> ## Documentation Index > Fetch the complete documentation index at: https://docs.baseten.co/llms.txt > Use this file to discover all available pages before exploring further. # Serving your trained model > How to deploy checkpoints from Baseten Training jobs as usable models. 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. **This feature works with HuggingFace compatible LLMs**, allowing you to easily deploy fine-tuned language models directly from your training checkpoints with a single command. For optimized inference performance with TensorRT-LLM, BEI and Baseten Inference Stack, see [Deploy with optimized inference engines](/training/deploy-with-engine-builder). To deploy checkpoints, first ensure you have a `TrainingJob` that's running with a `checkpointing_config` enabled. ```python theme={"system"} 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 via [`$BT_CHECKPOINT_DIR`](/reference/sdk/training#baseten-provided-environment-variables). 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).* The default location is "/tmp/training\_checkpoints". To deploy your checkpoint(s) as a `Deployment`, you can: ### CLI Deployment ```bash theme={"system"} truss train deploy_checkpoints [OPTIONS] ``` **Options:** | Option | Type | Description | | --------------------------- | ---- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `--job-id` | TEXT | Job ID to deploy checkpoints from. If not specified, deploys from the most recent training job. | | `--project` | TEXT | Project name or project ID. | | `--project-id` | TEXT | Project ID. | | `--trainer-id` | TEXT | Trainer ID. Deploy checkpoints from a trainer instead of a training job. Mutually exclusive with `--project`, `--project-id`, and `--job-id`. | | `--config` | TEXT | Path to a Python file that defines a `DeployCheckpointsConfig` (see [Advanced CLI Deployment](#advanced-cli-deployment)). | | `--dry-run` | FLAG | Generate a Truss config without deploying. Useful for inspecting or customizing the config before deployment. | | `--truss-config-output-dir` | TEXT | Path to output the Truss config to. Defaults to `truss_configs/_`, or `truss_configs/dry_run_` when using `--dry-run`. | | `--remote` | TEXT | Remote to use. | This will deploy the most recent checkpoint from your training job as an inference endpoint. ### UI Deployment You can also deploy checkpoints directly from the Baseten UI by pressing the dropdown menu on your completed training job and selecting "Deploy" on your selected checkpoint. ### Advanced CLI Deployment You can also: * run `truss train deploy_checkpoints [--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 `. When `deploy_checkpoints` is run, `truss` will construct a deployment `config.yml` and store it on disk. By default, the config is written to `truss_configs/_`. You can control the output location with `--truss-config-output-dir`. To inspect or customize the config before deploying, use `--dry-run` to generate the config without deploying: ```bash theme={"system"} truss train deploy_checkpoints --job-id --dry-run ``` If you'd like to modify the resulting deployment config, you can copy 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](../deployment/deployments) for more details. You can also reference training checkpoints using the `bt://` URI scheme in your [weights configuration](/development/model/bdn#baseten-training). After successful deployment, your model will be deployed on Baseten, where you can run inference requests and evaluate performance. See [Calling Your Model](/inference/calling-your-model) for more details. To download the files you saved to the checkpointing directory or understand the file structure, you can run `truss train get_checkpoint_urls [--job-id=]` to get a JSON file containing presigned URLs for each training job. The JSON file contains the following structure: ```json theme={"system"} { "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": "H200:2x8x128x1600", "name": "H200:2x8x128x1600 - 2 Nodes of 8 H200 GPUs, 1128 GiB VRAM, 128 vCPUs, 1600 GiB RAM", "memory_limit_mib": 1650000, "millicpu_limit": 127900, "gpu_count": 8, "gpu_type": "H200", "gpu_memory_limit_mib": 1155072 }, "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", "node_rank": 0 } ... ] } ``` **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 ## Complex and custom use cases * Custom Model Architectures * Weights Sharded Across Nodes Examine the structure of your files with `truss train get_checkpoint_urls --job-id=`. If a file looks like this: ```json theme={"system"} { "url": "https://bt-training-eqwnwwp-f815d6cd-19bf-4589-bfcb-da76cd8432c0.s3.amazonaws.com/training_projects/lqz9o34/jobs/03yv1l3/rank-4/checkpoint-10/weights.safetensors?AWSAccessKeyId=AKIARLZO4BEQO4Q2A5NH&Signature=0vdzJf0686wNE1d9bm4%2Bw9ik5lY%3D&Expires=1751291056", "relative_file_name": "checkpoint-10/weights.safetensors", "node_rank": 4 } ``` In your Truss configuration, add a section like this: Wildcards `*` match to an arbitrary number of chars while `?` matches to one. ```yaml theme={"system"} training_checkpoints: download_folder: /tmp/training_checkpoints artifact_references: - training_job_id: paths: - rank-*/checkpoint-10/ # Pull in all the files for checkpoint-10 across all nodes ``` When your model pod starts up, you can read the file from the path `/tmp/training_checkpoints/rank-[node-rank]/[relative_file_name]`. For the example above, the file can be read from: ``` /tmp/training_checkpoints//rank-4/checkpoint-10/weights.safetensors ```