hami-core 1.0.19

A Python library for batched backend scheduling

hami Towards Minimized User Input for Ensemble Pipeline Serving

torchpipe some hami plugins

torchpipe is an alternative choice for Triton Inference Server, mainly featuring similar functionalities such as Shared-momory, Ensemble, and BLS mechanism.

Version Migration Notes

The core functionality of TorchPipe (v0) has been extracted into this standalone Hami library.

TorchPipe (v1) is a collection of deep learning computation backends built on this Hami library, primarily integrating third-party libraries including TensorRT, OpenCV, and LibTorch.

Please note that the migration of all functionalities to TorchPipe (v1) is still in progress. We are actively working on completing this transition.

Overview

torchpipe is a multi-instance pipeline parallel library that acts as a bridge between lower-level acceleration libraries (such as TensorRT, OpenCV, CVCUDA) and RPC frameworks (like Thrift), ensuring a strict decoupling from them. It offers a thread-safe function interface for the PyTorch frontend at a higher level, while empowering users with fine-grained backend extension capabilities at a lower level.