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Owens, Ruby

This documentation describes how to install tensorflow package locally in your $HOME space.

Load python module

module load python/3.6-conda5.2

Clone python installation to local directory

Three alternative create commands are listed.  These cover the most common cases:

Owens, Pitzer
  • "Horovod is a distributed training framework for TensorFlow, Keras, PyTorch, and MXNet. The goal of Horovod is to make distributed Deep Learning fast and easy to use. The primary motivation for this project is to make it easy to take a single-GPU TensorFlow program and successfully train it on many GPUs faster."

Quote from Horovod Github documentation

Owens, Pitzer

metilene is a software tool to annotate differentally methylated regions (DMRs) and differentially methylated CpG sites (DMCs) from Methyl-seq data. metilene accounts for intra-group variances and offers different modes de-novo DMR detection, DMR detection within a known set of genomic features, and DMC detection.

Owens, Pitzer, Ruby

Introduction

OSCprojects is a command developed at OSC for use on OSC's systems and is used to view your logged in accounts project information.

Owens, Pitzer, Ruby

Introduction

OSCgetent is a command developed at OSC for use on OSC's systems and is similar to the standard getent command. It lets one view group information.

Owens, Pitzer, Ruby

Introduction

There are some commands that OSC has created custom versions of to be more useful to OSC users.

 

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

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.

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