mirror of
https://github.com/ROCm/ROCm.git
synced 2026-01-09 14:48:06 -05:00
Add TensorFlow compatibility docs (#4247)
* Add Tensorflow * WIP * WIP * minor fmt * PR feedbacks * fix missed inconsistent formatting * WIP WIP WIP WIP * minor formatting update tensorflow-rocm docker images to rocm6.3.1 fix urls * WIP * fix typo and update wordlist * fix tables not rendering * fix table headings * add period * update tf dockers * fix link * fix link * wording * update historical compat * fix tensile link --------- Co-authored-by: Mátyás Aradi <matyas@streamhpc.com> Co-authored-by: Istvan Kiss <neon60@gmail.com>
This commit is contained in:
Peter Park
@@ -151,8 +151,9 @@ HCA
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HGX
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HIPCC
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HIPExtension
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HIPification
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HIPIFY
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HIPification
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HIPify
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HPC
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HPCG
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HPE
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@@ -250,6 +251,7 @@ MyST
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NBIO
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NBIOs
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NCCL
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NCF
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NIC
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NICs
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NLI
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@@ -711,6 +713,7 @@ protobuf
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pseudorandom
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py
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recommender
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recommenders
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quantile
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quantizer
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quasirandom
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@@ -22,9 +22,9 @@ ROCm Version,6.3.1,6.3.0,6.2.4,6.2.2,6.2.1,6.2.0, 6.1.2, 6.1.1, 6.1.0, 6.0.2, 6.
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,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908
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,,,,,,,,,,,
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FRAMEWORK SUPPORT,.. _framework-support-compatibility-matrix-past-60:,,,,,,,,,,
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:doc:`PyTorch <../compatibility/pytorch-compatibility>`,"2.4, 2.3, 2.2, 2.1, 2.0, 1.13","2.4, 2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13"
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:doc:`TensorFlow <rocm-install-on-linux:install/3rd-party/tensorflow-install>`,"2.17.0, 2.16.2, 2.15.1","2.17.0, 2.16.2, 2.15.1","2.16.1, 2.15.1, 2.14.1","2.16.1, 2.15.1, 2.14.1","2.16.1, 2.15.1, 2.14.1","2.16.1, 2.15.1, 2.14.1","2.15.0, 2.14.0, 2.13.1","2.15.0, 2.14.0, 2.13.1","2.15.0, 2.14.0, 2.13.1","2.14.0, 2.13.1, 2.12.1","2.14.0, 2.13.1, 2.12.1"
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:doc:`JAX <rocm-install-on-linux:install/3rd-party/jax-install>`,0.4.31,0.4.31,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26
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:doc:`PyTorch <../compatibility/ml-compatibility/pytorch-compatibility>`,"2.4, 2.3, 2.2, 2.1, 2.0, 1.13","2.4, 2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13"
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:doc:`TensorFlow <../compatibility/ml-compatibility/tensorflow-compatibility>`,"2.17.0, 2.16.2, 2.15.1","2.17.0, 2.16.2, 2.15.1","2.16.1, 2.15.1, 2.14.1","2.16.1, 2.15.1, 2.14.1","2.16.1, 2.15.1, 2.14.1","2.16.1, 2.15.1, 2.14.1","2.15.0, 2.14.0, 2.13.1","2.15.0, 2.14.0, 2.13.1","2.15.0, 2.14.0, 2.13.1","2.14.0, 2.13.1, 2.12.1","2.14.0, 2.13.1, 2.12.1"
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:doc:`JAX <../compatibility/ml-compatibility/jax-compatibility>`,0.4.31,0.4.31,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26
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`ONNX Runtime <https://onnxruntime.ai/docs/build/eps.html#amd-migraphx>`_,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.14.1,1.14.1
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,,,,,,,,,,,
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THIRD PARTY COMMS,.. _thirdpartycomms-support-compatibility-matrix-past-60:,,,,,,,,,,
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@@ -71,7 +71,7 @@ ROCm Version,6.3.1,6.3.0,6.2.4,6.2.2,6.2.1,6.2.0, 6.1.2, 6.1.1, 6.1.0, 6.0.2, 6.
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:doc:`rocSOLVER <rocsolver:index>`,3.27.0,3.27.0,3.26.2,3.26.0,3.26.0,3.26.0,3.25.0,3.25.0,3.25.0,3.24.0,3.24.0
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:doc:`rocSPARSE <rocsparse:index>`,3.3.0,3.3.0,3.2.1,3.2.0,3.2.0,3.2.0,3.1.2,3.1.2,3.1.2,3.0.2,3.0.2
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:doc:`rocWMMA <rocwmma:index>`,1.6.0,1.6.0,1.5.0,1.5.0,1.5.0,1.5.0,1.4.0,1.4.0,1.4.0,1.3.0,1.3.0
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:doc:`Tensile <tensile:index>`,4.42.0,4.42.0,4.41.0,4.41.0,4.41.0,4.41.0,4.40.0,4.40.0,4.40.0,4.39.0,4.39.0
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:doc:`Tensile <tensile:src/index>`,4.42.0,4.42.0,4.41.0,4.41.0,4.41.0,4.41.0,4.40.0,4.40.0,4.40.0,4.39.0,4.39.0
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,,,,,,,,,,,
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PRIMITIVES,.. _primitivelibs-support-compatibility-matrix-past-60:,,,,,,,,,,
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:doc:`hipCUB <hipcub:index>`,3.3.0,3.3.0,3.2.1,3.2.0,3.2.0,3.2.0,3.1.0,3.1.0,3.1.0,3.0.0,3.0.0
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@@ -48,7 +48,7 @@ compatibility and system requirements.
