experiment-bridge

To automate running GPU experiments, you need a clearly defined EXPERIMENT_PLAN.md. The tool reads this file, implements the experiment code, deploys it to GPU, and collects initial results automatically.

An experiment automation loop bridges idea discovery with automated execution by converting a plan into deployed GPU experiments and feeding initial results into an auto-review loop for rapid iteration.

Yes, a clearly defined EXPERIMENT_PLAN.md is required to start automating ML experiments. The tool reads this file to implement experiment code and deploy it to GPU for execution.

You can deploy experiments to GPU and collect initial results automatically by providing an EXPERIMENT_PLAN.md and triggering the implementation process, which clones the base repo and runs sanity checks.

The best way to bridge idea discovery with automated execution is using a tool that reads an experiment plan, implements the code, deploys to GPU, and produces parseable results for an auto-review loop.

Limitations include the requirement for a clearly defined EXPERIMENT_PLAN.md and the ability to clone the base repo. The tool produces initial results for an auto-review loop, meaning manual validation of parseable outputs may still be needed.