ci: update version string in docs

---------

Co-authored-by: Jseam <hello.jseam@gmail.com>

.github/workflows/benchmarks.yml

@@ -6,22 +6,13 @@ on:
         description: "Test scenario tags"
 
 jobs:
-  bench_elgamal:
-    runs-on: self-hosted
-    steps:
-      - uses: actions/checkout@v4
-      - uses: actions-rs/toolchain@v1
-        with:
-          toolchain: nightly-2023-06-27
-          override: true
-          components: rustfmt, clippy
-      - name: Bench elgamal
-        run: cargo bench --verbose --bench elgamal
 
   bench_poseidon:
     runs-on: self-hosted
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2023-06-27
@@ -35,6 +26,8 @@ jobs:
     needs: [bench_poseidon]
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2023-06-27
@@ -48,6 +41,8 @@ jobs:
     needs: [bench_poseidon]
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2023-06-27
@@ -61,6 +56,8 @@ jobs:
     needs: [bench_poseidon]
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2023-06-27
@@ -74,6 +71,8 @@ jobs:
     needs: [bench_poseidon]
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2023-06-27
@@ -87,6 +86,8 @@ jobs:
     needs: [bench_poseidon]
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2023-06-27
@@ -100,6 +101,8 @@ jobs:
     needs: [bench_poseidon]
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2023-06-27
@@ -113,6 +116,8 @@ jobs:
     needs: [bench_poseidon]
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2023-06-27
@@ -126,6 +131,8 @@ jobs:
     needs: [bench_poseidon]
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2023-06-27
@@ -139,6 +146,8 @@ jobs:
     needs: [bench_poseidon]
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2023-06-27
@@ -152,6 +161,8 @@ jobs:
     needs: [bench_poseidon]
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2023-06-27

.github/workflows/engine.yml

@@ -20,6 +20,8 @@ jobs:
     if: startsWith(github.ref, 'refs/tags/')
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2024-07-18
@@ -51,7 +53,7 @@ jobs:
         run: |
           echo '{
             "name": "@ezkljs/engine",
-            "version": "${{ github.ref_name }}",
+            "version": "${RELEASE_TAG}",
             "dependencies": {
               "@types/json-bigint": "^1.0.1",
               "json-bigint": "^1.0.0"
@@ -190,15 +192,17 @@ jobs:
     if: startsWith(github.ref, 'refs/tags/')
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - name: Update version in package.json
         shell: bash
         env:
           RELEASE_TAG: ${{ github.ref_name }}
         run: |
-          sed -i "s|\"version\": \".*\"|\"version\": \"${{ github.ref_name }}\"|" in-browser-evm-verifier/package.json
+          sed -i "s|\"version\": \".*\"|\"version\": \"${RELEASE_TAG}\"|" in-browser-evm-verifier/package.json
       - name: Prepare tag and fetch package integrity
         run: |
-          CLEANED_TAG=${{ github.ref_name }} # Get the tag from ref_name
+          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)

.github/workflows/large-tests.yml

@@ -9,6 +9,8 @@ jobs:
     runs-on: kaiju
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2024-07-18

.github/workflows/pypi-gpu.yml

@@ -24,6 +24,8 @@ jobs:
         target: [x86_64]
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions/setup-python@v4
         with:
           python-version: 3.12

.github/workflows/pypi.yml

@@ -23,6 +23,8 @@ jobs:
         target: [x86_64, universal2-apple-darwin]
     steps:
       - uses: actions/checkout@v4
+        with: 
+          persist-credentials: false
       - uses: actions/setup-python@v4
         with:
           python-version: 3.12
@@ -69,6 +71,8 @@ jobs:
         target: [x64, x86]
     steps:
       - uses: actions/checkout@v4
+        with: 
+          persist-credentials: false
       - uses: actions/setup-python@v4
         with:
           python-version: 3.12
@@ -114,6 +118,8 @@ jobs:
         target: [x86_64]
     steps:
       - uses: actions/checkout@v4
+        with: 
+          persist-credentials: false
       - uses: actions/setup-python@v4
         with:
           python-version: 3.12
@@ -228,6 +234,8 @@ jobs:
           - x86_64-unknown-linux-musl
     steps:
       - uses: actions/checkout@v4
+        with: 
+          persist-credentials: false
       - uses: actions/setup-python@v4
         with:
           python-version: 3.12
@@ -292,6 +300,8 @@ jobs:
             arch: aarch64
     steps:
       - uses: actions/checkout@v4
+        with: 
+          persist-credentials: false
       - uses: actions/setup-python@v4
         with:
           python-version: 3.12
@@ -373,7 +383,8 @@ jobs:
     needs: pypi-publish
     steps:
       - uses: actions/checkout@v4
-
+        with: 
+          persist-credentials: false
       - name: Trigger RTDs build
         uses: dfm/rtds-action@v1
         with:

