
+        "res = ezkl.gen_witness(data_path_faulty, compiled_model_path, witness_path_faulty)\n",
        "assert os.path.isfile(witness_path_faulty)"
      ]
    },
@@ -312,7 +312,7 @@
        "# Serialize data into file:\n",
        "json.dump( data, open(data_path_truthy, 'w' ))\n",
        "\n",
-        "res = await ezkl.gen_witness(data_path_truthy, compiled_model_path, witness_path_truthy)\n",
+        "res = ezkl.gen_witness(data_path_truthy, compiled_model_path, witness_path_truthy)\n",
        "assert os.path.isfile(witness_path_truthy)"
      ]
    },
--- a/examples/notebooks/simple_demo_aggregated_proofs.ipynb
+++ b/examples/notebooks/simple_demo_aggregated_proofs.ipynb
@@ -171,7 +171,7 @@
                "json.dump(data, open(cal_path, 'w'))\n",
                "\n",
                "\n",
-                "await ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
+                "ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
            ]
        },
        {
@@ -205,7 +205,7 @@
            "source": [
                "# now generate the witness file \n",
                "\n",
-                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
                "assert os.path.isfile(witness_path)"
            ]
        },
@@ -404,4 +404,4 @@
    },
    "nbformat": 4,
    "nbformat_minor": 5
-}
+}
--- a/examples/notebooks/simple_demo_all_public.ipynb
+++ b/examples/notebooks/simple_demo_all_public.ipynb
@@ -171,7 +171,7 @@
                "json.dump(data, open(cal_path, 'w'))\n",
                "\n",
                "\n",
-                "await ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
+                "ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
            ]
        },
        {
@@ -205,7 +205,7 @@
            "source": [
                "# now generate the witness file \n",
                "\n",
-                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
                "assert os.path.isfile(witness_path)"
            ]
        },
@@ -304,4 +304,4 @@
    },
    "nbformat": 4,
    "nbformat_minor": 5
-}
+}
--- a/examples/notebooks/simple_demo_public_input_output.ipynb
+++ b/examples/notebooks/simple_demo_public_input_output.ipynb
@@ -169,7 +169,7 @@
                "json.dump(data, open(cal_path, 'w'))\n",
                "\n",
                "\n",
-                "await ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
+                "ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
            ]
        },
        {
@@ -203,7 +203,7 @@
            "source": [
                "# now generate the witness file \n",
                "\n",
-                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
                "assert os.path.isfile(witness_path)"
            ]
        },
@@ -302,4 +302,4 @@
    },
    "nbformat": 4,
    "nbformat_minor": 5
-}
+}
--- a/examples/notebooks/simple_demo_public_network_output.ipynb
+++ b/examples/notebooks/simple_demo_public_network_output.ipynb
@@ -170,7 +170,7 @@
                "json.dump(data, open(cal_path, 'w'))\n",
                "\n",
                "\n",
-                "await ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
+                "ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
            ]
        },
        {
@@ -204,7 +204,7 @@
            "source": [
                "# now generate the witness file \n",
                "\n",
-                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
                "assert os.path.isfile(witness_path)"
            ]
        },
@@ -303,4 +303,4 @@
    },
    "nbformat": 4,
    "nbformat_minor": 5
-}
+}
--- a/examples/notebooks/sklearn_mlp.ipynb
+++ b/examples/notebooks/sklearn_mlp.ipynb
@@ -149,7 +149,7 @@
                "json.dump(data, open(cal_path, 'w'))\n",
                "\n",
                "\n",
-                "await ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
+                "ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
            ]
        },
        {
@@ -183,7 +183,7 @@
            "source": [
                "# now generate the witness file \n",
                "\n",
-                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
                "assert os.path.isfile(witness_path)"
            ]
        },
--- a/examples/notebooks/solvency.ipynb
+++ b/examples/notebooks/solvency.ipynb
@@ -298,7 +298,7 @@
        "\n",
        "witness_path = \"witness.json\"\n",
        "\n",
-        "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path, vk_path)\n",
+        "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path, vk_path)\n",
        "assert os.path.isfile(witness_path)\n",
        "\n",
        "# we force the output to be 1 this corresponds to the solvency test being true -- and we set this to a fixed vis output\n",
@@ -412,7 +412,7 @@
      "source": [
        "# now generate the witness file\n",
        "\n",
-        "res = await ezkl.gen_witness(data_path_faulty, compiled_model_path, witness_path, vk_path)\n",
+        "res = ezkl.gen_witness(data_path_faulty, compiled_model_path, witness_path, vk_path)\n",
        "assert os.path.isfile(witness_path)\n",
        "\n",
        "# we force the output to be 1 this corresponds to the solvency test being true -- and we set this to a fixed vis output\n",
--- a/examples/notebooks/stacked_regression.ipynb
+++ b/examples/notebooks/stacked_regression.ipynb
@@ -167,7 +167,7 @@
                "res = ezkl.gen_settings(model_path, settings_path)\n",
                "assert res == True\n",
                "\n",
-                "res = await ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
+                "res = ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
                "assert res == True"
            ]
        },
@@ -187,7 +187,7 @@
                "json.dump(data, open(cal_path, 'w'))\n",
                "\n",
                "\n",
-                "await ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
+                "ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
            ]
        },
        {
@@ -221,7 +221,7 @@
            "source": [
                "# now generate the witness file \n",
                "\n",
-                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
                "assert os.path.isfile(witness_path)"
            ]
        },
--- a/examples/notebooks/svm.ipynb
+++ b/examples/notebooks/svm.ipynb
@@ -152,7 +152,7 @@
                "json.dump(data, open(cal_path, 'w'))\n",
                "\n",
                "\n",
-                "await ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
+                "ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
            ]
        },
        {
@@ -186,7 +186,7 @@
            "source": [
                "# now generate the witness file \n",
                "\n",
-                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
                "assert os.path.isfile(witness_path)"
            ]
        },
@@ -392,7 +392,7 @@
                "res = ezkl.gen_settings(model_path, settings_path)\n",
                "assert res == True\n",
                "\n",
-                "res = await ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
+                "res = ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n",
                "assert res == True"
            ]
        }
@@ -418,4 +418,4 @@
    },
    "nbformat": 4,
    "nbformat_minor": 5
-}
+}
--- a/examples/notebooks/tictactoe_autoencoder.ipynb
+++ b/examples/notebooks/tictactoe_autoencoder.ipynb
@@ -637,7 +637,7 @@
                "json.dump(data, open(cal_path, 'w'))\n",
                "\n",
                "\n",
-                "await ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\", scales = [11])"
+                "ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\", scales = [11])"
            ]
        },
        {
@@ -683,7 +683,7 @@
                "    data = json.load(f)\n",
                "    print(len(data['input_data'][0]))\n",
                "\n",
-                "await ezkl.gen_witness(data_path, compiled_model_path, witness_path)"
+                "ezkl.gen_witness(data_path, compiled_model_path, witness_path)"
            ]
        },
        {
@@ -758,4 +758,4 @@
    },
    "nbformat": 4,
    "nbformat_minor": 4
-}
+}
--- a/examples/notebooks/tictactoe_binary_classification.ipynb
+++ b/examples/notebooks/tictactoe_binary_classification.ipynb
@@ -525,7 +525,7 @@
                "json.dump(data, open(cal_path, 'w'))\n",
                "\n",
                "\n",
-                "await ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\", scales = [4])"
+                "ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\", scales = [4])"
            ]
        },
        {
@@ -572,7 +572,7 @@
                "    data = json.load(f)\n",
                "    print(len(data['input_data'][0]))\n",
                "\n",
-                "await ezkl.gen_witness(data_path, compiled_model_path, witness_path)"
+                "ezkl.gen_witness(data_path, compiled_model_path, witness_path)"
            ]
        },
        {
@@ -647,4 +647,4 @@
    },
    "nbformat": 4,
    "nbformat_minor": 4
-}
+}
--- a/examples/notebooks/univ3-da.ipynb
+++ b/examples/notebooks/univ3-da.ipynb
@@ -1,685 +0,0 @@
-{
-    "cells": [
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# univ3-da-ezkl\n",
-                "\n",
-                "Here's an example leveraging EZKL whereby the inputs to the model are read and attested to from an on-chain source. For this setup we make a single call to a view function that returns an array of UniV3 historical TWAP price data that we will attest to on-chain. \n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "First we import the necessary dependencies and set up logging to be as informative as possible. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 1,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# check if notebook is in colab\n",
-                "try:\n",
-                "    # install ezkl\n",
-                "    import google.colab\n",
-                "    import subprocess\n",
-                "    import sys\n",
-                "    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"ezkl\"])\n",
-                "    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"onnx\"])\n",
-                "\n",
-                "# rely on local installation of ezkl if the notebook is not in colab\n",
-                "except:\n",
-                "    pass\n",
-                "\n",
-                "\n",
-                "from torch import nn\n",
-                "import ezkl\n",
-                "import os\n",
-                "import json\n",
-                "import logging\n",
-                "\n",
-                "# uncomment for more descriptive logging \n",
-                "FORMAT = '%(levelname)s %(name)s %(asctime)-15s %(filename)s:%(lineno)d %(message)s'\n",
-                "logging.basicConfig(format=FORMAT)\n",
-                "logging.getLogger().setLevel(logging.DEBUG)\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Now we define our model. It is a very simple PyTorch model that has just one layer, an average pooling 2D layer. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 2,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import torch\n",
-                "# Defines the model\n",
-                "\n",
-                "class MyModel(nn.Module):\n",
-                "    def __init__(self):\n",
-                "        super(MyModel, self).__init__()\n",
-                "        self.layer = nn.AvgPool2d(2, 1, (1, 1))\n",
-                "\n",
-                "    def forward(self, x):\n",
-                "        return self.layer(x)[0]\n",
-                "\n",
-                "\n",
-                "circuit = MyModel()\n",
-                "\n",
-                "# this is where you'd train your model"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We omit training for purposes of this demonstration. We've marked where training would happen in the cell above. \n",
-                "Now we export the model to onnx and create a corresponding (randomly generated) input. This input data will eventually be stored on chain and read from according to the call_data field in the graph input.\n",
-                "\n",
-                "You can replace the random `x` with real data if you so wish. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 3,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "x = 0.1*torch.rand(1,*[3, 2, 2], requires_grad=True)\n",
-                "\n",
-                "# Flips the neural net into inference mode\n",
-                "circuit.eval()\n",
-                "\n",
-                "    # Export the model\n",
-                "torch.onnx.export(circuit,               # model being run\n",
-                "                      x,                   # model input (or a tuple for multiple inputs)\n",
-                "                      \"network.onnx\",            # where to save the model (can be a file or file-like object)\n",
-                "                      export_params=True,        # store the trained parameter weights inside the model file\n",
-                "                      opset_version=10,          # the ONNX version to export the model to\n",
-                "                      do_constant_folding=True,  # whether to execute constant folding for optimization\n",
-                "                      input_names = ['input'],   # the model's input names\n",
-                "                      output_names = ['output'], # the model's output names\n",
-                "                      dynamic_axes={'input' : {0 : 'batch_size'},    # variable length axes\n",
-                "                                    'output' : {0 : 'batch_size'}})\n",