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,,,
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FRAMEWORK SUPPORT,.. _framework-support-compatibility-matrix:,,
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:doc:`PyTorch <../compatibility/ml-compatibility/pytorch-compatibility>`,"2.4, 2.3, 2.2, 1.13","2.4, 2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13"
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:doc:`TensorFlow <rocm-install-on-linux:install/3rd-party/tensorflow-install>`,"2.17.0, 2.16.2, 2.15.1","2.17.0, 2.16.2, 2.15.1","2.16.1, 2.15.1, 2.14.1"
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:doc:`TensorFlow <../compatibility/ml-compatibility/tensorflow-compatibility>`,"2.17.0, 2.16.2, 2.15.1","2.17.0, 2.16.2, 2.15.1","2.16.1, 2.15.1, 2.14.1"
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:doc:`JAX <../compatibility/ml-compatibility/jax-compatibility>`,0.4.31,0.4.31,0.4.26
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`ONNX Runtime <https://onnxruntime.ai/docs/build/eps.html#amd-migraphx>`_,1.17.3,1.17.3,1.17.3
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,,,
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@@ -94,7 +94,7 @@ compatibility and system requirements.
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:doc:`rocSOLVER <rocsolver:index>`,3.27.0,3.27.0,3.26.0
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:doc:`rocSPARSE <rocsparse:index>`,3.3.0,3.3.0,3.2.0
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:doc:`rocWMMA <rocwmma:index>`,1.6.0,1.6.0,1.5.0
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:doc:`Tensile <tensile:index>`,4.42.0,4.42.0,4.41.0
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:doc:`Tensile <tensile:src/index>`,4.42.0,4.42.0,4.41.0
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,,,
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PRIMITIVES,.. _primitivelibs-support-compatibility-matrix:,,
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:doc:`hipCUB <hipcub:index>`,3.3.0,3.3.0,3.2.0
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@@ -54,8 +54,8 @@ Docker image compatibility
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AMD validates and publishes ready-made `JAX <https://hub.docker.com/r/rocm/jax/>`_
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images with ROCm backends on Docker Hub. The following Docker image tags and
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associated inventories are validated for
|
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`ROCm 6.3.1 <https://repo.radeon.com/rocm/apt/6.3.1/>`_. Click |docker-icon|
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to see the image on Docker Hub.
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`ROCm 6.3.1 <https://repo.radeon.com/rocm/apt/6.3.1/>`_. Click the |docker-icon|
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icon to view the image on Docker Hub.
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.. list-table:: JAX Docker image components
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:header-rows: 1
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@@ -605,7 +605,7 @@ ROCm.
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.. list-table::
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:header-rows: 1
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* - Data type
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* - Feature
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- Description
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- Since JAX
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* - Mixed Precision with TF32
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@@ -660,5 +660,4 @@ Use cases and recommendations
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performance for deep learning and other high-performance computing tasks on
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the MI300X GPU.
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For more use cases and recommendations, see `ROCm JAX blog posts <https://rocm.blogs.amd.com/blog/tag/jax.html>`_
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For more use cases and recommendations, see `ROCm JAX blog posts <https://rocm.blogs.amd.com/blog/tag/jax.html>`_.
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@@ -40,7 +40,7 @@ The upstream PyTorch includes an automatic HIPification solution that automatica
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source code from the CUDA backend. This approach allows PyTorch to support ROCm without requiring
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manual code modifications.
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ROCm's development is aligned with the stable release of PyTorch while upstream PyTorch testing uses
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Development of ROCm is aligned with the stable release of PyTorch while upstream PyTorch testing uses
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the stable release of ROCm to maintain consistency.
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.. _pytorch-docker-compat:
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@@ -55,7 +55,7 @@ Docker image compatibility
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AMD validates and publishes ready-made `PyTorch <https://hub.docker.com/r/rocm/pytorch>`_
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images with ROCm backends on Docker Hub. The following Docker image tags and
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associated inventories are validated for `ROCm 6.3.0 <https://repo.radeon.com/rocm/apt/6.3/>`_.
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Click |docker-icon| to see the image on Docker Hub.
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Click the |docker-icon| icon to view the image on Docker Hub.
