Coded computation is an emerging approach that applies ideas from coding theory to impart resource-efficient resilience against slowdowns and failures that occur in large-scale distributed computing systems. This repository contains a framework for exploring the use of machine learning to apply coded computation to broader classes of computations. More background on coded computation, its challenges, and the potential for machine learning within coded computation is provided in this blog post.
This repository focuses primarily on applying learning-based coded computation to impart resilience to distributed systems performing inference with neural networks.
This repository contains the code used for the following papers:
-
Learning-Based Coded Computation. Jack Kosaian, K. V. Rashmi, and Shivaram Venkataraman. In IEEE Journal on Selected Areas in Information Theory, 2020
-
Parity Models: Erasure-Coded Resilience for Prediction Serving Systems. Jack Kosaian, K. V. Rashmi, and Shivaram Venkataraman. In Proceedings of the 27th ACM Symposium on Operating Systems Principles (SOSP), 2019
-
Learning a Code: Machine Learning for Approximate Non-Linear Coded Computation. Jack Kosaian, K. V. Rashmi, and Shivaram Venkataraman. arXiv preprint arXiv:1806.01259, 2018
This repository originated as the artifact associated with the SOSP 2019 paper Parity Models: Erasure-Coded Resilience for Prediction Serving Systems. It has since evolved beyond serving this paper in isolation. The original artifact associated with the SOSP 2019 paper is located in the sosp2019-artifact branch.
- train: Code for training a neural network parity model
- clipper-parm: Code for ParM, a prediction serving system that employs learning-based coded computation to impart resilience against slowdowns and failures. For more details, see our paper.
Please see the READMEs in each of these subdirectories for more details.
If you are only interested in the training portion of the repository, you can clone the repository as:
git clone https://github.com/Thesys-lab/parity-models.gitIf you are interested in running ParM, the prediction serving system employing
parity models, you will need to clone required submodules using the
--recursive flag:
git clone --recursive https://github.com/Thesys-lab/parity-models.gitCopyright 2019, Carnegie Mellon University
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
We graciously acknowledge support from the National Science Foundation (NSF) under grant CNS-1850483, an NSF Graduate Research Fellowship (DGE-1745016 and DGE-1252522), and Amazon Web Services.