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.
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.
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).
| Field | Description |
|---|
| Job name | A name for the job. A random name is generated by default. |
| Command | The command that runs when the job starts, for example python train.py --epochs 10. |
| Working directory | (Optional) Where the command runs. Defaults to the image’s working directory. |
| Environment Variables | Add key-value variables, and link organization secrets instead of pasting credentials in plain text. |
| Hardware Configuration | Choose the GPU product (A100, H100, or CPU only), region, and GPU count. Each count shows its hourly price and availability. |
| Persistent volume | Attach an Object storage or Cluster storage volume with a mount path to keep data across runs. |
| Container image | A Managed image (PyTorch, CUDA, Python) or your own Custom image. |
Create from the CLI
Provide a cluster, resource spec, container image, command to run, and any volumes or environment variables you need.
vesslctl job create \
--name my-training-job \
--resource-spec <spec-slug> \
--image quay.io/vessl-ai/torch:2.9.1-cuda13.0.1-py3.13-slim \
--cmd "python train.py --epochs 10" \
--env WANDB_API_KEY=<your-key> \
--object-volume <volume-slug>:/output \
--tag training-run-2026-04
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. 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.
vesslctl job create \
--name my-training-job \
--resource-spec <spec-slug> \
--image quay.io/vessl-ai/torch:2.9.1-cuda13.0.1-py3.13-slim \
--object-volume <output-volume-slug>:/output \
--cmd "python train.py --output /output"
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.
Reuse a job configuration
Export an existing job’s configuration as JSON and resubmit it later:
vesslctl job export my-job-abc123 > job-config.json
vesslctl job create --file job-config.json
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.
See also