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.. list-table:: PyTorch Docker image components
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:header-rows: 1
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@@ -469,7 +469,7 @@ leveraging ROCm and CUDA as the underlying frameworks.
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.. list-table::
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:header-rows: 1
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* - Data type
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* - Feature
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- Description
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- Since PyTorch
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- Since ROCm
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@@ -537,14 +537,14 @@ torch.backends.cuda
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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``torch.backends.cuda`` is a PyTorch module that provides configuration options
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and flags to control the behavior of CUDA or ROCm operations. It is part of the
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and flags to control the behavior of ROCm or CUDA operations. It is part of the
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PyTorch backend configuration system, which allows users to fine-tune how
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PyTorch interacts with the CUDA or ROCm environment.
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PyTorch interacts with the ROCm or CUDA environment.
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|
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.. list-table::
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:header-rows: 1
|
||||
|
||||
* - Data type
|
||||
* - Feature
|
||||
- Description
|
||||
- Since PyTorch
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||||
- Since ROCm
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||||
@@ -588,12 +588,12 @@ PyTorch interacts with the CUDA or ROCm environment.
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torch.backends.cudnn
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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Supported ``torch`` options:
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Supported ``torch`` options include:
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|
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.. list-table::
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:header-rows: 1
|
||||
|
||||
* - Data type
|
||||
* - Option
|
||||
- Description
|
||||
- Since PyTorch
|
||||
- Since ROCm
|
||||
@@ -618,7 +618,7 @@ training and inference.
|
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.. list-table::
|
||||
:header-rows: 1
|
||||
|
||||
* - Data type
|
||||
* - Feature
|
||||
- Description
|
||||
- Since PyTorch
|
||||
- Since ROCm
|
||||
@@ -658,7 +658,7 @@ of computational resources and scalability for large-scale tasks.
|
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.. list-table::
|
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:header-rows: 1
|
||||
|
||||
* - Features
|
||||
* - Feature
|
||||
- Description
|
||||
- Since PyTorch
|
||||
- Since ROCm
|
||||
@@ -683,7 +683,7 @@ torch.compiler
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
|
||||
* - Features
|
||||
* - Feature
|
||||
- Description
|
||||
- Since PyTorch
|
||||
- Since ROCm
|
||||
@@ -717,7 +717,7 @@ The following ``torchaudio`` features are GPU-accelerated.
|
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.. list-table::
|
||||
:header-rows: 1
|
||||
|
||||
* - Features
|
||||
* - Feature
|
||||
- Description
|
||||
- Since torchaudio version
|
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- Since ROCm
|
||||
@@ -750,7 +750,7 @@ The following ``torchvision`` features are GPU-accelerated.
|
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.. list-table::
|
||||
:header-rows: 1
|
||||
|
||||
* - Features
|
||||
* - Feature
|
||||
- Description
|
||||
- Since torchvision version
|
||||
- Since ROCm
|
||||
@@ -834,7 +834,7 @@ The following are GPU-accelerated PyTorch features not currently supported by RO
|
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:widths: 30, 60, 10
|
||||
:header-rows: 1
|
||||
|
||||
* - Data type
|
||||
* - Feature
|
||||
- Description
|
||||
- Since PyTorch
|
||||
* - APEX batch norm
|
||||
@@ -919,4 +919,4 @@ Use cases and recommendations
|
||||
describes how PyTorch integrates with ROCm for AI workloads It outlines the use of PyTorch on the ROCm platform and
|
||||
focuses on how to efficiently leverage AMD GPU hardware for training and inference tasks in AI applications.
|
||||
|
||||
For more use cases and recommendations, see `ROCm PyTorch blog posts <https://rocm.blogs.amd.com/blog/tag/pytorch.html>`_
|
||||
For more use cases and recommendations, see `ROCm PyTorch blog posts <https://rocm.blogs.amd.com/blog/tag/pytorch.html>`_.
|
||||
|
||||
489
docs/compatibility/ml-compatibility/tensorflow-compatibility.rst
Normal file
489
docs/compatibility/ml-compatibility/tensorflow-compatibility.rst
Normal file
@@ -0,0 +1,489 @@
|
||||
.. meta::
|
||||
:description: TensorFlow compatibility
|
||||
:keywords: GPU, TensorFlow compatibility
|
||||
|
||||
*******************************************************************************
|
||||
TensorFlow compatibility
|
||||
*******************************************************************************
|
||||
|
||||
`TensorFlow <https://www.tensorflow.org/>`_ is an open-source library for
|
||||
solving machine learning, deep learning, and AI problems. It can solve many
|
||||
problems across different sectors and industries but primarily focuses on
|
||||
neural network training and inference. It is one of the most popular and
|
||||
in-demand frameworks and is very active in open-source contribution and
|
||||
development.
