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When you’ve prototyped in a workspace and want to run the same setup as a job — longer training or parameter sweeps — Convert to Job clones the environment in one click. The original workspace is untouched, so you can keep iterating in it while the job runs in parallel.

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What carries over

The job inherits everything needed to reproduce the workspace runtime, and drops the interactive pieces a job doesn’t use:
Cloned to the jobDropped
Container imageSSH keys
Resource spec (GPU, CPU, region)Custom exposed ports
Mounted Object and Cluster volumes (with mount paths)Init script
The workspace’s own root volume
Jobs run a command to completion and don’t need SSH access, custom ports, or a root volume.

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What you provide

  • Command — the job entrypoint, for example python train.py --epochs 50.
  • Environment variables or secret references (optional) — run-specific settings plus any secrets the script needs.
  • Override the resource spec, image, or volumes (optional) before submitting.

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Convert from the GUI

Workspace detail page showing the Convert to Job button in the top-right action bar, next to View history
  1. Open the workspace detail page (Workspaces → the workspace).
  2. Click Convert to Job in the top-right action bar.
  3. Fill in the Command and any env vars or secrets.
  4. Override the resource spec, image, or volumes if you need a different runtime for the run.
  5. Click Create job.
You’re redirected to the new job’s detail page. The source workspace stays exactly as it was, so you can keep developing in it while the job runs.

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Convert with a coding agent

VESSL Cloud extends beyond the web console to coding agents like Claude Code. The vesslctl skill includes a guide for converting a workspace into a job. Tell the coding agent which workspace to convert and ask in plain language: 
Convert this workspace to a job <workspace-slug>
It moves the environment over without the manual, multi-step flow. If a runtime setting needs to change, just ask the agent in plain language and follow its guidance.

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Tips

  • Store your data on a Cluster storage or Object storage volume. The workspace’s root volume isn’t carried over. Keep datasets, checkpoints, and converted scripts on it, mounted on both the workspace and the job, and the job reads them directly — no data to move.
  • Set a working directory. Separating the working directory from the command keeps runs consistent without repeating the path each time. The same .py can resolve to a different file depending on the working directory, so pinning it helps especially when you run experiments in parallel.
The conversion is a one-shot clone, not a live link. Changes you make to the workspace after conversion don’t flow into the running job, and vice versa.