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A training job in Baseten progresses through several states from creation to completion. Understanding these states helps you monitor and manage your training jobs effectively.

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Job states

StateDescriptionActiveTerminal
TRAINING_JOB_CREATEDInitial state when a job is first created. Baseten has received the training configuration and persisted it to our records.✅
TRAINING_JOB_DEPLOYINGBaseten is deploying the job, including provisioning compute resources and installing dependencies.✅
TRAINING_JOB_RUNNINGThe training code is actively executing.✅
TRAINING_JOB_COMPLETEDThe job has successfully finished execution. Any checkpoints or artifacts have been saved and uploaded.✅
TRAINING_JOB_DEPLOY_FAILEDThe job failed to deploy. This is likely due to a bad image or a resource allocation issue.✅
TRAINING_JOB_FAILEDThe job encountered an error and could not complete successfully. Check the logs for error details.✅
TRAINING_JOB_STOPPEDThe job was manually stopped by a user.✅

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State transitions

Jobs typically progress through states in the following order:
  1. TRAINING_JOB_CREATED → TRAINING_JOB_DEPLOYING: Automatic transition once resources are allocated
  2. TRAINING_JOB_DEPLOYING → TRAINING_JOB_RUNNING: Automatic transition once environment setup is complete
  3. TRAINING_JOB_RUNNING → TRAINING_JOB_COMPLETED: Automatic transition upon successful completion
A job may enter TRAINING_JOB_FAILED from any state if an error occurs. Similarly, TRAINING_JOB_STOPPED can be entered from any active state (DEPLOYING or RUNNING) when manually stopped. You can monitor these state transitions using the CLI command: 
truss train view # shows all active jobs
truss train view --job-id <your_job_id> # shows a specific job
Or track a specific job’s progress with: 
truss train logs --job-id <your_job_id> --tail
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