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* Changelog and tables updates for 7.1.1 release notes * Changelog synced * Naming udpated * Added upcoming changes for composable kernel * Update RELEASE.md Co-authored-by: Pratik Basyal <prbasyal@amd.com> * Update RELEASE.md * Highlights udpated for DGL, ROCm-DS, and HIP documentation * Changelog synced" * Offline, runfile and ROCm Bandwidth test updated * CK/AITER highlight added * Changelog synced * AI model highlight updated * PLDM version added * Changelog updated * Leo's feedback incorporated * Compatibility and PLDM versions udpated * New docs update added * ROCm resolved issue added * Review feedback added * Link added * PLDM updated * PLDM table udpated * Changes --------- Co-authored-by: spolifroni-amd <Sandra.Polifroni@amd.com>
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.. meta::
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:description: TensorFlow compatibility
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:keywords: GPU, TensorFlow, deep learning, framework compatibility
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.. version-set:: rocm_version latest
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*******************************************************************************
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TensorFlow compatibility
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*******************************************************************************
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`TensorFlow <https://www.tensorflow.org/>`__ is an open-source library for
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solving machine learning, deep learning, and AI problems. It can solve many
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problems across different sectors and industries, but primarily focuses on
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neural network training and inference. It is one of the most popular deep
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learning frameworks and is very active in open-source development.
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Support overview
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================================================================================
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- The ROCm-supported version of TensorFlow is maintained in the official `https://github.com/ROCm/tensorflow-upstream
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<https://github.com/ROCm/tensorflow-upstream>`__ repository, which differs from the
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`https://github.com/tensorflow/tensorflow <https://github.com/tensorflow/tensorflow>`__ upstream repository.
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- To get started and install TensorFlow on ROCm, use the prebuilt :ref:`Docker images <tensorflow-docker-compat>`,
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which include ROCm, TensorFlow, and all required dependencies.
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- See the :doc:`ROCm TensorFlow installation guide <rocm-install-on-linux:install/3rd-party/tensorflow-install>`
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for installation and setup instructions.
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- You can also consult the `TensorFlow API versions <https://www.tensorflow.org/versions>`__ list
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for additional context.
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Version support
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--------------------------------------------------------------------------------
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The `official TensorFlow repository <http://github.com/tensorflow/tensorflow>`__
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includes full ROCm support. AMD maintains a TensorFlow `ROCm repository
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<http://github.com/rocm/tensorflow-upstream>`__ in order to quickly add bug
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fixes, updates, and support for the latest ROCm versions.
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.. _tensorflow-docker-compat:
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Docker image compatibility
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================================================================================
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AMD provides preconfigured Docker images with TensorFlow and the ROCm backend.
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These images are published on `Docker Hub <https://hub.docker.com/r/rocm/tensorflow>`__ and are the
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recommended way to get started with deep learning with TensorFlow on ROCm.
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To find the right image tag, see the :ref:`TensorFlow on ROCm installation
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documentation <rocm-install-on-linux:tensorflow-docker-support>` for a list of
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available ``rocm/tensorflow`` images.
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Critical ROCm libraries for TensorFlow
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===============================================================================
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TensorFlow depends on multiple components and the supported features of those
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components can affect the TensorFlow ROCm supported feature set. The versions
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in the following table refer to the first TensorFlow version where the ROCm
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library was introduced as a dependency. The versions described
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are available in ROCm :version:`rocm_version`.
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.. list-table::
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:widths: 25, 10, 35, 30
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:header-rows: 1
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* - ROCm library
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- Version
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- Purpose
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- Used in
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* - `hipBLAS <https://github.com/ROCm/hipBLAS>`__
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- :version-ref:`hipBLAS rocm_version`
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- Provides GPU-accelerated Basic Linear Algebra Subprograms (BLAS) for
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matrix and vector operations.
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- Accelerates operations like ``tf.matmul``, ``tf.linalg.matmul``, and
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other matrix multiplications commonly used in neural network layers.
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* - `hipBLASLt <https://github.com/ROCm/hipBLASLt>`__
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- :version-ref:`hipBLASLt rocm_version`
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- Extends hipBLAS with additional optimizations like fused kernels and
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integer tensor cores.
