Jobs
A training job runs a training configuration against a dataset.
It brings together a workspace, dataset, training configuration, compute configuration, and optional constraints. When the job completes, it creates the model that can be inspected, evaluated, and deployed.
Training jobs are asynchronous. Create a job, then retrieve it to monitor status, progress, current stage, and the model produced by the run.
Create a training job
Create a training job when the dataset, workspace, training configuration, and compute configuration are ready.
The request names the model that will be created. It can also include individual model constraints, constraint groups, maximum runtime settings, and managed spot training.
Creating a training job requires an active workspace. Use the workspace id as the active workspace context when starting the job.
Managed spot training
managed_spot_training controls whether the job can use managed spot instances to reduce cost.
Checkpointing is not currently supported for managed spot training. If a spot instance is interrupted, training starts again from the beginning. This can lead to long waits or cause the job to time out, because spot availability and interruptions are unpredictable.
When managed spot training is enabled, maximum_wait_time_seconds controls how long the job may wait to start. maximum_runtime_seconds controls how long the job may run.
Monitor jobs
Retrieve jobs to monitor training progress or inspect previous runs.
To inspect a specific job, retrieve it by id.
Job responses include the job status, overall progress, current stage, and stage progress.
Training stages
An induction training job can move through these stages:
- Shard sampling
- Interactions
- Binning
- Training
- Build
- ESM
Update a job
Update a training job when you need to rename it.
Delete a job
Delete a training job when it should no longer be kept.

