Setup GPU Workbench
Create a GPU-enabled data science workbench with common ML frameworks pre-installed.
Usage
typescript
import { setupGpuWorkbench } from './skills/setup-gpu-workbench';
await setupGpuWorkbench({
projectName: "ml-experiments",
workbenchName: "training-notebook",
framework: "pytorch",
gpuCount: 1,
storageSize: "100Gi"
});
Parameters
| Parameter | Required | Description |
|---|---|---|
| projectName | Yes | Target OpenShift AI project |
| workbenchName | Yes | Name for the workbench |
| framework | No | ML framework: pytorch (default), tensorflow |
| gpuCount | No | Number of GPUs (default: 1) |
| containerSize | No | Container size: Medium (default), Large, X-Large |
| storageSize | No | Persistent storage size (default: 50Gi) |
What This Skill Does
- •Ensures the project exists
- •Creates a workbench with GPU support
- •Configures appropriate container resources
- •Attaches persistent storage for notebooks and data
- •Enables OAuth authentication
Framework Options
- •pytorch: CUDA-enabled PyTorch with Jupyter
- •tensorflow: CUDA-enabled TensorFlow with Jupyter