Deep Learning on SHARCNET: Best Practices
Please be aware that this webinar was developed for our legacy systems. As a consequence, some parts of the webinar or its entirety may not be applicable to the national systems (Graham, Cedar, Beluga etc.).
This webinar will focus on introducing the Deep learning hardware and software resources available on SHARCNET now and in the future, and also giving the guidance to properly choose them. A single GPU job (Torch example) and a multi-gpu job (Theano example) will be demonstrated as examples of running jobs on the cluster. Common bottlenecks like I/O will be also be discussed in this webinar.
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This webinar was presented by Fei Mao (SHARCNET) on February 1st 2017 as a part of a series of regular biweekly webinars ran by SHARCNET. The webinars cover different high performance computing (HPC) topics, are approximately 45 minutes in length, and are delivered by experts in the relevant fields. Further details can be found on this web page: https://www.sharcnet.ca/help/index.ph...
SHARCNET is a consortium of 18 Canadian academic institutions who share a network of high performance computers (http://www.sharcnet.ca). SHARCNET is a part of Compute Ontario (http://computeontario.ca/) and Compute Canada (https://computecanada.ca).
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