OPML: OPtimistic Machine Learning on Blockchain

OPML enables off-chain AI model inference using optimistic approach with an on chain interactive dispute engine implementing fault proofs.

For more in-depth information, refer to the project wiki.

You can also find a tutorial on building a straightforward handwritten digit recognition DNN model (MNIST) within OPML in the docs/tutorial.md.

Building

Pre-requisites: Go (Go 1.19), Node.js, Make, and CMake.

git clone git@github.com:hyperoracle/opml.git --recursive
make build

Examples

The script files demo/challenge_simple.sh presents an example scenario (a DNN model for MNIST) demonstrating the whole process of a fault proof, including the challenge game and single step verification.

To test the example, we should first start a local node:

npx hardhat node

Then we can run:

bash ./demo/challenge_simple.sh

A large language model, the llama example is provided in the branch "llama" (It also works for llama 2).

Roadmap

🔨 = Pending

🛠 = Work In Progress

✅ = Feature complete

Feature	Status
Supported Model
DNN for MNIST	✅
LLaMA	✅
General DNN Model (Onnx Support)	🛠
Traditional ML Algorithm (Decision Tree, KNN etc)	🔨
Mode
Inference	✅
Training	🔨
Fine-tuning	🔨
Optimization
ZK Fault Proof with zkOracle and zkWASM	🛠
GPU Acceleration	🛠
High Performance VM	🛠
Functionality
User-Friendly SDK	🛠

Project Structure

mlgo -- A tensor library for machine learning in pure Golang that can run on MIPS.
mlvm -- A MIPS runtime with ML execution
contracts -- A Merkleized MIPS processor on chain + the challenge logic

License

This code is MIT licensed.

Part of this code is borrowed from ethereum-optimism/cannon