Running Independent Tune Trials (Ray Tune)

Provides an end-to-end workflow for running independent Tune trials in HPE Ezmeral Unified Analytics Software.

Prerequisites

  • Sign in to HPE Ezmeral Unified Analytics Software.
  • Verify that the installed Ray client and server versions match. To verify, complete the following steps in the terminal:
    1. To switch to Ray's environment, run:
      source /opt/conda/etc/profile.d/conda.sh && conda activate ray
    2. To verify that the Ray client and server versions match, run :
      ray --version

About this task

In this tutorial, you will run N independent model training trials using Tune as a simple grid sweep.

You will complete the following steps:

Procedure

  1. Create a notebook server using the jupyter-data-science image with at least 3 CPUs and 4 Gi of memory in Kubeflow. See Creating and Managing Notebook Servers.
  2. In your notebook environment, activate the Ray-specific Python kernel.
  3. To ensure optimal performance, use dedicated directories containing only the essential files needed for that job submission as a working directory.

    For example, if you do not see the Ray-Tune folder in the <username> directory, copy the folder from the shared/ezua-tutorials/current-release/Data-Science/Ray/Ray-Tune directory into the <username> directory. The shared directory is accessible to all users. Editing or running examples from the shared directory is not advised. The <username> directory is specific to you and cannot be accessed by other users.

  4. Open the independent-tune-trials-executor.ipynb file in the <username>/Ray-Tune directory.
  5. Select the first cell of the independent-tune-trials-executor.ipynb notebook and click Run the selected cells and advance (play icon). Continue until you run all cells.

Results

After successful completion, you can view the trial metadata as follows:

To learn about this tutorial in detail, see Ray Tune Example from open-source Ray documentation.