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Modal Serverless Gpu

Checked
davila7·vlatest·cloud·MIT

Davila7's modal-serverless-gpu enables rapid deployment of GPU-accelerated workloads on Modal without server management.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add davila7/claude-code-templates --skill modal-serverless-gpu -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

davila7

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned davila7/claude-code-templates at 79182c51fb.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
79182c51fb
Manifest
cli-tool/components/skills/ai-research/infrastructure-modal/SKILL.md
Scanned
3 files, 1,199 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

modal-serverless-gpu

What it does

This skill enables AI agents to deploy and manage GPU-accelerated workloads using Modal’s serverless infrastructure. It automates the provisioning of compute resources, allowing agents to run intensive tasks like model training or inference without managing underlying servers.

When to use it

  • Running large language model inference or training jobs that require significant GPU memory.
  • Scaling machine learning pipelines dynamically based on demand without manual intervention.
  • Deploying data processing workflows that benefit from parallel GPU computation.
  • Testing and prototyping AI applications with instant, on-demand GPU access.

Key capabilities

  • Automatically provisions GPU instances via Modal’s serverless platform.
  • Supports containerized environments for reproducible ML workloads.
  • Handles resource scaling and teardown based on job completion.
  • Integrates with Python-based workflows for seamless execution.

Example prompts

  • "Deploy a PyTorch model inference service using a GPU instance."
  • "Run a batch training job on a dataset and scale resources as needed."
  • "Set up a serverless pipeline to process video frames with GPU acceleration."

Tips & gotchas

Ensure your environment has the necessary Modal SDK installed and configured before running GPU tasks. Be mindful of cost implications, as GPU usage is billed based on runtime and resource allocation.