Using GPU Devices

g-learn can run on CUDA-capable GPU devices with the following installed:

1. NVIDIA graphic driver,

2. CUDA libraries.

CUDA Version

The version of CUDA libraries installed on the user’s machine should match the version of the CUDA libraries that g-learn package was compiled with. This includes matching both major and minor parts of the version numbers. However, the version’s patch numbers do not need to be matched.

Note

The g-learn package that is installed with either pip or conda already has built-in support for CUDA Toolkit. The latest version of g-learn is compatible with CUDA 12.2.x, which should match the CUDA version installed on the user’s machine.

Methods of Setting up CUDA and g-learn

There are three ways to use g-learn with a compatible version of CUDA Toolkit:

1. Install NVIDIA CUDA Toolkit with a CUDA version compatible with an existing g-learn installation. In this way, you can keep the g-learn package that is already installed with pip or conda.

2. Compile g-learn from the source for a specific version of CUDA to use an existing CUDA library. glearn is a package used by g-learn. In this way, you can keep the current CUDA installation.

3. Use docker image with pre-installed g-learn, glearn, CUDA libraries, and NVIDIA graphic driver. This is the most convenient way as no compilation or installation of g-learn and CUDA Toolkit is required.

The above methods are described in the following sections:

Installing g-learn on GPU

• 1. Install NVIDIA CUDA Toolkit
  • 1.1. Install CUDA Runtime Libraries
    • 1.1.1. Add CUDA Repository
    • 1.1.2. Install Minimal CUDA Libraries
  • 1.2. Install NVIDIA Graphic Driver
  • 1.3. Install OpenMP
• 2. Compile g-learn from Source with CUDA
• 3. Use g-learn Docker Container on GPU
  • 3.1. Install Docker
  • 3.2. Install NVIDIA Container Toolkit
  • 3.3. Get g-learn Docker image
  • 3.4. Use g-learn Docker Container on GPU

Once you have set up g-learn on GPU devices, you can refer to the following sections to learn how to execute g-learn functions on GPUs:

Using g-learn on GPU

• 1. Inquiry GPU and CUDA with g-learn
  • 1.1. Locate CUDA Toolkit
  • 1.2. Detect NVIDIA Graphic Driver
  • 1.3. Detect GPU Devices
• 2. Run g-learn Functions on GPU
  • 2.1. A Simple Example
  • 2.2. Get Process Information
  • 2.3. Verbose Output
  • 2.4. Set Number of GPU Devices
• 3. Deploy g-learn on GPU Clusters
  • 3.1. Load Modules
  • 3.2. Interactive Session with SLURM
  • 3.3. Submit Jobs to GPU with SLURM