Torchtitan

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Torchtitan is a PyTorch-native platform for training generative AI models, offering a flexible foundation for rapid experimentation and large-scale model development. It supports multi-dimensional parallelism and various optimization techniques.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is torchtitan?

Torchtitan is a PyTorch-native platform designed for rapid experimentation and large-scale training of generative AI models. It serves as a minimal clean-room implementation of PyTorch native scaling techniques, providing a flexible foundation for developers to build upon. The platform emphasizes ease of understanding, use, and extension for different training purposes, with a bias towards a clean, minimal codebase. Key features include multi-dimensional composable parallelisms like FSDP2, Tensor Parallel, and Pipeline Parallel, along with support for activation checkpointing, distributed checkpointing, and interoperable checkpoints. Torchtitan also integrates with `torch.compile`, supports Float8 and MXFP8 training, and offers Supervised Fine-Tuning (SFT) with chat-formatted datasets. It provides debugging tools, flexible learning rate schedulers, and helper scripts for tokenizer downloads and checkpoint conversions, making it a comprehensive solution for advanced generative AI model development.

Best used for

Ideal for developers and data scientists who need to train large-scale generative AI models, experiment with new modeling architectures, and implement advanced distributed training techniques. Especially valuable for those working within the PyTorch ecosystem and requiring high flexibility and performance for LLM pre-training.

Common actions

train generative models

experiment with AI architectures

scale PyTorch models

implement distributed training

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Capabilities

Key features

Multi-dimensional composable parallelisms
FSDP2 with per-parameter sharding
Tensor Parallel (async TP)
Pipeline Parallel
Distributed checkpointing
Float8 and MXFP8 support
Supervised Fine-Tuning (SFT)

Target Audience

developerdata scientist

Integrations

hugging-facetensorboardweights-and-biases

Pricing & Plans

Open Source

Free

FAQs

What kind of generative AI models can be trained with torchtitan?

Torchtitan is designed for training various generative AI models, with a focus on large language models (LLMs). It currently supports pretraining Llama 3.1 LLMs of different sizes (8B, 70B, 405B) out of the box, and provides instructions for adding custom models.

What distributed training features does torchtitan support?

Torchtitan supports a comprehensive set of distributed training features, including FSDP2 with per-parameter sharding, Tensor Parallel (including async TP), Pipeline Parallel, and Context Parallel. It also offers distributed checkpointing and interoperable checkpoints for fine-tuning.

How can I install torchtitan?

You can install torchtitan from source, using nightly builds, or via stable releases. Source installation requires a PyTorch nightly build, while nightly builds of torchtitan can be installed via pip with specific index URLs for CUDA or ROCm. Stable releases are available via `pip` or `conda`.

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AI Agents & Automation › AI Frameworks & Infra Research & Education › Scientific Computing

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