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- Optimizes matrix multiplications and linear algebra operations used in
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layers like dense, convolutional, and RNNs in TensorFlow.
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* - `hipCUB <https://github.com/ROCm/hipCUB>`__
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- :version-ref:`hipCUB rocm_version`
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- Provides a C++ template library for parallel algorithms for reduction,
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scan, sort and select.
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- Supports operations like ``tf.reduce_sum``, ``tf.cumsum``, ``tf.sort``
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and other tensor operations in TensorFlow, especially those involving
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scanning, sorting, and filtering.
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* - `hipFFT <https://github.com/ROCm/hipFFT>`__
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- :version-ref:`hipFFT rocm_version`
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- Accelerates Fast Fourier Transforms (FFT) for signal processing tasks.
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- Used for operations like signal processing, image filtering, and
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certain types of neural networks requiring FFT-based transformations.
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* - `hipSOLVER <https://github.com/ROCm/hipSOLVER>`__
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- :version-ref:`hipSOLVER rocm_version`
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- Provides GPU-accelerated direct linear solvers for dense and sparse
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systems.
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- Optimizes linear algebra functions such as solving systems of linear
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equations, often used in optimization and training tasks.
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* - `hipSPARSE <https://github.com/ROCm/hipSPARSE>`__
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- :version-ref:`hipSPARSE rocm_version`
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- Optimizes sparse matrix operations for efficient computations on sparse
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data.
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- Accelerates sparse matrix operations in models with sparse weight
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matrices or activations, commonly used in neural networks.
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* - `MIOpen <https://github.com/ROCm/MIOpen>`__
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- :version-ref:`MIOpen rocm_version`
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- Provides optimized deep learning primitives such as convolutions,
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pooling,
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normalization, and activation functions.
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- Speeds up convolutional neural networks (CNNs) and other layers. Used
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in TensorFlow for layers like ``tf.nn.conv2d``, ``tf.nn.relu``, and
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``tf.nn.lstm_cell``.
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* - `RCCL <https://github.com/ROCm/rccl>`__
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- :version-ref:`RCCL rocm_version`
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- Optimizes for multi-GPU communication for operations like AllReduce and
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Broadcast.
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- Distributed data parallel training (``tf.distribute.MirroredStrategy``).
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Handles communication in multi-GPU setups.
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* - `rocThrust <https://github.com/ROCm/rocThrust>`__
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- :version-ref:`rocThrust rocm_version`
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- Provides a C++ template library for parallel algorithms like sorting,
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reduction, and scanning.
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- Reduction operations like ``tf.reduce_sum``, ``tf.cumsum`` for computing
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the cumulative sum of elements along a given axis or ``tf.unique`` to
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finds unique elements in a tensor can use rocThrust.
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Supported and unsupported features
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===============================================================================
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The following section maps supported data types and GPU-accelerated TensorFlow
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features to their minimum supported ROCm and TensorFlow versions.
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Data types
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---------------
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The data type of a tensor is specified using the ``dtype`` attribute or
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argument, and TensorFlow supports a wide range of data types for different use
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cases.
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The basic, single data types of `tf.dtypes <https://www.tensorflow.org/api_docs/python/tf/dtypes>`__
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are as follows:
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.. list-table::
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:header-rows: 1
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* - Data type
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- Description
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- Since TensorFlow
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- Since ROCm
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* - ``bfloat16``
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- 16-bit bfloat (brain floating point).
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- 1.0.0
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- 1.7
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* - ``bool``
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- Boolean.
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- 1.0.0
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- 1.7
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* - ``complex128``
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- 128-bit complex.
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- 1.0.0
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- 1.7
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* - ``complex64``
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- 64-bit complex.
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- 1.0.0
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- 1.7
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* - ``double``
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- 64-bit (double precision) floating-point.
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- 1.0.0
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- 1.7
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* - ``float16``
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- 16-bit (half precision) floating-point.
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- 1.0.0
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- 1.7
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* - ``float32``
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- 32-bit (single precision) floating-point.
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- 1.0.0
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- 1.7
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* - ``float64``
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- 64-bit (double precision) floating-point.
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- 1.0.0
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- 1.7
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* - ``half``
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- 16-bit (half precision) floating-point.
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- 2.0.0
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- 2.0
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* - ``int16``
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- Signed 16-bit integer.
