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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.
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.
