Baseten Training seamlessly integrates with Baseten’s model deployment capabilities. Once your TrainingJob has produced model checkpoints, you can deploy them as fully operational model endpoints. To leverage deploying checkpoints, first ensure you have a TrainingJob that’s running with a checkpointing_config enabled. 
runtime = definitions.Runtime(
    start_commands=[
        "/bin/sh -c './run.sh'",
    ],
    checkpointing_config=definitions.CheckpointingConfig(
        enabled=True,
    ),
)
In your training code or configuration, ensure that your checkpoints are being written to the checkpointing directory, which can be referenced in via $BT_CHECKPOINT_DIR. The contents of this directory are uploaded to Baseten’s storage and made immediately available for deployment. (You can optionally specify a checkpoint_path in your checkpointing_config if you prefer to write to a specific directory). To deploy your checkpoint(s) as a Deployment, you can:
  • run truss train deploy_checkpoints [--job-id <job_id>] and follow the setup wizard.
  • define an instance of a DeployCheckpointsConfig class (this is helpful for small changes that aren’t provided by the wizard) and run truss train deploy_checkpoints --config <path_to_config_file>.
When deploy_checkpoints is run, truss will construct a deployment config.yml and store it on disk in a temporary directory. If you’d like to preserve or modify the resulting deployment config, you can copy paste it into a permanent directory and customize it as needed. This file defines the source of truth for the deployment and can be deployed independently via truss push. See deployments for more details. After successful deployment, your model will be deployed on Baseten, where you can run inference requests and evaluate performance. See Calling Your Model for more details. To download the files you saved to the checkpointing directory, you can run truss train get_checkpoint_urls [--job-id=<job_id>] to get a JSON file containing presigned URLs for each training job. The JSON file contains the following structure: 
{
  "timestamp": "2025-06-23T13:44:16.485905+00:00",
  "job": {
    "id": "03yv1l3",
    "created_at": "2025-06-18T14:30:30.480Z",
    "current_status": "TRAINING_JOB_COMPLETED",
    "error_message": null,
    "instance_type": {
			"id": "H100:2x8x176x968",
			"name": "H100:2x8x176x968 - 2 Nodes of 8 H100 GPUs, 640 GiB VRAM, 176 vCPUs, 968 GiB RAM",
			"memory_limit_mib": 967512,
			"millicpu_limit": 176000,
			"gpu_count": 8,
			"gpu_type": "H100",
			"gpu_memory_limit_mib": 655360
		},
    "updated_at": "2025-06-18T14:30:30.510Z",
    "training_project_id": "lqz9o34",
    "training_project": {
      "id": "lqz9o34",
      "name": "checkpointing"
    }
  },
  "checkpoint_artifacts": [
    {
      "url": "https://bt-training-eqwnwwp-f815d6cd-19bf-4589-bfcb-da76cd8432c0.s3.amazonaws.com/training_projects/lqz9o34/jobs/03yv1l3/rank-0/checkpoint-24/tokenizer_config.json?AWSAccessKeyId=AKIARLZO4BEQO4Q2A5NH&Signature=0vdzJf0686wNE1d9bm4%2Bw9ik5lY%3D&Expires=1751291056",
      "relative_file_name": "checkpoint-24/tokenizer_config.json"
    }
    ...
  ]
}
Important notes about the presigned URLs:
  • The presigned URLs expire after 7 days from generation
  • These URLs are primarily intended for evaluation and testing purposes, not for long-term inference deployments
  • For production deployments, consider copying the checkpoint files to your Truss model directory and downloading them in the model’s load() function