TensorFlow™ is an open source software library for high performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs and GPUs), and from desktops to clusters of servers to mobile and edge devices. Originally developed by researchers and engineers from the Google Brain team within Google’s AI organization, it comes with strong support for machine learning and deep learning and the flexible numerical computation core is used across many other scientific domains.
CRC version of Tensorflow support both GPU and CPU. The following is an example of UGE job submission script requesting 1 GPU
#!/bin/bash #$ -M email@example.com # Email address for job notification #$ -m abe # Send mail when job begins, ends and aborts #$ -q gpu # Specify queue #$ -l gpu_card=1 # Specify number of GPU cards to use. #$ -N TFjob # Specify job name module load tensorflow python3 pythonscript.py
As you can see above, no python module manually loaded for a tensorflow job. The tensorflow module itself will bring it’s own version of python with several necessary packages preconfigured to use the CRC infrastructure.
$ module list Currently Loaded Modulefiles: 1) CRC_default/1.1 $ python3 --version Python 3.6.8 $ module load tensorflow $ python3 --version Python 3.9.5
Installing Python Packages Locally
If a python package is not installed with the CRC version of Tensorflow, you can easily install it locally in your personal AFS space using pip.
module load tensorflow pip3 install --user package_name