.github/workflows/release.yml

@@ -50,6 +50,9 @@ jobs:
           components: rustfmt, clippy
       - name: Checkout repo
         uses: actions/checkout@v4
+        with: 
+          persist-credentials: false
+
 
       - name: Get release version from tag
         shell: bash
@@ -132,6 +135,8 @@ jobs:
     steps:
       - name: Checkout repo
         uses: actions/checkout@v4
+        with: 
+          persist-credentials: false
 
       - name: Get release version from tag
         shell: bash

.github/workflows/rust.yml

@@ -24,6 +24,8 @@ jobs:
 
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2024-07-18
@@ -36,6 +38,8 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2024-07-18
@@ -48,6 +52,8 @@ jobs:
     runs-on: ubuntu-latest-32-cores
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2024-07-18
@@ -71,6 +77,8 @@ jobs:
   #     ENABLE_ICICLE_GPU: true
   #   steps:
   #     - uses: actions/checkout@v4
+        # with:
+        #   persist-credentials: false
   #     - uses: actions-rs/toolchain@v1
   #       with:
   #         toolchain: nightly-2024-07-18
@@ -104,6 +112,8 @@ jobs:
     runs-on: non-gpu
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2024-07-18
@@ -137,6 +147,8 @@ jobs:
     runs-on: non-gpu
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2024-07-18
@@ -170,6 +182,8 @@ jobs:
     runs-on: ubuntu-latest-16-cores
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2024-07-18
@@ -186,6 +200,8 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2024-07-18
@@ -212,6 +228,8 @@ jobs:
     needs: [build, library-tests, docs, python-tests, python-integration-tests]
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2024-07-18
@@ -275,6 +293,8 @@ jobs:
     needs: [build, library-tests, docs, python-tests, python-integration-tests]
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2024-07-18
@@ -285,6 +305,8 @@ jobs:
           crate: cargo-nextest
           locked: true
       - uses: actions/checkout@v3
+        with:
+          persist-credentials: false
       - name: Use pnpm 8
         uses: pnpm/action-setup@v2
         with:
@@ -354,6 +376,8 @@ jobs:
     needs: [build, library-tests, docs]
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2024-07-18
@@ -369,6 +393,8 @@ jobs:
       - name: Add rust-src
         run: rustup component add rust-src --toolchain nightly-2024-07-18-x86_64-unknown-linux-gnu
       - uses: actions/checkout@v3
+        with:
+          persist-credentials: false
       - name: Use pnpm 8
         uses: pnpm/action-setup@v2
         with:
@@ -431,6 +457,8 @@ jobs:
   #     ENABLE_ICICLE_GPU: true
   #   steps:
   #     - uses: actions/checkout@v4
+        # with:
+        #   persist-credentials: false
   #     - uses: actions-rs/toolchain@v1
   #       with:
   #         toolchain: nightly-2024-07-18
@@ -465,6 +493,8 @@ jobs:
     needs: [build, library-tests, docs, python-tests, python-integration-tests]
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2024-07-18
@@ -483,6 +513,8 @@ jobs:
   #     ENABLE_ICICLE_GPU: true
   #   steps:
   #     - uses: actions/checkout@v4
+        # with:
+        #   persist-credentials: false
   #     - uses: actions-rs/toolchain@v1
   #       with:
   #         toolchain: nightly-2024-07-18
@@ -500,6 +532,8 @@ jobs:
     needs: [build, library-tests, docs, python-tests, python-integration-tests]
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2024-07-18
@@ -517,6 +551,8 @@ jobs:
     needs: [build, library-tests, docs, python-tests, python-integration-tests]
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2024-07-18
@@ -538,6 +574,8 @@ jobs:
     needs: [build, library-tests, docs]
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions-rs/toolchain@v1
         with:
           toolchain: nightly-2024-07-18
@@ -555,6 +593,8 @@ jobs:
     needs: [build, library-tests, docs]
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions/setup-python@v4
         with:
           python-version: "3.12"
@@ -581,6 +621,8 @@ jobs:
     needs: [build, library-tests, docs, python-tests, python-integration-tests]
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions/setup-python@v4
         with:
           python-version: "3.12"
@@ -628,6 +670,8 @@ jobs:
           - 5432:5432
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - uses: actions/setup-python@v4
         with:
           python-version: "3.11"
@@ -680,6 +724,8 @@ jobs:
       runs-on: macos-latest
       steps:
         - uses: actions/checkout@v4
+          with:
+            persist-credentials: false
         - uses: actions-rs/toolchain@v1
           with:
             toolchain: nightly-2024-07-18
@@ -698,6 +744,8 @@ jobs:
 
