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==Graham==
 
==Graham==
 
[https://docs.computecanada.ca/wiki/Graham GRAHAM] is a heterogeneous cluster, suitable for a variety of workloads, and located at the University of Waterloo. A total of 35,520 cores and 320 GPU devices, spread across 1,107 nodes of different types. GPU nodes have 128 GB of memory, 16 cores/socket, 2 sockets/node, 2 NVIDIA P100 Pascal GPUs/node (12GB HBM2 memory). Intel "Broadwell" CPUs at 2.1Ghz, model E5-2683 v4. 1.6TB NVMe SSD.
 
[https://docs.computecanada.ca/wiki/Graham GRAHAM] is a heterogeneous cluster, suitable for a variety of workloads, and located at the University of Waterloo. A total of 35,520 cores and 320 GPU devices, spread across 1,107 nodes of different types. GPU nodes have 128 GB of memory, 16 cores/socket, 2 sockets/node, 2 NVIDIA P100 Pascal GPUs/node (12GB HBM2 memory). Intel "Broadwell" CPUs at 2.1Ghz, model E5-2683 v4. 1.6TB NVMe SSD.
 
==Minsky==
 
[https://www.sharcnet.ca/help/index.php/Minsky Minsky]  is an IBM S822LC server with dual power8+ chips, 10 cores per socket, 8 SMT (Simultaneous MultiThreading) per core. 4 NVIDIA Pascal P100 GPUs are connected with NVlinks. SSD is equipped as local /tmp storage to provide 700GB usable space.
 
 
==Copper==
 
[https://www.sharcnet.ca/help/index.php/Copper Copper] is a contributed cluster that has 8 GPU nodes with 4 K80 cards (8 GK210 devices) each node.
 
 
==Mosaic==
 
[https://www.sharcnet.ca/help/index.php/Mosaic Mosaic] is a contributed cluster that has 20 GPU nodes with 1 K20 card each node.
 
  
 
=Software Packages=
 
=Software Packages=
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General interest seminars:  
 
General interest seminars:  
* 2017/02/01 - [https://www.youtube.com/watch?v=NhHIHUUuHjo Deep Learning on SHARCNET: Best Practices], Fie Mao, [[Webinar 2017 Deep Learning on SHARCNET: Best Practices|Abstract]], [[Media:Deep_Learning_on_SHARCNET-Best_Practices.pdf|slides]]
+
* 2017/02/01 - [https://www.youtube.com/watch?v=NhHIHUUuHjo Deep Learning on SHARCNET: Best Practices], Fei Mao, [[Webinar 2017 Deep Learning on SHARCNET: Best Practices|Abstract]], [[Media:Deep_Learning_on_SHARCNET-Best_Practices.pdf|slides]]
 
* 2016/04/27 - [https://www.youtube.com/watch?v=bNrdvxZQK1s Deep Learning on SHARCNET: Tools you can use], Fei Mao, [[Webinar 2016 Deep Learning at SHARCNET: Tools you can use|Abstract]], [[Media:Deep-learning-tool-webinar-fei-2016.pdf |slides]]
 
* 2016/04/27 - [https://www.youtube.com/watch?v=bNrdvxZQK1s Deep Learning on SHARCNET: Tools you can use], Fei Mao, [[Webinar 2016 Deep Learning at SHARCNET: Tools you can use|Abstract]], [[Media:Deep-learning-tool-webinar-fei-2016.pdf |slides]]
 
* 2015/02/04 - [https://www.youtube.com/watch?v=el1iSlP1uOs Deep Learning on SHARCNET: From CPU to GPU cluster], Fei Mao, [[Webinar 2015 Deep Learning on SHARCNET: From CPU to GPU cluster|Abstract]], [[Media:DeepLearningonSHARCNET_2015.pdf|slides]]
 
* 2015/02/04 - [https://www.youtube.com/watch?v=el1iSlP1uOs Deep Learning on SHARCNET: From CPU to GPU cluster], Fei Mao, [[Webinar 2015 Deep Learning on SHARCNET: From CPU to GPU cluster|Abstract]], [[Media:DeepLearningonSHARCNET_2015.pdf|slides]]
 
=Staff Contacts=
 
 
The following staff have backgrounds in machine learning and data mining and may be able to help with domain specific issues.  Their contact information can be found in the [https://www.sharcnet.ca/my/contact/directory SHARCNET staff directory].
 
 
*Fei
 
 
  
  
 
[[Category:Computer Science and Engineering]]
 
[[Category:Computer Science and Engineering]]

Latest revision as of 16:46, 15 May 2019

Introduction

This page is intended to serve as a community and information hub for our machine learning/deep learning and data mining users.

Hardwares

Graham

GRAHAM is a heterogeneous cluster, suitable for a variety of workloads, and located at the University of Waterloo. A total of 35,520 cores and 320 GPU devices, spread across 1,107 nodes of different types. GPU nodes have 128 GB of memory, 16 cores/socket, 2 sockets/node, 2 NVIDIA P100 Pascal GPUs/node (12GB HBM2 memory). Intel "Broadwell" CPUs at 2.1Ghz, model E5-2683 v4. 1.6TB NVMe SSD.

Software Packages

This section is intended to list software that is being used at SHARCNET, as well as other popular packages that users may wish to consider using for their work. Optimally each package listed below will have it's own wiki page including installation, configuration and execution hints and tips, as well as a listing of different groups at SHARCNET that are experienced with the software.

Package Description User Guide
Caffe Caffe is a deep learning framework developed with cleanliness, readability, and speed in mind. Caffe-wiki
Theano Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Theano-wiki
BIDMach BIDMach is an interactive environment designed to make it extremely easy to build and use machine learning models. BIDMach-wiki
DIGITS The NVIDIA Deep Learning GPU Training System (DIGITS) puts the power of deep learning in the hands of data scientists and researchers DIGITS-wiki
cuDNN The NVIDIA CUDA Deep Neural Network library (cuDNN) is a GPU-accelerated library of primitives for deep neural networks cuDNN-wiki
Torch Torch is a scientific computing framework with wide support for machine learning algorithms. Torch-wiki
Tensorflow TensorFlow is an Open Source Software Library for Machine Intelligence. Tensorflow-wiki
IBM PowerAI The PowerAI platform includes the most popular machine learning frameworks and their dependencies, and it is built for easy and rapid deployment. Minksy-wiki

Events

General interest seminars: