tensorflow

Module Name

tensorflow

Last Updated On

08/29/2023

Support Level

Secondary Support

Software Access Level

Open Access

Home Page

https://www.tensorflow.org

Software Description

From the Tensorflow website: 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) communicated between them. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google\'s Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.

Mesabi

Tensorflow is available for use on MSI\'s Mesabi k40 nodes . To load tensorflow into your environment, use the following command:

module load tensorflow

Tensorflow is frequently used via its python interface. To use tensorflow in this manner after loading in the module, use the following procedure:

python

>>> import tensorflow as tf

There are a variety of tutorials available for using tensorflow on the official website .

General Linux

As a python package, Tensorflow is best installed at MSI using Anaconda. MSI provides certain Tensorflow versions as modules for compatibility and to fill dependencies in other modules, but users wishing to run their own Tensorflow calculations are better served by installing it locally. This offers the advantages that 1) users can select the appropriate version of Tensorflow for their work, and 2) users can also install any other python libraries that are needed. An example of a worflow for installing Tensorflow locally within a conda environment might look like:

module load python
mamba create -n my_tensorflow python=3 tensorflow -c conda-forge

Then, to use that environment in future sessions, you would run:

module load python
source activate my_tensorflow

You can also use conda to install other packages to your tensorflow environment that you may need. For documentation on managing packages in a conda environment, see the official conda documentation . If you would like to use Tensorflow on one of our GPU-enabled systems and take advantage of the available CUDA cores, you would need to modify the conda command:

mamba create -n my_tensorflow python=3 tensorflow-gpu -c anaconda

Default

Other Modules

1.3_gpu, 1.3_gpu_python3, 1.4_gpu, 1.4_gpu_python3

Default

1.4_gpu_python3

Other Modules

1.3_gpu, 1.3_gpu_python3, 1.4_gpu, 1.4_gpu_python3

Default

Other Modules

1.3_gpu, 1.3_gpu_python3, 1.4_gpu, 1.4_gpu_python3