Deploy and run ML experiments on local or remote GPU servers. Use when user says "run experiment", "deploy to server", "跑实验", or needs to launch training jobs.
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Deploy and run ML experiments on local or remote GPU servers. Use when user says "run experiment", "deploy to server", "跑实验", or needs to launch training jobs.
argument-hint
[experiment-description]
allowed-tools
Bash(*), Read, Grep, Glob, Edit, Write, Agent
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.
Vast.ai detection priority:
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
If no server info is found in CLAUDE.md, ask the user.
(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)
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:
After launching, update the experiment field in vast-instances.json for this instance.
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):
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)
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>.
