Tensorboards

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Available in VPC.

This section describes the Tensorboards interface. Use Tensorboards to create and delete Tensorboards or access active Tensorboards to monitor training processes.

Tensorboards

The Tensorboards interface includes the following components:

mlxp_console_tensorboards01_ko

Component Description
① Menu name Current menu name.
② Basic features Add or delete a Tensorboard.
③ Tensorboard list View active Tensorboards and their information.

View Tensorboards list

The Tensorboards list includes the following information:

  • Status: Tensorboard status
  • Name: Tensorboard name
  • Created At: Initial creation timestamp
  • LogsPath: Storage path for logs
  • CONNECT: Click to open the Tensorboard in a new window

Create a Tensorboard

To create a new Tensorboard:

  1. Click [Create].
  2. When the New Tensorboard popup appears, enter a Tensorboard name.
    • Use lowercase letters (a–z), numbers (0–9), and hyphens (-) within 3 to 61 characters.
    • Must start with a letter and end with a letter or number.
    • Must be unique.
  3. Verify the Project Namespace.
  4. Select a storage type for log storage.
    • Object Storage/Ncloud Storage: Use your Object Storage or Ncloud Storage
    • Volumes: Use a volume you created
  5. Enter the Mount Path for the selected storage type.
Note

If you select Object Storage or Ncloud Storage, enter the endpoint in S3 REST API format.

  1. Under Configurations, select the GPU Zone where the Tensorboard will run.
Caution
  • ML expert Platform currently provides GPU resources in a Private Zone. Select the GPU Zone assigned to your workspace.
  • For available GPU Zones, see View GPU Zone information.
  1. Click [Create].

Delete a Tensorboard

You can delete a Tensorboard as follows:

  1. Select the Tensorboard you want to delete and click [Delete] button.
  2. When the Delete Tensorboard popup appears, enter the Tensorboard name and click [Delete] button.
  3. Verify that the selected Tensorboard has been removed from the list.