       steps:
         - uses: actions/checkout@v4
+          with:
+            persist-credentials: false
         - uses: actions-rs/toolchain@v1
           with:
             toolchain: nightly-2024-07-18

.github/workflows/static-analysis.yml

@@ -0,0 +1,32 @@
+name: Static Analysis
+
+on:
+  push:
+    branches: [ main ]
+  pull_request:
+    branches: [ main ]
+
+jobs:
+  analyze:
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
+      - uses: actions-rs/toolchain@v1
+        with:
+          toolchain: nightly-2024-07-18
+          override: true
+          components: rustfmt, clippy
+
+      # Run Zizmor static analysis
+
+      - name: Install Zizmor
+        run: cargo install --locked zizmor
+
+      - name: Run Zizmor Analysis
+        run: zizmor .
+
+
+    

.github/workflows/swift-pm.yml

@@ -12,15 +12,18 @@ jobs:
     runs-on: macos-latest
     env:
       EZKL_SWIFT_PACKAGE_REPO: github.com/zkonduit/ezkl-swift-package.git
+      RELEASE_TAG: ${{ github.ref_name }}
 
     steps:
       - name: Checkout EZKL
         uses: actions/checkout@v3
+        with: 
+          persist-credentials: false
 
       - name: Extract TAG from github.ref_name
         run: |
           # github.ref_name is provided by GitHub Actions and contains the tag name directly.
-          TAG="${{ github.ref_name }}"
+          TAG="${RELEASE_TAG}"
           echo "Original TAG: $TAG"
           # Remove leading 'v' if present to match the Swift Package Manager version format.
           NEW_TAG=${TAG#v}
@@ -47,7 +50,8 @@ jobs:
 
       - name: Copy Test Files
         run: |
-          rm -rf ezkl-swift-package/Tests/EzklAssets/*
+          rm -rf ezkl-swift-package/Tests/EzklAssets/
+          mkdir -p ezkl-swift-package/Tests/EzklAssets/
           cp tests/assets/kzg ezkl-swift-package/Tests/EzklAssets/kzg.srs
           cp tests/assets/input.json ezkl-swift-package/Tests/EzklAssets/input.json
           cp tests/assets/model.compiled ezkl-swift-package/Tests/EzklAssets/network.ezkl
@@ -105,7 +109,6 @@ jobs:
           cd ezkl-swift-package
           git add Sources/EzklCoreBindings Tests/EzklAssets
           git commit -m "Automatically updated EzklCoreBindings for EZKL"
-          
           if ! git push origin; then
             echo "::error::Failed to push changes to ${{ env.EZKL_SWIFT_PACKAGE_REPO }}. Please ensure that EZKL_PORTER_TOKEN has the correct permissions."
             exit 1
@@ -115,7 +118,6 @@ jobs:
         run: |
           cd ezkl-swift-package
           source $GITHUB_ENV
-          
           # Tag the latest commit on the current branch
           if git rev-parse "$TAG" >/dev/null 2>&1; then
             echo "Tag $TAG already exists locally. Skipping tag creation."

