2 min read

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.

Read next

Nvidia unveils a new Turing architecture: “The world’s first ray tracing GPU”

AMD open sources V-EZ, the Vulkan wrapper library

Sugar operating system: A new OS to enhance GPU acceleration security in web apps

Data science enthusiast. Cycling, music, food, movies. Likes FPS and strategy games.


Please enter your comment!
Please enter your name here