serverless-modal
Run GPU workloads on Modal — training, fine-tuning, inference, batch processing. Zero-config serverless: no SSH, no Docker, auto scale-to-zero. Use when user says \"modal run\", \"modal training\", \"modal inference\", \"deploy to modal\", \"need a GPU\", \"run on modal\", \"serverless GPU\", or needs remote GPU compute.
npx skills add majiayu000/claude-skill-registry --skill serverless-modal --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Modal Cloud GPU — Training & Inference Task: $ARGUMENTS ## Overview **Modal** is a serverless GPU cloud. Key advantages over SSH-based platforms (vast.ai, remote servers): - **Zero config**: no SSH, no Docker, no port forwarding. Write Python → `modal run` → done. - **Auto scale-to-zero**: billing stops the instant your code finishes. No idle instances. - **Local-first**: run `modal run` from your laptop. Code, data, and results stay local; only the GPU function runs remotely. - **Reproducible environments**: dependencies declared in code via `modal.Image`, not system-level packages. **Best for**: Users without a local GPU who need to debug CUDA code, run small-scale tests, or iterate quickly on experiments. The $5 free tier (no card) is enough for code debugging; $30 (with card) covers most small-scale experiment runs. **Trade-off**: Modal costs more per GPU-hour than vast.ai or Lightning for some GPU tiers, but eliminates setup time and idle billing, often making it cheaper for short/medium workloads. For long training runs (>4 hours), consider vast.ai for lower $/hr. ## Authentication ```bash pip install modal modal setup # Opens browser login, writes token to ~/.modal.toml #
- Overview
- Authentication
- Pricing (source: modal.com/pricing, per-second billing)
- !! Cost Estimation Required !!
- Cost Estimation Template (required before every run)
- 7-8B BF16 Benchmark Cost Comparison
- Workflow
- Step 1: Analyze Task → Estimate Cost → Choose GPU
- Step 2: Generate Modal Launcher
- Step 3: Run
- Step 4: Verify & Monitor
- Step 5: Collect Results
- Step 6: Cleanup
- CLI Reference
pip install modal
modal setup # Opens browser login, writes token to ~/.modal.toml
modal run -q 'print("ok")'
modal run launcher.py # One-shot execution (most common for experiments)
modal deploy app.py # Persistent service deployment
modal app list # List running apps
modal app logs <app-name> # Stream logs
modal app stop <app-name> # Stop a deployed service
modal volume rm <volume-name> # Delete a volume when done
modal run app.py # Run onceWhat does the serverless-modal skill do?
Run GPU workloads on Modal — training, fine-tuning, inference, batch processing. Zero-config serverless: no SSH, no Docker, auto scale-to-zero. Use when user says \"modal run\", \"modal training\", \"modal inference\", \"deploy to modal\", \"need a GPU\", \"run on modal\", \"serverless GPU\", or needs remote GPU compute.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill serverless-modal --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.