.github/workflows/tagging.yml

@@ -12,6 +12,8 @@ jobs:
 
     steps:
       - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
       - name: Bump version and push tag
         id: tag_version
         uses: mathieudutour/github-tag-action@v6.2

Cargo.lock

@@ -2377,7 +2377,7 @@ dependencies = [
 [[package]]
 name = "halo2_gadgets"
 version = "0.2.0"
-source = "git+https://github.com/zkonduit/halo2#6d72498928cdb69ce0de9f2230d2873ca2cf5324"
+source = "git+https://github.com/zkonduit/halo2#d7ecad83c7439fa1cb450ee4a89c2d0b45604ceb"
 dependencies = [
  "arrayvec 0.7.4",
  "bitvec",
@@ -2394,7 +2394,7 @@ dependencies = [
 [[package]]
 name = "halo2_proofs"
 version = "0.3.0"
-source = "git+https://github.com/zkonduit/halo2#6d72498928cdb69ce0de9f2230d2873ca2cf5324#6d72498928cdb69ce0de9f2230d2873ca2cf5324"
+source = "git+https://github.com/zkonduit/halo2#ee4e1a09ebdb1f79f797685b78951c6034c430a6#ee4e1a09ebdb1f79f797685b78951c6034c430a6"
 dependencies = [
  "bincode",
  "blake2b_simd",

Cargo.toml

@@ -280,10 +280,10 @@ no-update = []
 
 
 [patch.'https://github.com/zkonduit/halo2']
-halo2_proofs = { git = "https://github.com/zkonduit/halo2#6d72498928cdb69ce0de9f2230d2873ca2cf5324", package = "halo2_proofs" }
+halo2_proofs = { git = "https://github.com/zkonduit/halo2#ee4e1a09ebdb1f79f797685b78951c6034c430a6", package = "halo2_proofs" }
 
 [patch.'https://github.com/zkonduit/halo2#0654e92bdf725fd44d849bfef3643870a8c7d50b']
-halo2_proofs = { git = "https://github.com/zkonduit/halo2#6d72498928cdb69ce0de9f2230d2873ca2cf5324", package = "halo2_proofs" }
+halo2_proofs = { git = "https://github.com/zkonduit/halo2#ee4e1a09ebdb1f79f797685b78951c6034c430a6", package = "halo2_proofs" }
 
 [patch.crates-io]
 uniffi_testing = { git = "https://github.com/ElusAegis/uniffi-rs", branch = "feat/testing-feature-build-fix" }

docs/python/src/conf.py

@@ -1,7 +1,7 @@
 import ezkl
 
 project = 'ezkl'
-release = '0.0.0'
+release = '17.1.4'
 version = release
 
 

examples/notebooks/felt_conversion_test.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -54,7 +54,7 @@
     "    gip_run_args.param_scale = 19\n",
     "    gip_run_args.logrows = 8\n",
     "    run_args = ezkl.gen_settings(py_run_args=gip_run_args)\n",
-    "    ezkl.get_srs(commitment=ezkl.PyCommitments.KZG)\n",
+    "    await ezkl.get_srs(commitment=ezkl.PyCommitments.KZG)\n",
     "    ezkl.compile_circuit()\n",
     "    res = await ezkl.gen_witness()\n",
     "    print(res)\n",

examples/notebooks/linear_regression.ipynb

@@ -1,279 +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"
-            ]
-        },
-        {
-            "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 = 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
+}

examples/notebooks/mean_postgres.ipynb

@@ -1,456 +1,459 @@
 {
-  "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",
-        "# Generate settings using ezkl\n",
-        "res = ezkl.gen_settings(onnx_filename, settings_filename)\n",
-        "\n",
-        "assert res == True\n",
-        "\n",
-        "res = await ezkl.calibrate_settings(input_filename, onnx_filename, settings_filename, \"resources\")\n",
-        "\n",
-        "assert res == True"
-      ]
-    },
-    {
-      "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": "Python 3",
-      "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": "3.12.2"
-    }
+ "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"
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 0
+  {
+   "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",
+    "# Generate settings using ezkl\n",
+    "res = ezkl.gen_settings(onnx_filename, settings_filename)\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
 }

