BharatMLStack is an open-source, end-to-end machine learning infrastructure stack built at Meesho to support real-time and batch ML workloads at Bharat scale
GraphPipe Machine Learning Model Deployment Made Simple What is it? GraphPipe is a protocol and collection of software designed to simplify machine learning model deployment and decouple it from framework-specific model implementations. The existing solutions for model serving are inconsistent and/or inefficient. There is no consistent protocol for communicating with these model servers so it is often necessary to build custom clients for each workload. GraphPipe solves these problems by standardizing on an efficient communication protocol and providing
simple model servers for the major ML frameworks. We hope that open sourcing GraphPipe makes the model serving landscape a friendlier place. Or browse the rest of the documentation. Features A minimalist machine learning transport specification based on [flatbuffers] Simple, efficient reference model servers for [Tensorflow], [Caffe2], and [ONNX]. Efficient client implementations in Go, Python, and Java. This repo contains documentation as well as the flatubuffer definition files that are used by other language specific repos. If you are looking
for GraphPipe clients, servers, and example code, check out our other GraphPipe repos: - the GraphPipe go client library - - - the GraphPipe client library for - a Python implementation of Building flatbuffer definitions If you've got flatc installed you can just but if you don't want to install it, you can and then . (Remember, make needs vars exported, not just on the command-line where you run make). This will produce the go, c, and python libraries, which can then be copied into
their projects graphpipe-go, graphpipe-tf-py, and graphpipe-py, respectively. Contributing All of the GraphPipe projects are open source. To find out how to contribute see CONTRIBUTING. md You can also chat us up on our Slack Channel.
GraphPipe is a protocol and collection of software designed to simplify machine
learning model deployment and decouple it from framework-specific model
implementations.
The existing solutions for model serving are inconsistent and/or inefficient.
There is no consistent protocol for communicating with these model servers so
it is often necessary to build custom clients for each workload. GraphPipe
solves these problems by standardizing on an efficient communication protocol
and providing simple model servers for the major ML frameworks.
We hope that open sourcing GraphPipe makes the model serving landscape a
friendlier place. See more about why we built it
here.
Efficient client implementations in Go, Python, and Java.
What is in this repo?
This repo contains documentation as well as the flatubuffer definition files
that are used by other language specific repos. If you are looking for
GraphPipe clients, servers, and example code, check out our other GraphPipe
repos:
If you've got flatc installed you can just make all but if you don't want
to install it, you can export USE_DOCKER=1 and then make all. (Remember,
make needs vars exported, not just on the command-line where you run make).
This will produce the go, c, and python libraries, which can then be copied
into their projects graphpipe-go, graphpipe-tf-py, and graphpipe-py,
respectively.
Contributing
All of the GraphPipe projects are open source. To find out how to contribute
see CONTRIBUTING.md
