doubleword-batch

You can submit batch inference jobs for large-scale LLM workloads using the autobatcher Python client to interface with the Doubleword Batch API. It streamlines batch submission, status polling, and result downloads for scalable processing.

Batch inference reduces API inference costs for data pipelines by processing large volumes asynchronously where latency is acceptable. The autobatcher client orchestrates cost-efficient batch submissions, polling, and result retrieval.

Batch inference status monitoring and result retrieval work through a consistent API surface that supports status polling, result downloads, and resumable downloads, allowing you to track and manage batch jobs programmatically.

Yes, you can use the autobatcher client for model evaluation and content generation. It applies to data pipelines where scalable, cost-effective batching is beneficial and latency is acceptable in exchange for cost savings.

Yes, the Doubleword Batch API supports resumable downloads for batch results. This allows you to retrieve large inference outputs reliably without restarting the entire download process if interruptions occur.

You should use batch inference instead of real-time API calls when processing large volumes of data where latency is acceptable. It is ideal for data pipelines, model evaluation, and content generation requiring cost-effective scaling.