src/bindings/python.rs

@@ -938,6 +938,45 @@ fn gen_settings(
     Ok(true)
 }
 
+/// Generates random data for the model
+///
+/// Arguments
+/// ---------
+/// model: str
+///     Path to the onnx file
+///
+/// output: str
+///     Path to create the data file
+///
+/// seed: int
+///     Random seed to use for generated data
+///
+/// variables
+/// Returns
+/// -------
+/// bool
+///
+#[pyfunction(signature = (
+    model=PathBuf::from(DEFAULT_MODEL),
+    output=PathBuf::from(DEFAULT_SETTINGS),
+    variables=Vec::from([("batch_size".to_string(), 1)]),
+    seed=DEFAULT_SEED.parse().unwrap(),
+))]
+#[gen_stub_pyfunction]
+fn gen_random_data(
+    model: PathBuf,
+    output: PathBuf,
+    variables: Vec<(String, usize)>,
+    seed: u64,
+) -> Result<bool, PyErr> {
+    crate::execute::gen_random_data(model, output, variables, seed).map_err(|e| {
+        let err_str = format!("Failed to generate settings: {}", e);
+        PyRuntimeError::new_err(err_str)
+    })?;
+
+    Ok(true)
+}
+
 /// Calibrates the circuit settings
 ///
 /// Arguments
@@ -2055,6 +2094,7 @@ fn ezkl(m: &Bound<'_, PyModule>) -> PyResult<()> {
     m.add_function(wrap_pyfunction!(get_srs, m)?)?;
     m.add_function(wrap_pyfunction!(gen_witness, m)?)?;
     m.add_function(wrap_pyfunction!(gen_settings, m)?)?;
+    m.add_function(wrap_pyfunction!(gen_random_data, m)?)?;
     m.add_function(wrap_pyfunction!(calibrate_settings, m)?)?;
     m.add_function(wrap_pyfunction!(aggregate, m)?)?;
     m.add_function(wrap_pyfunction!(mock_aggregate, m)?)?;

src/circuit/ops/chip.rs

@@ -75,6 +75,16 @@ impl FromStr for CheckMode {
     }
 }
 
+impl CheckMode {
+    /// Returns the value of the check mode
+    pub fn is_safe(&self) -> bool {
+        match self {
+            CheckMode::SAFE => true,
+            CheckMode::UNSAFE => false,
+        }
+    }
+}
+
 #[allow(missing_docs)]
 /// An enum representing the tolerance we can accept for the accumulated arguments, either absolute or percentage
 #[derive(Clone, Default, Debug, PartialEq, PartialOrd, Serialize, Deserialize, Copy)]

src/circuit/ops/layouts.rs

@@ -4038,7 +4038,7 @@ pub(crate) fn range_check<F: PrimeField + TensorType + PartialOrd + std::hash::H
     }
 
     let is_assigned = !w.any_unknowns()?;
-    if is_assigned && region.check_range() {
+    if is_assigned && region.check_range() && config.check_mode.is_safe() {
         // assert is within range
         let int_values = w.int_evals()?;
         for v in int_values.iter() {

src/commands.rs

@@ -90,6 +90,8 @@ pub const DEFAULT_USE_REDUCED_SRS_FOR_VERIFICATION: &str = "false";
 pub const DEFAULT_ONLY_RANGE_CHECK_REBASE: &str = "false";
 /// Default commitment
 pub const DEFAULT_COMMITMENT: &str = "kzg";
+/// Default seed used to generate random data
+pub const DEFAULT_SEED: &str = "21242";
 
