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Huggingface Accelerate

Checked
davila7·vlatest·ai-ml·MIT

This skill utilizes Hugging Face Accelerate for distributed training of large language models, speeding up development and experimentation.

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 huggingface-accelerate -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/distributed-training-accelerate/SKILL.md
Scanned
4 files, 1,635 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

huggingface-accelerate

What it does

This skill enables AI agents to programmatically interact with Hugging Face's Accelerate library, simplifying the setup and execution of distributed training jobs across various hardware backends. It automates configuration management for multi-GPU environments, allowing agents to scale model training efficiently without manual infrastructure intervention.

When to use it

  • Scaling large models: When a task requires training transformer models that exceed single-GPU memory limits.
  • Hardware abstraction: When an agent needs to switch between CPU, GPU, or TPU backends dynamically based on available resources.
  • Distributed workflows: When orchestrating data parallelism or model parallelism strategies for deep learning experiments.
  • Environment consistency: When ensuring training configurations remain consistent across different development and production environments.

Key capabilities

  • Automatic detection and configuration of available hardware accelerators (CPU, GPU, TPU).
  • Simplified initialization of Accelerator objects for distributed training setups.
  • Management of mixed precision training settings through integrated configurations.
  • Support for various deep learning frameworks including PyTorch, TensorFlow, and JAX via Accelerate's compatibility layer.

Example prompts

  • "Set up a multi-GPU training environment using Hugging Face Accelerate for my current PyTorch model."
  • "Configure distributed data parallelism with 4 GPUs and mixed precision enabled using the accelerate library."
  • "Create a training script that automatically detects available hardware and adjusts batch sizes accordingly via Accelerate."

Tips & gotchas

Ensure the accelerate package is installed in your environment before invoking this skill, as it serves as the core dependency for all distributed operations. Be mindful that while Accelerate simplifies setup, complex custom training loops may still require manual intervention to fully leverage advanced features like deepspeed integration or specific hardware optimizations.