|
||||
|
||||
The `official TensorFlow repository <http://github.com/tensorflow/tensorflow>`_
|
||||
includes full ROCm support. AMD maintains a TensorFlow `ROCm repository
|
||||
<http://github.com/rocm/tensorflow-upstream>`_ in order to quickly add bug
|
||||
fixes, updates, and support for the latest ROCM versions.
|
||||
|
||||
- ROCm TensorFlow release:
|
||||
|
||||
- Offers :ref:`Docker images <tensorflow-docker-compat>` with
|
||||
ROCm and TensorFlow pre-installed.
|
||||
|
||||
- ROCm TensorFlow repository: `<https://github.com/ROCm/tensorflow-upstream>`_
|
||||
|
||||
- See the :doc:`ROCm TensorFlow installation guide <rocm-install-on-linux:install/3rd-party/tensorflow-install>`
|
||||
to get started.
|
||||
|
||||
- Official TensorFlow release:
|
||||
|
||||
- Official TensorFlow repository: `<https://github.com/tensorflow/tensorflow>`_
|
||||
|
||||
- See the `TensorFlow API versions <https://www.tensorflow.org/versions>`_ list.
|
||||
|
||||
.. note::
|
||||
|
||||
The official TensorFlow documentation does not cover ROCm support. Use the
|
||||
ROCm documentation for installation instructions for Tensorflow on ROCm.
|
||||
See :doc:`rocm-install-on-linux:install/3rd-party/tensorflow-install`.
|
||||
|
||||
.. _tensorflow-docker-compat:
|
||||
|
||||
Docker image compatibility
|
||||
===============================================================================
|
||||
|
||||
.. |docker-icon| raw:: html
|
||||
|
||||
<i class="fab fa-docker"></i>
|
||||
|
||||
AMD validates and publishes ready-made `TensorFlow
|
||||
<https://hub.docker.com/r/rocm/tensorflow>`_ images with ROCm backends on
|
||||
Docker Hub. The following Docker image tags and associated inventories are
|
||||
validated for `ROCm 6.3.1 <https://repo.radeon.com/rocm/apt/6.3.1/>`_. Click
|
||||
the |docker-icon| icon to view the image on Docker Hub.
|
||||
|
||||
.. list-table:: TensorFlow Docker image components
|
||||
:header-rows: 1
|
||||
|
||||
* - Docker image
|
||||
- TensorFlow
|
||||
- Dev
|
||||
- Python
|
||||
- TensorBoard
|
||||
|
||||
* - .. raw:: html
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/tensorflow/rocm6.3.1-py3.12-tf2.17.0-dev/images/sha256-804121ee4985718277ba7dcec53c57bdade130a1ef42f544b6c48090ad379c17"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
|
||||
|
||||
- `tensorflow-rocm 2.17.0 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.3/tensorflow_rocm-2.17.0-cp312-cp312-manylinux_2_28_x86_64.whl>`_
|
||||
- dev
|
||||
- `Python 3.12 <https://www.python.org/downloads/release/python-3124/>`_
|
||||
- `TensorBoard 2.17.1 <https://github.com/tensorflow/tensorboard/tree/2.17.1>`_
|
||||
|
||||
* - .. raw:: html
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/tensorflow/rocm6.3.1-py3.10-tf2.17.0-dev/images/sha256-776837ffa945913f6c466bfe477810a11453d21d5b6afb200be1c36e48fbc08e"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
|
||||
|
||||
- `tensorflow-rocm 2.17.0 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.3/tensorflow_rocm-2.17.0-cp310-cp310-manylinux_2_28_x86_64.whl>`_
|
||||
- dev
|
||||
- `Python 3.10 <https://www.python.org/downloads/release/python-31012/>`_
|
||||
- `TensorBoard 2.17.0 <https://github.com/tensorflow/tensorboard/tree/2.17.0>`_
|
||||
|
||||
* - .. raw:: html
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/tensorflow/rocm6.3.1-py3.12-tf2.16.2-dev/images/sha256-c793e1483e30809c3c28fc5d7805bedc033c73da224f839fff370717cb100944"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
|
||||
|
||||
- `tensorflow-rocm 2.16.2 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.3/tensorflow_rocm-2.16.2-cp312-cp312-manylinux_2_28_x86_64.whl>`_
|
||||
- dev
|
||||
- `Python 3.12 <https://www.python.org/downloads/release/python-3124/>`_
|
||||
- `TensorBoard 2.16.2 <https://github.com/tensorflow/tensorboard/tree/2.16.2>`_
|
||||
|
||||
* - .. raw:: html
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/tensorflow/rocm6.3.1-py3.10-tf2.16.0-dev/images/sha256-263e78414ae85d7bcd52a025a94131d0a279872a45ed632b9165336dfdcd4443"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
|
||||
|
||||
- `tensorflow-rocm 2.16.2 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.3/tensorflow_rocm-2.16.2-cp310-cp310-manylinux_2_28_x86_64.whl>`_
|
||||
- dev
|
||||
- `Python 3.10 <https://www.python.org/downloads/release/python-31012/>`_
|
||||
- `TensorBoard 2.16.2 <https://github.com/tensorflow/tensorboard/tree/2.16.2>`_
|
||||
|
||||
* - .. raw:: html
|
||||
|
||||
<a href="https://hub.docker.com/layers/rocm/tensorflow/rocm6.3.1-py3.10-tf2.15.0-dev/images/sha256-479046a8477ca701a9494a813ab17e8ab4f6baa54641e65dc8d07629f1e6a880"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
|
||||
|
||||
- `tensorflow-rocm 2.15.1 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.3/tensorflow_rocm-2.15.1-cp310-cp310-manylinux_2_28_x86_64.whl>`_
|
||||
- dev
|
||||
- `Python 3.10 <https://www.python.org/downloads/release/python-31012/>`_
|
||||
- `TensorBoard 2.15.2 <https://github.com/tensorflow/tensorboard/tree/2.15.2>`_
|
||||
|
||||
Critical ROCm libraries for TensorFlow
|
||||
===============================================================================
|
||||
|
||||
TensorFlow depends on multiple components and the supported features of those
|
||||
components can affect the TensorFlow ROCm supported feature set. The versions
|
||||
in the following table refer to the first TensorFlow version where the ROCm
|
||||
library was introduced as a dependency.