 #[cfg(feature = "python-bindings")]
 /// Converts TranscriptType into a PyObject (Required for TranscriptType to be compatible with Python)
@@ -422,7 +424,21 @@ pub enum Commands {
         #[clap(flatten)]
         args: RunArgs,
     },
-
+    /// Generate random data for a model
+    GenRandomData {
+        /// The path to the .onnx model file
+        #[arg(short = 'M', long, default_value = DEFAULT_MODEL, value_hint = clap::ValueHint::FilePath)]
+        model: Option<PathBuf>,
+        /// The path to the .json data file
+        #[arg(short = 'D', long, default_value = DEFAULT_DATA, value_hint = clap::ValueHint::FilePath)]
+        data: Option<PathBuf>,
+        /// Hand-written parser for graph variables, eg. batch_size=1
+        #[cfg_attr(all(feature = "ezkl", not(target_arch = "wasm32")), arg(short = 'V', long, value_parser = crate::parse_key_val::<String, usize>, default_value = "batch_size->1", value_delimiter = ',', value_hint = clap::ValueHint::Other))]
+        variables: Vec<(String, usize)>,
+        /// random seed for reproducibility (optional)
+        #[arg(long, value_hint = clap::ValueHint::Other, default_value = DEFAULT_SEED)]
+        seed: u64,
+    },
     /// Calibrates the proving scale, lookup bits and logrows from a circuit settings file.
     CalibrateSettings {
         /// The path to the .json calibration data file.

src/execute.rs

@@ -65,6 +65,8 @@ use std::str::FromStr;
 use std::time::Duration;
 use tabled::Tabled;
 use thiserror::Error;
+use tract_onnx::prelude::IntoTensor;
+use tract_onnx::prelude::Tensor as TractTensor;
 
 use lazy_static::lazy_static;
 
@@ -134,6 +136,17 @@ pub async fn run(command: Commands) -> Result<String, EZKLError> {
             settings_path.unwrap_or(DEFAULT_SETTINGS.into()),
             args,
         ),
+        Commands::GenRandomData {
+            model,
+            data,
+            variables,
+            seed,
+        } => gen_random_data(
+            model.unwrap_or(DEFAULT_MODEL.into()),
+            data.unwrap_or(DEFAULT_DATA.into()),
+            variables,
+            seed,
+        ),
         Commands::CalibrateSettings {
             model,
             settings_path,
@@ -828,6 +841,71 @@ pub(crate) fn gen_circuit_settings(
     Ok(String::new())
 }
 
+/// Generate a circuit settings file
+pub(crate) fn gen_random_data(
+    model_path: PathBuf,
+    data_path: PathBuf,
+    variables: Vec<(String, usize)>,
+    seed: u64,
+) -> Result<String, EZKLError> {
+    let mut file = std::fs::File::open(&model_path).map_err(|e| {
+        crate::graph::errors::GraphError::ReadWriteFileError(
+            model_path.display().to_string(),
+            e.to_string(),
+        )
+    })?;
+
+    let (tract_model, _symbol_values) = Model::load_onnx_using_tract(&mut file, &variables)?;
+
+    let input_facts = tract_model
+        .input_outlets()
+        .map_err(|e| EZKLError::from(e.to_string()))?
+        .iter()
+        .map(|&i| tract_model.outlet_fact(i))
+        .collect::<tract_onnx::prelude::TractResult<Vec<_>>>()
+        .map_err(|e| EZKLError::from(e.to_string()))?;
+
+    /// Generates a random tensor of a given size and type.
+    fn random(
+        sizes: &[usize],
+        datum_type: tract_onnx::prelude::DatumType,
+        seed: u64,
+    ) -> TractTensor {
+        use rand::{Rng, SeedableRng};
+        let mut rng = rand::rngs::StdRng::seed_from_u64(seed);
+
+        let mut tensor = TractTensor::zero::<f32>(sizes).unwrap();
+        let slice = tensor.as_slice_mut::<f32>().unwrap();
+        slice.iter_mut().for_each(|x| *x = rng.gen());
+        tensor.cast_to_dt(datum_type).unwrap().into_owned()
+    }
+
+    fn tensor_for_fact(fact: &tract_onnx::prelude::TypedFact, seed: u64) -> TractTensor {
+        if let Some(value) = &fact.konst {
+            return value.clone().into_tensor();
+        }
+
+        random(
+            fact.shape
+                .as_concrete()
+                .expect("Expected concrete shape, found: {fact:?}"),
+            fact.datum_type,
+            seed,
+        )
+    }
+
+    let generated = input_facts
+        .iter()
+        .map(|v| tensor_for_fact(v, seed))
+        .collect_vec();
+
+    let data = GraphData::from_tract_data(&generated)?;
+
+    data.save(data_path)?;
+
+    Ok(String::new())
+}
+
 // not for wasm targets
 pub(crate) fn init_spinner() -> ProgressBar {
     let pb = indicatif::ProgressBar::new_spinner();

