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.. meta::
:description: Deep Graph Library (DGL) compatibility
:keywords: GPU, CPU, deep graph library, DGL, deep learning, framework 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
that if a deep graph model is a component in an end-to-end application, the rest of
the logic is implemented using PyTorch.
DGL provides a high-performance graph object that can reside on either CPUs or GPUs.
It bundles structural data features for better control and provides a variety of functions
for computing with graph objects, including efficient and customizable message passing
primitives for Graph Neural Networks.
Support overview
================================================================================
- The ROCm-supported version of DGL is maintained in the official `https://github.com/ROCm/dgl
<https://github.com/ROCm/dgl>`__ repository, which differs from the
`https://github.com/dmlc/dgl <https://github.com/dmlc/dgl>`__ upstream repository.
- To get started and install DGL on ROCm, use the prebuilt :ref:`Docker images <dgl-docker-compat>`,
which include ROCm, DGL, and all required dependencies.
- See the :doc:`ROCm DGL installation guide <rocm-install-on-linux:install/3rd-party/dgl-install>`
for installation and setup instructions.
- You can also consult the upstream `Installation guide <https://www.dgl.ai/pages/start.html>`__
for additional context.
Version support
--------------------------------------------------------------------------------
DGL is supported on `ROCm 6.4.0 <https://repo.radeon.com/rocm/apt/6.4/>`__.
Supported devices
--------------------------------------------------------------------------------
- **Officially Supported**: AMD Instinct™ MI300X (through `hipBLASlt <https://rocm.docs.amd.com/projects/hipBLASLt/en/latest/index.html>`__)
- **Partially Supported**: 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 models, a variety of use cases are supported:
- 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/tags>`__
with ROCm backends on Docker Hub. The following Docker image tags and associated
inventories represent the latest available DGL version from the official Docker Hub.
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 image
- ROCm
- 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>
- `6.4.0 <https://repo.radeon.com/rocm/apt/6.4/>`__.
- `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>
- `6.4.0 <https://repo.radeon.com/rocm/apt/6.4/>`__.
- `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>
- `6.4.0 <https://repo.radeon.com/rocm/apt/6.4/>`__.
- `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>
- `6.4.0 <https://repo.radeon.com/rocm/apt/6.4/>`__.
- `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
- ROCm 6.4.0 Version
- Purpose
* - `Composable Kernel <https://github.com/ROCm/composable_kernel>`_
- 1.1.0
- Enables faster execution of core operations like matrix multiplication
(GEMM), convolutions and transformations.
* - `hipBLAS <https://github.com/ROCm/hipBLAS>`_
- 2.4.0
- Provides GPU-accelerated Basic Linear Algebra Subprograms (BLAS) for
matrix and vector operations.
* - `hipBLASLt <https://github.com/ROCm/hipBLASLt>`_
- 0.12.0
- 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>`_
- 3.4.0
- Provides a C++ template library for parallel algorithms for reduction,
scan, sort and select.
* - `hipFFT <https://github.com/ROCm/hipFFT>`_
- 1.0.18
- Provides GPU-accelerated Fast Fourier Transform (FFT) operations.
* - `hipRAND <https://github.com/ROCm/hipRAND>`_
- 2.12.0
- Provides fast random number generation for GPUs.
* - `hipSOLVER <https://github.com/ROCm/hipSOLVER>`_
- 2.4.0
- Provides GPU-accelerated solvers for linear systems, eigenvalues, and
singular value decompositions (SVD).
* - `hipSPARSE <https://github.com/ROCm/hipSPARSE>`_
- 3.2.0
- Accelerates operations on sparse matrices, such as sparse matrix-vector
or matrix-matrix products.
* - `hipSPARSELt <https://github.com/ROCm/hipSPARSELt>`_
- 0.2.3
- Accelerates operations on sparse matrices, such as sparse matrix-vector
or matrix-matrix products.
* - `hipTensor <https://github.com/ROCm/hipTensor>`_
- 1.5.0
- Optimizes for high-performance tensor operations, such as contractions.
* - `MIOpen <https://github.com/ROCm/MIOpen>`_
- 3.4.0
- Optimizes deep learning primitives such as convolutions, pooling,
normalization, and activation functions.
* - `MIGraphX <https://github.com/ROCm/AMDMIGraphX>`_
- 2.12.0
- Adds graph-level optimizations, ONNX models and mixed precision support
and enable Ahead-of-Time (AOT) Compilation.
* - `MIVisionX <https://github.com/ROCm/MIVisionX>`_
- 3.2.0
- 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>`_
- 2.2.0
- Optimizes for multi-GPU communication for operations like AllReduce and
Broadcast.
* - `rocDecode <https://github.com/ROCm/rocDecode>`_
- 0.10.0
- Provides hardware-accelerated data decoding capabilities, particularly
for image, video, and other dataset formats.
* - `rocJPEG <https://github.com/ROCm/rocJPEG>`_
- 0.8.0
- Provides hardware-accelerated JPEG image decoding and encoding.
* - `RPP <https://github.com/ROCm/RPP>`_
- 1.9.10
- Speeds up data augmentation, transformation, and other preprocessing steps.
* - `rocThrust <https://github.com/ROCm/rocThrust>`_
- 3.3.0
- Provides a C++ template library for parallel algorithms like sorting,
reduction, and scanning.
* - `rocWMMA <https://github.com/ROCm/rocWMMA>`_
- 1.7.0
- 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 upstream are also supported in DGL on 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 Graph
* 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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