|
||||
|
||||
.. list-table::
|
||||
:widths: 25, 10, 35, 30
|
||||
:header-rows: 1
|
||||
|
||||
* - ROCm library
|
||||
- Version
|
||||
- Purpose
|
||||
- Used in
|
||||
* - `hipBLAS <https://github.com/ROCm/hipBLAS>`_
|
||||
- 2.3.0
|
||||
- Provides GPU-accelerated Basic Linear Algebra Subprograms (BLAS) for
|
||||
matrix and vector operations.
|
||||
- Accelerates operations like ``tf.matmul``, ``tf.linalg.matmul``, and
|
||||
other matrix multiplications commonly used in neural network layers.
|
||||
* - `hipBLASLt <https://github.com/ROCm/hipBLASLt>`_
|
||||
- 0.10.0
|
||||
- Extends hipBLAS with additional optimizations like fused kernels and
|
||||
integer tensor cores.
|
||||
- Optimizes matrix multiplications and linear algebra operations used in
|
||||
layers like dense, convolutional, and RNNs in TensorFlow.
|
||||
* - `hipCUB <https://github.com/ROCm/hipCUB>`_
|
||||
- 3.3.0
|
||||
- Provides a C++ template library for parallel algorithms for reduction,
|
||||
scan, sort and select.
|
||||
- Supports operations like ``tf.reduce_sum``, ``tf.cumsum``, ``tf.sort``
|
||||
and other tensor operations in TensorFlow, especially those involving
|
||||
scanning, sorting, and filtering.
|
||||
* - `hipFFT <https://github.com/ROCm/hipFFT>`_
|
||||
- 1.0.17
|
||||
- Accelerates Fast Fourier Transforms (FFT) for signal processing tasks.
|
||||
- Used for operations like signal processing, image filtering, and
|
||||
certain types of neural networks requiring FFT-based transformations.
|
||||
* - `hipSOLVER <https://github.com/ROCm/hipSOLVER>`_
|
||||
- 2.3.0
|
||||
- Provides GPU-accelerated direct linear solvers for dense and sparse
|
||||
systems.
|
||||
- Optimizes linear algebra functions such as solving systems of linear
|
||||
equations, often used in optimization and training tasks.
|
||||
* - `hipSPARSE <https://github.com/ROCm/hipSPARSE>`_
|
||||
- 3.1.2
|
||||
- Optimizes sparse matrix operations for efficient computations on sparse
|
||||
data.
|
||||
- Accelerates sparse matrix operations in models with sparse weight
|
||||
matrices or activations, commonly used in neural networks.
|
||||
* - `MIOpen <https://github.com/ROCm/MIOpen>`_
|
||||
- 3.3.0
|
||||
- Provides optimized deep learning primitives such as convolutions,
|
||||
pooling,
|
||||
normalization, and activation functions.
|
||||
- Speeds up convolutional neural networks (CNNs) and other layers. Used
|
||||
in TensorFlow for layers like ``tf.nn.conv2d``, ``tf.nn.relu``, and
|
||||
``tf.nn.lstm_cell``.
|
||||
* - `RCCL <https://github.com/ROCm/rccl>`_
|
||||
- 2.21.5
|
||||
- Optimizes for multi-GPU communication for operations like AllReduce and
|
||||
Broadcast.
|
||||
- Distributed data parallel training (``tf.distribute.MirroredStrategy``).
|
||||
Handles communication in multi-GPU setups.
|
||||
* - `rocThrust <https://github.com/ROCm/rocThrust>`_
|
||||
- 3.3.0
|
||||
- Provides a C++ template library for parallel algorithms like sorting,
|
||||
reduction, and scanning.
