Here’s a quote from the developers:
ROCm Tensorflow 1.8 Release
We are excited to announce the release of ROCm enabled TensorFlow v1.8 for AMD GPUs. This post demonstrates the steps to install and use TensorFlow on AMD GPUs.
First, you’ll need to install the open-source ROCm stack. Details can be found here: https://rocm.github.io/ROCmInstall.html
Then, install these other relevant ROCm packages:
sudo apt update sudo apt install rocm-libs miopen-hip cxlactivitylogger
And finally, install TensorFlow itself (via our 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
Now that TensorFlow is installed, let’s run a few workloads.
We’ll use one of TensorFlow’s tutorials as a quick and easy Inception-v3 image recognition workload: https://www.tensorflow.org/tutorials/image_recognition
Here’s how to run it:
cd ~ && git clone https://github.com/tensorflow/models.git cd ~/models/tutorials/image/imagenet python3 classify_image.py
After, you should see a list of labels with associated scores. Since the above script is for classifying a supplied image of a panda, that’s what the result indicates:
giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca (score = 0.89103) indri, indris, Indri indri, Indri brevicaudatus (score = 0.00810) lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens (score = 0.00258) custard apple (score = 0.00149) earthstar (score = 0.00141)