Visual Studio Code Workload¶

This workload deploys a basic Visual Studio Code environment on top of any image with Python (pip) pre-installed. It is ideal for interactive development sessions and experimentation with other workloads.

Persistent Settings¶

By default, this workload is configured to persist VSCode settings, extensions, and workspace configurations across pod restarts using a Persistent Volume Claim (PVC). This means:

Settings: Your VSCode settings, themes, and preferences are preserved
Extensions: Installed extensions persist and don't need to be reinstalled
Workspace: Your workspace configurations and project settings are maintained
User Data: All user customizations and data are stored persistently

The persistent configuration is stored in /workload/{user_id}/.vscode-server/ on the PVC.

Disabling Persistence¶

If you prefer ephemeral storage (settings reset on pod restart), you can disable persistence by setting:

persistent_storage:
  enabled: false

Configuration Parameters¶

You can configure the following parameters in the values.yaml file or override them via the command line:

Parameter	Description	Default
`image`	Container image repository and tag	`rocm/pytorch:rocm6.4_ubuntu24.04_py3.12_pytorch_release_2.6.0`
`imagePullPolicy`	Image pull policy	`Always`
`gpus`	Number of GPUs to allocate (set to 0 for CPU-only mode)	`1`
`memory_per_gpu`	Memory allocated per GPU (in Gi)	`128`
`cpu_per_gpu`	CPU cores allocated per GPU	`4`
`storage.ephemeral`	Ephemeral storage configuration	`128Gi`, `mlstorage`, `ReadWriteOnce`
`persistent_storage.enabled`	Enable persistent storage for VSCode settings and extensions	`false`
`deployment.ports.http`	HTTP port exposed by the service	`8080`
`entrypoint`	Custom entrypoint script	See `values.yaml` for details

For more details see the values.yaml file.

Deploying the Workload¶

To deploy the chart with the release name example, run the following command from the helm/ directory:

helm template example . | kubectl apply -f -

Note: If you set the gpus value greater than 0, ensure you specify a GPU-capable image to utilize the allocated resources properly.

Accessing the Workload Locally¶

To access Visual Studio Code locally, forward the service port to your machine:

kubectl port-forward services/dev-workspace-vscode-example 8080:80

Then, open your browser and navigate to http://localhost:8080.

Accessing the Workload via URL¶

To access the workload through a URL, you can enable either an Ingress or HTTPRoute in the values.yaml file. The following parameters are available:

Parameter	Description	Default
`ingress.enabled`	Enable Ingress resource	`false`
`httproute.enabled`	Enable HTTPRoute resource	`false`

See the corresponding template files in the templates/ directory. For more details on configuring Ingress or HTTPRoute, refer to the Ingress documentation and HTTPRoute documentation, or documentation of the particular gateway implementation you may use, like KGateway. Check with your cluster administrator for the correct configuration for your environment.