During the outage in mid-March we have upgraded the hardware in our GPU partitions (namely
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:
- Use the
- Install TensorFlow 2.2.0 (specifically
pipinside 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 (
~/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
~/work). Write the following
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.
In the future we will make TensorFlow automatically available in a module format on our new software modules. Watch this space for updates.