Running Ray GPU Example

Describes how to run the Ray GPU example in HPE Ezmeral Unified Analytics Software.

Prerequisites

About this task

In this tutorial, you will run the sample Ray GPU example and analyze logs to ensure that Unified Analytics is running the GPU-accelerated jobs.

You will complete the following steps:

Procedure

  1. Create a notebook server using the jupyter-tensorflow-cuda-full image with at least 3 CPUs and 4 Gi of memory in Kubeflow. See Creating GPU-Enabled 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.
    NOTE

    If you do not see the Ray-GPU folder in the <username> directory, copy the folder from the shared/ezua-tutorials/current-release/Data-Science/Ray 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.

    If the Ray-GPU folder is not available in the shared/ezua-tutorials/current-release/Data-Science/Ray directory, perform:
    1. Go to GitHub repository for tutorials.
    2. Clone the repository.
    3. Navigate to ezua-tutorials/Data-Science/Ray.
    4. Navigate back to the <username> directory.
    5. Copy the Ray-GPU folder from the ezua-tutorials/Data-Science/Ray directory into the <username> directory.
  4. Open the ray-gpu-executor.ipynb file in the <username>/Ray-GPU directory.
  5. Select the first cell of the ray-gpu-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 that Unified Analytics is running the GPU-accelerated Ray job.