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- 1.0.0
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- 1.7
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* - ``int32``
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- Signed 32-bit integer.
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- 1.0.0
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- 1.7
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* - ``int64``
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- Signed 64-bit integer.
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- 1.0.0
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- 1.7
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* - ``int8``
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- Signed 8-bit integer.
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- 1.0.0
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- 1.7
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* - ``qint16``
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- Signed quantized 16-bit integer.
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- 1.0.0
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- 1.7
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* - ``qint32``
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- Signed quantized 32-bit integer.
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- 1.0.0
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- 1.7
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* - ``qint8``
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- Signed quantized 8-bit integer.
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- 1.0.0
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- 1.7
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* - ``quint16``
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- Unsigned quantized 16-bit integer.
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- 1.0.0
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- 1.7
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* - ``quint8``
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- Unsigned quantized 8-bit integer.
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- 1.0.0
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- 1.7
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* - ``resource``
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- Handle to a mutable, dynamically allocated resource.
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- 1.0.0
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- 1.7
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* - ``string``
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- Variable-length string, represented as byte array.
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- 1.0.0
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- 1.7
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* - ``uint16``
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- Unsigned 16-bit (word) integer.
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- 1.0.0
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- 1.7
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* - ``uint32``
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- Unsigned 32-bit (dword) integer.
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- 1.5.0
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- 1.7
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* - ``uint64``
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- Unsigned 64-bit (qword) integer.
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- 1.5.0
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- 1.7
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* - ``uint8``
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- Unsigned 8-bit (byte) integer.
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- 1.0.0
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- 1.7
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* - ``variant``
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- Data of arbitrary type (known at runtime).
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- 1.4.0
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- 1.7
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Features
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---------------
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This table provides an overview of key features in TensorFlow and their
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availability in ROCm.
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.. list-table::
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:header-rows: 1
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* - Module
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- Description
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- Since TensorFlow
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- Since ROCm
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* - ``tf.linalg`` (Linear Algebra)
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- Operations for matrix and tensor computations, such as
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``tf.linalg.matmul`` (matrix multiplication), ``tf.linalg.inv``
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(matrix inversion) and ``tf.linalg.cholesky`` (Cholesky decomposition).
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These leverage GPUs for high-performance linear algebra operations.
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- 1.4
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- 1.8.2
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* - ``tf.nn`` (Neural Network Operations)
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- GPU-accelerated building blocks for deep learning models, such as 2D
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convolutions with ``tf.nn.conv2d``, max pooling operations with
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``tf.nn.max_pool``, activation functions like ``tf.nn.relu`` or softmax
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for output layers with ``tf.nn.softmax``.
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- 1.0
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- 1.8.2
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* - ``tf.image`` (Image Processing)
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- GPU-accelerated functions for image preprocessing and augmentations,
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such as resize images with ``tf.image.resize``, flip images horizontally
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with ``tf.image.flip_left_right`` and adjust image brightness randomly
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with ``tf.image.random_brightness``.
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- 1.1
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- 1.8.2
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* - ``tf.keras`` (High-Level API)
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- GPU acceleration for Keras layers and models, including dense layers
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(``tf.keras.layers.Dense``), convolutional layers
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(``tf.keras.layers.Conv2D``) and recurrent layers
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(``tf.keras.layers.LSTM``).
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- 1.4
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- 1.8.2
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* - ``tf.math`` (Mathematical Operations)
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- GPU-accelerated mathematical operations, such as sum across dimensions
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with ``tf.math.reduce_sum``, elementwise exponentiation with
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``tf.math.exp`` and sigmoid activation (``tf.math.sigmoid``).
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- 1.5
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- 1.8.2
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* - ``tf.signal`` (Signal Processing)
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- Functions for spectral analysis and signal transformations.
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- 1.13
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- 2.1
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* - ``tf.data`` (Data Input Pipeline)
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- GPU-accelerated data preprocessing for efficient input pipelines,
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Prefetching with ``tf.data.experimental.AUTOTUNE``. GPU-enabled
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transformations like map and batch.
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- 1.4
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- 1.8.2
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* - ``tf.distribute`` (Distributed Training)
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- Enabling to scale computations across multiple devices on a single
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machine or across multiple machines.