-                "\n",
-                "data_array = ((x).detach().numpy()).reshape([-1]).tolist()\n",
-                "\n",
-                "data = dict(input_data = [data_array])\n",
-                "\n",
-                "    # Serialize data into file:\n",
-                "json.dump(data, open(\"input.json\", 'w' ))\n",
-                "\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We now define a function that will create a new anvil instance which we will deploy our test contract too. This contract will contain in its storage the data that we will read from and attest to. In production you would not need to set up a local anvil instance. Instead you would replace RPC_URL with the actual RPC endpoint of the chain you are deploying your verifiers too, reading from the data on said chain."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 4,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import subprocess\n",
-                "import time\n",
-                "import threading\n",
-                "\n",
-                "# make sure anvil is running locally\n",
-                "# $ anvil -p 3030\n",
-                "\n",
-                "RPC_URL = \"http://localhost:3030\"\n",
-                "\n",
-                "# Save process globally\n",
-                "anvil_process = None\n",
-                "\n",
-                "def start_anvil():\n",
-                "    global anvil_process\n",
-                "    if anvil_process is None:\n",
-                "        anvil_process = subprocess.Popen([\"anvil\", \"-p\", \"3030\", \"--fork-url\", \"https://arb1.arbitrum.io/rpc\", \"--code-size-limit=41943040\"])\n",
-                "        if anvil_process.returncode is not None:\n",
-                "            raise Exception(\"failed to start anvil process\")\n",
-                "        time.sleep(3)\n",
-                "\n",
-                "def stop_anvil():\n",
-                "    global anvil_process\n",
-                "    if anvil_process is not None:\n",
-                "        anvil_process.terminate()\n",
-                "        anvil_process = None\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We define our `PyRunArgs` objects which contains the visibility parameters for out model. \n",
-                "- `input_visibility` defines the visibility of the model inputs\n",
-                "- `param_visibility` defines the visibility of the model weights and constants and parameters \n",
-                "- `output_visibility` defines the visibility of the model outputs\n",
-                "\n",
-                "Here we create the following setup:\n",
-                "- `input_visibility`: \"public\"\n",
-                "- `param_visibility`: \"private\"\n",
-                "- `output_visibility`: public\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 5,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import ezkl\n",
-                "\n",
-                "model_path = os.path.join('network.onnx')\n",
-                "compiled_model_path = os.path.join('network.compiled')\n",
-                "pk_path = os.path.join('test.pk')\n",
-                "vk_path = os.path.join('test.vk')\n",
-                "settings_path = os.path.join('settings.json')\n",
-                "srs_path = os.path.join('kzg.srs')\n",
-                "data_path = os.path.join('input.json')\n",
-                "\n",
-                "run_args = ezkl.PyRunArgs()\n",
-                "run_args.input_visibility = \"public\"\n",
-                "run_args.param_visibility = \"private\"\n",
-                "run_args.output_visibility = \"public\"\n",
-                "run_args.decomp_legs=5\n",
-                "run_args.num_inner_cols = 1\n",
-                "run_args.variables = [(\"batch_size\", 1)]"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Now we generate a settings file. This file basically instantiates a bunch of parameters that determine their circuit shape, size etc... Because of the way we represent nonlinearities in the circuit (using Halo2's [lookup tables](https://zcash.github.io/halo2/design/proving-system/lookup.html)), it is often best to _calibrate_ this settings file as some data can fall out of range of these lookups.\n",
-                "\n",
-                "You can pass a dataset for calibration that will be representative of real inputs you might find if and when you deploy the prover. Here we create a dummy calibration dataset for demonstration purposes. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# TODO: Dictionary outputs\n",
-                "res = ezkl.gen_settings(model_path, settings_path, py_run_args=run_args)\n",
-                "assert res == True"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# generate a bunch of dummy calibration data\n",
-                "cal_data = {\n",
-                "    \"input_data\": [(0.1*torch.rand(2, *[3, 2, 2])).flatten().tolist()],\n",
-                "}\n",
-                "\n",
-                "cal_path = os.path.join('val_data.json')\n",
-                "# save as json file\n",
-                "with open(cal_path, \"w\") as f:\n",
-                "    json.dump(cal_data, f)\n",
-                "\n",
-                "res = await ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "res = ezkl.compile_circuit(model_path, compiled_model_path, settings_path)\n",
-                "assert res == True"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "The graph input for on chain data sources is formatted completely differently compared to file based data sources.\n",
-                "\n",
-                "- For file data sources, the raw floating point values that eventually get quantized, converted into field elements and stored in `witness.json` to be consumed by the circuit are stored. The output data contains the expected floating point values returned as outputs from running your vanilla pytorch model on the given inputs.\n",
-                "- For on chain data sources, the input_data field contains all the data necessary to read and format the on chain data into something digestable by EZKL (aka field elements :-D). \n",
-                "Here is what the schema for an on-chain data source graph input file should look like for a single call data source:\n",
-                "    \n",
-                "```json\n",
-                "{\n",
-                "    \"input_data\": {\n",
-                "        \"rpc\": \"http://localhost:3030\", // The rpc endpoint of the chain you are deploying your verifier to\n",
-                "        \"call\": {\n",
-                "            \"call_data\": \"1f3be514000000000000000000000000c6962004f452be9203591991d15f6b388e09e8d00000000000000000000000000000000000000000000000000000000000000040000000000000000000000000000000000000000000000000000000000000000c000000000000000000000000000000000000000000000000000000000000000b000000000000000000000000000000000000000000000000000000000000000a0000000000000000000000000000000000000000000000000000000000000009000000000000000000000000000000000000000000000000000000000000000800000000000000000000000000000000000000000000000000000000000000070000000000000000000000000000000000000000000000000000000000000006000000000000000000000000000000000000000000000000000000000000000500000000000000000000000000000000000000000000000000000000000000040000000000000000000000000000000000000000000000000000000000000003000000000000000000000000000000000000000000000000000000000000000200000000000000000000000000000000000000000000000000000000000000010000000000000000000000000000000000000000000000000000000000000000\", // The abi encoded call data to a view function that returns an array of on-chain data points we are attesting to. \n",
-                "            \"decimals\": 0, // The number of decimal places of the large uint256 value. This is our way of representing large wei values as floating points on chain, since the evm only natively supports integer values.\n",
-                "            \"address\": \"9A213F53334279C128C37DA962E5472eCD90554f\", // The address of the contract that we are calling to get the data. \n",
-                "            \"len\": 12 // The number of data points returned by the view function (the length of the array)\n",
-                "        }\n",
-                "    }\n",
-                "}\n",
-                "```"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "from web3 import Web3, HTTPProvider\n",
-                "from solcx import compile_standard\n",
-                "from decimal import Decimal\n",
-                "import json\n",
-                "import os\n",
-                "import torch\n",
-                "import requests\n",
-                "\n",
-                "def count_decimal_places(num):\n",
-                "    num_str = str(num)\n",
-                "    if '.' in num_str:\n",
-                "        return len(num_str) - 1 - num_str.index('.')\n",
-                "    else:\n",
-                "        return 0\n",
-                "\n",
-                "w3 = Web3(HTTPProvider(RPC_URL)) \n",
-                "\n",
-                "def on_chain_data(tensor):\n",
-                "    data = tensor.view(-1).tolist()\n",
-                "    secondsAgo = [len(data) - 1 - i for i in range(len(data))]\n",
-                "\n",
-                "    contract_source_code = '''\n",
-                "    // SPDX-License-Identifier: MIT\n",
-                "    pragma solidity ^0.8.20;\n",
-                "\n",
-                "    interface IUniswapV3PoolDerivedState {\n",
-                "        function observe(\n",
-                "            uint32[] calldata secondsAgos\n",
-                "        ) external view returns (\n",
-                "            int56[] memory tickCumulatives,\n",
-                "            uint160[] memory secondsPerLiquidityCumulativeX128s\n",
-                "        );\n",
-                "    }\n",
-                "\n",
-                "    contract UniTickAttestor {\n",
-                "        int256[] private cachedTicks;\n",
-                "\n",
-                "        function consult(\n",
-                "            IUniswapV3PoolDerivedState pool,\n",
-                "            uint32[] memory secondsAgo\n",
-                "        ) public view returns (int256[] memory tickCumulatives) {\n",
-                "            tickCumulatives = new int256[](secondsAgo.length);\n",
-                "            (int56[] memory _ticks,) = pool.observe(secondsAgo);\n",
-                "            for (uint256 i = 0; i < secondsAgo.length; i++) {\n",
-                "                tickCumulatives[i] = int256(_ticks[i]);\n",
-                "            }\n",
-                "        }\n",
-                "\n",
-                "        function cache_price(\n",
-                "            IUniswapV3PoolDerivedState pool,\n",
-                "            uint32[] memory secondsAgo\n",
-                "        ) public {\n",
-                "            (int56[] memory _ticks,) = pool.observe(secondsAgo);\n",
-                "            cachedTicks = new int256[](_ticks.length);\n",
-                "            for (uint256 i = 0; i < _ticks.length; i++) {\n",
-                "                cachedTicks[i] = int256(_ticks[i]);\n",
-                "            }\n",
-                "        }\n",
-                "\n",
-                "        function readPriceCache() public view returns (int256[] memory) {\n",
-                "            return cachedTicks;\n",
-                "        }\n",
-                "    }\n",
-                "    '''\n",
-                "\n",
-                "    compiled_sol = compile_standard({\n",
-                "        \"language\": \"Solidity\",\n",
-                "        \"sources\": {\"UniTickAttestor.sol\": {\"content\": contract_source_code}},\n",
-                "        \"settings\": {\"outputSelection\": {\"*\": {\"*\": [\"metadata\", \"evm.bytecode\", \"abi\"]}}}\n",
-                "    })\n",
-                "\n",
-                "    bytecode = compiled_sol['contracts']['UniTickAttestor.sol']['UniTickAttestor']['evm']['bytecode']['object']\n",
-                "    abi = json.loads(compiled_sol['contracts']['UniTickAttestor.sol']['UniTickAttestor']['metadata'])['output']['abi']\n",
-                "\n",
-                "    # Deploy contract\n",
-                "    UniTickAttestor = w3.eth.contract(abi=abi, bytecode=bytecode)\n",
-                "    tx_hash = UniTickAttestor.constructor().transact()\n",
-                "    tx_receipt = w3.eth.wait_for_transaction_receipt(tx_hash)\n",
-                "    contract = w3.eth.contract(address=tx_receipt['contractAddress'], abi=abi)\n",
-                "\n",
-                "    # Step 4: Store data via cache_price transaction\n",
-                "    tx_hash = contract.functions.cache_price(\n",
-                "        \"0xC6962004f452bE9203591991D15f6b388e09E8D0\",\n",
-                "        secondsAgo\n",
-                "    ).transact()\n",
-                "    tx_receipt = w3.eth.wait_for_transaction_receipt(tx_hash)\n",
-                "\n",
-                "    # Step 5: Prepare calldata for readPriceCache\n",
-                "    call = contract.functions.readPriceCache().build_transaction()\n",
-                "    calldata = call['data'][2:]\n",
-                "\n",
-                "    # Get stored data\n",
-                "    result = contract.functions.readPriceCache().call()\n",
-                "    print(f'Cached ticks: {result}')\n",
-                "\n",
-                "    decimals = [0] * len(data)\n",
-                "\n",
-                "    call_to_account = {\n",
-                "        'call_data': calldata,\n",
-                "        'decimals': decimals,\n",
-                "        'address': contract.address[2:],\n",
