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ROCm/docs/compatibility/ml-compatibility/dgl-compatibility.rst
anisha-amd ae54add299 Sphinx warning for ROCm fixed (#5077) (#5082) 
* Sphinx warning for DGL fixed

* Update dgl-compatibility.rst

removed benchmark line and updated link

---------

Co-authored-by: Pratik Basyal <prbasyal@amd.com>
2025-07-22 10:51:15 -04:00

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:orphan:
.. meta::
:description: Deep Graph Library (DGL) compatibility
:keywords: GPU, DGL compatibility
.. version-set:: rocm_version latest
********************************************************************************
DGL compatibility
********************************************************************************
Deep Graph Library `(DGL) <https://www.dgl.ai/>`_ is an easy-to-use, high-performance and scalable
Python package for deep learning on graphs. DGL is framework agnostic, meaning
if a deep graph model is a component in an end-to-end application, the rest of
the logic is implemented using PyTorch.
* ROCm support for DGL is hosted in the `https://github.com/ROCm/dgl <https://github.com/ROCm/dgl>`_ repository.
* Due to independent compatibility considerations, this location differs from the `https://github.com/dmlc/dgl <https://github.com/dmlc/dgl>`_ upstream repository.
* Use the prebuilt :ref:`Docker images <dgl-docker-compat>` with DGL, PyTorch, and ROCm preinstalled.
* See the :doc:`ROCm DGL installation guide <rocm-install-on-linux:install/3rd-party/dgl-install>`
to install and get started.
Supported devices
================================================================================
- **Officially Supported**: TF32 with AMD Instinct MI300X (through hipblaslt)
- **Partially Supported**: TF32 with AMD Instinct MI250X
.. _dgl-recommendations:
Use cases and recommendations
================================================================================
DGL can be used for Graph Learning, and building popular graph models like
GAT, GCN and GraphSage. Using these we can support a variety of use-cases such as:
- Recommender systems
- Network Optimization and Analysis
- 1D (Temporal) and 2D (Image) Classification
- Drug Discovery
Multiple use cases of DGL have been tested and verified.
However, a recommended example follows a drug discovery pipeline using the ``SE3Transformer``.
Refer to the `AMD ROCm blog <https://rocm.blogs.amd.com/>`_,
where you can search for DGL examples and best practices to optimize your training workflows on AMD GPUs.
Coverage includes:
- Single-GPU training/inference
- Multi-GPU training
.. _dgl-docker-compat:
Docker image compatibility
================================================================================
.. |docker-icon| raw:: html
<i class="fab fa-docker"></i>
AMD validates and publishes `DGL images <https://hub.docker.com/r/rocm/dgl>`_
with ROCm and Pytorch backends on Docker Hub. The following Docker image tags and associated
inventories were tested on `ROCm 6.4.0 <https://repo.radeon.com/rocm/apt/6.4/>`_.
Click the |docker-icon| to view the image on Docker Hub.
.. list-table:: DGL Docker image components
:header-rows: 1
:class: docker-image-compatibility
* - Docker
- DGL
- PyTorch
- Ubuntu
- Python
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/dgl/dgl-2.4_rocm6.4_ubuntu24.04_py3.12_pytorch_release_2.6.0/images/sha256-8ce2c3bcfaa137ab94a75f9e2ea711894748980f57417739138402a542dd5564"><i class="fab fa-docker fa-lg"></i></a>
- `2.4.0 <https://github.com/dmlc/dgl/releases/tag/v2.4.0>`_
- `2.6.0 <https://github.com/ROCm/pytorch/tree/release/2.6>`_
- 24.04
- `3.12.9 <https://www.python.org/downloads/release/python-3129/>`_
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/dgl/dgl-2.4_rocm6.4_ubuntu24.04_py3.12_pytorch_release_2.4.1/images/sha256-cf1683283b8eeda867b690229c8091c5bbf1edb9f52e8fb3da437c49a612ebe4"><i class="fab fa-docker fa-lg"></i></a>
- `2.4.0 <https://github.com/dmlc/dgl/releases/tag/v2.4.0>`_
- `2.4.1 <https://github.com/ROCm/pytorch/tree/release/2.4>`_
- 24.04
- `3.12.9 <https://www.python.org/downloads/release/python-3129/>`_
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/dgl/dgl-2.4_rocm6.4_ubuntu22.04_py3.10_pytorch_release_2.4.1/images/sha256-4834f178c3614e2d09e89e32041db8984c456d45dfd20286e377ca8635686554"><i class="fab fa-docker fa-lg"></i></a>
- `2.4.0 <https://github.com/dmlc/dgl/releases/tag/v2.4.0>`_
- `2.4.1 <https://github.com/ROCm/pytorch/tree/release/2.4>`_
- 22.04
- `3.10.16 <https://www.python.org/downloads/release/python-31016/>`_
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/dgl/dgl-2.4_rocm6.4_ubuntu22.04_py3.10_pytorch_release_2.3.0/images/sha256-88740a2c8ab4084b42b10c3c6ba984cab33dd3a044f479c6d7618e2b2cb05e69"><i class="fab fa-docker fa-lg"></i></a>
- `2.4.0 <https://github.com/dmlc/dgl/releases/tag/v2.4.0>`_
- `2.3.0 <https://github.com/ROCm/pytorch/tree/release/2.3>`_
- 22.04
- `3.10.16 <https://www.python.org/downloads/release/python-31016/>`_
Key ROCm libraries for DGL
================================================================================
DGL on ROCm depends on specific libraries that affect its features and performance.
