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TensorFlow ROCm port
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whchung Merge pull request #423 from ROCmSoftwarePlatform/develop-upstream-di…

 Disabling gemm auto-tuner on the XLA path (in ROCm mode).  Failures in `rocm` path should be environment issue.
Latest commit 4f5d661 Apr 23, 2019
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rocm_docs Updating SYNC_UPSTREAM to record conflict resolution Apr 16, 2019
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tools Merge remote-tracking branch 'google_upstream/master' into develop-up… Apr 8, 2019
.bazelrc Re-enabling MKL-DNN contraction kernel. Prevent and _gru… Apr 1, 2019
.gitignore Adds new podspecs for the TensorFlow Lite iOS libraries. Mar 22, 2019
ACKNOWLEDGMENTS TensorFlow: Improve performance of Alexnet Nov 20, 2015 Internal file cleanup. Oct 18, 2016
AUTHORS Merge changes from github. Dec 7, 2017
CODEOWNERS Fix tensorflow nccl CODEOWNERS Dec 7, 2018 Merge pull request tensorflow#23063 from bfinan:bfinan-patch-1 Oct 19, 2018 Merge pull request tensorflow#22949 from dksb:master Oct 16, 2018
LICENSE Update license year Feb 15, 2019 Update doc for ROCm2.3 compatibility Apr 13, 2019 Merge remote-tracking branch 'google_upstream/master' into develop-up… Apr 16, 2019 Resolve merge conflict for 190422 Apr 22, 2019 fix md link format Jun 13, 2018
WORKSPACE Updates versions for Apple and Swift Bazel rules. Apr 11, 2019
build_rocm Modify the bazel build scripts to force hipcc on HCC path Jan 10, 2019
build_rocm_python3 Modify the bazel build scripts to force hipcc on HCC path Jan 10, 2019
build_rocm_verbs Merge remote-tracking branch 'upstream/master' into develop-upstream-… Nov 5, 2018
build_rocm_xla_python3 Build script for xla Feb 21, 2019
configure Merge changes from github. Mar 13, 2018 Merge remote-tracking branch 'google_upstream/master' into develop-up… Apr 22, 2019
models.BUILD Make models.BUILD filegroup include everything but metadata files and… Jan 10, 2017


TensorFlow is an open source software library for numerical computation using data flow graphs. The graph nodes represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture enables you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code. TensorFlow also includes TensorBoard, a data visualization toolkit.

TensorFlow was originally developed by researchers and engineers working on the Google Brain team within Google's Machine Intelligence Research organization for the purposes of conducting machine learning and deep neural networks research. The system is general enough to be applicable in a wide variety of other domains, as well.

TensorFlow provides stable Python and C APIs as well as non-guaranteed backwards compatible API's for C++, Go, Java, JavaScript, and Swift.

Keep up to date with release announcements and security updates by subscribing to

Tensorflow ROCm port This project is based on TensorFlow 1.13.1. It has been verified to work with the latest ROCm2.3 release. Please follow the instructions here to set up your ROCm stack. A docker container: rocm/tensorflow:latest( is readily available to be used:

alias drun='sudo docker run -it --network=host --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v $HOME/dockerx:/dockerx'
drun rocm/tensorflow

We maintain tensorflow-rocm whl packages on PyPI here, to install tensorflow-rocm package using pip:

# Install some ROCm dependencies
sudo apt install rocm-libs miopen-hip cxlactivitylogger

# Pip3 install the whl package from PyPI
pip3 install --user tensorflow-rocm --upgrade

For details on Tensorflow ROCm port, please take a look at the ROCm-specific README file.


To install the current release for CPU-only:

pip install tensorflow

Use the GPU package for CUDA-enabled GPU cards:

pip install tensorflow-gpu

See Installing TensorFlow for detailed instructions, and how to build from source.

People who are a little more adventurous can also try our nightly binaries:

Nightly pip packages * We are pleased to announce that TensorFlow now offers nightly pip packages under the tf-nightly and tf-nightly-gpu project on PyPi. Simply run pip install tf-nightly or pip install tf-nightly-gpu in a clean environment to install the nightly TensorFlow build. We support CPU and GPU packages on Linux, Mac, and Windows.

Try your first TensorFlow program

$ python
>>> import tensorflow as tf
>>> tf.enable_eager_execution()
>>> tf.add(1, 2).numpy()
>>> hello = tf.constant('Hello, TensorFlow!')
>>> hello.numpy()
'Hello, TensorFlow!'

Learn more examples about how to do specific tasks in TensorFlow at the tutorials page of

Contribution guidelines

If you want to contribute to TensorFlow, be sure to review the contribution guidelines. This project adheres to TensorFlow's code of conduct. By participating, you are expected to uphold this code.

We use GitHub issues for tracking requests and bugs, so please see TensorFlow Discuss for general questions and discussion, and please direct specific questions to Stack Overflow.

The TensorFlow project strives to abide by generally accepted best practices in open-source software development:

CII Best Practices

Continuous build status

Official Builds

Build Type Status Artifacts
Linux CPU Status pypi
Linux GPU Status pypi
Linux XLA Status TBA
MacOS Status pypi
Windows CPU Status pypi
Windows GPU Status pypi
Android Status Download
Raspberry Pi 0 and 1 Status Status Py2 Py3
Raspberry Pi 2 and 3 Status Status Py2 Py3

Community Supported Builds

Build Type Status Artifacts
IBM s390x Build Status TBA
Linux ppc64le CPU Nightly Build Status Nightly
Linux ppc64le CPU Stable Release Build Status Release
Linux ppc64le GPU Nightly Build Status Nightly
Linux ppc64le GPU Stable Release Build Status Release
Linux CPU with Intel® MKL-DNN Nightly Build Status Nightly
Linux CPU with Intel® MKL-DNN
Supports Python 2.7, 3.4, 3.5 and 3.6
Build Status 1.13.1 pypi

For more information

Learn more about the TensorFlow community at the community page of for a few ways to participate.


Apache License 2.0

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