/exp-run

To automate machine learning experiment deployment from a wiki, use a tool that reads experiment plans, generates reproducible code packages, and executes them on local or remote SSH environments. This process turns planned wiki pages into running code while monitoring status transitions automatically.

Yes, you can run machine learning experiments remotely over SSH and collect results. The execution process supports remote SSH deployments by generating code, starting runs, monitoring state, and collecting multi-seed result JSON files back into your wiki.

To collect and report experiment results with mean and standard deviation, execute a collection phase that gathers per-seed JSON files from the results directory. It aggregates metrics and summarizes them into the wiki with per-metric mean±std reporting.

The best way to ensure reproducibility when generating machine learning experiment code is to scaffold a consistent package including train.py, config.yaml, and run.sh. This deterministic generation supports checkpointing and multi-seed result saving for reliable reproduction.

Yes, the experiment execution process supports an optional code review gate before deployment. By enabling a review flag during the code generation phase, an LLM reviews the generated code to improve correctness before the run starts.

Yes, you can deploy only the experiment code without collecting results immediately. The execution process supports deploy-only, collect-only, and full execution modes, allowing you to separate code deployment from result collection.