-                "    }\n",
-                "\n",
-                "    return call_to_account\n",
-                "\n",
-                "start_anvil()\n",
-                "call_to_account = on_chain_data(x)\n",
-                "\n",
-                "data = dict(input_data =  {'rpc': RPC_URL, 'call': call_to_account })\n",
-                "json.dump(data, open(\"input.json\", 'w'))"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "As we use Halo2 with KZG-commitments we need an SRS string from (preferably) a multi-party trusted setup ceremony. For an overview of the procedures for such a ceremony check out [this page](https://blog.ethereum.org/2023/01/16/announcing-kzg-ceremony). The `get_srs` command retrieves a correctly sized SRS given the calibrated settings file from [here](https://github.com/han0110/halo2-kzg-srs). \n",
-                "\n",
-                "These SRS were generated with [this](https://github.com/privacy-scaling-explorations/perpetualpowersoftau) ceremony. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "res = await ezkl.get_srs( settings_path)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We now need to generate the circuit witness. These are the model outputs (and any hashes) that are generated when feeding the previously generated `input.json` through the circuit / model. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# !export RUST_BACKTRACE=1\n",
-                "\n",
-                "witness_path = \"witness.json\"\n",
-                "\n",
-                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Here we setup verifying and proving keys for the circuit. As the name suggests the proving key is needed for ... proving and the verifying key is needed for ... verifying. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# HERE WE SETUP THE CIRCUIT PARAMS\n",
-                "# WE GOT KEYS\n",
-                "# WE GOT CIRCUIT PARAMETERS\n",
-                "# EVERYTHING ANYONE HAS EVER NEEDED FOR ZK\n",
-                "res = ezkl.setup(\n",
-                "        compiled_model_path,\n",
-                "        vk_path,\n",
-                "        pk_path,\n",
-                "    )\n",
-                "\n",
-                "assert res == True\n",
-                "assert os.path.isfile(vk_path)\n",
-                "assert os.path.isfile(pk_path)\n",
-                "assert os.path.isfile(settings_path)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Now we generate a full proof. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# GENERATE A PROOF\n",
-                "\n",
-                "proof_path = os.path.join('test.pf')\n",
-                "\n",
-                "res = ezkl.prove(\n",
-                "        witness_path,\n",
-                "        compiled_model_path,\n",
-                "        pk_path,\n",
-                "        proof_path,\n",
-                "        \"single\",\n",
-                "    )\n",
-                "\n",
-                "print(res)\n",
-                "assert os.path.isfile(proof_path)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "And verify it as a sanity check. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# VERIFY IT\n",
-                "\n",
-                "res = ezkl.verify(\n",
-                "        proof_path,\n",
-                "        settings_path,\n",
-                "        vk_path,\n",
-                "    )\n",
-                "\n",
-                "assert res == True\n",
-                "print(\"verified\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We can now create and then deploy a vanilla evm verifier."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "abi_path = 'test.abi'\n",
-                "sol_code_path = 'test.sol'\n",
-                "\n",
-                "res = await ezkl.create_evm_verifier(\n",
-                "        vk_path,\n",
-                "        settings_path,\n",
-                "        sol_code_path,\n",
-                "        abi_path,\n",
-                "    )\n",
-                "assert res == True"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import json\n",
-                "\n",
-                "addr_path_verifier = \"addr_verifier.txt\"\n",
-                "\n",
-                "res = await ezkl.deploy_evm(\n",
-                "    addr_path_verifier,\n",
-                "    sol_code_path,\n",
-                "    'http://127.0.0.1:3030'\n",
-                ")\n",
-                "\n",
-                "assert res == True"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "With the vanilla verifier deployed, we can now create the data attestation contract, which will read in the instances from the calldata to the verifier, attest to them, call the verifier and then return the result. "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "\n",
-                "abi_path = 'test.abi'\n",
-                "sol_code_path = 'test.sol'\n",
-                "input_path = 'input.json'\n",
-                "\n",
-                "res = await ezkl.create_evm_data_attestation(\n",
-                "        input_path,\n",
-                "        settings_path,\n",
-                "        sol_code_path,\n",
-                "        abi_path,\n",
-                "    )"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Now we can deploy the data attest verifier contract. For security reasons, this binding will only deploy to a local anvil instance, using accounts generated by anvil. \n",
-                "So should only be used for testing purposes."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "addr_path_da = \"addr_da.txt\"\n",
-                "\n",
-                "res = await ezkl.deploy_da_evm(\n",
-                "        addr_path_da,\n",
-                "        input_path,\n",
-                "        settings_path,\n",
-                "        sol_code_path,\n",
-                "        RPC_URL,\n",
-                "    )\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Here we need to regenerate the witness, prove and then verify all within the same cell. This is because we want to reduce the amount of latency between reading on-chain state and verifying it on-chain. This is because the attest input values read from the oracle are time sensitive (their values are derived from computing on block.timestamp) and can change between the time of reading and the time of verifying.\n",
-                "\n",
-                "Call the view only verify method on the contract to verify the proof. Since it is a view function this is safe to use in production since you don't have to pass your private key."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# !export RUST_BACKTRACE=1\n",
-                "\n",
-                "# print(res)\n",
-                "assert os.path.isfile(proof_path)\n",
-                "# read the verifier address\n",
-                "addr_verifier = None\n",
-                "with open(addr_path_verifier, 'r') as f:\n",
-                "    addr = f.read()\n",
-                "#read the data attestation address\n",
-                "addr_da = None\n",
-                "with open(addr_path_da, 'r') as f:\n",
-                "    addr_da = f.read()\n",
-                "\n",
-                "res = await ezkl.verify_evm(\n",
-                "    addr,\n",
-                "    proof_path,\n",
-                "    RPC_URL,\n",
-                "    addr_da,\n",
-                ")"
-            ]
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": ".env",
-            "language": "python",
-            "name": "python3"
-        },
-        "language_info": {
-            "codemirror_mode": {
-                "name": "ipython",
-                "version": 3
-            },
-            "file_extension": ".py",
-            "mimetype": "text/x-python",
-            "name": "python",
-            "nbconvert_exporter": "python",
-            "pygments_lexer": "ipython3",
-            "version": "3.11.5"
-        },
-        "orig_nbformat": 4
-    },
-    "nbformat": 4,
-    "nbformat_minor": 2
-}
--- a/examples/notebooks/variance.ipynb
+++ b/examples/notebooks/variance.ipynb
@@ -458,7 +458,7 @@
        "\n",
        "\n",
        "ezkl.gen_settings(onnx_filename, settings_filename)\n",
-        "await ezkl.calibrate_settings(\n",
+        "ezkl.calibrate_settings(\n",
        "    input_filename, onnx_filename, settings_filename, \"resources\", scales = [4])\n",
        "res = await ezkl.get_srs(settings_filename)\n",
        "ezkl.compile_circuit(onnx_filename, compiled_filename, settings_filename)\n",
@@ -527,7 +527,7 @@
        "\n",
        "witness_path = \"witness.json\"\n",
        "\n",
-        "res = await ezkl.gen_witness(input_filename, compiled_filename, witness_path)\n",
+        "res = ezkl.gen_witness(input_filename, compiled_filename, witness_path)\n",
        "assert os.path.isfile(witness_path)"
      ]
    },
@@ -666,7 +666,7 @@
    },
    {
      "cell_type": "code",
-      "execution_count": 11,
+      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
@@ -689,8 +689,8 @@
        "# await\n",
        "res = await ezkl.deploy_evm(\n",
        "    address_path,\n",
+        "    'http://127.0.0.1:3030',\n",
        "    sol_code_path,\n",
-        "    'http://127.0.0.1:3030'\n",
        ")\n",
        "\n",
        "assert res == True\n",
@@ -701,7 +701,7 @@
    },
    {
      "cell_type": "code",
-      "execution_count": 12,
+      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
@@ -722,8 +722,8 @@
        "\n",
        "res = await ezkl.verify_evm(\n",
        "    addr,\n",
+        "    \"http://127.0.0.1:3030\",\n",
        "    proof_path,\n",
-        "    \"http://127.0.0.1:3030\"\n",
        ")\n",
        "assert res == True"
      ]
@@ -743,7 +743,8 @@
      "provenance": []
    },
    "kernelspec": {
-      "display_name": "Python 3",
+      "display_name": ".env",
+      "language": "python",
      "name": "python3"
    },
    "language_info": {
@@ -756,9 +757,9 @@
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
-      "version": "3.12.2"
+      "version": "3.12.9"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
-}
+}
--- a/examples/notebooks/voice_judge.ipynb
+++ b/examples/notebooks/voice_judge.ipynb
@@ -629,7 +629,7 @@
            "source": [
                "\n",
                "\n",
-                "res = await ezkl.calibrate_settings(val_data, model_path, settings_path, \"resources\", scales = [4])\n",
+                "res = ezkl.calibrate_settings(val_data, model_path, settings_path, \"resources\", scales = [4])\n",
                "assert res == True\n",
                "print(\"verified\")\n"
            ]
@@ -680,7 +680,7 @@
                "\n",
                "witness_path = \"witness.json\"\n",
                "\n",
-                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
                "assert os.path.isfile(witness_path)"
            ]
        },
@@ -849,8 +849,8 @@
                "\n",
                "res = await ezkl.deploy_evm(\n",
                "    address_path,\n",
+                "    'http://127.0.0.1:3030',\n",
                "    sol_code_path,\n",
-                "    'http://127.0.0.1:3030'\n",
                ")\n",
                "\n",
                "assert res == True\n",
@@ -870,8 +870,8 @@
                "\n",
                "res = await ezkl.verify_evm(\n",
                "    addr,\n",
-                "    proof_path,\n",
-                "    \"http://127.0.0.1:3030\"\n",
+                "    \"http://127.0.0.1:3030\",\n",
+                "    proof_path\n",
                ")\n",
                "assert res == True"
            ]
--- a/examples/notebooks/world_rotation.ipynb
+++ b/examples/notebooks/world_rotation.ipynb
@@ -1,539 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "cf69bb3f-94e6-4dba-92cd-ce08df117d67",
-   "metadata": {},
-   "source": [
-    "## World rotation\n",
-    "\n",
-    "Here we demonstrate how to use the EZKL package to rotate an on-chain world. \n",
-    "\n",
-    "![zk-gaming-diagram-transformed](https://hackmd.io/_uploads/HkApuQGV6.png)\n",
-    "> **A typical ZK application flow**. For the shape rotators out there — this is an easily digestible example. A user computes a ZK-proof that they have calculated a valid rotation of a world. They submit this proof to a verifier contract which governs an on-chain world, along with a new set of coordinates, and the world rotation updates. Observe that it’s possible for one player to initiate a *global* change.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "95613ee9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# check if notebook is in colab\n",
-    "try:\n",
-    "    # install ezkl\n",
-    "    import google.colab\n",
-    "    import subprocess\n",
-    "    import sys\n",
-    "    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"ezkl\"])\n",
-    "    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"onnx\"])\n",
-    "\n",
-    "# rely on local installation of ezkl if the notebook is not in colab\n",
-    "except:\n",
-    "    pass\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch import nn\n",
-    "import ezkl\n",
-    "import os\n",
-    "import json\n",
-    "import torch\n",
-    "import math\n",
-    "\n",
-    "# these are constants for the rotation\n",
-    "phi = torch.tensor(5 * math.pi / 180)\n",
-    "s = torch.sin(phi)\n",
-    "c = torch.cos(phi)\n",
-    "\n",
-    "\n",
-    "class RotateStuff(nn.Module):\n",
-    "    def __init__(self):\n",
-    "        super(RotateStuff, self).__init__()\n",
-    "\n",
-    "        # create a rotation matrix -- the matrix is constant and is transposed for convenience\n",
-    "        self.rot = torch.stack([torch.stack([c, -s]),\n",
-    "                   torch.stack([s, c])]).t()\n",
-    "\n",
