NZ RSE hackathon: use machine learning to optimise computational resources

New Zealand eScience Infrastructure(NeSI) designs, builds, and operates a shared national platform of advanced computing resources and data services. All researchers in Aotearoa New Zealand have access to NeSI’s platforms. Through NeSI, researchers can access high-capacity CPUs, GPUs and high memory nodes; tailored software environments; expertise in research software and data science engineering; and skills training in computational  tools and approaches. NeSI enables researchers to investigate large, unique, or complex projects more easily, quickly, and accurately.  To learn more, get in touch: info@nesi.org.nz.

New Zealand eScience Infrastructure (NeSI)

NeSI and the NZ Centre for Earthquake Resilience are partnering to offer a machine learning hackathon as a precursor to NZ RSE Conference.

<p>NeSI is hosting its first machine learning hackathon this September and you are invited to participate! </p><p>Over four days you will apply machine learning techniques to a real data science problem that comes from the NZ research community.  This self-paced challenge is ideal for individuals or teams wanting to gain confidence in using machine learning to analyse real datasets. Researchers and research software engineers from all domains are encouraged to participate.  </p><h2>The Challenge</h2><p>NeSI is proud to partner with the NZ Centre for Earthquake Resilience (QuakeCoRE), which is conducting large-scale, computationally intensive numerical ground motion simulations to assess earthquake risks around New Zealand.  A particular challenge is that modelling low frequency ground motion can take between a few compute core-hours to thousands of core-hours on NeSI’s platforms. In this hackathon, participants will develop a model to predict the number of core hours required to run a model, based on a number of features (problem size, number of of parallel processes, domain decomposition, etc). The solutions gathered will be judged based on the solution performance (details on judgement criteria TBD). </p><h2>Event Details</h2><p><strong>Start:</strong> 3:00pm Thursday, September 3th </p><p><strong>End: </strong>3:00pm Tuesday September 8th</p><p><strong>Location:</strong> online, solution submission guidelines TBD</p><p><strong>Other info: </strong>This challenge was designed so that a decent solution can be generated in under 10 hours. The problem size is suitable to run a on a laptop, <strong>you do not need to be a NeSI user to participate</strong></p><p>At 9:00am on September 4th the challenge will kick-off with all hackathon participants receiving a full challenge brief via email.  For support during the hackathon we will have zoom &#x27;office hours&#x27; where you can get help or guidance as required.  </p><p>The top solution for the hackathon will be announced at the <a href="https://www.rseconference.nz/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">NZ Research Software Engineering Conference</a> on 11 September 2020. </p><h2>Who is this event for?</h2><p>This event is for researchers and research software engineers (RSEs) who are interested in machine learning and want to gain experience working on a real data science problem.  Both individuals and teams are welcome to register. </p><p>If you are unsure if this event is for you please message training@nesi.org.nz and we will be glad to guide you.</p><h2>More Information</h2><p>More information about solution submissions to come. If you have any questions or would like more information about this session, please email <a href="mailto:training@nesi.org.nz" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">training@nesi.org.nz</a>.</p>

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NZ RSE hackathon: use machine learning to optimise computational resources

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NZ RSE hackathon: use machine learning to optimise computational resources

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