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Workload Management

A workload is a collection of containers (e.g., a Kubernetes Deployment, DaemonSet, or StatefulSet) that run a particular application or service inside a workspace. Most commonly a workspace and workload has a one-to-one relationship which simplifies management. When you start a Workspace, you effectively launch its associated workload, and stopping or deleting the Workspace likewise terminates the underlying workload. For example, interactive workloads such as Jupyter Notebook or VSCode environments.

Viewing and Controlling Workloads

  1. Workloads Dashboard

    • In the Workloads section (or via the MemVerge AI UI’s “Workloads” page), you’ll see a list of running workloads.
    • Each row typically shows the Name of the workload, its Status (e.g., Running, Pending), the Project, and how long ago it was Launched.

    Workload Dashboard

  2. Actions

    • From this view, you can Stop a workload (equivalent to stopping the corresponding Workspace).
    • You can also Delete a workload entirely if it’s no longer needed. Doing so removes the Workspace and its running container from the cluster.

  3. Detailed Workload Information

    • Clicking on a workload often reveals logs, events, or detailed container metrics. These insights help troubleshoot issues like unexpected restarts or resource bottlenecks.

Best Practices

  • Use Projects: Assign your workloads (and hence Workspaces) to a relevant Project for logical grouping and easier management.
  • Monitor Status: Keep an eye on workload statuses in case of errors (e.g., CrashLoopBackOff) that might require adjustments to your container image or resource limits.
  • Resource Efficiency: Stop or delete workloads that are no longer active to free cluster resources (GPUs, CPUs, memory).
  • Persistent Data: Attach volumes to any workload-based session (Workspace) that needs to retain data beyond the lifecycle of the container.