mirror of
https://github.com/MPCStats/zk-stats-demo.git
synced 2026-01-09 21:58:10 -05:00
281 lines
18 KiB
Plaintext
281 lines
18 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 39,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import torch\n",
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"from zkstats.core import (\n",
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" prover_gen_settings,\n",
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" prover_gen_proof,\n",
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" verifier_setup,\n",
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" verifier_verify,\n",
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")\n",
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"from zkstats.computation import computation_to_model, State\n",
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"\n",
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"cwd = os.getcwd()\n",
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"\n",
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"# FIXME: fill this in with the path to your data\n",
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"data_path = f\"{cwd}/data.json\"\n",
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"\n",
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"# Paths to the output files\n",
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"output_dir = f\"{cwd}/out\"\n",
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"os.makedirs(output_dir, exist_ok=True)\n",
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"model_onnx_path = f\"{output_dir}/model.onnx\"\n",
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"compiled_model_path = f\"{output_dir}/model.compiled\"\n",
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"\n",
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"pk_path = f\"{output_dir}/model.pk\"\n",
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"vk_path = f\"{output_dir}/model.vk\"\n",
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"proof_path = f\"{output_dir}/model.pf\"\n",
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"settings_path = f\"{output_dir}/settings.json\"\n",
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"witness_path = f\"{output_dir}/witness.json\"\n",
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"comb_data_path = f\"{output_dir}/comb_data.json\""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## User-defined Computation\n",
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"\n",
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"A computation should be of type `TComputation`. For example, the following code snippet defines a computation that computes the sum of the private data.\n",
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"\n",
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"```python\n",
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"def computation(state: State, x: list[torch.Tensor]):\n",
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" out_0 = state.median(x[0])\n",
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" out_1 = state.median(x[1])\n",
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" return state.mean(torch.tensor([out_0, out_1]).reshape(1,-1,1))\n",
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"```\n",
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"\n",
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"FIXME: The following code snippet is entirely from the user. You MUST check\n",
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"1. the code only performs zkstats-related operations.\n",
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"2. the computation must not leak any information about the private data."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 40,
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"metadata": {},
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"outputs": [],
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"source": [
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"# This is just a dummy computation. Replace it with user's computation\n",
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"# def computation(state: State, x: list[torch.Tensor]):\n",
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"# x_0 = x[0]\n",
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"# out_0 = state.median(x_0)\n",
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"# out_1 = state.median(x_0)\n",
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"# return state.mean(torch.tensor([out_0, out_1]).reshape(1,-1,1))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Generate settings and setup with user's computation."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 41,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/var/folders/t3/5psrvr1x0w1_6n9kx2n7d9700000gn/T/ipykernel_97835/3906390486.py:5: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.\n",
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" return state.mean(torch.tensor([out_0, out_1]).reshape(1,-1,1))\n",
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"/var/folders/t3/5psrvr1x0w1_6n9kx2n7d9700000gn/T/ipykernel_97835/3906390486.py:5: TracerWarning: Converting a tensor to a Python float might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
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" return state.mean(torch.tensor([out_0, out_1]).reshape(1,-1,1))\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"==== Generate & Calibrate Setting ====\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"spawning module 0\n",
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"spawning module 2\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"scale: default\n",
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"setting: {\"run_args\":{\"tolerance\":{\"val\":0.0,\"scale\":1.0},\"input_scale\":8,\"param_scale\":8,\"scale_rebase_multiplier\":10,\"lookup_range\":[-25112,24986],\"logrows\":16,\"num_inner_cols\":2,\"variables\":[[\"batch_size\",1]],\"input_visibility\":{\"Hashed\":{\"hash_is_public\":true,\"outlets\":[]}},\"output_visibility\":\"Public\",\"param_visibility\":\"Private\"},\"num_rows\":14432,\"total_assignments\":12049,\"total_const_size\":1816,\"model_instance_shapes\":[[1],[1]],\"model_output_scales\":[0,8],\"model_input_scales\":[8],\"module_sizes\":{\"kzg\":[],\"poseidon\":[14432,[1]],\"elgamal\":[0,[0]]},\"required_lookups\":[\"Abs\",{\"GreaterThan\":{\"a\":0.0}},\"KroneckerDelta\"],\"check_mode\":\"UNSAFE\",\"version\":\"7.0.0\",\"num_blinding_factors\":null}\n",
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"==== setting up ezkl ====\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"spawning module 0\n",
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"spawning module 2\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Time setup: 12.489933013916016 seconds\n"
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]
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}
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],
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"source": [
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"_, model = computation_to_model(computation)\n",
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"prover_gen_settings(\n",
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" [data_path],\n",
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" comb_data_path,\n",
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" model,\n",
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" model_onnx_path,\n",
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" \"default\",\n",
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" \"resources\",\n",
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" settings_path,\n",
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")\n",
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"verifier_setup(model_onnx_path, compiled_model_path, settings_path, vk_path, pk_path)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Generate proof with your data and user's computation."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 42,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"==== Generating Witness ====\n",
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"witness boolean: 1.0\n",
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"witness result 1 : 49.55078125\n",
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"==== Generating Proof ====\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"spawning module 0\n",
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"spawning module 2\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"proof: {'instances': [[[3042937791208075219, 8157070662846698822, 3804781648660056856, 172406108020799675], [12436184717236109307, 3962172157175319849, 7381016538464732718, 1011752739694698287], [18341455175509539295, 12796101019039945164, 1607286914885633240, 1929881192315725821]]], 'proof': '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'transcript_type': 'EVM'}\n",
|
|
"Time gen prf: 17.092010021209717 seconds\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"prover_gen_proof(\n",
|
|
" model_onnx_path,\n",
|
|
" comb_data_path,\n",
|
|
" witness_path,\n",
|
|
" compiled_model_path,\n",
|
|
" settings_path,\n",
|
|
" proof_path,\n",
|
|
" pk_path,\n",
|
|
")\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Verify the proof to ensure it is correct"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 43,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"!@# res: True\n",
|
|
"prf instances: [[[3042937791208075219, 8157070662846698822, 3804781648660056856, 172406108020799675], [12436184717236109307, 3962172157175319849, 7381016538464732718, 1011752739694698287], [18341455175509539295, 12796101019039945164, 1607286914885633240, 1929881192315725821]]]\n",
|
|
"num_inputs: 1\n",
|
|
"proof boolean: 1.0\n",
|
|
"proof result 0 : 49.55078125\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"verifier_verify(proof_path, settings_path, vk_path)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Print the file paths. You should share the following files back to the user for them to verify the proof. You **SHOULD NOT** share more files otherwise data might be leaked."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 44,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Model onnx:\t\t /Users/mhchia/projects/work/pse/demo-next/public/assets/out/model.onnx\n",
|
|
"Settings:\t\t /Users/mhchia/projects/work/pse/demo-next/public/assets/out/settings.json\n",
|
|
"Proof:\t\t\t /Users/mhchia/projects/work/pse/demo-next/public/assets/out/model.pf\n",
|
|
"Verification key:\t /Users/mhchia/projects/work/pse/demo-next/public/assets/out/model.vk\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"model_onnx_path, settings_path, proof_path, vk_path\n",
|
|
"print(\"Model onnx:\\t\\t\", model_onnx_path)\n",
|
|
"print(\"Settings:\\t\\t\", settings_path)\n",
|
|
"print(\"Proof:\\t\\t\\t\", proof_path)\n",
|
|
"print(\"Verification key:\\t\", vk_path)"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": ".venv",
|
|
"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": "3.9.6"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|