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 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:
PipelineOptions
to specify the maximum number of parallel API calls. Use direct_num_workers
for the Direct Runner or max_num_workers
for the Google Cloud Dataflow Runner. Refer to the Beam Capability Matrix for information about other runners.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:
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:
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.