> ## Documentation Index
> Fetch the complete documentation index at: https://docs.archetypeai.app/llms.txt
> Use this file to discover all available pages before exploring further.

> ## Agent Instructions
> Start with /introduction/getting-started. Use the Direct Query API (POST /query) with the Newton Fusion model (text, image, and video reasoning) or the Newton Omega encoder (time-series embeddings). ATAI_API_ENDPOINT must include the version path: /v0.5 for most APIs, /v0.6 for the Fine-Tuning Service. Pages whose descriptions are marked (Archived) document the legacy Lens runtime — do not use them for new projects.

# Batch Manager

> Create, manage, and monitor batch processing jobs from the Batch Manager dashboard in the Developer Console.

The [Developer Console](https://console.u1.archetypeai.app/) supports processing large datasets asynchronously using
the **Batch Manager**. The Batch Manager lets you create and manage batch processing jobs, as well as monitor their
status.

Selecting the Batch Manager in the console presents you with the Batch Manager dashboard, which shows existing jobs,
along with filters to let you see only jobs in the desired state, job search functionality, and other tools to help
you manage and monitor jobs easily.

## Creating a Batch Processing Job

To create a batch processing job:

1. Click the **Create** button on the Batch Manager dashboard page.
2. Enter a name for the job.
3. Choose the workflow key to use when running the job. This specifies the base model you want
   to use.
4. Enter the configuration of the job using YAML. For example, for the Machine State
   Classification workflow, make sure the column names in the YAML configuration match the
   names of the columns in your data files.
5. Select or upload the files to run the job against. You can select up to 50 files.
6. For the Machine State Classification workflow, select N-shot example files to train with. Be sure to add
   appropriate labels for each.
7. *If you want to use a fine-tuned model*, enter the checkpoint ID of the fine-tuning job that
   generated the fine-tuned model under **Fine-tuned Checkpoint**. See
   [Fine-Tuning](/developer-console/fine-tuning) for details on creating fine-tuned models. Make
   sure to use a fine-tuned model that corresponds to the workflow key you selected.
8. When everything's configured the way you want it, click **Create Job**.

## Viewing the Status of a Batch Processing Job

To view status information about a job, such as its progress, its logs, and other job details, click on the job in the
Batch Manager dashboard. The job status page displays information about the job, including:

* The job's status (`PENDING`, `ADMITTED`, `RUNNING`, `PAUSED`, `COMPLETED`, `INTERRUPTED`, `FAILED`, `PREEMPTED`, or `CANCELLED`)
* The amount of time the job has spent running
* The total amount of time the job has spent in the queue
* The job's ID
* The job's task type
* When the job was created
* When the job started running
* The YAML configuration of the job
* Processing details about the job

The processing details pane offers three tabs:

* **Progress** shows the execution process of the job, such as the timestamp at which training and inference began for
  each step of the job.
* **Logs** shows detailed logs for the batch processing job.
* **Artifacts** lists all of the artifacts generated by the job.

## Downloading Batch Artifacts

To download the artifacts generated by completed jobs, you have two options:

* To download all of the artifacts for a batch at once, click the **Download Artifacts** button to the right of the
  job title. This will download a Zip archive containing all the artifact files.
* To download the artifact file for a specific job, click the download icon at the right side of that artifact's row
  in the **Job Artifacts** view.

## Canceling a Batch Processing Job

To cancel a running job, click on the job in the Batch Manager dashboard, then click the **Cancel** button to the
right of the job's title.