Distributed Llm Pretraining Torchtitan

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by orchestra-research · vlatest · Repository

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

Install on your platform

We auto-selected Claude Code based on this skill’s supported platforms.

1

Run in terminal (recommended)

terminal
claude mcp add orchestra-research-distributed-llm-pretraining-torchtitan npx -- -y @trustedskills/orchestra-research-distributed-llm-pretraining-torchtitan
2

Or manually add to ~/.claude/settings.json

~/.claude/settings.json
{
  "mcpServers": {
    "orchestra-research-distributed-llm-pretraining-torchtitan": {
      "command": "npx",
      "args": [
        "-y",
        "@trustedskills/orchestra-research-distributed-llm-pretraining-torchtitan"
      ]
    }
  }
}

Requires Claude Code (claude CLI). Run claude --version to verify your install.

About This Skill

What it does

This skill facilitates distributed Large Language Model (LLM) pretraining using the TorchTitan framework. It enables scaling LLM training across multiple GPUs and machines, optimizing for performance and efficiency. Specifically, it supports features like data parallelism and model sharding to handle very large models that wouldn't fit on a single device.

When to use it

  • Training extremely large language models (e.g., billions or trillions of parameters) where a single GPU is insufficient.
  • Accelerating LLM pretraining by leveraging multiple GPUs across several machines.
  • Experimenting with different distributed training strategies and configurations for optimal performance.
  • Reproducing research results involving large-scale LLM pretraining setups.

Key capabilities

  • Distributed LLM pretraining
  • TorchTitan framework integration
  • Data parallelism support
  • Model sharding capabilities

Example prompts

  • "Run a distributed pretraining job for my language model using 8 GPUs."
  • "Configure the data parallel settings for training with TorchTitan."
  • "Shard my LLM across multiple machines to reduce memory footprint per device."

Tips & gotchas

  • Requires familiarity with PyTorch and distributed training concepts.
  • Proper hardware setup (multiple GPUs, network connectivity) is essential for successful execution.

Tags

🛡️

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Details

Version
vlatest
License
Author
orchestra-research
Installs
26

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Passed automated security scans.