Monkey: Platform-Agnostic Hybrid-Cloud Cluster Compute Orchestration Designed for AI/ML

As AI/ML research progresses, the amount of compute needed to train and evaluate state-of-the-art AI algorithms consistently increases. With increasing needs for compute, researchers spend time designing distributed systems to scalably train and hyper-parameter optimize their latest model rather than focusing on their core research. We aim to build a fault-tolerant distributed system capable of cheaply and flexibly scheduling reproducible research training jobs on heterogeneous hybrid-cloud compute clusters including local machines and provider agnostic cloud machines. Our system focuses on ML researchers with two main goals, minimizing costs (using preemptible/spot-instances) and user friendliness. The system aims to require minimal user setup and configuration, allowing researchers to quickly get started training models. The Monkey System includes a web console and visualization dashboard to track, evaluate, and compare multiple jobs’ progress and results.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2021

Author dc:creator

• Lamp, Avery

Advisor dc:contributor.advisor

• Agrawal, Pulkit

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/139258

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/139258