Run Experiment

Deploy and run ML experiment: $ARGUMENTS

Workflow

Step 1: Detect Environment

Read the project's

CLAUDE.md

• 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.

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

ssh_port

vast-instances.json

vastai ssh-url <INSTANCE_ID>

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)

Check the project's

CLAUDE.md

code_sync

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.

Option C: Vast.ai instance

Sync code to the vast.ai instance (always rsync, code dir is

/workspace/project/

rsync -avz -e "ssh -p <PORT>" \
  --include='*.py' --include='*.yaml' --include='*.yml' --include='*.json' \
  --include='*.txt' --include='*.sh' --include='*/' \
  --exclude='*.pt' --exclude='*.pth' --exclude='*.ckpt' \
  --exclude='__pycache__' --exclude='.git' --exclude='data/' \
  --exclude='wandb/' --exclude='outputs/' \
  ./ root@<HOST>:/workspace/project/

If

requirements.txt

scp -P <PORT> requirements.txt root@<HOST>:/workspace/
ssh -p <PORT> root@<HOST> "pip install -q -r /workspace/requirements.txt"

Step 3.5: W&B Integration (when

wandb: true

in CLAUDE.md)

Skip this step entirely if

wandb

false

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>'"

Vast.ai instance

No conda needed — the Docker image has the environment. Use

/workspace/project/

ssh -p <PORT> root@<HOST> "screen -dmS <exp_name> bash -c '\
  cd /workspace/project && \
  CUDA_VISIBLE_DEVICES=<gpu_id> python <script> <args> 2>&1 | tee /workspace/<log_file>'"

After launching, update the

experiment

vast-instances.json

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

Step 5: Verify Launch

Remote (SSH):

ssh <server> "screen -ls"

Remote (Vast.ai):

ssh -p <PORT> root@<HOST> "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)

Step 7: Auto-Destroy Vast.ai Instance (when

gpu: vast

and

auto_destroy: true

)

Skip this step if not using vast.ai or

auto_destroy

false

After the experiment completes (detected via

/monitor-experiment

1. Download results from the instance:

   rsync -avz -e "ssh -p <PORT>" root@<HOST>:/workspace/project/results/ ./results/
   

2. Download logs:

   scp -P <PORT> root@<HOST>:/workspace/*.log ./logs/
   

3. Destroy the instance to stop billing:

   vastai destroy instance <INSTANCE_ID>
   

4. Update

   vast-instances.json

   — mark status as

   destroyed

   .

5. Report cost:

   Vast.ai instance <ID> auto-destroyed.
   - Duration: ~X.X hours
   - Estimated cost: ~$X.XX
   - Results saved to: ./results/
   

This ensures users are never billed for idle instances. When

auto_destroy: true

(the default), the full lifecycle is automatic: rent → setup → run → collect → destroy.

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
• Vast.ai cost awareness: When using

  gpu: vast

  , always report the running cost. If

  auto_destroy: true

  , destroy the instance as soon as all experiments on it complete

CLAUDE.md Example

Users should add their server info to their project's

CLAUDE.md

## Remote Server
- gpu: remote               # use pre-configured SSH 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)

## Vast.ai
- gpu: vast                  # rent on-demand GPU from vast.ai
- auto_destroy: true         # auto-destroy after experiment completes (default: true)
- max_budget: 5.00           # optional: max total $ to spend per experiment

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

Vast.ai setup: Run

pip install vastai && vastai set api-key YOUR_KEY

. Upload your SSH public key at https://cloud.vast.ai/manage-keys/. Set

gpu: vast

in your

CLAUDE.md

—

/run-experiment

will automatically rent an instance, run the experiment, and destroy it when done.

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>

.