run-experiment

Run Experiment

Deploy and run ML experiment: $ARGUMENTS

Workflow

Step 1: Detect Environment

Read the project's CLAUDE.md to determine the experiment environment:

• Local GPU (gpu: local): Look for local CUDA/MPS setup info
• Remote server (gpu: remote): Look for SSH alias, conda env, code directory
• Vast.ai (gpu: vast): Check for vast-instances.json at project root — if a running instance exists, use it. Also check CLAUDE.md for a ## Vast.ai section.
• Modal (gpu: modal): Serverless GPU via Modal. No SSH, no Docker, auto scale-to-zero. Delegate to /serverless-modal.

Modal detection: If CLAUDE.md has gpu: modal or a ## Modal section, the entire deployment is handled by /serverless-modal. Jump to Step 4: Deploy (Modal) — Steps 2-3 are not needed (Modal handles code sync and GPU allocation automatically).

Vast.ai detection priority:

1. If CLAUDE.md has gpu: vast or a ## Vast.ai section:
   • If vast-instances.json exists and has a running instance → use that instance
   • If no running instance → call /vast-gpu provision which analyzes the task, presents cost-optimized GPU options, and rents the user's choice
2. If no server info is found in CLAUDE.md, ask the user.

Step 2: Pre-flight Check

Check GPU availability on the target machine:

Remote (SSH):

ssh <server> nvidia-smi --query-gpu=index,memory.used,memory.total --format=csv,noheader

Remote (Vast.ai):

ssh -p <PORT> root@<HOST> nvidia-smi --query-gpu=index,memory.used,memory.total --format=csv,noheader

(Read ssh_host and ssh_port from vast-instances.json, or run vastai ssh-url <INSTANCE_ID> which returns ssh://root@HOST:PORT)

Local:

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)