-    "    def forward(self, x):\n",
-    "        x_rot = x @ self.rot   # same as x_rot = (rot @ x.t()).t() due to rot in O(n) (SO(n) even)\n",
-    "        return x_rot\n",
-    "\n",
-    "\n",
-    "circuit = RotateStuff()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This will showcase the principle directions of rotation by plotting the rotation of a single unit vector."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from matplotlib import pyplot\n",
-    "pyplot.figure(figsize=(3, 3))\n",
-    "pyplot.arrow(0, 0, 1, 0, width=0.02, alpha=0.5)\n",
-    "pyplot.arrow(0, 0, 0, 1, width=0.02, alpha=0.5)\n",
-    "pyplot.arrow(0, 0, circuit.rot[0, 0].item(), circuit.rot[0, 1].item(), width=0.02)\n",
-    "pyplot.arrow(0, 0, circuit.rot[1, 0].item(), circuit.rot[1, 1].item(), width=0.02)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b37637c4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model_path = os.path.join('network.onnx')\n",
-    "compiled_model_path = os.path.join('network.compiled')\n",
-    "pk_path = os.path.join('test.pk')\n",
-    "vk_path = os.path.join('test.vk')\n",
-    "settings_path = os.path.join('settings.json')\n",
-    "srs_path = os.path.join('kzg.srs')\n",
-    "witness_path = os.path.join('witness.json')\n",
-    "data_path = os.path.join('input.json')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "82db373a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "\n",
-    "# initial principle vectors for the rotation are as in the plot above\n",
-    "x = torch.tensor([[1, 0], [0, 1]], dtype=torch.float32)\n",
-    "\n",
-    "# Flips the neural net into inference mode\n",
-    "circuit.eval()\n",
-    "\n",
-    "    # Export the model\n",
-    "torch.onnx.export(circuit,               # model being run\n",
-    "                      x,                   # model input (or a tuple for multiple inputs)\n",
-    "                      model_path,            # where to save the model (can be a file or file-like object)\n",
-    "                      export_params=True,        # store the trained parameter weights inside the model file\n",
-    "                      opset_version=10,          # the ONNX version to export the model to\n",
-    "                      do_constant_folding=True,  # whether to execute constant folding for optimization\n",
-    "                      input_names = ['input'],   # the model's input names\n",
-    "                      output_names = ['output'], # the model's output names\n",
-    "                      )\n",
-    "\n",
-    "data_array = ((x).detach().numpy()).reshape([-1]).tolist()\n",
-    "\n",
-    "data = dict(input_data = [data_array])\n",
-    "\n",
-    "    # Serialize data into file:\n",
-    "json.dump( data, open(data_path, 'w' ))\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### World rotation in 2D on-chain"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For demo purposes we deploy these coordinates to a contract running locally using Anvil. This creates our on-chain world. We then rotate the world using the EZKL package and submit the proof to the contract. The contract then updates the world rotation. For demo purposes we do this repeatedly, rotating the world by 1 transform each time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import subprocess\n",
-    "import time\n",
-    "import threading\n",
-    "\n",
-    "# make sure anvil is running locally\n",
-    "# $ anvil -p 3030\n",
-    "\n",
-    "RPC_URL = \"http://localhost:3030\"\n",
-    "\n",
-    "# Save process globally\n",
-    "anvil_process = None\n",
-    "\n",
-    "def start_anvil():\n",
-    "    global anvil_process\n",
-    "    if anvil_process is None:\n",
-    "        anvil_process = subprocess.Popen([\"anvil\", \"-p\", \"3030\", \"--code-size-limit=41943040\"])\n",
-    "        if anvil_process.returncode is not None:\n",
-    "            raise Exception(\"failed to start anvil process\")\n",
-    "        time.sleep(3)\n",
-    "\n",
-    "def stop_anvil():\n",
-    "    global anvil_process\n",
-    "    if anvil_process is not None:\n",
-    "        anvil_process.terminate()\n",
-    "        anvil_process = None\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We define our `PyRunArgs` objects which contains the visibility parameters for out model. \n",
-    "- `input_visibility` defines the visibility of the model inputs\n",
-    "- `param_visibility` defines the visibility of the model weights and constants and parameters \n",
-    "- `output_visibility` defines the visibility of the model outputs\n",
-    "\n",
-    "Here we create the following setup:\n",
-    "- `input_visibility`: \"public\"\n",
-    "- `param_visibility`: \"fixed\"\n",
-    "- `output_visibility`: public"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d5e374a2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "py_run_args = ezkl.PyRunArgs()\n",
-    "py_run_args.input_visibility = \"public\"\n",
-    "py_run_args.output_visibility = \"public\"\n",
-    "py_run_args.param_visibility = \"private\" # private by default\n",
-    "py_run_args.scale_rebase_multiplier = 10\n",
-    "\n",
-    "res = ezkl.gen_settings(model_path, settings_path, py_run_args=py_run_args)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3aa4f090",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "res = ezkl.compile_circuit(model_path, compiled_model_path, settings_path)\n",
-    "assert res == True"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We also define a contract that holds out test data. This contract will contain in its storage the data that we will read from and attest to. In production you would not need to set up a local anvil instance. Instead you would replace RPC_URL with the actual RPC endpoint of the chain you are deploying your verifiers too, reading from the data on said chain."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2007dc77",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ezkl.setup_test_evm_data(\n",
-    "    data_path,\n",
-    "    compiled_model_path,\n",
-    "    # we write the call data to the same file as the input data\n",
-    "    data_path,\n",
-    "    input_source=ezkl.PyTestDataSource.OnChain,\n",
-    "    output_source=ezkl.PyTestDataSource.File,\n",
-    "    rpc_url=RPC_URL)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ab993958",
-   "metadata": {},
-   "source": [
-    "As we use Halo2 with KZG-commitments we need an SRS string from (preferably) a multi-party trusted setup ceremony. For an overview of the procedures for such a ceremony check out [this page](https://blog.ethereum.org/2023/01/16/announcing-kzg-ceremony). The `get_srs` command retrieves a correctly sized SRS given the calibrated settings file from [here](https://github.com/han0110/halo2-kzg-srs). \n",
-    "\n",
-    "These SRS were generated with [this](https://github.com/privacy-scaling-explorations/perpetualpowersoftau) ceremony. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8b74dcee",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# srs path\n",
-    "res = await ezkl.get_srs( settings_path)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "18c8b7c7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# now generate the witness file \n",
-    "\n",
-    "witness = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
-    "assert os.path.isfile(witness_path)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ad58432e",
-   "metadata": {},
-   "source": [
-    "Here we setup verifying and proving keys for the circuit. As the name suggests the proving key is needed for ... proving and the verifying key is needed for ... verifying. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b1c561a8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "res = ezkl.setup(\n",
-    "        compiled_model_path,\n",
-    "        vk_path,\n",
-    "        pk_path,\n",
-    "        \n",
-    "    )\n",
-    "\n",
-    "assert res == True\n",
-    "assert os.path.isfile(vk_path)\n",
-    "assert os.path.isfile(pk_path)\n",
-    "assert os.path.isfile(settings_path)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1746c8d1",
-   "metadata": {},
-   "source": [
-    "We can now create an EVM verifier contract from our circuit. This contract will be deployed to the chain we are using. In this case we are using a local anvil instance."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d1920c0f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "abi_path = 'test.abi'\n",
-    "sol_code_path = 'test.sol'\n",
-    "\n",
-    "res = await ezkl.create_evm_verifier(\n",
-    "        vk_path,\n",
-    "        settings_path,\n",
-    "        sol_code_path,\n",
-    "        abi_path,\n",
-    "    )\n",
-    "assert res == True"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0fd7f22b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "\n",
-    "addr_path_verifier = \"addr_verifier.txt\"\n",
-    "\n",
-    "res = await ezkl.deploy_evm(\n",
-    "    addr_path_verifier,\n",
-    "    sol_code_path,\n",
-    "    'http://127.0.0.1:3030'\n",
-    ")\n",
-    "\n",
-    "assert res == True"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9c0dffab",
-   "metadata": {},
-   "source": [
-    "With the vanilla verifier deployed, we can now create the data attestation contract, which will read in the instances from the calldata to the verifier, attest to them, call the verifier and then return the result. \n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c2db14d7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "abi_path = 'test.abi'\n",
-    "sol_code_path = 'test.sol'\n",
-    "input_path = 'input.json'\n",
-    "\n",
-    "res = await ezkl.create_evm_data_attestation(\n",
-    "        input_path,\n",
-    "        settings_path,\n",
-    "        sol_code_path,\n",
-    "        abi_path,\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5a018ba6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "addr_path_da = \"addr_da.txt\"\n",
-    "\n",
-    "res = await ezkl.deploy_da_evm(\n",
-    "        addr_path_da,\n",
-    "        input_path,\n",
-    "        settings_path,\n",
-    "        sol_code_path,\n",
-    "        RPC_URL,\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2adad845",
-   "metadata": {},
-   "source": [
-    "Now we can pull in the data from the contract and calculate a new set of coordinates. We then rotate the world by 1 transform and submit the proof to the contract. The contract could then update the world rotation (logic not inserted here). For demo purposes we do this repeatedly, rotating the world by 1 transform. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c384cbc8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# GENERATE A PROOF\n",
-    "\n",
-    "\n",
-    "proof_path = os.path.join('test.pf')\n",
-    "\n",
-    "res = ezkl.prove(\n",
-    "        witness_path,\n",
-    "        compiled_model_path,\n",
-    "        pk_path,\n",
-    "        proof_path,\n",
-    "        \n",
-    "        \"single\",\n",
-    "    )\n",
-    "\n",
-    "print(res)\n",
-    "assert os.path.isfile(proof_path)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "90eda56e",
-   "metadata": {},
-   "source": [
-    "Call the view only verify method on the contract to verify the proof. Since it is a view function this is safe to use in production since you don't have to pass your private key."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "76f00d41",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# read the verifier address\n",
-    "addr_verifier = None\n",
-    "with open(addr_path_verifier, 'r') as f:\n",
-    "    addr = f.read()\n",
-    "#read the data attestation address\n",
-    "addr_da = None\n",
-    "with open(addr_path_da, 'r') as f:\n",
-    "    addr_da = f.read()\n",
-    "\n",
-    "res = ezkl.verify_evm(\n",
-    "    addr,\n",
-    "    proof_path,\n",
-    "    RPC_URL,\n",
-    "    addr_da,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As a sanity check lets plot the rotations of the unit vectors. We can see that the unit vectors rotate as expected by the output of the circuit. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "witness['outputs'][0][0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "settings = json.load(open(settings_path, 'r'))\n",
-    "out_scale = settings[\"model_output_scales\"][0]\n",
-    "\n",
-    "from matplotlib import pyplot\n",
-    "pyplot.figure(figsize=(3, 3))\n",
-    "pyplot.arrow(0, 0, 1, 0, width=0.02, alpha=0.5)\n",
-    "pyplot.arrow(0, 0, 0, 1, width=0.02, alpha=0.5)\n",
-    "\n",
-    "arrow_x = ezkl.felt_to_float(witness['outputs'][0][0], out_scale)\n",
-    "arrow_y = ezkl.felt_to_float(witness['outputs'][0][1], out_scale)\n",
-    "pyplot.arrow(0, 0, arrow_x, arrow_y, width=0.02)\n",
-    "arrow_x = ezkl.felt_to_float(witness['outputs'][0][2], out_scale)\n",
-    "arrow_y = ezkl.felt_to_float(witness['outputs'][0][3], out_scale)\n",
-    "pyplot.arrow(0, 0, arrow_x, arrow_y, width=0.02)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".env",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
--- a/examples/notebooks/xgboost.ipynb
+++ b/examples/notebooks/xgboost.ipynb
@@ -193,7 +193,7 @@
                "with open(cal_path, \"w\") as f:\n",
                "    json.dump(cal_data, f)\n",
                "\n",
-                "res = await ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
+                "res = ezkl.calibrate_settings(cal_path, model_path, settings_path, \"resources\")"
            ]
        },
        {
@@ -227,7 +227,7 @@
            "source": [
                "# now generate the witness file \n",
                "\n",
-                "res = await ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
+                "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n",
                "assert os.path.isfile(witness_path)"
            ]
        },