|
||||
- Reduction operations like ``tf.reduce_sum``, ``tf.cumsum`` for computing
|
||||
the cumulative sum of elements along a given axis or ``tf.unique`` to
|
||||
finds unique elements in a tensor can use rocThrust.
|
||||
|
||||
Supported and unsupported features
|
||||
===============================================================================
|
||||
|
||||
The following section maps supported data types and GPU-accelerated TensorFlow
|
||||
features to their minimum supported ROCm and TensorFlow versions.
|
||||
|
||||
Data types
|
||||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||||
|
||||
The data type of a tensor is specified using the ``dtype`` attribute or
|
||||
argument, and TensorFlow supports a wide range of data types for different use
|
||||
cases.
|
||||
|
||||
The basic, single data types of `tf.dtypes <https://www.tensorflow.org/api_docs/python/tf/dtypes>`_
|
||||
are as follows:
|
||||
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
|
||||
* - Data type
|
||||
- Description
|
||||
- Since TensorFlow
|
||||
- Since ROCm
|
||||
* - ``bfloat16``
|
||||
- 16-bit bfloat (brain floating point).
|
||||
- 1.0.0
|
||||
- 1.7
|
||||
* - ``bool``
|
||||
- Boolean.
|
||||
- 1.0.0
|
||||
- 1.7
|
||||
* - ``complex128``
|
||||
- 128-bit complex.
|
||||
- 1.0.0
|
||||
- 1.7
|
||||
* - ``complex64``
|
||||
- 64-bit complex.
|
||||
- 1.0.0
|
||||
- 1.7
|
||||
* - ``double``
|
||||
- 64-bit (double precision) floating-point.
|
||||
- 1.0.0
|
||||
- 1.7
|
||||
* - ``float16``
|
||||
- 16-bit (half precision) floating-point.
|
||||
- 1.0.0
|
||||
- 1.7
|
||||
* - ``float32``
|
||||
- 32-bit (single precision) floating-point.
|
||||
- 1.0.0
|
||||
- 1.7
|
||||
* - ``float64``
|
||||
- 64-bit (double precision) floating-point.
|
||||
- 1.0.0
|
||||
- 1.7
|
||||
* - ``half``
|
||||
- 16-bit (half precision) floating-point.
|
||||
- 2.0.0
|
||||
- 2.0
|
||||
* - ``int16``
|
||||
- Signed 16-bit integer.
|
||||
- 1.0.0
|
||||
- 1.7
|
||||
* - ``int32``
|
||||
- Signed 32-bit integer.
|
||||
- 1.0.0
|
||||
- 1.7
|
||||
* - ``int64``
|
||||
- Signed 64-bit integer.
|
||||
- 1.0.0
|
||||
- 1.7
|
||||
* - ``int8``
|
||||
- Signed 8-bit integer.
|
||||
- 1.0.0
|
||||
- 1.7
|
||||
* - ``qint16``
|
||||
- Signed quantized 16-bit integer.
|
||||
- 1.0.0
|
||||
- 1.7
|
||||
* - ``qint32``
|
||||
- Signed quantized 32-bit integer.
|
||||
- 1.0.0
|
||||
- 1.7
|
||||
* - ``qint8``
|
||||
- Signed quantized 8-bit integer.
|
||||
- 1.0.0
|
||||
- 1.7
|
||||
* - ``quint16``
|
||||
- Unsigned quantized 16-bit integer.
|
||||
- 1.0.0
|
||||
- 1.7
|
||||
* - ``quint8``
|
||||
- Unsigned quantized 8-bit integer.
|
||||
- 1.0.0
|
||||
- 1.7
|
||||
* - ``resource``
|
||||
- Handle to a mutable, dynamically allocated resource.
|
||||
- 1.0.0
|
||||
- 1.7
|
||||
* - ``string``
|
||||
- Variable-length string, represented as byte array.
|
||||
- 1.0.0
|
||||
- 1.7
|
||||
* - ``uint16``
|
||||
- Unsigned 16-bit (word) integer.
|
||||
- 1.0.0
|
||||
- 1.7
|
||||
* - ``uint32``
|
||||
- Unsigned 32-bit (dword) integer.
|
||||
- 1.5.0
|
||||
- 1.7
|
||||
* - ``uint64``
|
||||
- Unsigned 64-bit (qword) integer.
|
||||
- 1.5.0
|
||||
- 1.7
|
||||
* - ``uint8``
|
||||
- Unsigned 8-bit (byte) integer.
|
||||
- 1.0.0
|
||||
- 1.7
|
||||
* - ``variant``
|
||||
- Data of arbitrary type (known at runtime).
|
||||
- 1.4.0
|
||||
- 1.7
|
||||
|
||||
Features
|
||||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||||
|
||||
This table provides an overview of key features in TensorFlow and their
|
||||
availability in ROCm.
