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Overview

A job runs a command in a container and exits when the command finishes. Use it for training, inference, and batch processing workloads. There are three ways to create a job:
  • Console — fill in the Create Job form and submit. Best for your first few jobs.
  • CLI — run vesslctl job create. Best for automation, scripting, and reproducible runs.
  • Convert a workspace — clone a workspace’s image, resource spec, and volumes into a job. See Convert a workspace to a Job.

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Prerequisites

  • Available credit balance: Job creation is blocked when the balance is zero or negative. Add a payment method and top up from Billing.
  • (CLI only) Authenticated vesslctl: Run vesslctl auth login to complete the browser OAuth flow. See the CLI Quickstart for the full setup.
  • (Optional) Persistent volume for outputs: Create an Object storage or Cluster storage volume ahead of time if you need to keep model checkpoints or other artifacts.

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Create from the console

Open Jobs in the sidebar and click Create Job, then fill in the form. Most fields match the workspace creation flow — a job adds a command to run and drops the interactive pieces (SSH, ports).
Create a new Job form showing job name, command, working directory, environment variables with organization secrets, hardware configuration (GPU product, region, and count), persistent volume, container image, and a pricing summary with a Create button
Review the Pricing summary on the right and click Create to submit. You’re taken to the job’s detail page, where you can watch status, logs, and metrics.

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Create from the CLI

Provide a cluster, resource spec, container image, command to run, and any volumes or environment variables you need.
Run vesslctl cluster list and vesslctl resource-spec list to discover available clusters and GPU specs. See vesslctl job create for the full flag reference.

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Persist job output

Jobs run in ephemeral containers — anything written outside a mounted volume disappears when the job ends. Attach at least one Object storage or Cluster storage volume so your outputs survive.
  • Object storage (--object-volume): Shared across clusters, ideal for final artifacts like trained models and evaluation metrics. Mount at a dedicated path such as /output.
  • Cluster storage (--cluster-volume): Fast in-cluster storage, ideal for intermediate checkpoints during long training. Mount at /workspace or similar.
Temporary storage is cleared when a job ends, even after succeeded. If your training script writes to /tmp or the current directory without a mounted volume, the results are lost.

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Reuse a job configuration

Export an existing job’s configuration as JSON and resubmit it later:

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Submit a job from inside a workspace

Workspaces ship with vesslctl pre-authenticated with a workload token, so you can iterate on a script in JupyterLab and submit the same code as a batch job from the same shell. See vesslctl workspace for details.

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See also