modal-projects/modal-auto-research-skills/modal-gpu-dev

Claude Code and agent skill by modal-projects/modal-auto-research-skills

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Interactive GPU sandboxes on Modal with SSH access for debugging, profiling, and prototyping. Launch a container with any GPU (T4 to B200), SSH in, iterate on code with pre-installed ML tools. Use when you need hands-on GPU access for debugging, profiling with nsys/ncu, prototyping training scripts, or testing model serving.

Install

npx vskill@latest install modal-projects/modal-auto-research-skills/modal-gpu-dev

SKILL.md

GPU Debugging

Interactive GPU containers on Modal with SSH access. Use for debugging, profiling, prototyping, or any work where you need hands-on access to a GPU environment. Containers come pre-installed with PyTorch, Transformers, SGLang, and common ML tools. Your workspace is persisted to a Modal Volume and synced every 30 seconds.

For general Modal platform usage (app structure, function types, CLI, deployment), see the modal-basic-skills skill.

Quick Reference

Launch a Sandbox
bash
# Launch with specific GPU (runs in background)
_SANDBOX_GPU=H100 python -m modal run modal-gpu-dev/tools/gpu_sandbox.py \
  --key-path ~/.ssh/id_ed25519.pub --gpu H100 --sandbox-id my-sandbox > /tmp/sandbox.log 2>&1 &
 
# Wait for SSH info (written to volume — reliable, not buffered)
while true; do
  modal volume get gpu-sandbox-workspace /ssh-info/my-sandbox.json /tmp/ssh-info.json 2>/dev/null && break
  sleep 3
done
HOST=$(python3 -c "import json; d=json.load(open('/tmp/ssh-info.json')); print(d['host'])")
PORT=$(python3 -c "import json; d=json.load(open('/tmp/ssh-info.json')); print(d['port'])")
 
# SSH in (available immediately — sshd starts before volume sync)
ssh -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null \
  -i ~/.ssh/id_ed25519 -p $PORT root@$HOST "nvidia-smi"

Sandboxes auto-terminate after 4 hours.

SSH Commands
bash
# Run a command
ssh -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null \
  -o LogLevel=ERROR -i ~/.ssh/id_ed25519 -p $PORT root@$HOST "nvidia-smi"
 
# Upload/download files
scp -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null \
  -i ~/.ssh/id_ed25519 -P $PORT ./local_file.py root@$HOST:/tmp/
scp -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null \
  -i ~/.ssh/id_ed25519 -P $PORT root@$HOST:/tmp/results.json ./
 
# Rsync a directory
rsync -avz --exclude .git/ --exclude __pycache__/ --exclude .venv/ \
  -e "ssh -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null -i ~/.ssh/id_ed25519 -p $PORT" \
  ./my_code/ root@$HOST:/tmp/my_code/
Workspace Persistence

The workspace at /root/workspace is backed by a Modal Volume (gpu-sandbox-workspace) and synced every 30 seconds. Data persists across sandbox restarts.

bash
# List files on the volume
modal volume ls gpu-sandbox-workspace /workspace
 
# Download/upload via CLI
modal volume get gpu-sandbox-workspace /workspace/results.json ./results.json
modal volume put gpu-sandbox-workspace ./data.tar.gz /workspace/data.tar.gz
Compute Options

Modal offers GPUs from T4 (16 GB) to B200 (192 GB), with up to 8 GPUs per container. H100 requests may be auto-upgraded to H200 at no extra cost.

See ./references/compute.md for the full GPU table, selection guide, and CUDA details.

Development Workflow
  1. Launch a sandbox with the GPU you need
  2. Upload your code via scp/rsync
  3. SSH in and iterate: run, debug, profile
  4. Once the code works, write a proper Modal training app (see the modal-gpu-experiment skill)
  5. Kill the sandbox

References

  • ./references/development.md — Full development workflow, environment details, use cases
  • ./references/compute.md — GPU options and selection guide

See Also

  • For writing training apps: use the modal-gpu-experiment skill
  • For running multiple sandboxes in parallel (multi-agent): use the sub-agents skill

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