|
||||
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
|
||||
* - Module
|
||||
- Description
|
||||
- Since TensorFlow
|
||||
- Since ROCm
|
||||
* - ``tf.linalg`` (Linear Algebra)
|
||||
- Operations for matrix and tensor computations, such as
|
||||
``tf.linalg.matmul`` (matrix multiplication), ``tf.linalg.inv``
|
||||
(matrix inversion) and ``tf.linalg.cholesky`` (Cholesky decomposition).
|
||||
These leverage GPUs for high-performance linear algebra operations.
|
||||
- 1.4
|
||||
- 1.8.2
|
||||
* - ``tf.nn`` (Neural Network Operations)
|
||||
- GPU-accelerated building blocks for deep learning models, such as 2D
|
||||
convolutions with ``tf.nn.conv2d``, max pooling operations with
|
||||
``tf.nn.max_pool``, activation functions like ``tf.nn.relu`` or softmax
|
||||
for output layers with ``tf.nn.softmax``.
|
||||
- 1.0
|
||||
- 1.8.2
|
||||
* - ``tf.image`` (Image Processing)
|
||||
- GPU-accelerated functions for image preprocessing and augmentations,
|
||||
such as resize images with ``tf.image.resize``, flip images horizontally
|
||||
with ``tf.image.flip_left_right`` and adjust image brightness randomly
|
||||
with ``tf.image.random_brightness``.
|
||||
- 1.1
|
||||
- 1.8.2
|
||||
* - ``tf.keras`` (High-Level API)
|
||||
- GPU acceleration for Keras layers and models, including dense layers
|
||||
(``tf.keras.layers.Dense``), convolutional layers
|
||||
(``tf.keras.layers.Conv2D``) and recurrent layers
|
||||
(``tf.keras.layers.LSTM``).
|
||||
- 1.4
|
||||
- 1.8.2
|
||||
* - ``tf.math`` (Mathematical Operations)
|
||||
- GPU-accelerated mathematical operations, such as sum across dimensions
|
||||
with ``tf.math.reduce_sum``, elementwise exponentiation with
|
||||
``tf.math.exp`` and sigmoid activation (``tf.math.sigmoid``).
|
||||
- 1.5
|
||||
- 1.8.2
|
||||
* - ``tf.signal`` (Signal Processing)
|
||||
- Functions for spectral analysis and signal transformations.
|
||||
- 1.13
|
||||
- 2.1
|
||||
* - ``tf.data`` (Data Input Pipeline)
|
||||
- GPU-accelerated data preprocessing for efficient input pipelines,
|
||||
Prefetching with ``tf.data.experimental.AUTOTUNE``. GPU-enabled
|
||||
transformations like map and batch.
|
||||
- 1.4
|
||||
- 1.8.2
|
||||
* - ``tf.distribute`` (Distributed Training)
|
||||
- Enabling to scale computations across multiple devices on a single
|
||||
machine or across multiple machines.
|
||||
- 1.13
|
||||
- 2.1
|
||||
* - ``tf.random`` (Random Number Generation)
|
||||
- GPU-accelerated random number generation
|
||||
- 1.12
|
||||
- 1.9.2
|
||||
* - ``tf.TensorArray`` (Dynamic Array Operations)
|
||||
- Enables dynamic tensor manipulation on GPUs.
|
||||
- 1.0
|
||||
- 1.8.2
|
||||
* - ``tf.sparse`` (Sparse Tensor Operations)
|
||||
- GPU-accelerated sparse matrix manipulations.
|
||||
- 1.9
|
||||
- 1.9.0
|
||||
* - ``tf.experimental.numpy``
|
||||
- GPU-accelerated NumPy-like API for numerical computations.
|
||||
- 2.4
|
||||
- 4.1.1
|
||||
* - ``tf.RaggedTensor``
|
||||
- Handling of variable-length sequences and ragged tensors with GPU
|
||||
support.
|
||||
- 1.13
|
||||
- 2.1
|
||||
* - ``tf.function`` with XLA (Accelerated Linear Algebra)
|
||||
- Enable GPU-accelerated functions in optimization.
|
||||
- 1.14
|
||||
- 2.4
|
||||
* - ``tf.quantization``
|
||||
- Quantized operations for inference, accelerated on GPUs.
|
||||
- 1.12
|
||||
- 1.9.2
|
||||
|
||||
Distributed library features
|
||||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||||
|
||||
Enables developers to scale computations across multiple devices on a single machine or
|
||||
across multiple machines.
|
||||
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
|
||||
* - Feature
|
||||
- Description
|
||||
- Since TensorFlow
|
||||
- Since ROCm
|
||||
* - ``MultiWorkerMirroredStrategy``
|
||||
- Synchronous training across multiple workers using mirrored variables.
|
||||
- 2.0
|
||||
- 3.0
|
||||
* - ``MirroredStrategy``
|
||||
- Synchronous training across multiple GPUs on one machine.
|
||||
- 1.5
|
||||
- 2.5
|
||||
* - ``TPUStrategy``
|
||||
- Efficiently trains models on Google TPUs.
