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AMD has announced the support for TensorFlow v1.8 for their ROCm-enabled GPUs. This includes the Radeon Instinct MI25. ROCm stands for Radeon Open Compute and it is an open-source Hyperscale-class (HPC) platform for GPUs. The platform is programming-language independent.

This is a major milestone in AMD’s efforts towards accelerating deep learning. ROCm, the Radeon Open Ecosystem is AMD’s open-source software foundation for GPU computing on Linux. Mayank Daga, Director, Deep Learning Software, AMD stated: “Our TensorFlow implementation leverages MIOpen, a library of highly optimized GPU routines for deep learning.

There is a pre-built whl package made available for a simple install similar to the installation of generic TensorFlow in Linux. They also provide a pre-built Docker image for fast installation.

AMD is also working towards upstreaming all the ROCm-specific enhancements to the TensorFlow master repository in addition to supporting TensorFlow v1.8. While they work towards fully upstreaming the enhancements, AMD will be releasing and maintaining future ROCm-enabled TensorFlow versions, like v1.10.

In the post, Daga stated, “We believe the future of deep learning optimization, portability, and scalability has its roots in domain-specific compilers. We are motivated by the early results of XLA, and are also working towards enabling and optimizing XLA for AMD GPUs.

Current CPUs which support PCIe Gen3 + PCIe Atomics are:

  • AMD Ryzen CPUs
  • Intel Xeon E7 V3 or newer CPUs
  • Intel Xeon E5 v3 or newer CPUs
  • Intel Xeon E3 v3 or newer CPUs
  • Intel Core i7 v4, Core i5 v4, Core i3 v4 or newer CPUs (i.e. Haswell family or newer).

The installation is simple,

First, you’ll need the open-source ROCm stack. Then, install the rocm library needs to be installed via APT:

sudo apt update
sudo apt install rocm-libs miopen-hip cxlactivitylogger

And finally, you install TensorFlow itself (via AMD’s pre-built whl package):

sudo apt install wget python3-pip
wget http://repo.radeon.com/rocm/misc/tensorflow/tensorflow-1.8.0-cp35-cp35m-manylinux1_x86_64.whl
pip3 install ./tensorflow-1.8.0-cp35-cp35m-manylinux1_x86_64.whl

For more details on how to get started, visit the GitHub repository. There are also examples on image recognition, audio recognition, and multi-gpu training on ImageNet in the GPUOpen website.

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