Using the DGL Docker container or building it with the provided docker file or a ROCm base image is recommended.
If you prefer to build it yourself, ensure the following dependencies are installed:
.. list-table::
:header-rows: 1
* - ROCm library
- Version
- Purpose
* - `Composable Kernel <https://github.com/ROCm/composable_kernel>`_
- :version-ref:`"Composable Kernel" rocm_version`
- Enables faster execution of core operations like matrix multiplication
(GEMM), convolutions and transformations.
* - `hipBLAS <https://github.com/ROCm/hipBLAS>`_
- :version-ref:`hipBLAS rocm_version`
- Provides GPU-accelerated Basic Linear Algebra Subprograms (BLAS) for
matrix and vector operations.
* - `hipBLASLt <https://github.com/ROCm/hipBLASLt>`_
- :version-ref:`hipBLASLt rocm_version`
- hipBLASLt is an extension of the hipBLAS library, providing additional
features like epilogues fused into the matrix multiplication kernel or
use of integer tensor cores.
* - `hipCUB <https://github.com/ROCm/hipCUB>`_
- :version-ref:`hipCUB rocm_version`
- Provides a C++ template library for parallel algorithms for reduction,
scan, sort and select.
* - `hipFFT <https://github.com/ROCm/hipFFT>`_
- :version-ref:`hipFFT rocm_version`
- Provides GPU-accelerated Fast Fourier Transform (FFT) operations.
* - `hipRAND <https://github.com/ROCm/hipRAND>`_
- :version-ref:`hipRAND rocm_version`
- Provides fast random number generation for GPUs.
* - `hipSOLVER <https://github.com/ROCm/hipSOLVER>`_
- :version-ref:`hipSOLVER rocm_version`
- Provides GPU-accelerated solvers for linear systems, eigenvalues, and
singular value decompositions (SVD).
* - `hipSPARSE <https://github.com/ROCm/hipSPARSE>`_
- :version-ref:`hipSPARSE rocm_version`
- Accelerates operations on sparse matrices, such as sparse matrix-vector
or matrix-matrix products.
* - `hipSPARSELt <https://github.com/ROCm/hipSPARSELt>`_
- :version-ref:`hipSPARSELt rocm_version`
- Accelerates operations on sparse matrices, such as sparse matrix-vector
or matrix-matrix products.
* - `hipTensor <https://github.com/ROCm/hipTensor>`_
- :version-ref:`hipTensor rocm_version`
- Optimizes for high-performance tensor operations, such as contractions.
* - `MIOpen <https://github.com/ROCm/MIOpen>`_
- :version-ref:`MIOpen rocm_version`
- Optimizes deep learning primitives such as convolutions, pooling,
normalization, and activation functions.
* - `MIGraphX <https://github.com/ROCm/AMDMIGraphX>`_
- :version-ref:`MIGraphX rocm_version`
- Adds graph-level optimizations, ONNX models and mixed precision support
and enable Ahead-of-Time (AOT) Compilation.
* - `MIVisionX <https://github.com/ROCm/MIVisionX>`_
- :version-ref:`MIVisionX rocm_version`
- Optimizes acceleration for computer vision and AI workloads like
preprocessing, augmentation, and inferencing.
* - `rocAL <https://github.com/ROCm/rocAL>`_
- :version-ref:`rocAL rocm_version`
- Accelerates the data pipeline by offloading intensive preprocessing and
augmentation tasks. rocAL is part of MIVisionX.
* - `RCCL <https://github.com/ROCm/rccl>`_
- :version-ref:`RCCL rocm_version`
- Optimizes for multi-GPU communication for operations like AllReduce and
Broadcast.
* - `rocDecode <https://github.com/ROCm/rocDecode>`_
- :version-ref:`rocDecode rocm_version`
- Provides hardware-accelerated data decoding capabilities, particularly
for image, video, and other dataset formats.
* - `rocJPEG <https://github.com/ROCm/rocJPEG>`_
- :version-ref:`rocJPEG rocm_version`
- Provides hardware-accelerated JPEG image decoding and encoding.
* - `RPP <https://github.com/ROCm/RPP>`_
- :version-ref:`RPP rocm_version`
- Speeds up data augmentation, transformation, and other preprocessing steps.
* - `rocThrust <https://github.com/ROCm/rocThrust>`_
- :version-ref:`rocThrust rocm_version`
- Provides a C++ template library for parallel algorithms like sorting,
reduction, and scanning.
* - `rocWMMA <https://github.com/ROCm/rocWMMA>`_
- :version-ref:`rocWMMA rocm_version`
- Accelerates warp-level matrix-multiply and matrix-accumulate to speed up matrix
multiplication (GEMM) and accumulation operations with mixed precision
support.
Supported features
================================================================================
Many functions and methods available in DGL Upstream are also supported in DGL ROCm.
Instead of listing them all, support is grouped into the following categories to provide a general overview.
* DGL Base
* DGL Backend
* DGL Data
* DGL Dataloading
* DGL DGLGraph
* DGL Function
* DGL Ops
* DGL Sampling
* DGL Transforms
* DGL Utils
* DGL Distributed
* DGL Geometry
* DGL Mpops
* DGL NN
* DGL Optim
* DGL Sparse
Unsupported features
================================================================================
* Graphbolt
* Partial TF32 Support (MI250x only)
* Kineto/ ROCTracer integration
Unsupported functions
================================================================================
* ``more_nnz``
* ``format``
* ``multiprocess_sparse_adam_state_dict``
* ``record_stream_ndarray``
* ``half_spmm``
* ``segment_mm``
* ``gather_mm_idx_b``
* ``pgexplainer``
* ``sample_labors_prob``
* ``sample_labors_noprob``
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