src/graph/input.rs

@@ -557,6 +557,34 @@ impl GraphData {
         Ok(inputs)
     }
 
+    // not wasm
+    #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
+    /// Convert the tract data to tract data
+    pub fn from_tract_data(tensors: &[TractTensor]) -> Result<Self, GraphError> {
+        use tract_onnx::prelude::DatumType;
+
+        let mut input_data = vec![];
+        for tensor in tensors {
+            match tensor.datum_type() {
+                tract_onnx::prelude::DatumType::Bool => {
+                    let tensor = tensor.to_array_view::<bool>()?;
+                    let tensor = tensor.iter().map(|e| FileSourceInner::Bool(*e)).collect();
+                    input_data.push(tensor);
+                }
+                _ => {
+                    let cast_tensor = tensor.cast_to_dt(DatumType::F64)?;
+                    let tensor = cast_tensor.to_array_view::<f64>()?;
+                    let tensor = tensor.iter().map(|e| FileSourceInner::Float(*e)).collect();
+                    input_data.push(tensor);
+                }
+            }
+        }
+        Ok(GraphData {
+            input_data: DataSource::File(input_data),
+            output_data: None,
+        })
+    }
+
     ///
     pub fn new(input_data: DataSource) -> Self {
         GraphData {

src/graph/mod.rs

@@ -280,7 +280,13 @@ impl GraphWitness {
         })?;
 
         let reader = std::io::BufReader::with_capacity(*EZKL_BUF_CAPACITY, file);
-        serde_json::from_reader(reader).map_err(|e| e.into())
+        let witness: GraphWitness =
+            serde_json::from_reader(reader).map_err(Into::<GraphError>::into)?;
+
+        // check versions match
+        crate::check_version_string_matches(witness.version.as_deref().unwrap_or(""));
+
+        Ok(witness)
     }
 
     /// Save the model input to a file
@@ -572,10 +578,14 @@ impl GraphSettings {
         // buf reader
         let reader =
             std::io::BufReader::with_capacity(*EZKL_BUF_CAPACITY, std::fs::File::open(path)?);
-        serde_json::from_reader(reader).map_err(|e| {
+        let settings: GraphSettings = serde_json::from_reader(reader).map_err(|e| {
             error!("failed to load settings file at {}", e);
             std::io::Error::new(std::io::ErrorKind::Other, e)
-        })
+        })?;
+
+        crate::check_version_string_matches(&settings.version);
+
+        Ok(settings)
     }
 
     /// Export the ezkl configuration as json
@@ -697,6 +707,9 @@ impl GraphCircuit {
         let reader = std::io::BufReader::with_capacity(*EZKL_BUF_CAPACITY, f);
         let result: GraphCircuit = bincode::deserialize_from(reader)?;
 
+        // check the versions matche
+        crate::check_version_string_matches(&result.core.settings.version);
+
         Ok(result)
     }
 }

src/graph/model.rs

@@ -621,16 +621,16 @@ impl Model {
     /// * `scale` - The scale to use for quantization.
     /// * `public_params` - Whether to make the params public.
     #[cfg(all(feature = "ezkl", not(target_arch = "wasm32")))]
-    fn load_onnx_using_tract(
+    pub(crate) fn load_onnx_using_tract(
         reader: &mut dyn std::io::Read,
-        run_args: &RunArgs,
+        variables: &[(String, usize)],
     ) -> Result<TractResult, GraphError> {
         use tract_onnx::tract_hir::internal::GenericFactoid;
 
         let mut model = tract_onnx::onnx().model_for_read(reader)?;
 
         let variables: std::collections::HashMap<String, usize> =
-            std::collections::HashMap::from_iter(run_args.variables.clone());
+            std::collections::HashMap::from_iter(variables.iter().map(|(k, v)| (k.clone(), *v)));
 
