Speaker(s):

RunInference: Machine Learning Inferences in Beam

By Andy Ye
12:00-12:25 CDT
Local room: 202

Users of machine learning frameworks must currently implement their own PTransforms for predictions or inferences. Only TensorFlow makes a RunInference beam transform available, but it’s not highly accessible since it’s hosted in the TFX-BSL repo.

We are creating implementations of RunInference for two popular machine learning frameworks, scikit-learn and PyTorch. These will take advantage of both internal optimizations like shared resource models, as well as framework-specific optimizations such as vectorization. It will have a clean simple unified interface, and use types intuitive to developers for inputs and outputs (numpy for scikit-learn, Tensors for PyTorch).

The eventual goal is to support this for many more ML frameworks (e.g. XGBoost, mxnet, Statsmodels, JAX, TensorRT) and remote services (e.g. Vertex AI).

Link to notebook: https://colab.research.google.com/drive/10iPQTCmaLJL4_OohS00R9Wmor6d57JkS