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Connecting to TensorBoard

Once you launch a Deep Learning workload using Run:AI, you may want to view its progress. A popular tool for viewing progress is TensorBoard.

The document below explains how to use TensorBoard to view the progress or a Run:AI Job.

Submit a Workload

When you submit a workload, your workload must save TensorBoard logs which can later be viewed. Follow this document on how to do this. You can also view the Run:AI sample code here.

The code shows:

  • A reference to a log directory:
log_dir = "logs/fit/" +"%Y%m%d-%H%M%S")
  • A registered Keras callback for TensorBoard:
tensorboard_callback = TensorBoard(log_dir=log_dir, histogram_freq=1), y_train,
        callbacks=[..., tensorboard_callback])

The logs directory must be saved on a Network File Server such that it can be accessed by the TensorBoard Job. For example, by running the Job as follows:

runai submit train-with-logs -i tensorflow/tensorflow:1.14.0-gpu-py3 \
  -v /mnt/nfs_share/john:/mydir -g 1  --working-dir /mydir --command -- ./

Note the volume flag (-v) and working directory flag (--working-dir). The logs directory will be created on /mnt/nfs_share/john/logs/fit.

Submit a TensorBoard Job

Run the following:

runai submit tb -i tensorflow/tensorflow:latest --interactive --service-type=portforward --port 8888:8888  --working-dir /mydir  -v /mnt/nfs_share/john:/mydir   --command -- tensorboard --logdir logs/fit --port 8888 --host

The terminal will show the following:

The job 'tb' has been submitted successfully
You can run `runai describe job tb -p team-a` to check the job status
INFO[0006] Waiting for job to start
Waiting for job to start
INFO[0014] Job started
Open access point(s) to service from localhost:8888
Forwarding from -> 8888
Forwarding from [::1]:8888 -> 8888

Browse to http://localhost:8888/ to view TensorBoard.


A single TensorBoard Job can be used to view multiple deep learning Jobs, provided it has access to the logs directory for these Jobs.

Last update: January 3, 2021