TensorFlow
Description
TensorFlow is an open source software library for numerical computation using 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 (easier to use) neural networks API, written in Python and capable of running on top of TensorFlow.
Required Modules
Run module spider tensorflow
to find out what environment modules are available for this application.
System Variables
- HPC_TENSORFLOW_DIR - installation directory
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