mirror of
https://github.com/MPCStats/zk-stats-lib.git
synced 2026-01-10 05:57:55 -05:00
240 lines
19 KiB
Plaintext
240 lines
19 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import ezkl\n",
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"import torch\n",
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"from torch import nn\n",
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"import json\n",
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"import os\n",
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"import time\n",
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"import scipy\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import statistics\n",
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"import math"
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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": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"from zkstats.core import create_dummy, verifier_define_calculation, prover_gen_settings, setup, prover_gen_proof, verifier_verify, generate_data_commitment"
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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": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"# init path\n",
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"os.makedirs(os.path.dirname('shared/'), exist_ok=True)\n",
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"os.makedirs(os.path.dirname('prover/'), exist_ok=True)\n",
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"verifier_model_path = os.path.join('shared/verifier.onnx')\n",
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"prover_model_path = os.path.join('prover/prover.onnx')\n",
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"verifier_compiled_model_path = os.path.join('shared/verifier.compiled')\n",
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"prover_compiled_model_path = os.path.join('prover/prover.compiled')\n",
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"pk_path = os.path.join('shared/test.pk')\n",
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"vk_path = os.path.join('shared/test.vk')\n",
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"proof_path = os.path.join('shared/test.pf')\n",
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"settings_path = os.path.join('shared/settings.json')\n",
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"srs_path = os.path.join('shared/kzg.srs')\n",
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"witness_path = os.path.join('prover/witness.json')\n",
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"# this is private to prover since it contains actual data\n",
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"sel_data_path = os.path.join('prover/sel_data.json')\n",
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"# this is just dummy random value\n",
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"sel_dummy_data_path = os.path.join('shared/sel_dummy_data.json')\n",
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"data_commitment_path = os.path.join('shared/data_commitment.json')"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"======================= ZK-STATS FLOW ======================="
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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": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"data_path = os.path.join('data.json')\n",
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"dummy_data_path = os.path.join('shared/dummy_data.json')\n",
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"create_dummy(data_path, dummy_data_path)"
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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": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"scales = [3]\n",
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"selected_columns = ['col_name']\n",
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"generate_data_commitment(data_path, scales, data_commitment_path)"
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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": 6,
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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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"/Users/jernkun/Desktop/zk-stats-lib/zkstats/computation.py:172: 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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" is_precise_aggregated = torch.tensor(1.0)\n",
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"/Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages/torch/onnx/symbolic_opset9.py:2174: FutureWarning: 'torch.onnx.symbolic_opset9._cast_Bool' is deprecated in version 2.0 and will be removed in the future. Please Avoid using this function and create a Cast node instead.\n",
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" return fn(g, to_cast_func(g, input, False), to_cast_func(g, other, False))\n",
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"/Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages/torch/onnx/utils.py:1703: UserWarning: The exported ONNX model failed ONNX shape inference. The model will not be executable by the ONNX Runtime. If this is unintended and you believe there is a bug, please report an issue at https://github.com/pytorch/pytorch/issues. Error reported by strict ONNX shape inference: [ShapeInferenceError] (op_type:Where, node name: /Where): Y has inconsistent type tensor(float) (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/jit/serialization/export.cpp:1490.)\n",
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" _C._check_onnx_proto(proto)\n"
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]
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}
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],
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"source": [
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"# Verifier/ data consumer side: send desired calculation\n",
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"from zkstats.computation import computation_to_model, State\n",
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"\n",
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"\n",
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"def computation(s: State, data: list[torch.Tensor]) -> torch.Tensor:\n",
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" x = data[0]\n",
