This page provides a description for the procedure to install tensorflow on the test cluster.
After log in to phoenix It is possible to access to the following nodes:
- test-u19-n01.test.cluster AMD Epyc
- test-u21-n01.test.cluster Skylake, 4x V100
- test-u23-n01.test.cluster AMD Ryzen
- test-u25-n01.test.cluster Skylake, 2x V100
- test-u25-n02.test.cluster Cascadelake
- test-u36-n01.test.cluster Broadwell, 2x P100
This procedure will describe the installation for tensorflow 2.0
Create the virtual environment
Download cudnn-7, and unzip it. At the end of ~/tensorflow-2.0-gpu-venv/bin/activate add the following:
Activate the virtualenv, and installl tensorflow
In order to use the pip version you need to check the tensorflow version, the cuda library and the cudnn library.
More information can be found at https://www.tensorflow.org/install/source#linux
On the test cluster is available cuda-10.0, cudnn can be download and unpack in the user directory.
TensorBoard is a tool for providing the measurements and visualizations needed during the machine learning workflow.
It enables tracking experiment metrics like loss and accuracy, visualizing the model graph, projecting embeddings to a
lower dimensional space, and much more.
If you are interested in this tool start from here https://www.tensorflow.org/tensorboard/get_started
All the examples in the guide assume that the user run tensorflow inside jupyter. However, in a production scenario,
the user will run either interactively or by using a job script. In such case tensorboard can be invoked like
You can now visualize the results in your browser by typing the address http://test-u36-n01.test.cluster:6006.
Sometimes it might be required the insert the ip manullay. For test-u360-n01.test.cluster is 10.91.1.17.
You can also run tensorflow through shifter. For and introduction to shifter please look at https://scitas-data.epfl.ch/confluence/display/DOC/Running+Docker+images+using+Shifter
You can upload your own image if necessary. However, if you want to experiment with an already available image you can follow the template provided below.
The example above load the image tensorflow-gpu/tensorflow/tensorflow:2.0.0-gpu which provides tensorflow 2.0.