During the outage in mid-March we have upgraded the hardware in our GPU partitions (namely gpuk80) to CUDA driver 440.33.01 which supports TensorFlow up to version 2.2. The following guide will show you how to install them with a virtual environment or Anaconda.

The following guide largely repeats the instructions in our software environments guide. The only specific instructions we have added are:

  1. Use the cuda/10.1 software module.
  2. Install TensorFlow 2.2.0 (specifically 2.2.0rc0) with pip inside your environment.

Read below for a step-by-step walkthrough.

Option 1: Virtual Environments

TensorFlow typically requires the penultimate version of Python. To use a virtual environment to install TensorFlow 2 you must load a Python 3 module and select a location for a virtual environment. We strongly recommend using the new ~/code location inside your home directory. Do not install programs on our Lustre filesystem (~/scratch and ~/work). The following steps will build the virtual environment and install TensorFlow inside. Lines prefixed with the hash symbol are comments for your reference.

# start an interactive session on a GPU node
interact -p debug -c 6 -t 60 -g 1
# navigate to your ~/code directory or another suitable location
cd ~/code
# load the Python and CUDA modules
ml python/3.7.7
ml cuda/10.1
# build the environment in a folder called ./venv
python -m venv ./venv
# activate the environment
source ./venv/bin/activate
# install tensorflow 2.2
pip install tensorflow==2.2.0rc0
# confirm that it is installed
python -c 'import tensorflow'

After installing the environment you will access it with the following commands. Note that the path to your environment may be different it if you installed it elsewhere.

ml cuda/10.1
source ~/code/venv/bin/activate

Note that we recommend installing TensorFlow 2.2.0 for use with CUDA 10.1.

Option 2: Anaconda

Anaconda allows you to carefully control the exact version of Python and install additional supporting packages.

Select a location, ideally in ~/code or on the ~/data filesystem (but not our Lustre filesystem at ~/scratch and ~/work). Write the following reqs.yaml file.

dependencies:
  - python==3.7
  - pip
  - pip:
    - tensorflow==2.2.0rc0

Install the environment to your chosen location with the following commands.

# start an interactive session on a GPU node
interact -p debug -c 6 -t 60 -g 1
# navigate to your ~/code directory or another suitable location
cd ~/code
# load the Anaconda and CUDA modules
ml anaconda/2019.03
ml cuda/10.1
# build the environment at your chosen location 
conda env update --file ./reqs.yaml -p ./env
# activate the environment
conda activate ./env
# confirm that it works
python -c 'import tensorflow'

After installing this environment, you can access the environment with the following commands.

ml anaconda/2019.03
ml cuda/10.2
conda activate ~/code/env

Note that the paths above may be different if you install your environment to a different location.

Pending updates

In the future we will make TensorFlow automatically available in a module format on our new software modules. Watch this space for updates.