This position is offered through the ANU Computing Internship (COMP3820 / COMP4820 / COMP8830)
Company
ML Research Labs supports Trellis Data Group with world-leading machine learning research and implementation.
Project - Democratise Deep Learning Through Algorithmic Optimisation - Inference
Recently, deep learning models have grown in performance and in size. For example, GPT-3 has 175 billion parameters, while Google’s PaLM has over 500 billion parameters. They can no longer be trained or deployed on regular computers. For example, the electricity used to train GPT-3 is equivalent of an entire suburb’s electricity consumption over a month.
That means most people won’t have the resources to train or run large-scale deep learning models. In order to let deep learning models to run on regular computers again, we need to use the computation resources more efficiently:
Training and inference optimisation (i.e. More GPU efficient, Large-scale dataset, large-scale model, fine-tuning larger models)
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This project will focus on inference
- Model quantisation capability
Required technical skills
Software Engineering, Python, Pytorch
Required/preferred professional and other skills
Professional Written and Oral Communication
Delivery Mode
Hybrid
Student location
Student must be located in Canberra only
Project’s Special Requirements/ Conditions
Intern requires Police Check
Type of internship
Paid
How to apply
Applications will be invited only from those students who have been confirmed as the eligible students to apply for the Computing Internship courses COMP3820 or COMP4820 or COMP8830 in the Main Round but unsuccessful to secure a placement in the Main Round.
No new applications will be accepted, see information available on the Computing Internship page.
Eligible students will receive the Supplementary Project Submission Form page between Wednesday 7th November to Wednesday 15th November via an email from studentemployability.cecc@anu.edu.au.
You can nominate multiple preferred Internship projects and host organisations through the one online application form.
As you have already submitted your resume in the Main Round, for the Supplementary Round Application will require you to upload only the following documents.
- An Expression of Interest (limit 350 words) for each organisation you wish to apply to (for organisations with multiple projects only submit one Expression of Interest but state clearly which project/s you wish to be considered for).
Semester 1 2024 Supplementary Project Submissions open on Wednesday 8th November 2023 to Wednesday15th November 2023.