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ROCm/docs/compatibility/ml-compatibility/megablocks-compatibility.rst

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.. meta::
:description: Megablocks compatibility
:keywords: GPU, megablocks, compatibility
.. version-set:: rocm_version latest
********************************************************************************
Megablocks compatibility
********************************************************************************
Megablocks is a light-weight library for mixture-of-experts (MoE) training.
The core of the system is efficient "dropless-MoE" and standard MoE layers.
Megablocks is integrated with `https://github.com/stanford-futuredata/Megatron-LM <https://github.com/stanford-futuredata/Megatron-LM>`_,
where data and pipeline parallel training of MoEs is supported.
* ROCm support for Megablocks is hosted in the official `https://github.com/ROCm/megablocks <https://github.com/ROCm/megablocks>`_ 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 <megablocks-docker-compat>` with ROCm, PyTorch, and Megablocks preinstalled.
* See the :doc:`ROCm Megablocks installation guide <rocm-install-on-linux:install/3rd-party/megablocks-install>` to install and get started.
.. note::
Megablocks is supported on ROCm 6.3.0.
Supported devices
================================================================================
- **Officially Supported**: AMD Instinct MI300X
- **Partially Supported** (functionality or performance limitations): AMD Instinct MI250X, MI210X
Supported models and features
================================================================================
This section summarizes the Megablocks features supported by ROCm.
* Distributed Pre-training
* Activation Checkpointing and Recomputation
* Distributed Optimizer
* Mixture-of-Experts
* dropless-Mixture-of-Experts
.. _megablocks-recommendations:
Use cases and recommendations
================================================================================
The `ROCm Megablocks blog posts <https://rocm.blogs.amd.com/artificial-intelligence/Megablocks/README.html>`_
guide how to leverage the ROCm platform for pre-training using the Megablocks framework.
It features how to pre-process datasets and how to begin pre-training on AMD GPUs through:
* Single-GPU pre-training
* Multi-GPU pre-training
.. _megablocks-docker-compat:
Docker image compatibility
================================================================================
.. |docker-icon| raw:: html
<i class="fab fa-docker"></i>
AMD validates and publishes `ROCm Megablocks images <https://hub.docker.com/r/rocm/megablocks/tags>`_
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
- ROCm
- Megablocks
- PyTorch
- Ubuntu
- Python
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/megablocks/megablocks-0.7.0_rocm6.3.0_ubuntu24.04_py3.12_pytorch2.4.0/images/sha256-372ff89b96599019b8f5f9db469c84add2529b713456781fa62eb9a148659ab4"><i class="fab fa-docker fa-lg"></i> rocm/megablocks</a>
- `6.3.0 <https://repo.radeon.com/rocm/apt/6.3/>`_
- `0.7.0 <https://github.com/databricks/megablocks/releases/tag/v0.7.0>`_
- `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/>`_