Submit a training job
Launch a Workbench training job from a preset or from a custom runtime, compute, and output configuration.
Training jobs run finite ML workloads on Kubernetes compute. The New Training Job modal supports two creation modes:
| Mode | Use when |
|---|---|
| Preset | Your team already has a saved training configuration. This is the fastest and safest path. |
| Custom | You need to choose the runtime, sizing, output destination, trainer overrides, or labels manually. |
Open Training Jobs
Click Workbench in the left sidebar, then click Train > Training > Training Jobs.
Start a new job
Click New Training Job. The modal opens with a Create mode toggle.
Choose Preset or Custom mode
For Preset mode, select a preset, optional compute pool, job name, and optional TTL. Preset mode derives the runtime and sizing from the selected preset.
For Custom mode, enter a job name, runtime kind, runtime name, optional compute pool, node count, CPU per node, memory per node, GPUs per node, and optional GPU vendor.
Configure optional output and overrides
Use Override output destination in Preset mode, or Trainer overrides in Custom mode, when you need to change where outputs are written or how the runtime starts your workload.
| Option | What it does |
|---|---|
| Output type | Use the cluster default, an S3 URI, or a PVC destination. |
| Trainer command | JSON array that overrides the runtime container entrypoint. |
| Trainer args | JSON array of argument strings passed to the trainer. |
| Environment | KEY=VALUE pairs passed to the trainer. |
| Labels | KEY=VALUE pairs attached to the job for filtering and ownership. |
Launch the job
Click Launch job.
Monitor the job
The job appears in the Training Jobs list. Click the row to inspect status, runtime, sizing, logs, output destination, and related Kubernetes resources.
Success looks like this: the training job is created, appears in the list, and moves from pending to running when the requested compute is available.
Use Preset mode unless you need to override the runtime or resource sizing. Presets reduce repeated configuration and help teams submit jobs with known-good settings.