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Pytorch Lightning

Checked
davila7·vlatest·ai-ml·MIT

Pytorch Lightning streamlines PyTorch deep learning workflows by handling boilerplate, boosting training speed & simplifying experiment 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 pytorch-lightning -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

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Live repository

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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

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  • Pinned to a commit: passed

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

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Commit
79182c51fb
Manifest
cli-tool/components/skills/ai-research/distributed-training-pytorch-lightning/SKILL.md
Scanned
4 files, 1,654 lines
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About this skill

What it does

The pytorch-lightning skill provides a high-level framework for training and deploying deep learning models using PyTorch. It simplifies the process of writing training loops, handling data loading, and managing distributed training across multiple GPUs or nodes.

When to use it

  • You want to streamline your PyTorch code with built-in logging, checkpointing, and model management features.
  • You are working on a project that requires distributed training over multiple GPUs or TPUs.
  • You need to quickly prototype models without writing boilerplate training loops.
  • Your team is looking for a standardized way to manage deep learning experiments.

Key capabilities

  • Simplified training loop with Trainer class
  • Built-in support for logging, checkpointing, and progress tracking
  • Easy integration with distributed computing resources (GPUs, TPUs)
  • Compatibility with popular PyTorch modules and datasets

Example prompts

  • "Create a PyTorch Lightning module for a neural network model."
  • "Set up distributed training on multiple GPUs using PyTorch Lightning."
  • "Log training metrics to TensorBoard during model training."

Tips & gotchas

  • Ensure that your environment has PyTorch installed before using this skill.
  • While PyTorch Lightning simplifies many tasks, it may not be suitable for highly customized training loops or research-level experimentation.