Environments to Accelerate Machine Learning-Based Discovery

Bringing together Machine Learning (ML) tools, libraries, and access to data, across large Graphic Processing Unit (GPU) deployments nationally to provide a consolidated platform for ML-based research, deployed at Monash University and University of Queensland.

The confluence of big data, Machine Learning (ML) techniques and parallel computing is making AI useful across a range of research areas. There is increasing sophistication, insight and accuracy which is driving a strong and growing appetite across research groups for access to ML capacity, services, libraries, expertise and training.

The environment developed for Machine Learning will support core ML tools for preprocessing, annotating, training, and validation, and integrate with software development environments. A national outreach and training program will engage the researcher community and increase knowledge.

Start date 10 April 2020

Expected completion date 3 February 2022

Investment by ARDC $484,800

Co-investment partners

Monash University

NCI

Pawsey Supercomputing Centre

QCIF

University of Queensland

Lead node

1 Integrated Development Environment for ML

Fundamental and applied ML researchers will have integrated access to low and high end GPUs, ML tools & libraries, data duration tools and reference datasets.

2 Efficient Tools, and improve knowledge and support for common tools

We will improve our knowledge about deploying and configuration of the most widely used ML tools and libraries, including Tensorflow, PyTorch, Keras; and Data preprocessing tools, such as Pandas, Matlab and Moa. This will enable quick and efficient customisation, deployment and support of ML tools and libraries and will best leverage the limited hardware available.

3 Training and Outreach

We will facilitate knowledge sharing, best practice frameworks and collaborations through training programs and Communities of Practices across the participating universities.

Core features

Accelerate research

We will provide a friction-free environment to test ML ideas, demonstrate success and sometimes ‘fail fast’

Promote interdisciplinary research

Promotion of interaction across disciplines through collaborations with tools and libraries, and annotated datasets

Improve efficiency

Usage of ML tools will be more efficient, and users will be able to better leverage available hardware, software and data capabilities.

Who is this project for?

Researchers who require access to machine learning tools and computing, and are interested in upskilling their machine learning expertise across a wide range of domains including life sciences, engineering, computer science, and increasingly the humanities and social sciences.
Infrastructure providers

What does this project enable?

The project will enable ML researchers and users from a variety of research areas to do their research efficiently and effectively with available reference data and tools, which will greatly facilitate new discoveries. It will also enable infrastructure providers to provide better data and computing infrastructure for supporting ML researchers.