--- a/examples/onnx/fr_age/heaptrack.ezkl.3356626.gz
+++ b/examples/onnx/fr_age/heaptrack.ezkl.3356626.gz
--- a/examples/onnx/fr_age/lol.txt
+++ b/examples/onnx/fr_age/lol.txt
--- a/examples/onnx/large_mlp/gen.py
+++ b/examples/onnx/large_mlp/gen.py
@@ -0,0 +1,79 @@
+from torch import nn
+import torch.nn.init as init
+import torch
+import json
+
+N = 100
+
+class Model(nn.Module):
+    def __init__(self, inplace=False):
+        super(Model, self).__init__()
+
+        self.aff1 = nn.Linear(N,N)
+        self.aff2 = nn.Linear(N,N)
+        self.aff3 = nn.Linear(N,N)
+        self.aff4 = nn.Linear(N,N)
+        self.aff5 = nn.Linear(N,N)
+        self.aff6 = nn.Linear(N,N)
+        self.aff7 = nn.Linear(N,N)
+        self.aff8 = nn.Linear(N,N)
+        self.aff9 = nn.Linear(N,N)
+        self.relu = nn.ReLU()
+        self._initialize_weights()
+
+    def forward(self, x):
+        # concat 10 x along dim 0
+        x = x.repeat(10, 1)
+        x = self.aff1(x)
+        x = self.relu(x)
+        x = self.aff2(x)
+        x = self.relu(x)
+        x = self.aff3(x)
+        x = self.relu(x)
+        x = self.aff4(x)
+        x = self.relu(x)
+        x = self.aff5(x)
+        x = self.relu(x)
+        x = self.aff6(x)
+        x = self.relu(x)
+        x = self.aff7(x)
+        x = self.relu(x)
+        x = self.aff8(x)
+        x = self.relu(x)
+        x = self.aff9(x)
+        return x
+
+
+    def _initialize_weights(self):
+        init.orthogonal_(self.aff1.weight)
+
+model = Model()
+
+# Flips the neural net into inference mode
+model.eval()
+model.to('cpu')
+
+
+x = torch.randn(1, N)
+# Export the model
+torch.onnx.export(model,               # model being run
+                  # model input (or a tuple for multiple inputs)
+                  x,
+                  # where to save the model (can be a file or file-like object)
+                  "network.onnx",
+                  export_params=True,        # store the trained parameter weights inside the model file
+                  opset_version=12,          # the ONNX version to export the model to
+                  do_constant_folding=True,  # whether to execute constant folding for optimization
+                  input_names=['input'],   # the model's input names
+                  output_names=['output'],  # the model's output names
+                  dynamic_axes={'input': {0: 'batch_size'},    # variable length axes
+                                'output': {0: 'batch_size'}})
+
+data_array = ((x).detach().numpy()).reshape([-1]).tolist()
+
+data_json = dict(input_data=[data_array])
+
+print(data_json)
+
+# Serialize data into file:
+json.dump(data_json, open("input.json", 'w'))
--- a/examples/onnx/large_mlp/input.json
+++ b/examples/onnx/large_mlp/input.json
@@ -0,0 +1 @@
+{"input_data": [[0.33088353276252747, -0.8819183707237244, 1.245591163635254, -1.807046890258789, 1.9922369718551636, -0.3360576629638672, 0.4529011845588684, -0.3590165674686432, 0.08356846123933792, 0.5126393437385559, 0.44627535343170166, 1.4916497468948364, 0.49731069803237915, -0.9748706817626953, -0.4923185408115387, 1.3548223972320557, 0.2306872010231018, 1.125955581665039, -1.7063908576965332, 0.3777385354042053, -2.7988760471343994, -1.1846797466278076, 0.7473157048225403, 1.490412950515747, 0.017497723922133446, 2.113945245742798, -1.2141249179840088, -0.16120357811450958, 0.021127669140696526, 0.7207374572753906, -1.369688868522644, -0.7369781732559204, -0.630584180355072, -0.4520200788974762, 0.29123976826667786, 0.6334688067436218, -0.869332492351532, -1.258501648902893, 0.3012596666812897, -0.5507447123527527, 0.669975757598877, 0.15088629722595215, -0.1050339788198471, 0.5505334138870239, -0.1287376880645752, -1.4297826290130615, -0.01703289896249771, -1.2296998500823975, 0.5122153162956238, -0.16924428939819336, -0.415036678314209, -1.1979341506958008, 0.05831022188067436, -0.4411357045173645, 2.0713791847229004, 1.4611141681671143, -0.9357407093048096, -0.333297461271286, -0.676478385925293, 1.390028476715088, -0.05827632546424866, 1.535687804222107, 0.3060210347175598, -0.03171076253056526, -0.614985466003418, 1.2040390968322754, 0.31318482756614685, -1.2134959697723389, 0.13110508024692535, -1.4880926609039307, 1.7007993459701538, 1.5412729978561401, 0.09260450303554535, 0.7649128437042236, -0.5009126663208008, -0.5356241464614868, -0.069572813808918, -0.011717632412910461, 0.21314217150211334, -0.1985170543193817, -0.0223808903247118, 1.2128918170928955, 0.8334696888923645, 1.9029873609542847, -0.11491120606660843, -0.10303237289190292, -0.2467050403356552, 1.557223916053772, -1.1108328104019165, -0.9065343141555786, -0.2271333783864975, 0.6959827542304993, -0.48698121309280396, 0.5689510703086853, 1.115319013595581, -0.8907430768013, -0.24722427129745483, -0.7437837719917297, 0.6742106676101685, -1.7830933332443237]]}
--- a/examples/onnx/large_mlp/network.onnx
+++ b/examples/onnx/large_mlp/network.onnx
--- a/examples/onnx/multihead_attention/gen.py
+++ b/examples/onnx/multihead_attention/gen.py
@@ -104,5 +104,5 @@ json.dump(data, open("input.json", 'w'))
 # ezkl.gen_settings("network.onnx", "settings.json")

 # !RUST_LOG = full
-# res = await ezkl.calibrate_settings(
+# res = ezkl.calibrate_settings(
 #     "input.json", "network.onnx", "settings.json", "resources")
--- a/examples/tensor_contraction.rs
+++ b/examples/tensor_contraction.rs
@@ -0,0 +1,182 @@
+use ezkl::circuit::einsum::analysis::analyze_einsum_usage;
+use ezkl::circuit::poly::PolyOp;
+use ezkl::circuit::*;
+use ezkl::tensor::*;
+use halo2_proofs::circuit::floor_planner::V1;
+use halo2_proofs::dev::MockProver;
+use halo2_proofs::{
+    arithmetic::Field,
+    circuit::{Layouter, Value},
+    plonk::{Circuit, ConstraintSystem, Error},
+};
+use halo2curves::bn256::Fr;
+use halo2curves::ff::PrimeField;
+use itertools::Itertools;
+use rand::rngs::OsRng;
+use std::collections::HashMap;
+use std::marker::PhantomData;
+
+static mut LEN: usize = 4;
+const K: usize = 11;
+
+#[derive(Clone)]
+struct MyCircuit<F: PrimeField + TensorType + PartialOrd> {
+    inputs: [ValTensor<F>; 2],
+    einsum: Einsum<F>,
+}
+
+#[derive(Clone, Default)]
+struct Einsum<F: PrimeField + TensorType + PartialOrd> {
+    equation: String,
+    input_axes_to_dims: HashMap<char, usize>,
+    _marker: PhantomData<F>,
+}
+
+impl<F: PrimeField + TensorType + PartialOrd> Einsum<F> {
+    pub fn new(equation: &str, inputs: &[&Tensor<Value<F>>]) -> Result<Self, CircuitError> {
+        let mut eq = equation.split("->");
+        let inputs_eq = eq.next().ok_or(CircuitError::InvalidEinsum)?;
+        let inputs_eq = inputs_eq.split(',').collect::<Vec<_>>();
+
+        // Check that the number of inputs matches the number of inputs in the equation
+        if inputs.len() != inputs_eq.len() {
+            return Err(TensorError::DimMismatch("einsum".to_string()).into());
+        }
+
+        let mut input_axes_to_dims = HashMap::new();
+        for (i, input) in inputs.iter().enumerate() {
+            for j in 0..inputs_eq[i].len() {
+                let c = inputs_eq[i]
+                    .chars()
+                    .nth(j)
+                    .ok_or(CircuitError::InvalidEinsum)?;
+                if let std::collections::hash_map::Entry::Vacant(e) = input_axes_to_dims.entry(c) {
+                    e.insert(input.dims()[j]);
+                } else if input_axes_to_dims[&c] != input.dims()[j] {
+                    return Err(TensorError::DimMismatch("einsum".to_string()).into());
+                }
+            }
+        }
+
+        Ok(Self {
+            equation: equation.to_owned(),
+            input_axes_to_dims,
+            _marker: PhantomData,
+        })
+    }
+}
+
+impl Circuit<Fr> for MyCircuit<Fr> {
+    type Config = BaseConfig<Fr>;
+    type FloorPlanner = V1;
+    type Params = Einsum<Fr>;
+
+    fn without_witnesses(&self) -> Self {
+        self.clone()
+    }
+
+    fn configure_with_params(cs: &mut ConstraintSystem<Fr>, params: Self::Params) -> Self::Config {
+        let len = unsafe { LEN };
+
+        let a = VarTensor::new_advice(cs, K, 1, len);
+        let b = VarTensor::new_advice(cs, K, 1, len);
+        let output = VarTensor::new_advice(cs, K, 1, len);
+
+        let mut config = Self::Config::configure(cs, &[a, b], &output, CheckMode::UNSAFE);
+
+        let mut equations = HashMap::new();
+        equations.insert((0, params.equation), params.input_axes_to_dims);
+        let analysis = analyze_einsum_usage(&equations).unwrap();
+        let num_einsum_inner_cols = 2;
+        config
+            .configure_einsums(cs, &analysis, num_einsum_inner_cols, K)
+            .unwrap();
+        let _constant = VarTensor::constant_cols(cs, K, 2, false);
+
+        config
+    }
+
+    fn params(&self) -> Self::Params {
+        Einsum::<Fr>::new(
+            &self.einsum.equation,
+            &[
+                &self.inputs[0].get_inner().unwrap(),
+                &self.inputs[1].get_inner().unwrap(),
+            ],
+        )
+        .unwrap()
+    }
+
+    fn configure(_cs: &mut ConstraintSystem<Fr>) -> Self::Config {
+        unimplemented!("call configure_with_params instead")
+    }
+
+    fn synthesize(
+        &self,
+        mut config: Self::Config,
+        mut layouter: impl Layouter<Fr>,
+    ) -> Result<(), Error> {
+        let challenges = config
+            .einsums
+            .as_ref()
+            .ok_or(Error::Synthesis)?
+            .challenges()
+            .unwrap()
+            .iter()
+            .map(|c| layouter.get_challenge(*c))
+            .collect_vec();
+
+        layouter.assign_region(
+            || "",
+            |region| {
+                let mut region = region::RegionCtx::new_with_challenges(
+                    region,
+                    0,
+                    1,
+                    1024,
+                    2,
+                    challenges.clone(),
+                );
+                config
+                    .layout(
+                        &mut region,
+                        &self.inputs.iter().collect_vec(),
+                        Box::new(PolyOp::Einsum {
+                            equation: self.einsum.equation.clone(),
+                        }),
+                    )
+                    .unwrap();
+                Ok(())
+            },
+        )?;
+        Ok(())
+    }
+}
+
+fn runmatmul() {
+    let i = 10;
+    let n = 10;
+    let j = 40;
+    let k = 10;
+
+    let mut a = Tensor::from((0..i * n * j).map(|_| Value::known(Fr::random(OsRng))));
+    a.reshape(&[i, n, j]).unwrap();
+
+    // parameters
+    let mut b = Tensor::from((0..j * k).map(|_| Value::known(Fr::random(OsRng))));
+    b.reshape(&[j, k]).unwrap();
+
+    let einsum = Einsum::<Fr>::new("inj,jk->ik", &[&a, &b]).unwrap();
+
+    let circuit = MyCircuit {
+        inputs: [ValTensor::from(a), ValTensor::from(b)],
+        einsum,
+    };
+
+    let mock_prover = MockProver::run(K as u32, &circuit, vec![]).unwrap();
+    mock_prover.assert_satisfied();
+}
+
+pub fn main() {
+    runmatmul()
+}
--- a/ezkl.pyi
+++ b/ezkl.pyi
@@ -160,30 +160,6 @@ def compile_circuit(model:str | os.PathLike | pathlib.Path,compiled_circuit:str
    """
    ...

-def create_evm_data_attestation(input_data:str | os.PathLike | pathlib.Path,settings_path:str | os.PathLike | pathlib.Path,sol_code_path:str | os.PathLike | pathlib.Path,abi_path:str | os.PathLike | pathlib.Path,witness_path:typing.Optional[str | os.PathLike | pathlib.Path]) -> typing.Any:
-    r"""
-    Creates an EVM compatible data attestation verifier, you will need solc installed in your environment to run this
-    
-    Arguments
-    ---------
-    input_data: str
-        The path to the .json data file, which should contain the necessary calldata and account addresses needed to read from all the on-chain view functions that return the data that the network ingests as inputs
-    
-    settings_path: str
-        The path to the settings file
-    
-    sol_code_path: str
-        The path to the create the solidity verifier
-    
-    abi_path: str
-        The path to create the ABI for the solidity verifier
-    
-    Returns
-    -------
-    bool
-    """
-    ...
-
 def create_evm_verifier(vk_path:str | os.PathLike | pathlib.Path,settings_path:str | os.PathLike | pathlib.Path,sol_code_path:str | os.PathLike | pathlib.Path,abi_path:str | os.PathLike | pathlib.Path,srs_path:typing.Optional[str | os.PathLike | pathlib.Path],reusable:bool) -> typing.Any:
    r"""
    Creates an EVM compatible verifier, you will need solc installed in your environment to run this
@@ -247,7 +223,7 @@ def create_evm_verifier_aggr(aggregation_settings:typing.Sequence[str | os.PathL
    """
    ...

-def create_evm_vka(vk_path:str | os.PathLike | pathlib.Path,settings_path:str | os.PathLike | pathlib.Path,sol_code_path:str | os.PathLike | pathlib.Path,abi_path:str | os.PathLike | pathlib.Path,srs_path:typing.Optional[str | os.PathLike | pathlib.Path]) -> typing.Any:
+def create_evm_vka(vk_path:str | os.PathLike | pathlib.Path,settings_path:str | os.PathLike | pathlib.Path,vka_path:str | os.PathLike | pathlib.Path,srs_path:typing.Optional[str | os.PathLike | pathlib.Path]) -> typing.Any:
    r"""
    Creates an Evm VK artifact. This command generated a VK with circuit specific meta data encoding in memory for use by the reusable H2 verifier.
    This is useful for deploying verifier that were otherwise too big to fit on chain and required aggregation.
@@ -260,8 +236,8 @@ def create_evm_vka(vk_path:str | os.PathLike | pathlib.Path,settings_path:str |
    settings_path: str
        The path to the settings file
    
-    sol_code_path: str
-        The path to the create the solidity verifying key.
+    vka_path: str
+        The path to the create the vka calldata.
    
    abi_path: str
        The path to create the ABI for the solidity verifier
@@ -275,12 +251,6 @@ def create_evm_vka(vk_path:str | os.PathLike | pathlib.Path,settings_path:str |
    """
    ...

