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Argonne Leadership Computing Facility

TensorBoard Instructions

If you are able to install TensorBoard on your local machine, it is often easiest to copy the requisite files from ALCF file systems (via sftp, scp, Globus, etc.) to your local machine and run a TensorBoard there.

However, if that is not possible, or if you have many and/or large files that TensorBoard needs to process located on ALCF file systems, there are several ways to run a TensorBoard server remotely.

TensorBoard server on a ThetaGPU compute node

This approach can be useful to have TensorBoard analyze live training progress. After you have logged into ThetaGPU, and have an interactive job running, you'll need to know the name of one of your worker nodes so you can SSH to it.

# Select a theta login node N where N=[1-6] ssh -L $PORT0:localhost:$PORT1 $ 

# after reaching thetaloginN 

# Replace NN with your thetagpu worker node ssh -L $PORT1:thetagpuNN:$PORT3 $USER@thetagpusn1 
# after reaching thetagpusn1 

# login to worker node 
ssh thetagpuNN 

# now setup your tensorflow environment 
# for instance run the conda script created during the script 

# now run tensorboard 
tensorboard --logdir </path/to/logs> --port $PORT3 --bind_all

TensorBoard server on a ThetaKNL login node

If you do not require the use of a GPU during analysis while TensorBoard runs, and you do not require a cutting-edge version of TensorBoard (this will load version 2.6.0), you can avoid additional SSH tunnel hops by running the TensorBoard server on a ThetaKNL login node:

ssh -D <some-port-number>

module load conda/2021-09-22
export LD_LIBRARY_PATH=/soft/thetagpu/cuda/cuda_11.3.0_465.19.01_linux/lib64:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=/soft/thetagpu/cuda/cuda_11.3.0_465.19.01_linux/extras/CUPTI/lib64/:$LD_LIBRARY_PATH