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chore: add colab/notebook examples for readme blurb examples (#506)
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,290 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "id": "cf69bb3f-94e6-4dba-92cd-ce08df117d67", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## EZKL Jupyter Notebook Demo \n", | ||
| "\n", | ||
| "Here we demonstrate the use of the EZKL package in a Jupyter notebook whereby all components of the circuit are public or pre-committed to. This is the simplest case of using EZKL (proof of computation)." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "95613ee9", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# check if notebook is in colab\n", | ||
| "try:\n", | ||
| " # install ezkl\n", | ||
| " import google.colab\n", | ||
| " import subprocess\n", | ||
| " import sys\n", | ||
| " subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"ezkl\"])\n", | ||
| " subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"onnx\"])\n", | ||
| "\n", | ||
| "# rely on local installation of ezkl if the notebook is not in colab\n", | ||
| "except:\n", | ||
| " pass\n", | ||
| "\n", | ||
| "\n", | ||
| "# here we create and (potentially train a model)\n", | ||
| "\n", | ||
| "# make sure you have the dependencies required here already installed\n", | ||
| "from torch import nn\n", | ||
| "import ezkl\n", | ||
| "import os\n", | ||
| "import json\n", | ||
| "import torch\n", | ||
| "\n", | ||
| "\n", | ||
| "# Defines the model\n", | ||
| "# we got convs, we got relu, we got linear layers\n", | ||
| "# What else could one want ????\n", | ||
| "\n", | ||
| "class MyModel(nn.Module):\n", | ||
| " def __init__(self):\n", | ||
| " super(MyModel, self).__init__()\n", | ||
| "\n", | ||
| " self.conv1 = nn.Conv2d(in_channels=1, out_channels=2, kernel_size=5, stride=2)\n", | ||
| " self.conv2 = nn.Conv2d(in_channels=2, out_channels=3, kernel_size=5, stride=2)\n", | ||
| "\n", | ||
| " self.relu = nn.ReLU()\n", | ||
| "\n", | ||
| " self.d1 = nn.Linear(48, 48)\n", | ||
| " self.d2 = nn.Linear(48, 10)\n", | ||
| "\n", | ||
| " def forward(self, x):\n", | ||
| " # 32x1x28x28 => 32x32x26x26\n", | ||
| " x = self.conv1(x)\n", | ||
| " x = self.relu(x)\n", | ||
| " x = self.conv2(x)\n", | ||
| " x = self.relu(x)\n", | ||
| "\n", | ||
| " # flatten => 32 x (32*26*26)\n", | ||
| " x = x.flatten(start_dim = 1)\n", | ||
| "\n", | ||
| " # 32 x (32*26*26) => 32x128\n", | ||
| " x = self.d1(x)\n", | ||
| " x = self.relu(x)\n", | ||
| "\n", | ||
| " # logits => 32x10\n", | ||
| " logits = self.d2(x)\n", | ||
| "\n", | ||
| " return logits\n", | ||
| "\n", | ||
| "\n", | ||
| "circuit = MyModel()\n", | ||
| "\n", | ||
| "# Train the model as you like here (skipped for brevity)\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", | ||
| "srs_path = os.path.join('kzg.srs')\n", | ||
| "witness_path = os.path.join('witness.json')\n", | ||
| "data_path = os.path.join('input.json')" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "82db373a", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "\n", | ||
| "\n", | ||
| "# After training, export to onnx (network.onnx) and create a data file (input.json)\n", | ||
| "x = 0.1*torch.rand(1,*[1, 28, 28], requires_grad=True)\n", | ||
| "\n", | ||
| "# Flips the neural net into inference mode\n", | ||
| "circuit.eval()\n", | ||
| "\n", | ||
| " # Export the model\n", | ||
| "torch.onnx.export(circuit, # model being run\n", | ||
| " x, # model input (or a tuple for multiple inputs)\n", | ||
| " model_path, # where to save the model (can be a file or file-like object)\n", | ||
| " export_params=True, # store the trained parameter weights inside the model file\n", | ||
| " opset_version=10, # the ONNX version to export the model to\n", | ||
| " do_constant_folding=True, # whether to execute constant folding for optimization\n", | ||
| " input_names = ['input'], # the model's input names\n", | ||
| " output_names = ['output'], # the model's output names\n", | ||
| " dynamic_axes={'input' : {0 : 'batch_size'}, # variable length axes\n", | ||
| " 'output' : {0 : 'batch_size'}})\n", | ||
| "\n", | ||
| "data_array = ((x).detach().numpy()).reshape([-1]).tolist()\n", | ||
| "\n", | ||
| "data = dict(input_data = [data_array])\n", | ||
| "\n", | ||
| " # Serialize data into file:\n", | ||
| "json.dump( data, open(data_path, 'w' ))\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "d5e374a2", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "\n", | ||
| "py_run_args = ezkl.PyRunArgs()\n", | ||
| "py_run_args.input_visibility = \"public\"\n", | ||
| "py_run_args.output_visibility = \"public\"\n", | ||
| "py_run_args.param_visibility = \"fixed\" # \"fixed\" for params means that the committed to params are used for all proofs\n", | ||
| "\n", | ||
| "res = ezkl.gen_settings(model_path, settings_path, py_run_args=py_run_args)\n", | ||
| "assert res == True\n", | ||
| "\n", | ||
| "res = await ezkl.calibrate_settings(data_path, model_path, settings_path, \"resources\")\n", | ||
| "assert res == True" | ||
| ] | ||
| }, | ||
| { | ||
| "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 = ezkl.get_srs(srs_path, settings_path)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "18c8b7c7", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# now generate the witness file \n", | ||
| "\n", | ||
| "res = ezkl.gen_witness(data_path, compiled_model_path, witness_path)\n", | ||
| "assert os.path.isfile(witness_path)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "b1c561a8", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "\n", | ||
| "# HERE WE SETUP THE CIRCUIT PARAMS\n", | ||
| "# WE GOT KEYS\n", | ||
| "# WE GOT CIRCUIT PARAMETERS\n", | ||
| "# EVERYTHING ANYONE HAS EVER NEEDED FOR ZK\n", | ||
| "\n", | ||
| "\n", | ||
| "\n", | ||
| "res = ezkl.setup(\n", | ||
| " compiled_model_path,\n", | ||
| " vk_path,\n", | ||
| " pk_path,\n", | ||
| " srs_path,\n", | ||
| " )\n", | ||
| "\n", | ||
| "assert res == True\n", | ||
| "assert os.path.isfile(vk_path)\n", | ||
| "assert os.path.isfile(pk_path)\n", | ||
| "assert os.path.isfile(settings_path)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "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", | ||
| " srs_path,\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", | ||
| " srs_path,\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 | ||
| } |
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