TensorFlow
Description
TensorFlow is an open-source software library that is commonly used for implementing artificial neural networks and for deep learning. TensorFlow's numerical computation model is based on data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture lets you deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code. TensorFlow also includes TensorBoard, a data visualization toolkit, and Keras, a high-level (and easier-to-use) neural networks API for Python.
Environment Modules
To use TensorFlow on HiPerGator, you first need to load one of the TensorFlow environment modules.
Run module spider tensorflow to find out which environment modules are available for TensorFlow.
Once you determine which module you want to use, load it as follows (using tensorflow/2.4.1 as an example):
module load tensorflow/2.4.1
Additional Information
As of version 2.0 tensorflow includes keras as tf.keras module. This is the module that you should use. See keras vs. tf.keras article for details.
To start a tensorflow session on a GPU or GPUs on HiPerGator you must request the '--gpus' resource and specify the 'gpu' partition in your job script or on the command line as described in GPU Access help page. E.g.
srun --partition=gpu --gpus=1 --ntasks=1 --mem=4gb --time=08:00:00 --pty -u bash -i
Citation
If you publish research that uses tensorflow you have to cite it as follows:
https://www.tensorflow.org/versions/r0.11/resources/bib.html