experiment-bridge

To automate deployment of GPU experiments from a markdown plan, you provide an EXPERIMENT_PLAN.md file. The system parses run order, datasets, and metrics, implements the code, deploys to GPUs, and collects initial results.

The process for turning an experiment plan into runnable GPU-backed code involves parsing the experiment plan for setup details, automatically implementing the experiment code, performing sanity checks, and deploying directly to GPUs for live evaluation.

Yes, you can use this pipeline to collect initial metrics and prepare for auto-review loops. It deploys live evaluations to GPUs and gathers initial results specifically to satisfy automated review requirements.

Yes, you need an EXPERIMENT_PLAN.md file located in the refine-logs directory to trigger automated experiment implementation. This file provides the run order, datasets, metrics, and setup details required for deployment.

The best way to bridge idea discovery into executable GPU experiments is to define your run order and metrics in an EXPERIMENT_PLAN.md, then trigger the bridge to automatically implement code, deploy, and collect live evaluation results.

Yes, there are configurable safeguards for automated experiment deployment pipelines. The system includes configurable inputs and safeguards during the parsing, implementation, and deployment phases to ensure controlled execution.