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Once you’ve submitted training jobs, Baseten provides tools to manage your TrainingProjects and individual TrainingJobs. You can use the CLI or the API to manage your jobs.

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TrainingProject management

  • Listing Projects: To view all your training projects: 
    truss train view
    This command will list all TrainingProjects you have access to, typically showing their names and IDs. Additionally, this command will show all active jobs.
  • Viewing Jobs within a Project: To see all jobs associated with a specific project, use its project (obtained when creating the project or from truss train view): 
    truss train view --project <project_id or project_name>
  • Deleting a TrainingProject: You can delete a training project through the API, or through the dashboard. Using the API: 
    curl -X DELETE https://api.baseten.co/v1/training_projects/<training_project_id> \
  -H "Authorization: Bearer YOUR_API_KEY"
    From the Baseten dashboard:
    1. Select the training project you want to delete.
    2. Type the project name (for example, demo/qwen3-0.6b) to confirm.
    3. Select Delete.
    When you delete a project, the following data is permanently deleted with no archival or recovery option:
    • All undeployed checkpoints from every job in the project
    • All data in the project’s training cache ($BT_PROJECT_CACHE_DIR)
    Checkpoints that have been deployed aren’t affected.

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TrainingJob management

After submitting a job with truss train push config.py, you receive a project_id and job_id.
  • Listing Jobs: As shown above, you can list all jobs within a project using: 
    truss train view --project <project_id or project_name>
    This will typically show job IDs, statuses, creation times, etc.
  • Checking Status and Retrieving Logs: To view the logs for a specific job, you can tail them in real-time or fetch existing logs.
    • To view logs for the most recently submitted job in the current context (for example, if you just pushed a job from your current terminal directory): 
      truss train logs --tail
    • To view logs for a specific job using its job-id: 
      truss train logs --job-id <your_job_id> [--tail]
      Add --tail to follow the logs live.
  • Understanding Job Statuses: The truss train view and truss train logs commands will help you track which status a job is in. For more on the job lifecycle, see the Lifecycle page.
  • Stopping a TrainingJob: If you need to stop a running or pending job, use the stop command with the job’s project ID and job ID: 
    truss train stop --job-id <your_job_id>
truss train stop --all # Stops all active jobs; Will prompt the user for confirmation.
    This will transition the job to the TRAINING_JOB_STOPPED state.
  • Deleting a TrainingJob: You can delete a training job through the API, or through the dashboard. Using the API: 
    curl -X DELETE https://api.baseten.co/v1/training_projects/<training_project_id>/jobs/<training_job_id> \
  -H "Authorization: Bearer YOUR_API_KEY"
    From the Baseten dashboard:
    1. Select the project containing the job.
    2. Select the job you want to delete.
    3. Type the job name (for example, job-2) to confirm.
    4. Select Delete.
    When you delete a job, all undeployed checkpoints are deleted permanently. There’s no archival or recovery option. Checkpoints that have been deployed aren’t affected.
  • Understanding Job Outputs & Checkpoints:
    • The primary outputs of a successful TrainingJob are model checkpoints (if checkpointing is enabled and configured).
    • These checkpoints are stored by Baseten. For more information on how CheckpointingConfig works, see Checkpoints.
    • When you are ready to deploy a model, you will specify which checkpoints to use. The model_name you assign during deployment (using DeployCheckpointsConfig) becomes the identifier for this trained model version derived from your specific job’s checkpoints.
    • You can see the available checkpoints for a job through the Training API.

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Queue priority

When GPU capacity is full, new training jobs wait in a queue (the TRAINING_JOB_PENDING state). Set --priority on truss train push to control the order jobs leave that queue: 
truss train push config.py --priority 100
Higher values run first. Jobs default to priority 0. Raise a job above 0 to run it sooner, or drop it below 0 to hold it behind default-priority work; the value is an unbounded integer. Priority only reorders jobs that are waiting for capacity. It doesn’t pause a running job or add capacity, so raising priority helps only when jobs are queued and capacity frees up. Set priority when you submit the job. To change a pending job’s priority, stop it with truss train stop and resubmit at the new value.