googletensorflow-serving

Launch TensorBoard

NOTE: The Bitnami TensorFlow Serving Stack is configured to deploy the TensorFlow ResNet API. This image also ships other tools like Bazel or the TensorFlow Python library for training models. Training operations will require higher hardware requirements in terms of CPU, RAM and disk. It is highly recommended to check the requirements for these operations and scale your server accordingly.

  • Execute the TensorBoard server:

    $ tensorboard --logdir=path/to/log-directory
    

By default the port for the TensorBoard service is 6006. You will need to create an SSH tunnel to access it. Refer to the FAQ if you need help with this.

For more information, please check this the TensorBoard: Visualizing Learning guide.

Last modification January 4, 2019