-def deploy_da_evm(addr_path:str | os.PathLike | pathlib.Path,input_data:str | os.PathLike | pathlib.Path,settings_path:str | os.PathLike | pathlib.Path,sol_code_path:str | os.PathLike | pathlib.Path,rpc_url:typing.Optional[str],optimizer_runs:int,private_key:typing.Optional[str]) -> typing.Any:
-    r"""
-    deploys the solidity da verifier
-    """
-    ...
-
 def deploy_evm(addr_path:str | os.PathLike | pathlib.Path,sol_code_path:str | os.PathLike | pathlib.Path,rpc_url:typing.Optional[str],contract_type:str,optimizer_runs:int,private_key:typing.Optional[str]) -> typing.Any:
    r"""
    deploys the solidity verifier
@@ -706,35 +676,6 @@ def setup_aggregate(sample_snarks:typing.Sequence[str | os.PathLike | pathlib.Pa
    """
    ...

-def setup_test_evm_data(data_path:str | os.PathLike | pathlib.Path,compiled_circuit_path:str | os.PathLike | pathlib.Path,test_data:str | os.PathLike | pathlib.Path,input_source:PyTestDataSource,output_source:PyTestDataSource,rpc_url:typing.Optional[str]) -> typing.Any:
-    r"""
-    Setup test evm data
-    
-    Arguments
-    ---------
-    data_path: str
-        The path to the .json data file, which should include both the network input (possibly private) and the network output (public input to the proof)
-    
-    compiled_circuit_path: str
-        The path to the compiled model file (generated using the compile-circuit command)
-    
-    test_data: str
-        For testing purposes only. The optional path to the .json data file that will be generated that contains the OnChain data storage information derived from the file information in the data .json file. Should include both the network input (possibly private) and the network output (public input to the proof)
-    
-    input_sources: str
-        Where the input data comes from
-    
-    output_source: str
-        Where the output data comes from
-    
-    rpc_url: str
-        RPC URL for an EVM compatible node, if None, uses Anvil as a local RPC node
-    
-    Returns
-    -------
-    bool
-    """
-    ...

 def swap_proof_commitments(proof_path:str | os.PathLike | pathlib.Path,witness_path:str | os.PathLike | pathlib.Path) -> None:
    r"""
@@ -823,7 +764,7 @@ def verify_aggr(proof_path:str | os.PathLike | pathlib.Path,vk_path:str | os.Pat
    """
    ...

-def verify_evm(addr_verifier:str,proof_path:str | os.PathLike | pathlib.Path,rpc_url:typing.Optional[str],addr_da:typing.Optional[str],addr_vk:typing.Optional[str]) -> typing.Any:
+def verify_evm(addr_verifier:str,proof_path:str | os.PathLike | pathlib.Path,rpc_url:typing.Optional[str],vka_path:typing.Optional[str]) -> typing.Any:
    r"""
    verifies an evm compatible proof, you will need solc installed in your environment to run this
    
@@ -838,11 +779,8 @@ def verify_evm(addr_verifier:str,proof_path:str | os.PathLike | pathlib.Path,rpc
    rpc_url: str
        RPC URL for an Ethereum node, if None will use Anvil but WON'T persist state
    
-    addr_da: str
-        does the verifier use data attestation ?
-    
-    addr_vk: str
-        The addess of the separate VK contract (if the verifier key is rendered as a separate contract)
+    vka_path: str
+        The path to the VKA calldata bytes file (generated using the create_evm_vka command)
    Returns
    -------
    bool
--- a/in-browser-evm-verifier/README.md
+++ b/in-browser-evm-verifier/README.md
@@ -1,60 +0,0 @@
-# inbrowser-evm-verify
-
-We would like the Solidity verifier to be canonical and usually all you ever need. For this, we need to be able to run that verifier in browser.
-
-## How to use (Node js)
-
-```ts 
-import localEVMVerify from '@ezkljs/verify';
-
-// Load in the proof file as a buffer
-const proofFileBuffer = fs.readFileSync(`${path}/${example}/proof.pf`)
-
-// Stringified EZKL evm verifier bytecode (this is just an example don't use in production)
-const bytecode = '0x608060405234801561001057600080fd5b5060d38061001f6000396000f3fe608060405234801561001057600080fd5b50600436106100415760003560e01c8063cfae321714610046575b600080fd5b6100496100f1565b60405161005691906100f1565b60405180910390f35b'
-
-const result = await localEVMVerify(proofFileBuffer, bytecode)
-
-console.log('result', result)
-```
-
-**Note**: Run `ezkl create-evm-verifier` to get the Solidity verifier, with which you can retrieve the bytecode once compiled. We recommend compiling to the Shanghai hardfork target, else you will have to pass an additional parameter specifying the EVM version to the `localEVMVerify` function like so (for Paris hardfork):
-
-```ts
-import localEVMVerify, { hardfork } from '@ezkljs/verify';
-
-const result = await localEVMVerify(proofFileBuffer, bytecode, hardfork['Paris'])
-```
-
-**Note**: You can also verify separated vk verifiers using the `localEVMVerify` function. Just pass the vk verifier bytecode as the third parameter like so:
-```ts
-import localEVMVerify from '@ezkljs/verify';
-
-const result = await localEVMVerify(proofFileBuffer, verifierBytecode, VKBytecode)
-```
-
-
-## How to use (Browser)
-
-```ts
-import localEVMVerify from '@ezkljs/verify';
-
-// Load in the proof file as a buffer using the web apis (fetch, FileReader, etc)
-// We use fetch in this example to load the proof file as a buffer
-const proofFileBuffer = await fetch(`${path}/${example}/proof.pf`).then(res => res.arrayBuffer())
-
-// Stringified EZKL evm verifier bytecode (this is just an example don't use in production)
-const bytecode = '0x608060405234801561001057600080fd5b5060d38061001f6000396000f3fe608060405234801561001057600080fd5b50600436106100415760003560e01c8063cfae321714610046575b600080fd5b6100496100f1565b60405161005691906100f1565b60405180910390f35b'
-
-const result = await browserEVMVerify(proofFileBuffer, bytecode)
-
-console.log('result', result)
-```
-
-Output:
-
-```ts  
-result: true
-```
-
-
--- a/in-browser-evm-verifier/package.json
+++ b/in-browser-evm-verifier/package.json
@@ -1,42 +0,0 @@
-{
-  "name": "@ezkljs/verify",
-  "version": "v10.4.2",
-  "publishConfig": {
-    "access": "public"
-  },
-  "description": "Evm verify EZKL proofs in the browser.",
-  "main": "dist/commonjs/index.js",
-  "module": "dist/esm/index.js",
-  "types": "dist/commonjs/index.d.ts",
-  "files": [
-    "dist",
-    "LICENSE",
-    "README.md"
-  ],
-  "scripts": {
-    "clean": "rm -r dist || true",
-    "build:commonjs": "tsc --project tsconfig.commonjs.json && resolve-tspaths -p tsconfig.commonjs.json",
-    "build:esm": "tsc --project tsconfig.esm.json && resolve-tspaths -p tsconfig.esm.json",
-    "build": "npm run clean && npm run build:commonjs && npm run build:esm"
-  },
-  "dependencies": {
-    "@ethereumjs/common": "4.0.0",
-    "@ethereumjs/evm": "2.0.0",
-    "@ethereumjs/statemanager": "2.0.0",
-    "@ethereumjs/tx": "5.0.0",
-    "@ethereumjs/util": "9.0.0",
-    "@ethereumjs/vm": "7.0.0",
-    "@ethersproject/abi": "5.7.0",
-    "@ezkljs/engine": "10.4.2",
-    "ethers": "6.7.1",
-    "json-bigint": "1.0.0"
-  },
-  "devDependencies": {
-    "@types/node": "^20.8.3",
-    "ts-loader": "^9.5.0",
-    "ts-node": "^10.9.1",
-    "resolve-tspaths": "^0.8.16",
-    "tsconfig-paths": "^4.2.0",
-    "typescript": "^5.2.2"
-  }
-}
--- a/in-browser-evm-verifier/pnpm-lock.yaml
+++ b/in-browser-evm-verifier/pnpm-lock.yaml
--- a/in-browser-evm-verifier/src/index.ts
+++ b/in-browser-evm-verifier/src/index.ts
@@ -1,144 +0,0 @@
-import { defaultAbiCoder as AbiCoder } from '@ethersproject/abi'
-import { Address, hexToBytes } from '@ethereumjs/util'
-import { Chain, Common, Hardfork } from '@ethereumjs/common'
-import { LegacyTransaction, LegacyTxData } from '@ethereumjs/tx'
-// import { DefaultStateManager } from '@ethereumjs/statemanager'
-// import { Blockchain } from '@ethereumjs/blockchain'
-import { VM } from '@ethereumjs/vm'
-import { EVM } from '@ethereumjs/evm'
-import { buildTransaction, encodeDeployment } from './utils/tx-builder'
-import { getAccountNonce, insertAccount } from './utils/account-utils'
-import { encodeVerifierCalldata } from '../nodejs/ezkl';
-
-async function deployContract(
-  vm: VM,
-  common: Common,
-  senderPrivateKey: Uint8Array,
-  deploymentBytecode: string
-): Promise<Address> {
-  // Contracts are deployed by sending their deployment bytecode to the address 0
-  // The contract params should be abi-encoded and appended to the deployment bytecode.
-  // const data =
-  const data = encodeDeployment(deploymentBytecode)
-  const txData = {
-    data,
-    nonce: await getAccountNonce(vm, senderPrivateKey),
-  }
-
-  const tx = LegacyTransaction.fromTxData(
-    buildTransaction(txData) as LegacyTxData,
-    { common, allowUnlimitedInitCodeSize: true },
-  ).sign(senderPrivateKey)
-
-  const deploymentResult = await vm.runTx({
-    tx,
-    skipBlockGasLimitValidation: true,
-    skipNonce: true
-  })
-
-  if (deploymentResult.execResult.exceptionError) {
-    throw deploymentResult.execResult.exceptionError
-  }
-
-  return deploymentResult.createdAddress!
-}
-
-async function verify(
-  vm: VM,
-  contractAddress: Address,
-  caller: Address,
-  proof: Uint8Array | Uint8ClampedArray,
-  vkAddress?: Address | Uint8Array,
-): Promise<boolean> {
-  if (proof instanceof Uint8Array) {
-    proof = new Uint8ClampedArray(proof.buffer)
-  }
-  if (vkAddress) {
-    const vkAddressBytes = hexToBytes(vkAddress.toString())
-    const vkAddressArray = Array.from(vkAddressBytes)
-
-    let string = JSON.stringify(vkAddressArray)
-
-    const uint8Array = new TextEncoder().encode(string);
-
-    // Step 3: Convert to Uint8ClampedArray
-    vkAddress = new Uint8Array(uint8Array.buffer);
-
-    // convert uitn8array of length
-    console.error('vkAddress', vkAddress)
-  }
-  const data = encodeVerifierCalldata(proof, vkAddress)
-
-  const verifyResult = await vm.evm.runCall({
-    to: contractAddress,
-    caller: caller,
-    origin: caller, // The tx.origin is also the caller here
-    data: data,
-  })
-
-  if (verifyResult.execResult.exceptionError) {
-    throw verifyResult.execResult.exceptionError
-  }
-
-  const results = AbiCoder.decode(['bool'], verifyResult.execResult.returnValue)
-
-  return results[0]
-}
-
-/**
- * Spins up an ephemeral EVM instance for executing the bytecode of a solidity verifier
- * @param proof Json serialized proof file
- * @param bytecode The bytecode of a compiled solidity verifier.
- * @param bytecode_vk The bytecode of a contract that stores the vk. (Optional, only required if the vk is stored in a separate contract)
- * @param evmVersion The evm version to use for the verification. (Default: London)
- * @returns The result of the evm verification.
