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ROCm/docs/compatibility/ml-compatibility/stanford-megatron-lm-compatibility.rst

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.. meta::
:description: Stanford Megatron-LM compatibility
:keywords: Stanford, Megatron-LM, compatibility
.. version-set:: rocm_version latest
********************************************************************************
Stanford Megatron-LM compatibility
********************************************************************************
Stanford Megatron-LM is a large-scale language model training framework developed by NVIDIA `https://github.com/NVIDIA/Megatron-LM <https://github.com/NVIDIA/Megatron-LM>`_. It is
designed to train massive transformer-based language models efficiently by model and data parallelism.
* ROCm support for Stanford Megatron-LM is hosted in the official `https://github.com/ROCm/Stanford-Megatron-LM <https://github.com/ROCm/Stanford-Megatron-LM>`_ repository.
* Due to independent compatibility considerations, this location differs from the `https://github.com/stanford-futuredata/Megatron-LM <https://github.com/stanford-futuredata/Megatron-LM>`_ upstream repository.
* Use the prebuilt :ref:`Docker image <megatron-lm-docker-compat>` with ROCm, PyTorch, and Megatron-LM preinstalled.
* See the :doc:`ROCm Stanford Megatron-LM installation guide <rocm-install-on-linux:install/3rd-party/stanford-megatron-lm-install>` to install and get started.
.. note::
Stanford Megatron-LM is supported on ROCm 6.3.0.
Supported Devices
================================================================================
- **Officially Supported**: AMD Instinct MI300X
- **Partially Supported** (functionality or performance limitations): AMD Instinct MI250X, MI210
Supported models and features
================================================================================
This section details models & features that are supported by the ROCm version on Stanford Megatron-LM.
Models:
* Bert
* GPT
* T5
* ICT
Features:
* Distributed Pre-training
* Activation Checkpointing and Recomputation
* Distributed Optimizer
* Mixture-of-Experts
.. _megatron-lm-recommendations:
Use cases and recommendations
================================================================================
See the `Efficient MoE training on AMD ROCm: How-to use Megablocks on AMD GPUs blog <https://rocm.blogs.amd.com/artificial-intelligence/megablocks/README.html>`_ post
to leverage the ROCm platform for pre-training by using the Stanford Megatron-LM framework of pre-processing datasets on AMD GPUs.
Coverage includes:
* Single-GPU pre-training
* Multi-GPU pre-training
.. _megatron-lm-docker-compat:
Docker image compatibility
================================================================================
.. |docker-icon| raw:: html
<i class="fab fa-docker"></i>
AMD validates and publishes `Stanford Megatron-LM images <https://hub.docker.com/r/rocm/megatron-lm>`_
with ROCm and Pytorch backends on Docker Hub. The following Docker image tags and associated
inventories represent the latest Megatron-LM version from the official Docker Hub.
The Docker images have been validated for `ROCm 6.3.0 <https://repo.radeon.com/rocm/apt/6.3/>`_.
Click |docker-icon| to view the image on Docker Hub.
.. list-table::
:header-rows: 1
:class: docker-image-compatibility
* - Docker image
- Stanford Megatron-LM
- PyTorch
- Ubuntu
- Python
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/stanford-megatron-lm/stanford-megatron-lm85f95ae_rocm6.3.0_ubuntu24.04_py3.12_pytorch2.4.0/images/sha256-070556f078be10888a1421a2cb4f48c29f28b02bfeddae02588d1f7fc02a96a6"><i class="fab fa-docker fa-lg"></i></a>
- `85f95ae <https://github.com/stanford-futuredata/Megatron-LM/commit/85f95aef3b648075fe6f291c86714fdcbd9cd1f5>`_
- `2.4.0 <https://github.com/ROCm/pytorch/tree/release/2.4>`_
- 24.04
- `3.12.9 <https://www.python.org/downloads/release/python-3129/>`_