TensorFlow - Office of Research

QuickStart TensorFlow

Tensorflow is a symbolic math library based on dataflow and differentiable programming.

Use Global Protect VPN: Whether on or Off-Campus

In the top menu bar access (globe icon).

Be sure vpn-groups selected when you connect.
Start an SSH Session

SSH your_netid@hpc.kennesaw.edu
To Test or Develop, Reserve a Node (for non-gpu)

$ qsub -I -q batch -l nodes=1:ppn=24,walltime=1:00:00
To Test or Develop, Reserve a Node (gpu)

$ qsub -I -q gpuq -l nodes=1:ppn=24:gpus=2,walltime=1:00:00
Due to the Variety of Ways TensorFlow is Used, It is Now Advised to Build Your Own Python Virtual Environment to Access a Recent Copy of TensorFlow

You will want to utilize the Anaconda module to create a Conda environment.

From your Conda environment, you can have access to TensorFlow with GPU support that should work without a GPU.

Once created, to use the environment in an interactive session or from within a PBS job submission script, you will need to load Anaconda and activate your new TensorFlow-aware conda environment.

Step by step instructions have been prepared by the KSU Sysadmins on the KSU HPCdocs wiki: TensorFlow Example.
Write a Job Submission Script (run_tf2.pbs)

Check the PBS file example at hpcdocs.
Submit Your Job to the Scheduler

Use the qsub sumission example at the bottom of the PBS file example.
TensorFlow Tutorials and Guides

Visit TensorFlow Tutorials.