How do I run batch inference on a hosted model endpoint?

Batch inference on a hosted model endpoint usually means sending many inputs in one job rather than calling the endpoint one request at a time. The exact steps depend on the platform, but the workflow is generally:

Typical workflow

1. Prepare your inputs

   • Put your prompts/examples into a file, often JSONL, CSV, or plain JSON.
   • One record per line is common for batch jobs.

2. Choose the batch method

   • Offline batch job: upload a file, create a batch job, and get results later.
   • Synchronous bulk requests: send multiple inputs in one API call if the endpoint supports arrays.
   • Client-side batching: group requests in your code and send them concurrently.

3. Submit the batch job

   • Upload the input file to the service.
   • Create a batch/inference job pointing to the hosted endpoint/model.
   • Configure any parameters like max tokens, temperature, or response format.

4. Monitor job status

   • Poll for statuses like queued, running, completed, or failed.

5. Download results

   • Retrieve the output file or paginated results.
   • Match each output back to its original input using an ID.

Common JSONL format example

{"id":"1","input":"Summarize this article..."}
{"id":"2","input":"Classify this review..."}
{"id":"3","input":"Translate this sentence..."}

Example pseudo-code

# 1. Upload file
input_file_id = upload_file("batch_inputs.jsonl")

# 2. Create batch job
job = create_batch_job(
    endpoint_id="your-hosted-endpoint-id",
    input_file_id=input_file_id,
    parameters={"temperature": 0.2, "max_tokens": 200}
)

# 3. Poll until complete
while job.status not in ("completed", "failed"):
    job = get_batch_job(job.id)
    sleep(30)

# 4. Download results
if job.status == "completed":
    download_file(job.output_file_id, "results.jsonl")

Important considerations

• Throughput vs latency: batch jobs optimize throughput, not immediate response time.
• Input size limits: each batch item and the total batch may have size limits.
• Ordering: outputs may not be returned in the same order; use IDs.
• Retries: handle failed records individually if the platform supports partial failures.
• Rate limits: batching helps reduce request overhead and avoid throttling.

If you’re using a specific platform

The exact API differs depending on whether you mean:

• OpenAI / Azure OpenAI
• Hugging Face Inference Endpoints
• AWS SageMaker endpoints
• Google Vertex AI
• Anthropic / other hosted inference services

If you tell me which platform you’re using, I can give you the exact commands or code.