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TensorFlow

TensorFlow is TensorFlow is an end-to-end open source machine learning platform., used for ML Framework & Development Digital twins Virtual Reality . This product integrates TensorFlow, which is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.

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Prepare​

When referring to this document to use TensorFlow, please read and ensure the following points:

  • Login to Websoft9 Console and find or install TensorFlow:

    • Go to My Apps listing applications
    • Go to App Store installing target application

  • This application is installed by Websoft9 console.

  • The purpose of this application complies with the apache2 open source license agreement.

  • Configure the domain name or server security group opens external network ports for application access.

Getting Started​

Initial Setup​

  1. After completing the installation of TensorFlow in the Websoft9 Console, retrieve the application's Overview and Access information from My Apps.

  2. Access the Jupyter URL locally, and you will be prompted to enter a login token.

  3. Log in to the Jupyter backend using the token or set a password.

Run TensorBoard​

  1. In the Jupyter backend, go to New > Python 3 (ipykernel).

  2. Refer to Using TensorBoard in Notebooks, and run the example programs in sequence. Add the parameter --host 0.0.0.0 to the last command (to allow external access).

  3. TensorBoard will now be displayed in the Notebook.

Configuration Options​

  • Container Ports:
    • 8888: Jupyter port
    • 6006: TensorBoard port

Administration​

Troubleshooting​

TensorBoard Not Visible in Notebook?​

  • Ensure that the TensorBoard command is started with the --host 0.0.0.0 flag.
  • Ensure the host port for the container's 6006 port mapping is enabled.