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Distributed Llm Pretraining Torchtitan

Listed
davila7·vlatest·ai-ml·MIT

Provides LLMs guidance and assistance for building AI and machine learning applications.

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 distributed-llm-pretraining-torchtitan -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 failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • No pipe-to-shell installers: failed

    Runs code fetched at install time in 2 places.

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

    What it found

    • cli-tool/components/skills/ai-research/model-architecture-torchtitan/SKILL.md:186pip install straight from a URL
      USE_CPP=0 pip install git+https://github.com/pytorch/ao.git
    • cli-tool/components/skills/ai-research/model-architecture-torchtitan/references/float8.md:13pip install straight from a URL
      USE_CPP=0 pip install git+https://github.com/pytorch/ao.git

  • 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.

  • 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/model-architecture-torchtitan/SKILL.md
Scanned
5 files, 941 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

The distributed-llm-pretraining-torchtitan skill leverages PyTorch's TorchTitan library to orchestrate large-scale, multi-GPU pretraining workflows for language models. It manages the complex coordination required to distribute model weights and gradients across a cluster of machines efficiently.

When to use it

  • Scaling up LLM pretraining from single GPU experiments to massive multi-node clusters.
  • Managing high-throughput training jobs where inter-process communication overhead must be minimized.
  • Implementing custom distributed strategies for specific hardware topologies or network fabrics.
  • Automating the setup of fault-tolerant training environments using TorchTitan primitives.

Key capabilities

  • Integration with PyTorch's native distributed data parallel (DDP) and tensor parallelism mechanisms.
  • Utilization of TorchTitan for optimized collective communication operations across GPU clusters.
  • Support for dynamic scaling of training jobs based on available compute resources.
  • Streamlined handling of model checkpointing and state synchronization in distributed settings.

Example prompts

  • "Set up a distributed pretraining job using TorchTitan across 8 GPUs with gradient accumulation enabled."
  • "Configure a multi-node LLM training cluster using TorchTitan's collective communication primitives for efficient synchronization."
  • "Optimize the distributed data parallel setup for a large language model using TorchTitan's latest performance features."

Tips & gotchas

Ensure your environment has compatible versions of PyTorch and TorchTitan installed before attempting to run distributed jobs. Be aware that TorchTitan requires specific hardware configurations and network setups to function correctly in a multi-node environment.