|
||||
- 1.9
|
||||
- ❌
|
||||
* - ``ParameterServerStrategy``
|
||||
- Asynchronous training using parameter servers for variable management.
|
||||
- 2.1
|
||||
- 4.0
|
||||
* - ``CentralStorageStrategy``
|
||||
- Keeps variables on a single device and performs computation on multiple
|
||||
devices.
|
||||
- 2.3
|
||||
- 4.1
|
||||
* - ``CollectiveAllReduceStrategy``
|
||||
- Synchronous training across multiple devices and hosts.
|
||||
- 1.14
|
||||
- 3.5
|
||||
* - Distribution Strategies API
|
||||
- High-level API to simplify distributed training configuration and
|
||||
execution.
|
||||
- 1.10
|
||||
- 3.0
|
||||
|
||||
Unsupported TensorFlow features
|
||||
===============================================================================
|
||||
|
||||
The following are GPU-accelerated TensorFlow features not currently supported by
|
||||
ROCm.
|
||||
|
||||
.. list-table::
|
||||
:header-rows: 1
|
||||
|
||||
* - Feature
|
||||
- Description
|
||||
- Since TensorFlow
|
||||
* - Mixed Precision with TF32
|
||||
- Mixed precision with TF32 is used for matrix multiplications,
|
||||
convolutions, and other linear algebra operations, particularly in
|
||||
deep learning workloads like CNNs and transformers.
|
||||
- 2.4
|
||||
* - ``tf.distribute.TPUStrategy``
|
||||
- Efficiently trains models on Google TPUs.
|
||||
- 1.9
|
||||
|
||||
Use cases and recommendations
|
||||
===============================================================================
|
||||
|
||||
* The `Training a Neural Collaborative Filtering (NCF) Recommender on an AMD
|
||||
GPU <https://rocm.blogs.amd.com/artificial-intelligence/ncf/README.html>`_
|
||||
blog post discusses training an NCF recommender system using TensorFlow. It
|
||||
explains how NCF improves traditional collaborative filtering methods by
|
||||
leveraging neural networks to model non-linear user-item interactions. The
|
||||
post outlines the implementation using the recommenders library, focusing on
|
||||
the use of implicit data (for example, user interactions like viewing or
|
||||
purchasing) and how it addresses challenges like the lack of negative values.
|
||||
|
||||
* The `Creating a PyTorch/TensorFlow code environment on AMD GPUs
|
||||
<https://rocm.blogs.amd.com/software-tools-optimization/pytorch-tensorflow-env/README.html>`_
|
||||
blog post provides instructions for creating a machine learning environment
|
||||
for PyTorch and TensorFlow on AMD GPUs using ROCm. It covers steps like
|
||||
installing the libraries, cloning code repositories, installing dependencies,
|
||||
and troubleshooting potential issues with CUDA-based code. Additionally, it
|
||||
explains how to HIPify code (port CUDA code to HIP) and manage Docker images
|
||||
for a better experience on AMD GPUs. This guide aims to help data scientists
|
||||
and ML practitioners adapt their code for AMD GPUs.
|
||||
|
||||
For more use cases and recommendations, see the `ROCm Tensorflow blog posts <https://rocm.blogs.amd.com/blog/tag/tensorflow.html>`_.
|
||||
@@ -40,6 +40,7 @@ all_article_info_author = ""
|
||||
article_pages = [
|
||||
{"file": "about/release-notes", "os": ["linux", "windows"], "date": "2024-12-20"},
|
||||
{"file": "compatibility/ml-compatibility/pytorch-compatibility", "os": ["linux"]},
|
||||
{"file": "compatibility/ml-compatibility/tensorflow-compatibility", "os": ["linux"]},
|
||||
{"file": "compatibility/ml-compatibility/jax-compatibility", "os": ["linux"]},
|
||||
{"file": "how-to/deep-learning-rocm", "os": ["linux"]},
|
||||
{"file": "how-to/rocm-for-ai/index", "os": ["linux"]},
|
||||
|
||||
@@ -15,10 +15,9 @@ The following guides provide information on compatibility and supported
|
||||
features for these ROCm-enabled deep learning frameworks.
|
||||
|
||||
* :doc:`PyTorch compatibility <../compatibility/ml-compatibility/pytorch-compatibility>`
|
||||
* :doc:`TensorFlow compatibility <../compatibility/ml-compatibility/tensorflow-compatibility>`
|
||||
* :doc:`JAX compatibility <../compatibility/ml-compatibility/jax-compatibility>`
|
||||
|
||||
.. * :doc:`TensorFlow compatibility <../compatibility/ml-compatibility/tensorflow-compatibility>`
|
||||
|
||||
This chart steps through typical installation workflows for installing deep learning frameworks for ROCm.
|
||||
|
||||
.. image:: ../data/how-to/framework_install_2024_07_04.png
|
||||
|
||||
Reference in New Issue
Block a user