A collaboration between Google Cloud and NVIDIA has enabled Apache Beam users to maximize the performance of ML models within their data processing pipelines, using NVIDIA TensorRTand NVIDIA GPUs alongside the new Apache Beam TensorRTEngineHandler.
The NVIDIA TensorRT SDK provides high-performance, neural network inference that lets developers optimize and deploy trained ML models on NVIDIA GPUs with the highest throughput and lowest latency, while preserving model prediction accuracy. TensorRT was specifically designed to support multiple classes of deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and Transformer-based models.
Deploying and managing end-to-end ML inference pipelines while maximizing infrastructure utilization and minimizing total costs is a hard problem. Integrating ML models in a production data processing pipeline to extract insights requires addressing challenges associated with the three main workflow segments:
Preprocess large volumes of raw data from multiple data sources to use as inputs to train ML models to “infer / predict” results, and then leverage the ML model outputs downstream for incorporation into business processes.
Call ML models within data processing pipelines while supporting different inference use-cases: batch, streaming, ensemble models, remote inference, or local inference. Pipelines are not limited to a single model and often require an ensemble of models to produce the desired business outcomes.
Optimize the performance of the ML models to deliver results within the application’s accuracy, throughput, and latency constraints. For pipelines that use complex, computate-intensive models for use-cases like NLP or that require multiple ML models together, the response time of these models often becomes a performance bottleneck. This can cause poor hardware utilization and requires more compute resources to deploy your pipelines in production, leading to potentially higher costs of operations.
Google Cloud Dataflow is a fully managed runner for stream or batch processing pipelines written with Apache Beam. To enable developers to easily incorporate ML models in data processing pipelines, Dataflow recently announced support for Apache Beam’s generic machine learning prediction and inference transform, RunInference. The RunInference transform simplifies the ML pipeline creation process by allowing developers to use models in production pipelines without needing lots of boilerplate code.
You can see an example of its usage with Apache Beam in the following code sample. Note that the engine_handler is passed as a configuration to the RunInference transform, which abstracts the user from the implementation details of running the model.
- [StructValue([(u’code’, u”engine_handler = TensorRTEngineHandlerNumPy(rn min_batch_size=4,rn max_batch_size=4,rn engine_path=rn ‘gs://gcp_bucket/single_tensor_features_engine.trt’)rnrnpcoll = pipeline | beam.Create(SINGLE_FEATURE_EXAMPLES)rnpredictions = pcoll | RunInference(engine_handler)”), (u’language’, u”), (u’caption’, <wagtail.wagtailcore.rich_text.RichText object at 0x3ee440b59b90>)])]
Along with the Dataflow runner and the TensorRT engine, Apache Beam enables users to address the three main challenges. The Dataflow runner takes care of pre-processing data at scale, preparing the data for use as model input. Apache Beam’s single API for batch and streaming pipelines means that RunInference is automatically available for both use cases. Apache Beam’s ability to define complex multi-path pipelines also makes it easier to create pipelines that have multiple models. With TensorRT support, Dataflow now also has the ability to optimize the inference performance of models on NVIDIA GPUs.
For more details and samples to start using this feature today please have a look at the NVIDIA Technical Blog, “Simplifying and Accelerating Machine Learning Predictions in Apache Beam with NVIDIA TensorRT.” Documentation for RunInference can be found at the Apache Beam document site and for Dataflow docs.