Using TensorFlow on RCC Systems
There are two main ways to make use of TensorFlow on RCC Systems. The first is to use one of the default installations available through the Anaconda modules. For detailed documentation, examples and tutorials on programming with TensorFlow, refer to the Documentation.
Using the Default TensorFlow in Anaconda
module load anaconda3.8.3 python >> import tensorflow as tf
Building Your Own TensorFlow Version
The current TensorFlow version available in the Anaconda modules is version 2.2.0. However, if your job requires a different version of TensorFlow, it is possible to install that version in your home directory with a Python
virtualenv virtual environment.
virtualenv --system-site-packages --extra-search-dir=/gpfs/research/software/anaconda3.8.3 NAME pip install tensorflow --user
From here, you can install the latest TensorFlow by running the
pip install command. For more information, please refer to the virtualenv documentation.
TensorFlow on the GPU
The current version of TensorFlow on the HPC system includes support for GPU devices which support CUDA. These are all NVIDIA GPUs. If you want to run your code with GPU support, this is automatically included in the available install of TensorFlow. Documentation on usage of TensorFlow with GPU support can be found here. When submitting a GPU job with TensorFlow, you will need to include the cuda module as well as the anaconda module. A sample submit script might look like this:
#!/bin/bash #SBATCH -p backfill2 #SBATCH -n 12 #SBATCH --gres=gpu:1 module load cuda module load gnu-openmpi module load anaconda3.8.3 srun python MYCODE.py