- * @throws If the verify transaction reverts
- */
-export default async function localEVMVerify(
-  proof: Uint8Array | Uint8ClampedArray,
-  bytecode_verifier: string,
-  bytecode_vk?: string,
-  evmVersion?: Hardfork,
-): Promise<boolean> {
-  try {
-    const hardfork = evmVersion ? evmVersion : Hardfork['Shanghai']
-    const common = new Common({ chain: Chain.Mainnet, hardfork })
-    const accountPk = hexToBytes(
-      '0xe331b6d69882b4cb4ea581d88e0b604039a3de5967688d3dcffdd2270c0fd109', // anvil deterministic Pk 
-    )
-
-    const evm = new EVM({
-      allowUnlimitedContractSize: true,
-      allowUnlimitedInitCodeSize: true,
-    })
-
-    const vm = await VM.create({ common, evm })
-    const accountAddress = Address.fromPrivateKey(accountPk)
-
-    await insertAccount(vm, accountAddress)
-
-    const verifierAddress = await deployContract(
-      vm,
-      common,
-      accountPk,
-      bytecode_verifier
-    )
-
-    if (bytecode_vk) {
-      const accountPk = hexToBytes("0xac0974bec39a17e36ba4a6b4d238ff944bacb478cbed5efcae784d7bf4f2ff80"); // anvil deterministic Pk
-      const accountAddress = Address.fromPrivateKey(accountPk)
-      await insertAccount(vm, accountAddress)
-      const output = await deployContract(vm, common, accountPk, bytecode_vk)
-      const result = await verify(vm, verifierAddress, accountAddress, proof, output)
-      return true
-    }
-
-    const result = await verify(vm, verifierAddress, accountAddress, proof)
-
-    return result
-  } catch (error) {
-    // log or re-throw the error, depending on your needs
-    console.error('An error occurred:', error)
-    throw error
-  }
-}
--- a/in-browser-evm-verifier/src/utils/account-utils.ts
+++ b/in-browser-evm-verifier/src/utils/account-utils.ts
@@ -1,32 +0,0 @@
-import { VM } from '@ethereumjs/vm'
-import { Account, Address } from '@ethereumjs/util'
-
-export const keyPair = {
-  secretKey:
-    '0x3cd7232cd6f3fc66a57a6bedc1a8ed6c228fff0a327e169c2bcc5e869ed49511',
-  publicKey:
-    '0x0406cc661590d48ee972944b35ad13ff03c7876eae3fd191e8a2f77311b0a3c6613407b5005e63d7d8d76b89d5f900cde691497688bb281e07a5052ff61edebdc0',
-}
-
-export const insertAccount = async (vm: VM, address: Address) => {
-  const acctData = {
-    nonce: 0,
-    balance: BigInt('1000000000000000000'), // 1 eth
-  }
-  const account = Account.fromAccountData(acctData)
-
-  await vm.stateManager.putAccount(address, account)
-}
-
-export const getAccountNonce = async (
-  vm: VM,
-  accountPrivateKey: Uint8Array,
-) => {
-  const address = Address.fromPrivateKey(accountPrivateKey)
-  const account = await vm.stateManager.getAccount(address)
-  if (account) {
-    return account.nonce
-  } else {
-    return BigInt(0)
-  }
-}
--- a/in-browser-evm-verifier/src/utils/tx-builder.ts
+++ b/in-browser-evm-verifier/src/utils/tx-builder.ts
@@ -1,59 +0,0 @@
-import { Interface, defaultAbiCoder as AbiCoder } from '@ethersproject/abi'
-import {
-  AccessListEIP2930TxData,
-  FeeMarketEIP1559TxData,
-  TxData,
-} from '@ethereumjs/tx'
-
-type TransactionsData =
-  | TxData
-  | AccessListEIP2930TxData
-  | FeeMarketEIP1559TxData
-
-export const encodeFunction = (
-  method: string,
-  params?: {
-    // eslint-disable-next-line @typescript-eslint/no-explicit-any
-    types: any[]
-    values: unknown[]
-  },
-): string => {
-  const parameters = params?.types ?? []
-  const methodWithParameters = `function ${method}(${parameters.join(',')})`
-  const signatureHash = new Interface([methodWithParameters]).getSighash(method)
-  const encodedArgs = AbiCoder.encode(parameters, params?.values ?? [])
-
-  return signatureHash + encodedArgs.slice(2)
-}
-
-export const encodeDeployment = (
-  bytecode: string,
-  params?: {
-    // eslint-disable-next-line @typescript-eslint/no-explicit-any
-    types: any[]
-    values: unknown[]
-  },
-) => {
-  const deploymentData = '0x' + bytecode
-  if (params) {
-    const argumentsEncoded = AbiCoder.encode(params.types, params.values)
-    return deploymentData + argumentsEncoded.slice(2)
-  }
-  return deploymentData
-}
-
-export const buildTransaction = (
-  data: Partial<TransactionsData>,
-): TransactionsData => {
-  const defaultData: Partial<TransactionsData> = {
-    gasLimit: 3_000_000_000_000_000,
-    gasPrice: 7,
-    value: 0,
-    data: '0x',
-  }
-
-  return {
-    ...defaultData,
-    ...data,
-  }
-}
--- a/in-browser-evm-verifier/tsconfig.commonjs.json
+++ b/in-browser-evm-verifier/tsconfig.commonjs.json
@@ -1,7 +0,0 @@
-{
-  "extends": "./tsconfig.json",
-  "compilerOptions": {
-    "module": "CommonJS",
-    "outDir": "./dist/commonjs"
-  }
-}
--- a/in-browser-evm-verifier/tsconfig.esm.json
+++ b/in-browser-evm-verifier/tsconfig.esm.json
@@ -1,7 +0,0 @@
-{
-  "extends": "./tsconfig.json",
-  "compilerOptions": {
-    "module": "ES2020",
-    "outDir": "./dist/esm"
-  }
-}
--- a/in-browser-evm-verifier/tsconfig.json
+++ b/in-browser-evm-verifier/tsconfig.json
@@ -1,62 +0,0 @@
-{
-  "compilerOptions": {
-    "rootDir": "src",
-    "target": "es2017",
-    "outDir": "dist",
-    "declaration": true,
-    "lib": [
-      "dom",
-      "dom.iterable",
-      "esnext"
-    ],
-    "allowJs": true,
-    "checkJs": true,
-    "skipLibCheck": true,
-    "strict": true,
-    "forceConsistentCasingInFileNames": true,
-    "noEmit": false,
-    "esModuleInterop": true,
-    "module": "CommonJS",
-    "moduleResolution": "node",
-    "resolveJsonModule": true,
-    "isolatedModules": true,
-    "jsx": "preserve",
-    // "incremental": true,
-    "noUncheckedIndexedAccess": true,
-    "baseUrl": ".",
-    "paths": {
-      "@/*": [
-        "./src/*"
-      ]
-    }
-  },
-  "include": [
-    "src/**/*.ts",
-    "src/**/*.tsx",
-    "src/**/*.cjs",
-    "src/**/*.mjs"
-  ],
-  "exclude": [
-    "node_modules"
-  ],
-  // NEW: Options for file/directory watching
-  "watchOptions": {
-    // Use native file system events for files and directories
-    "watchFile": "useFsEvents",
-    "watchDirectory": "useFsEvents",
-    // Poll files for updates more frequently
-    // when they're updated a lot.
-    "fallbackPolling": "dynamicPriority",
-    // Don't coalesce watch notification
-    "synchronousWatchDirectory": true,
-    // Finally, two additional settings for reducing the amount of possible
-    // files to track  work from these directories
-    "excludeDirectories": [
-      "**/node_modules",
-      "_build"
-    ],
-    "excludeFiles": [
-      "build/fileWhichChangesOften.ts"
-    ]
-  }
-}
--- a/requirements.txt
+++ b/requirements.txt
@@ -9,6 +9,6 @@ pytest==8.1.1
 tomli==2.0.1
 typing-extensions==4.10.0
 zipp==3.18.1
-onnx==1.15.0
+onnx==1.17.0
 onnxruntime==1.17.1
 numpy==1.26.4
--- a/2
+++ b/2
@@ -1,3 +1,3 @@
 [toolchain]
-channel = "nightly-2025-02-17"
+channel = "nightly-2025-05-01"
 components = ["rustfmt", "clippy"]
--- a/setup-gpu.sh
+++ b/setup-gpu.sh
@@ -0,0 +1,229 @@
+#!/bin/bash
+
+set -e
+
+# Colors for output
+RED='\033[0;31m'
+GREEN='\033[0;32m'
+YELLOW='\033[1;33m'
+NC='\033[0m' # No Color
+
+# Default installation directory
+DEFAULT_INSTALL_DIR="/opt/icicle/lib/backend/halo2"
+
+# Halo2 repository details
+HALO2_REPO="https://github.com/zkonduit/halo2"
+HALO2_BRANCH="ac/conditional-compilation-icicle2"
+
+# Parse command line arguments
+AUTO_YES=false
+for arg in "$@"; do
+    case $arg in
+        -y|--yes)
+            AUTO_YES=true
+            shift
+            ;;
+        -h|--help)
+            echo "Usage: $0 [OPTIONS]"
+            echo "Options:"
+            echo "  -y, --yes    Automatically answer 'yes' to all prompts"
+            echo "  -h, --help   Show this help message"
+            exit 0
+            ;;
+        *)
+            echo "Unknown option: $arg"
+            echo "Use -h or --help for usage information"
+            exit 1
+            ;;
+    esac
+done
+
+echo -e "${GREEN}EZKL GPU Setup Script${NC}"
+echo -e "${GREEN}=====================${NC}"
+echo ""
+
+# Parse commit hash from Cargo.lock
+echo "Parsing halo2 commit hash from Cargo.lock..."
+if [ ! -f "Cargo.lock" ]; then
+    echo -e "${RED}Error: Cargo.lock not found. Please run this script from the project root.${NC}"
+    exit 1
+fi
+
+HALO2_COMMIT=$(grep "github\.com/zkonduit/halo2?" Cargo.lock | grep -v "halo2wrong" | head -1 | grep -o "#[a-f0-9]\{40\}" | cut -c2-)
+
+if [ -z "$HALO2_COMMIT" ]; then
+    echo -e "${RED}Error: Could not parse halo2 commit hash from Cargo.lock${NC}"
+    exit 1
+fi
+
+echo -e "${GREEN}Found halo2 commit: $HALO2_COMMIT${NC}"
+echo ""
+echo "This script will:"
+echo "1. Sparse checkout the halo2 repository at commit $HALO2_COMMIT"
+echo "2. Extract only the icicle/backend/cuda/ directory"
+echo "3. Set the ICICLE_BACKEND_INSTALL_DIR environment variable"
+echo ""
+
+# Check if user wants to override the default directory
+if [ "$AUTO_YES" = true ]; then
+    INSTALL_DIR="$DEFAULT_INSTALL_DIR"
+    echo -e "${GREEN}Using default installation directory: ${INSTALL_DIR}${NC}"
+else
+    echo -e "${YELLOW}Default installation directory: ${DEFAULT_INSTALL_DIR}${NC}"
+    read -p "Do you want to use a different directory? [y/N]: " -n 1 -r
+    echo
+    if [[ $REPLY =~ ^[Yy]$ ]]; then
+        read -p "Enter the installation directory: " INSTALL_DIR
+        INSTALL_DIR="${INSTALL_DIR/#\~/$HOME}"  # Expand ~ to $HOME
+    else
+        INSTALL_DIR="$DEFAULT_INSTALL_DIR"
+    fi
+
+    # Confirm the installation directory
+    echo ""
+    echo -e "${YELLOW}Installation directory: ${INSTALL_DIR}${NC}"
+    read -p "Continue with this directory? [y/N]: " -n 1 -r
+    echo
+    if [[ ! $REPLY =~ ^[Yy]$ ]]; then
+        echo -e "${RED}Setup cancelled by user.${NC}"
+        exit 1
+    fi
+fi
+
+# Check if ICICLE_BACKEND_INSTALL_DIR is already set
+if [ ! -z "$ICICLE_BACKEND_INSTALL_DIR" ] && [ "$AUTO_YES" = false ]; then
+    echo ""
+    echo -e "${YELLOW}Warning: ICICLE_BACKEND_INSTALL_DIR is already set to: $ICICLE_BACKEND_INSTALL_DIR${NC}"
+    read -p "Do you want to override it? [y/N]: " -n 1 -r
+    echo
+    if [[ ! $REPLY =~ ^[Yy]$ ]]; then
+        echo -e "${RED}Setup cancelled by user.${NC}"
+        exit 1
+    fi
+elif [ ! -z "$ICICLE_BACKEND_INSTALL_DIR" ] && [ "$AUTO_YES" = true ]; then
+    echo -e "${GREEN}Overriding existing ICICLE_BACKEND_INSTALL_DIR (was: $ICICLE_BACKEND_INSTALL_DIR)${NC}"
+fi
+
+echo ""
+echo -e "${GREEN}Starting GPU setup...${NC}"
+
+# Create installation directory
+echo "Creating installation directory..."