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- 1.13
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- 2.1
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* - ``tf.random`` (Random Number Generation)
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- GPU-accelerated random number generation
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- 1.12
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- 1.9.2
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* - ``tf.TensorArray`` (Dynamic Array Operations)
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- Enables dynamic tensor manipulation on GPUs.
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- 1.0
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- 1.8.2
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* - ``tf.sparse`` (Sparse Tensor Operations)
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- GPU-accelerated sparse matrix manipulations.
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- 1.9
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- 1.9.0
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* - ``tf.experimental.numpy``
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- GPU-accelerated NumPy-like API for numerical computations.
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- 2.4
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- 4.1.1
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* - ``tf.RaggedTensor``
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- Handling of variable-length sequences and ragged tensors with GPU
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support.
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- 1.13
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- 2.1
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* - ``tf.function`` with XLA (Accelerated Linear Algebra)
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- Enable GPU-accelerated functions in optimization.
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- 1.14
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- 2.4
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* - ``tf.quantization``
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- Quantized operations for inference, accelerated on GPUs.
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- 1.12
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- 1.9.2
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Distributed library features
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-----------------------------------
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Enables developers to scale computations across multiple devices on a single machine or
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across multiple machines.
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.. list-table::
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:header-rows: 1
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* - Feature
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- Description
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- Since TensorFlow
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- Since ROCm
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* - ``MultiWorkerMirroredStrategy``
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- Synchronous training across multiple workers using mirrored variables.
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- 2.0
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- 3.0
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* - ``MirroredStrategy``
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- Synchronous training across multiple GPUs on one machine.
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- 1.5
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- 2.5
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* - ``TPUStrategy``
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- Efficiently trains models on Google TPUs.
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- 1.9
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- ❌
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* - ``ParameterServerStrategy``
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- Asynchronous training using parameter servers for variable management.
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- 2.1
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- 4.0
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* - ``CentralStorageStrategy``
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- Keeps variables on a single device and performs computation on multiple
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devices.
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- 2.3
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- 4.1
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* - ``CollectiveAllReduceStrategy``
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- Synchronous training across multiple devices and hosts.
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- 1.14
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- 3.5
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* - Distribution Strategies API
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- High-level API to simplify distributed training configuration and
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execution.
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- 1.10
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- 3.0
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Unsupported TensorFlow features
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===============================================================================
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The following are GPU-accelerated TensorFlow features not currently supported by
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ROCm.
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.. list-table::
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:header-rows: 1
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* - Feature
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- Description
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- Since TensorFlow
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* - Mixed Precision with TF32
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- Mixed precision with TF32 is used for matrix multiplications,
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convolutions, and other linear algebra operations, particularly in
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deep learning workloads like CNNs and transformers.
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- 2.4
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* - ``tf.distribute.TPUStrategy``
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- Efficiently trains models on Google TPUs.
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- 1.9
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Use cases and recommendations
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===============================================================================
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* The `Training a Neural Collaborative Filtering (NCF) Recommender on an AMD
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GPU <https://rocm.blogs.amd.com/artificial-intelligence/ncf/README.html>`__
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blog post discusses training an NCF recommender system using TensorFlow. It
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explains how NCF improves traditional collaborative filtering methods by
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leveraging neural networks to model non-linear user-item interactions. The
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post outlines the implementation using the recommenders library, focusing on
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the use of implicit data (for example, user interactions like viewing or
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purchasing) and how it addresses challenges like the lack of negative values.
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* The `Creating a PyTorch/TensorFlow code environment on AMD GPUs
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<https://rocm.blogs.amd.com/software-tools-optimization/pytorch-tensorflow-env/README.html>`__
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blog post provides instructions for creating a machine learning environment
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for PyTorch and TensorFlow on AMD GPUs using ROCm. It covers steps like
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installing the libraries, cloning code repositories, installing dependencies,
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and troubleshooting potential issues with CUDA-based code. Additionally, it
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explains how to HIPify code (port CUDA code to HIP) and manage Docker images
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for a better experience on AMD GPUs. This guide aims to help data scientists
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and ML practitioners adapt their code for AMD GPUs.
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For more use cases and recommendations, see the `ROCm Tensorflow blog posts <https://rocm.blogs.amd.com/blog/tag/tensorflow.html>`__.
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