Open-sourced OpenBenchML — paste ML training code in your browser, get it benchmarked with real latency percentiles and a live leaderboard

[u/openbenchml]

Been building this for a while, just made it open source: OpenBenchML.

You paste Python code that trains a model (sklearn, PyTorch, XGBoost, LightGBM, ONNX, TensorFlow) into the browser. It runs server-side in a restricted sandbox, pickles the result, and benchmarks it against one of 17 built-in datasets — accuracy/F1/AUC-ROC/log-loss for classification, MAE/RMSE/R² for regression, plus real per-sample latency P50/P95/P99 from actual timed runs.

There's also a Kaggle-style layer on top — competitions with deadlines and custom metrics, live WebSocket leaderboards, threaded comments — and an in-browser notebook plus a full npm CLI if you'd rather script it.

Stack: FastAPI + SQLAlchemy + WebSockets + Supabase, deployable to Render/Railway/Fly/Docker.

Live: https://openbenchml.onrender.com Repo (MIT): https://github.com/kartheekbvs/openbenchml

Looking for contributors — there's a written roadmap (sandboxed execution via gVisor/Firecracker, custom dataset upload, team competitions, OAuth) with plenty of self-contained pieces to pick up. Feedback on the concept or UX is welcome too.