+mkdir -p "$INSTALL_DIR"
+
+# Create temporary directory for sparse checkout
+TEMP_DIR=$(mktemp -d)
+echo "Using temporary directory: $TEMP_DIR"
+
+# Clone with sparse checkout
+echo "Cloning halo2 repository with sparse checkout..."
+cd "$TEMP_DIR"
+git clone --filter=blob:none --sparse "$HALO2_REPO" halo2
+cd halo2
+
+# Checkout the specific branch and commit
+echo "Checking out branch $HALO2_BRANCH at commit $HALO2_COMMIT..."
+git checkout "$HALO2_BRANCH"
+git checkout "$HALO2_COMMIT"
+
+# Configure sparse checkout
+echo "Configuring sparse checkout for icicle/backend/cuda/..."
+git sparse-checkout init --cone
+git sparse-checkout set icicle/backend/cuda/
+
+# Copy the icicle directory to the installation location
+if [ -d "icicle/backend/cuda" ]; then
+    echo "Copying icicle/backend/cuda/ to $INSTALL_DIR..."
+    cp -r icicle/backend/cuda/* "$INSTALL_DIR/"
+    echo -e "${GREEN}Files copied successfully!${NC}"
+else
+    echo -e "${RED}Error: icicle/backend/cuda directory not found in the repository${NC}"
+    exit 1
+fi
+
+# Clean up temporary directory
+echo "Cleaning up temporary files..."
+rm -rf "$TEMP_DIR"
+
+# Ask user about setting environment variable permanently
+SETUP_PERMANENT_ENV=false
+if [ "$AUTO_YES" = true ]; then
+    SETUP_PERMANENT_ENV=true
+    echo ""
+    echo -e "${GREEN}Setting ICICLE_BACKEND_INSTALL_DIR environment variable permanently...${NC}"
+else
+    echo ""
+    echo -e "${YELLOW}Do you want to set ICICLE_BACKEND_INSTALL_DIR environment variable permanently?${NC}"
+    echo "This will add 'export ICICLE_BACKEND_INSTALL_DIR=\"$INSTALL_DIR\"' to your shell configuration file."
+    read -p "Set environment variable permanently? [y/N]: " -n 1 -r
+    echo
+    if [[ $REPLY =~ ^[Yy]$ ]]; then
+        SETUP_PERMANENT_ENV=true
+    fi
+fi
+
+if [ "$SETUP_PERMANENT_ENV" = true ]; then
+    echo "Setting ICICLE_BACKEND_INSTALL_DIR environment variable..."
+
+    # Detect shell and set environment variable accordingly
+    if [ -n "$ZSH_VERSION" ]; then
+        SHELL_RC="$HOME/.zshrc"
+    elif [ -n "$BASH_VERSION" ]; then
+        SHELL_RC="$HOME/.bashrc"
+    else
+        # Try to detect based on $SHELL
+        case "$SHELL" in
+            */zsh)
+                SHELL_RC="$HOME/.zshrc"
+                ;;
+            */bash)
+                SHELL_RC="$HOME/.bashrc"
+                ;;
+            *)
+                SHELL_RC="$HOME/.profile"
+                ;;
+        esac
+    fi
+
+    # Add environment variable to shell configuration
+    ENV_EXPORT="export ICICLE_BACKEND_INSTALL_DIR=\"$INSTALL_DIR\""
+
+    # Check if the variable is already set in the file
+    if [ -f "$SHELL_RC" ] && grep -q "ICICLE_BACKEND_INSTALL_DIR" "$SHELL_RC"; then
+        # Replace existing line
+        if [[ "$OSTYPE" == "darwin"* ]]; then
+            # macOS
+            sed -i '' "s|export ICICLE_BACKEND_INSTALL_DIR=.*|$ENV_EXPORT|" "$SHELL_RC"
+        else
+            # Linux
+            sed -i "s|export ICICLE_BACKEND_INSTALL_DIR=.*|$ENV_EXPORT|" "$SHELL_RC"
+        fi
+        echo "Updated existing ICICLE_BACKEND_INSTALL_DIR in $SHELL_RC"
+    else
+        # Add new line
+        echo "$ENV_EXPORT" >> "$SHELL_RC"
+        echo "Added ICICLE_BACKEND_INSTALL_DIR to $SHELL_RC"
+    fi
+
+    echo -e "${GREEN}Environment variable set permanently.${NC}"
+else
+    echo "Skipping permanent environment variable setup."
+fi
+
+# Export for current session regardless
+export ICICLE_BACKEND_INSTALL_DIR="$INSTALL_DIR"
+echo "Environment variable set for current session."
+
+echo ""
+echo -e "${GREEN}GPU setup completed successfully!${NC}"
+echo ""
+echo -e "${YELLOW}Important:${NC}"
+echo "1. The ICICLE_BACKEND_INSTALL_DIR environment variable has been set to: $INSTALL_DIR"
+if [ "$SETUP_PERMANENT_ENV" = true ]; then
+    echo "2. Please restart your terminal or run: source $SHELL_RC"
+else
+    echo "2. To use GPU features, set: export ICICLE_BACKEND_INSTALL_DIR=\"$INSTALL_DIR\""
+fi
+echo "3. You can now build with GPU support using: cargo build --features gpu-accelerated"
+echo ""
+echo -e "${GREEN}Setup complete!${NC}"
--- a/src/bin/ezkl.rs
+++ b/src/bin/ezkl.rs
@@ -1,12 +1,5 @@
 // ignore file if compiling for wasm

-#[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
-use mimalloc::MiMalloc;
-
-#[global_allocator]
-#[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
-static GLOBAL: MiMalloc = MiMalloc;
-
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
 use clap::{CommandFactory, Parser};
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
@@ -22,9 +15,6 @@ use log::{error, info};
 #[cfg(not(any(target_arch = "wasm32", feature = "no-banner")))]
 use rand::prelude::SliceRandom;
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
-#[cfg(feature = "icicle")]
-use std::env;
-
 #[tokio::main(flavor = "current_thread")]
 #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
 pub async fn main() {
@@ -38,12 +28,7 @@ pub async fn main() {
        init_logger();
        #[cfg(not(any(target_arch = "wasm32", feature = "no-banner")))]
        banner();
-        #[cfg(feature = "icicle")]
-        if env::var("ENABLE_ICICLE_GPU").is_ok() {
-            info!("Running with ICICLE GPU");
-        } else {
-            info!("Running with CPU");
-        }
+
        debug!(
            "command: \n {}",
            &command.as_json().to_colored_json_auto().unwrap()
--- a/src/bindings/mod.rs
+++ b/src/bindings/mod.rs
@@ -3,7 +3,7 @@
 pub mod python;
 /// Universal bindings for all platforms
 #[cfg(any(
-    feature = "ios-bindings",
+    feature = "universal-bindings",
    all(target_arch = "wasm32", target_os = "unknown")
 ))]
 pub mod universal;
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
github-actions[bot]	36c10459c8	ci: update version string in docs	2025-10-08 11:34:48 +00:00
dante	365d92a5f2	feat: implement generalized Freivalds' algorithm for arbitrary einsum (#990 ) --------- Co-authored-by: DoHoon Kim <59155248+DoHoonKim8@users.noreply.github.com> Co-authored-by: therealyingtong <yingtong.lai@gmail.com> Co-authored-by: DoHoonKim <dohoon1097819@gmail.com>	2025-10-08 07:34:32 -04:00
dante	d64749fc71	refactor: make default visibility public (#1004 )	2025-08-15 23:58:04 -04:00
dante	d7b04d0d25	chore: update icicle to latest (#998 )	2025-08-14 18:21:06 -04:00
GarmashAlex	d50a4b7d59	fix: correct commitment type in ipa_commit function (#997 )	2025-08-10 23:03:45 +01:00
dante	fad31de5b4	refactor: make reusable-verifier feat default (#996 )	2025-07-30 14:54:08 -04:00
dante	2f1a3f430e	feat: optional rebase scale override (#993 )	2025-07-27 21:05:54 +01:00
dante	edd4d7f5b8	fix: less bleeding edge toolchain (#992 )	2025-07-25 10:19:13 -04:00
dante	1c3ae450e1	fix: ios build	2025-07-25 09:57:23 -04:00
dante	afb4ca9f06	fix: release pipeline (#991 )	2025-07-25 09:34:57 -04:00
dante	0e79d36238	fix: ln arg is too restrictive on distances (#989 )	2025-07-24 18:56:50 -04:00
dante	e1c126c735	fix: ci (#988 )	2025-07-23 20:11:31 -04:00
dante	9077b8debc	refactor: shuffle + dynamic lookup params	2025-07-22 14:36:39 -04:00
dante	28594c7651	chore: simplify `DataSource` (#986 )	2025-07-03 10:52:31 -04:00
DoHoon Kim	b9c5ae76c0	chore: bump halo2_solidity_verifier (#985 )	2025-07-03 09:54:46 -04:00
dante	c34ce000ff	chore: rm git creds from ci (#984 )	2025-06-28 09:30:12 +01:00
Ethan Cemer	3ea68f08b3	feat: vka hashing squash (#982 )	2025-06-27 22:58:10 +02:00
dante	e81d93a73a	chore: display calibration fail reasons (#983 )	2025-06-21 19:24:30 +02:00
dante	40ce9dfde9	chore: rm lots of clones (#980 )	2025-05-26 10:54:09 -04:00
dante	839030ce10	chore: rm halo2proofs patches (#976 )	2025-04-29 10:58:35 -04:00
dante	cfccc5460c	refactor: rm postgres (#977 )	2025-04-29 08:59:14 -04:00
dante	0de0682bfa	refactor: configurable div epsilon (#968 )	2025-04-23 09:12:24 +01:00
dante	bf9cf14ab7	refactor!: rpc url should be required (#965 ) BREAKING CHANGE: in python the order of arguments for evm related functions has changed	2025-04-22 12:45:36 +01:00
dante	6818962ac2	chore: pass in raw data for gen-witness from file (#964 )	2025-04-06 14:08:11 -04:00
dante	70469e3bf9	chore: add `min/max` to `gen-random-data` (#960 )	2025-03-25 19:32:15 +00:00
				`@@ -0,0 +1 @@`
				{"input_data": [[0.33088353276252747, -0.8819183707237244, 1.245591163635254, -1.807046890258789, 1.9922369718551636, -0.3360576629638672, 0.4529011845588684, -0.3590165674686432, 0.08356846123933792, 0.5126393437385559, 0.44627535343170166, 1.4916497468948364, 0.49731069803237915, -0.9748706817626953, -0.4923185408115387, 1.3548223972320557, 0.2306872010231018, 1.125955581665039, -1.7063908576965332, 0.3777385354042053, -2.7988760471343994, -1.1846797466278076, 0.7473157048225403, 1.490412950515747, 0.017497723922133446, 2.113945245742798, -1.2141249179840088, -0.16120357811450958, 0.021127669140696526, 0.7207374572753906, -1.369688868522644, -0.7369781732559204, -0.630584180355072, -0.4520200788974762, 0.29123976826667786, 0.6334688067436218, -0.869332492351532, -1.258501648902893, 0.3012596666812897, -0.5507447123527527, 0.669975757598877, 0.15088629722595215, -0.1050339788198471, 0.5505334138870239, -0.1287376880645752, -1.4297826290130615, -0.01703289896249771, -1.2296998500823975, 0.5122153162956238, -0.16924428939819336, -0.415036678314209, -1.1979341506958008, 0.05831022188067436, -0.4411357045173645, 2.0713791847229004, 1.4611141681671143, -0.9357407093048096, -0.333297461271286, -0.676478385925293, 1.390028476715088, -0.05827632546424866, 1.535687804222107, 0.3060210347175598, -0.03171076253056526, -0.614985466003418, 1.2040390968322754, 0.31318482756614685, -1.2134959697723389, 0.13110508024692535, -1.4880926609039307, 1.7007993459701538, 1.5412729978561401, 0.09260450303554535, 0.7649128437042236, -0.5009126663208008, -0.5356241464614868, -0.069572813808918, -0.011717632412910461, 0.21314217150211334, -0.1985170543193817, -0.0223808903247118, 1.2128918170928955, 0.8334696888923645, 1.9029873609542847, -0.11491120606660843, -0.10303237289190292, -0.2467050403356552, 1.557223916053772, -1.1108328104019165, -0.9065343141555786, -0.2271333783864975, 0.6959827542304993, -0.48698121309280396, 0.5689510703086853, 1.115319013595581, -0.8907430768013, -0.24722427129745483, -0.7437837719917297, 0.6742106676101685, -1.7830933332443237]]}