         for (i, id) in model.clone().inputs.iter().enumerate() {
             let input = model.node_mut(id.node);
@@ -655,7 +655,7 @@ impl Model {
         }
 
         let mut symbol_values = SymbolValues::default();
-        for (symbol, value) in run_args.variables.iter() {
+        for (symbol, value) in variables.iter() {
             let symbol = model.symbols.sym(symbol);
             symbol_values = symbol_values.with(&symbol, *value as i64);
             debug!("set {} to {}", symbol, value);
@@ -683,7 +683,7 @@ impl Model {
     ) -> Result<ParsedNodes, GraphError> {
         let start_time = instant::Instant::now();
 
-        let (model, symbol_values) = Self::load_onnx_using_tract(reader, run_args)?;
+        let (model, symbol_values) = Self::load_onnx_using_tract(reader, &run_args.variables)?;
 
         let scales = VarScales::from_args(run_args);
         let nodes = Self::nodes_from_graph(
@@ -964,7 +964,7 @@ impl Model {
             GraphError::ReadWriteFileError(model_path.display().to_string(), e.to_string())
         })?;
 
-        let (model, _) = Model::load_onnx_using_tract(&mut file, run_args)?;
+        let (model, _) = Model::load_onnx_using_tract(&mut file, &run_args.variables)?;
 
         let datum_types: Vec<DatumType> = model
             .input_outlets()?

src/lib.rs

@@ -420,3 +420,30 @@ where
     let b = s[pos + 2..].parse()?;
     Ok((a, b))
 }
+
+/// Check if the version string matches the artifact version
+/// If the version string does not match the artifact version, log a warning
+pub fn check_version_string_matches(artifact_version: &str) {
+    if artifact_version == "0.0.0"
+        || artifact_version == "source - no compatibility guaranteed"
+        || artifact_version.is_empty()
+    {
+        log::warn!("Artifact version is 0.0.0, skipping version check");
+        return;
+    }
+
+    let version = crate::version();
+
+    if version == "source - no compatibility guaranteed" {
+        log::warn!("Compiled source version is not guaranteed to match artifact version");
+        return;
+    }
+
+    if version != artifact_version {
+        log::warn!(
+            "Version mismatch: CLI version is {} but artifact version is {}",
+            version,
+            artifact_version
+        );
+    }
+}

src/tensor/var.rs

@@ -558,7 +558,7 @@ impl VarTensor {
 
                 // duplicates every nth element to adjust for column overflow
                 let v = v.duplicate_every_n(duplication_freq, num_repeats, duplication_offset).unwrap();
-                let mut res: ValTensor<F> = {
+                let mut res: ValTensor<F> =
                     v.enum_map(|coord, k| {
 
                     let step = self.num_inner_cols();
@@ -579,12 +579,18 @@ impl VarTensor {
                         prev_cell = Some(cell.clone());
                     } else if coord > 0 && at_beginning_of_column  {
                         if let Some(prev_cell) = prev_cell.as_ref() {
-                            let cell = cell.cell().ok_or({
+                            let cell = if let Some(cell) = cell.cell() {
+                                cell
+                            } else {
                                 error!("Error getting cell: {:?}", (x,y));
-                                halo2_proofs::plonk::Error::Synthesis})?;
-                            let prev_cell = prev_cell.cell().ok_or({
-                                error!("Error getting cell: {:?}", (x,y));
-                                halo2_proofs::plonk::Error::Synthesis})?;
+                                return Err(halo2_proofs::plonk::Error::Synthesis);
+                            };
+                            let prev_cell = if let Some(prev_cell) = prev_cell.cell() {
+                                prev_cell
+                            } else {
+                                error!("Error getting prev cell: {:?}", (x,y));
+                                return Err(halo2_proofs::plonk::Error::Synthesis);
+                            };
                             region.constrain_equal(prev_cell,cell)?;
                         } else {
                             error!("Previous cell was not set");
@@ -594,7 +600,8 @@ impl VarTensor {
 
                     Ok(cell)
 
-                })?.into()};
+                })?.into();
+
                 let total_used_len = res.len();
                 res.remove_every_n(duplication_freq, num_repeats, duplication_offset).unwrap();