run-experiment

# Run Experiment Deploy and run ML experiment: $ARGUMENTS ## Workflow ### Step 1: Detect Environment Read the project's `AGENTS.md` to determine the experiment environment: - **Local GPU**: Look for local CUDA/MPS setup info - **Remote server**: Look for SSH alias, conda env, code directory If no server info is found in `AGENTS.md`, ask the user. ### Step 2: Pre-flight Check Check GPU availability on the target machine: **Remote:** ```bash ssh <server> nvidia-smi --query-gpu=index,memory.used,memory.total --format=csv,noheader ``` **Local:** ```bash nvidia-smi --query-gpu=index,memory.used,memory.total --format=csv,noheader # or for Mac MPS: python -c "import torch; print('MPS available:', torch.backends.mps.is_available())" ``` Free GPU = memory.used < 500 MiB. ### Step 3: Sync Code (Remote Only) Check the project's `AGENTS.md` for a `code_sync` setting. If not specified, default to `rsync`. #### Option A: rsync (default) Only sync necessary files — NOT data, checkpoints, or large files: ```bash rsync -avz --include='*.py' --exclude='*' <local_src>/ <server>:<remote_dst>/ ``` #### Option B: git (when `code_sync: git` is set in AGENTS.md) Push local changes to remote repo, then pull on the server: ```bash # 1. Push from local git add -A && git commit -m "sync: experiment deployment" && git push # 2. Pull on server ssh <server> "cd <remote_dst> && git pull" ``` Benefits: version-tracked, multi-server sync with one push, no rsync include/exclude rules needed....