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" return s.stdev(x)\n",
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"\n",
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"error = 0.01\n",
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"_, verifier_model = computation_to_model(computation, error)\n",
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"\n",
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"verifier_define_calculation(dummy_data_path, selected_columns, sel_dummy_data_path, verifier_model, verifier_model_path)"
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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": 7,
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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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"\n",
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"\n",
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" <------------- Numerical Fidelity Report (input_scale: 3, param_scale: 3, scale_input_multiplier: 1) ------------->\n",
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"\n",
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"+--------------+--------------+-----------+--------------+----------------+------------------+---------------+---------------+--------------------+--------------------+------------------------+\n",
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"| mean_error | median_error | max_error | min_error | mean_abs_error | median_abs_error | max_abs_error | min_abs_error | mean_squared_error | mean_percent_error | mean_abs_percent_error |\n",
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"+--------------+--------------+-----------+--------------+----------------+------------------+---------------+---------------+--------------------+--------------------+------------------------+\n",
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"| -0.022477627 | -0.044955254 | 0 | -0.044955254 | 0.022477627 | 0.044955254 | 0.044955254 | 0 | 0.0010104874 | -0.0015416706 | 0.0015416706 |\n",
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"+--------------+--------------+-----------+--------------+----------------+------------------+---------------+---------------+--------------------+--------------------+------------------------+\n",
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"\n",
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"\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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"scale: [3]\n",
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"setting: {\"run_args\":{\"tolerance\":{\"val\":0.0,\"scale\":1.0},\"input_scale\":3,\"param_scale\":3,\"scale_rebase_multiplier\":1,\"lookup_range\":[-20110,20332],\"logrows\":16,\"num_inner_cols\":2,\"variables\":[[\"batch_size\",1]],\"input_visibility\":{\"Hashed\":{\"hash_is_public\":true,\"outlets\":[]}},\"output_visibility\":\"Public\",\"param_visibility\":\"Private\",\"div_rebasing\":false,\"rebase_frac_zero_constants\":false,\"check_mode\":\"UNSAFE\"},\"num_rows\":14432,\"total_assignments\":9944,\"total_const_size\":2118,\"model_instance_shapes\":[[1],[1]],\"model_output_scales\":[0,3],\"model_input_scales\":[3],\"module_sizes\":{\"kzg\":[],\"poseidon\":[14432,[1]]},\"required_lookups\":[\"Abs\",{\"GreaterThan\":{\"a\":0.0}}],\"required_range_checks\":[[-4,4]],\"check_mode\":\"UNSAFE\",\"version\":\"9.1.0\",\"num_blinding_factors\":null,\"timestamp\":1709715644083}\n"
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]
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}
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],
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"source": [
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"# Prover/ data owner side\n",
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"_, prover_model = computation_to_model(computation, error)\n",
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"\n",
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"prover_gen_settings(data_path, selected_columns, sel_data_path, prover_model,prover_model_path, scales, \"resources\", settings_path)"
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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": 8,
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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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"==== setting up ezkl ====\n",
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"Time setup: 6.912375211715698 seconds\n",
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"=======================================\n",
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"==== Generating Witness ====\n",
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"witness boolean: 1.0\n",
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"witness result 1 : 14.625\n",
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"==== Generating Proof ====\n",
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"proof: {'instances': [['d57f47950cdabf2cb79306e0f33e75726a2c2960806e902b0fc88d3ff949a108', '0100000000000000000000000000000000000000000000000000000000000000', '7500000000000000000000000000000000000000000000000000000000000000']], 'proof': '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'transcript_type': 'EVM'}\n",
|
|
"Time gen prf: 8.47111177444458 seconds\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Here verifier & prover can concurrently call setup since all params are public to get pk.\n",
|
|
"# Here write as verifier function to emphasize that verifier must calculate its own vk to be sure\n",
|
|
"setup(verifier_model_path, verifier_compiled_model_path, settings_path,vk_path, pk_path )\n",
|
|
"\n",
|
|
"print(\"=======================================\")\n",
|
|
"# Prover generates proof\n",
|
|
"prover_gen_proof(prover_model_path, sel_data_path, witness_path, prover_compiled_model_path, settings_path, proof_path, pk_path)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Verifier gets result: [14.625]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Verifier verifies\n",
|
|
"res = verifier_verify(proof_path, settings_path, vk_path, selected_columns, data_commitment_path)\n",
|
|
"print(\"Verifier gets result:\", res)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.11.4"
|
|
},
|
|
"orig_nbformat": 4
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|