GPU environments (Jupyter Notebooks with Python with GPU)

With GPU environments, you can reduce the training time needed for compute-intensive machine learning models you create in a notebook. With more compute power, you can run more training iterations while fine-tuning your machine learning models. GPU environments are available for Python only.

Service GPU environments are not available by default. An administrator must install the Jupyter notebooks with Python for GPU service on the IBM Cloud Pak for Data platform. To determine whether the service is installed, open the Services catalog and check whether the service is enabled.

GPU environment templates

To use a GPU environment, you must create a new GPU environment template.

You must have the Admin or Editor role within the project to create an environment template.

To create a GPU environment template:

  1. From the Manage tab of your project, select the Environments page, then under Templates, click New template.

  2. Enter a name and a description.

  3. Select the GPU environment configuration type.

  4. Select the hardware configuration. Select the size dependent on the complexity of the model operations and the number of model training iterations you'd like to perform.

    • Specify the hardware size depending on the complexity of your analytics workload and available resources. The default size is 1 GPU and 1 vCPU and 2 GB RAM.

  5. Select the Python software version.

The environment template details are displayed. You can change your hardware settings by hovering over the setting.

The GPU environments with Python 3.10 include data science libraries from the 22.2 Runtime release that work with the NVIDIA CUDA Toolkit 11.4. The GPU environments with Python 3.9 include data science libraries from the 22.1 Runtime release that work with the NVIDIA CUDA Toolkit 11.2. Runtime 22.1 on Python 3.9 is deprecated.

You can add your own custom libraries in addition to those that are pre-installed for you by creating a customization. See Customizing environment templates.

Using GPU environments in notebooks

After you have created a GPU environment template, you can select to run your notebook in that environment at the time you create the notebook.

In a project, you can run more than one notebook using the same GPU environment template. This means that if you open a second notebook with the same environment template in the same project, a second kernel is started in the same runtime. The runtime resources are shared by the Jupyter kernels that you start in the runtime. The runtime is started per single user and not per notebook.

Next steps

Parent topic: Environments