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: Look for local CUDA/MPS setup info
• Remote server: Look for SSH alias, conda env, code directory

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

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)

Check the project's CLAUDE.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:

rsync -avz --include='*.py' --exclude='*' <local_src>/ <server>:<remote_dst>/

Option B: git (when code_sync: git is set in CLAUDE.md)

Push local changes to remote repo, then pull on the server:

# 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.

Step 3.5: W&B Integration (when wandb: true in CLAUDE.md)

Skip this step entirely if wandb is not set or is false in CLAUDE.md.

Before deploying, ensure the experiment scripts have W&B logging:

1. Check if wandb is already in the script — look for import wandb or wandb.init. If present, skip to Step 4.

2. If not present, add W&B logging to the training script:

   import wandb
   wandb.init(project=WANDB_PROJECT, name=EXP_NAME, config={...hyperparams...})
   
   # Inside training loop:
   wandb.log({"train/loss": loss, "train/lr": lr, "step": step})
   
   # After eval:
   wandb.log({"eval/loss": eval_loss, "eval/ppl": ppl, "eval/accuracy": acc})
   
   # At end:
   wandb.finish()
   

3. Metrics to log (add whichever apply to the experiment):

   • train/loss — training loss per step
   • train/lr — learning rate
   • eval/loss, eval/ppl, eval/accuracy — eval metrics per epoch
   • gpu/memory_used — GPU memory (via torch.cuda.max_memory_allocated())
   • speed/samples_per_sec — throughput
   • Any custom metrics the experiment already computes

4. Verify wandb login on the target machine:

   ssh <server> "wandb status"  # should show logged in
   # If not logged in:
   ssh <server> "wandb login <WANDB_API_KEY>"
   

The W&B project name and API key come from CLAUDE.md (see example below). The experiment name is auto-generated from the script name + timestamp.

Step 4: Deploy

Remote (via SSH + screen)

For each experiment, create a dedicated screen session with GPU binding:

ssh <server> "screen -dmS <exp_name> bash -c '\
  eval \"\$(<conda_path>/conda shell.bash hook)\" && \
  conda activate <env> && \
  CUDA_VISIBLE_DEVICES=<gpu_id> python <script> <args> 2>&1 | tee <log_file>'"

Local

# Linux with CUDA
CUDA_VISIBLE_DEVICES=<gpu_id> python <script> <args> 2>&1 | tee <log_file>

# Mac with MPS (PyTorch uses MPS automatically)
python <script> <args> 2>&1 | tee <log_file>

For local long-running jobs, use run_in_background: true to keep the conversation responsive.

Step 5: Verify Launch

Remote:

ssh <server> "screen -ls"

Local: Check process is running and GPU is allocated.

Step 6: Feishu Notification (if configured)

After deployment is verified, check ~/.claude/feishu.json:

• Send experiment_done notification: which experiments launched, which GPUs, estimated time
• If config absent or mode "off": skip entirely (no-op)

Key Rules

• ALWAYS check GPU availability first — never blindly assign GPUs
• Each experiment gets its own screen session + GPU (remote) or background process (local)
• Use tee to save logs for later inspection
• Run deployment commands with run_in_background: true to keep conversation responsive
• Report back: which GPU, which screen/process, what command, estimated time
• If multiple experiments, launch them in parallel on different GPUs

CLAUDE.md Example

Users should add their server info to their project's CLAUDE.md:

## Remote Server
- SSH: `ssh my-gpu-server`
- GPU: 4x A100 (80GB each)
- Conda: `eval "$(/opt/conda/bin/conda shell.bash hook)" && conda activate research`
- Code dir: `/home/user/experiments/`
- code_sync: rsync          # default. Or set to "git" for git push/pull workflow
- wandb: false              # set to "true" to auto-add W&B logging to experiment scripts
- wandb_project: my-project # W&B project name (required if wandb: true)
- wandb_entity: my-team     # W&B team/user (optional, uses default if omitted)

## Local Environment
- Mac MPS / Linux CUDA
- Conda env: `ml` (Python 3.10 + PyTorch)

W&B setup: Run wandb login on your server once (or set WANDB_API_KEY env var). The skill reads project/entity from CLAUDE.md and adds wandb.init() + wandb.log() to your training scripts automatically. Dashboard: https://wandb.ai/<entity>/<project>.