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Owens, Pitzer, Ruby

Before Conda environments may be used by Jupyter Notebooks they must be installed so that Jupyter knows about them. Older versions of Conda automatically installed a Jupyter kernel for themselves, that installation process now must be performed manually by the user.

To perform the installation the user should load their prefered version of Python, activate their Conda environment and run the following command:

Owens

 PyTorch is a Python package that provides Tensor computation (like NumPy) with strong GPU acceleration and     Deep neural networks built on a tape-based autograd system.

Quote from Pytorch Github documentation

Availability and Restrictions

Versions

The following version of Pytorch is available on OSC clusters:

Owens

Memory Limit:

It is strongly suggested to consider the memory use to the available per-core memory when users request OSC resources for their jobs. On Owens, it equates to 4GB/core or 124GB/node.

Thursday, April 18th 

9:00 - 10:00 am

Breakfast Assortment

Thursday, April 18th 

9:00 - 10:00 am

Breakfast Assortment

Intro

XDMoD can be used to look at the performance of past jobs. This tutorial will explain how to retreive this job performance data and how to use this data to best utilize OSC resources.

First, log into XDMoD.

See XDMoD Tool webpage for details about XDMoD and how to log in.

You will be sent to the Summary Tab in XDMoD:

Owens, Pitzer, Ruby

The Job Viewer Tab displays information about individual HPC jobs and includes a search interface that allows jobs to be selected based on a wide range of filters:

1. Click on the Job Viewer tab near the top of the page.

2. Click Search in the top left-hand corner of the page

screenshot of the XDMoD displaying the above text

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