EZKL zkML Tutorial: Proving PyTorch Model Inference with Zero-Knowledge SNARKs

Picture this: you’re running a PyTorch model in production, crunching sensitive data, and you need to prove to the world – or at least your Ethereum L2 dApp – that the inference happened exactly as claimed, without leaking a single input bit. Enter EZKL, the zkML powerhouse that’s turning pytorch zero knowledge proofs into a breeze. This tutorial dives headfirst into using EZKL to generate SNARK proofs for your model inference, making verifiable ML inference not just possible, but stupidly efficient. Buckle up, because we’re about to zk-proof your neural net like pros.

Unlocking EZKL: Your Gateway to SNARK Proofs PyTorch Style

EZKL isn’t just another library; it’s a command-line beast and Python powerhouse built for ezkl zkml domination. Forked from zkonduit/ezkl on GitHub, it slurps up deep learning models from PyTorch or TensorFlow, spits out ONNX graphs, and compiles them into zk-SNARK circuits faster than you can say ‘provable computation. ‘ The killer feature? Proofs that verify model execution without rerunning the whole shebang – perfect for zkml ethereum l2 apps where gas is king and trust is zero.

Why obsess over this? In DeFi alpha hunts or private AI oracles, you can’t afford opaque black boxes. EZKL delivers snark proofs pytorch that anyone with a verification key can check in milliseconds. Recent vibes from the EZKL Discord crew highlight quantization tweaks slashing prove times by 50%, and the fresh ezkl-lib PyPI drop makes Python integration seamless. We’re talking Halo2-based proofs that scale, baby!

Gear Up: Installing EZKL and Prepping Your Setup

Let’s hit the ground running. No fluff, just fire. Grab Rust first if you’re CLI-bound – curl –proto ‘=https’ –tlsv1.2 -sSf https://sh.rustup.rs or sh. Then, clone the repo: git clone https://github.com/zkonduit/ezkl.git and and cd ezkl. Cargo build –release, and boom, you’re wielding ezkl binary.

For Python junkies, pip install ezkl-py seals the deal. Pro tip: Docker image ezkl/ezkl: latest skips the hassle entirely. Test it with ezkl –version; expect something like v21. x. x. Now, snag a sample PyTorch model – MNIST classifier screams beginner-friendly. Load torchvision. models. resnet18(pretrained=True), but we’ll quantize later for speed.

Inputs? Vectors or images as JSON or NumPy. EZKL loves fixed-point arithmetic, so scale your floats to integers – think 16-bit for balance between accuracy and prove cost. NP Labs nailed it: ONNX conversion is step zero, dodging floating-point pitfalls that bloat circuits.

PyTorch to ONNX: The Alchemy of ZK-Ready Models

Time to transform your PyTorch beast into ONNX gold. Fire up a script: import torch, load your model, torch. onnx. export(model, dummy_input, ‘model. onnx’, opset_version=11). Dummy input matches inference shape – say, (1, 3,224, 224) for images. EZKL chokes on dynamic shapes, so static it is.

Quantize aggressively for zkML wins. Torch’s post-training quantization or QAT shrinks weights, trading micro-accuracy for mega-prove-speed. Vid Kersic drops truth: even public inputs shine because verification smokes full inference. ChainScore Labs echoes: circuit design here is exporting, proving, verifying – EZKL automates the grind.

Validate your ONNX with ezkl compile-model model. onnx settings. json –output-circuit circuit. ezkl. Settings. json packs the magic: num-bits=24, scale=1e9, lookup-table=true. Tweak for your verifiable ml inference needs. Output? A. ezkl circuit ready for proof gen. DIA loves this for Ethereum verifies – trust minimized, gas optimized.

Hexens spotlights EZKL’s Halo2 proofs making neural nets practical. TikTok trustless? EZKL’s got the sauce. Next up, we’ll smash inputs through the circuit and crank out SNARKs, but savor this setup – it’s the foundation of your zkML empire.

Inputs locked and loaded? Time to fire up the proof engine and generate those snark proofs pytorch that make auditors weep with joy. EZKL’s prove command is your Excalibur: ezkl prove model. onnx input. json witness. json settings. json –proof proof. pf –vk-digest vk-digest. txt –strategy eve. Witness. json holds your scaled inputs as JSON arrays – no leaks, all zk magic. Settings. json? Dial in run-time assertions for output ranges, like softmax bounded

Crank SNARK Proofs from ONNX – EZKL Supercharge! 🚀

Gen Tuned Settings

Kick off the magic: `ezkl gen-settings -M model.onnx input.json –settings settings.json`. This auto-tunes circuit params for your model – calibration wizardry in seconds!

Scale Inputs to Precision

Supercharge input.json: Multiply values by 1e9 (or your scale) for razor-sharp fixed-point precision. Dodge float drama – EZKL loves integers!

Prove the Forward Pass

Unleash the beast: `ezkl prove –settings settings.json model.onnx input.json –proof proof.pf –pk pk.key`. Full proof in <30s for quantized MNIST – ZK pow!

Export Tiny Proof & Keys

Snag your compact proof.pf (KB-sized gem!), pk.key & vk.key. Verify anywhere – Ethereum-ready, trustless glory!

Pro Tips: Turbo Mode

lookup-table=true for 4x speed bursts, rescale=true vs overflows, Eve strategy for batching pros. Visit ezkl.xyz for more hacks!

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Tweak for glory: lookup-table=true slashes multiplications; rescale=true fights overflow. Community Discord raves about 4x speedups on quantized ResNet-18. HackMD notes zk-SNARK verification trumps recompute – milliseconds vs. seconds. Your pytorch zero knowledge proofs now tamper-proof, ready for chain submission.

ZK-Proof Your PyTorch Model: 5 Turbocharged EZKL Steps! ⚡

1. Generate Settings from Model & Input

Kick things off in your terminal: `ezkl settings -M model.onnx -i input.json -o settings.json`. This auto-generates the ZK config tuned to your PyTorch model’s vibes and inputs—pure rocket fuel for proving! 🎉

2. Prove with ezkl prove Command

Unleash the proof beast: `ezkl prove settings.json –model model.onnx –input input.json -o proof.json`. EZKL crunches your inference into a tiny SNARK proof—fast, private, and epic! 🚀

3. Export VK for Verification

Snag your verification key: `ezkl export-vk settings.json -o vk.json` then `ezkl export-verifier vk.json -o verifier.sol`. Now the world can trust your proof at a glance! 🔑

4. Verify Proof Locally

Double-check the magic: `ezkl verify –proof proof.json –vk vk.json`. Instant validation without rerunning the model—ZK wins again, zero drama! 😎

5. Deploy to Ethereum L2

Ship it to the chain! Paste verifier.sol into Remix or Hardhat, deploy to Optimism/Arbitrum. Boom—your ZKML proof lives on L2, verifiable by all! 🌐

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Verify Like a Boss: Instant Checks, Zero Reruns

Verification is EZKL’s mic drop. Grab vk. bin from compile, then ezkl verify –proof proof. pf –vk vk. bin –input witness. json. Green light? Your inference is canon. Spectral-Finance echoes: anyone with vk verifies sans model rerun. Ethereum L2? vk-digest on-chain, proof off-chain, verify via precompile – gas under 300k. DIA’s take: trustless oracles for DeFi, where verifiable ml inference feeds prices without front-running.

Python flow? ezkl. verify(proof, vk, input_visibility=[Output]). Batch verifies? Accumulate proofs. Vid Kersic drops: even public inputs win on speed. ChainScore Labs: on-chain settlement for AI disputes. NP Labs warns accuracy-prove tradeoffs – quantize smart, or circuits balloon.

L2 Domination: Deploying EZKL Proofs to zkML Ethereum L2

Picture your proof hitting Polygon zkEVM or Optimism: Solidity verifier contract swallows proof. pf bytes, vk digest, spits true/false. EZKL exports Solidity glue – ezkl settings-to-sol settings. json Verifier. sol. Deploy, submitProof(txHash, proof, inputs), callback fires on success. Gas? Tiny, thanks to Halo2 recursion vibes from Hexens. TikTok-scale trustless feeds? EZKL scales it.

Real talk: in my high-risk DeFi plays, EZKL fraud proofs catch model drifts instantly. Community polls Discord: 80% slash prove costs via quantization. ZKML future? Private model serving, verifiable agents, oracle swarms. EZKL’s your turbocharger – from PyTorch script to L2 atomic proof in hours.

Dive into ezkl. xyz docs, fork the repo, quantize wild. Your neural nets just got verifiable superpowers. Trade fast, prove privately – zkML empire awaits.