learning/prompts/documentation-lookup/42_ai_custom_inference.md

Prompt:
How can I use custom remote inference calls in my Apache Beam pipeline?

Response:
The optimal approach for conducting inference within an Apache Beam pipeline is by leveraging the [RunInference API](https://beam.apache.org/releases/pydoc/current/apache_beam.ml.inference.html#apache_beam.ml.inference.RunInference) provided by the Apache Beam Python SDK. This feature allows you to seamlessly incorporate ML models into your pipeline or execute remote inference calls.

When developing custom inference calls, consider the following factors:
* **API quotas**: heavy loads may lead to reaching API quota limits. You can use [`PipelineOptions`](https://beam.apache.org/documentation/programming-guide/#configuring-pipeline-options) to specify the maximum number of parallel API calls. Use `direct_num_workers` for the [Direct Runner](https://beam.apache.org/documentation/runners/direct/) or `max_num_workers` for the [Google Cloud Dataflow Runner](https://beam.apache.org/documentation/runners/dataflow/). Refer to the [Beam Capability Matrix](https://beam.apache.org/documentation/runners/capability-matrix/) for information about other runners.
* **Error handling**: it is essential to handle errors in case of API call failures. Consider implementing exponential backoff for retries or utilizing dead-letter queues for failed API calls.
* **Monitoring**: incorporate monitoring and performance metrics to track the performance of your inference calls and the health of your pipeline.
* **Batching**: batching can be used to send multiple inputs in a single API call for improved efficiency.

To execute external API calls with the `RunInference` transform, you will need to create a `beam.DoFn` in the form of a custom model handler:

```python
class CustomModelHandler(ModelHandler):
  """DoFn that accepts a batch of inputs and sends that batch to the remote API for inference"""

  def initialize_client(self):
    """Initiate the Custom remote API client."""
    client = ... # Initialize the client
    return client

  def run_inference(self, batch, model, inference):

    # Initialize the client.
    client = self.initialize_client()

    # Prepare a batch request for all inputs in the batch.
    inputs = ... # Process inputs from the batch
    input_requests = ... # Prepare input requests for the model
    batch_request = ... # Prepare batch request for the model

    # Send the batch request to the remote endpoint.
    responses = client.request(request=batch_request).responses

    return responses
```

Integrate this custom model handler into your pipeline as demonstrated in the following example:

```python
with beam.Pipeline() as pipeline:
  _ = (pipeline | "Create inputs" >> beam.Create(<read input data>)
                | "Inference" >> RunInference(model_handler=CustomModelHandler())
                | "Process outputs" >> beam.Map(<write output data>)
  )
```

For a comprehensive example of using the RunInference API for remote inference calls, refer to the [Apache Beam GitHub repository](https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/custom_remote_inference.ipynb).