How to use TensorFlow with AMD GPU’s
Most machine learning frameworks that run with a GPU support Nvidia GPUs, but if you own a AMD GPU you are out of luck.
Recently AMD has made some progress with their ROCm platform for GPU computing and does now provide a TensorFlow build for their gpus.
Since I work with tensorflow and own a AMD GPU it was time to give it a try. I stumpled upon these instructions for TensorFlow 1.8 but since they are outdated, I decided to write down what I did.
1. Set up Linux
It looks like there is currently no ROCm support for Windows. And no, WSL aka Bash for Windows does not work. But there are packages for CentOS/RHEL 7 and Ubuntu. I used Ubuntu 18.04.
2. Install ROCm
Just follow the ROCm install instructions.
3. Install TensorFlow
AMD provides a special build of TensorFlow. Currently they support TensorFlow 1.12.0. You can build it yourself, but the most convenient way to use it, is to install the package from PyPI:
sudo apt install python3-pip pip3 install --user tensorflow-rocm
4. Train a Model
To test your setup you can run the image recognition task from the Tensorflow tutorials.
git clone https://github.com/tensorflow/models.git cd models/tutorials/image/imagenet python3 classify_image.py
and the result should look like this:
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)
Extra: Monitor your GPU
If you like to check that your model fully utilize your GPU, you can use the (radeontop)[https://github.com/clbr/radeontop] tool:
Install it with
sudo apt-get install radeontop
and run it
This will dump the statistics to the command line.
sudo radeontop -d -