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Linux Self Guided
We run RHEL/CentOS 6 Linux on our high-performance systems.
If you have never used Linux before or have had very limited use, read this useful guide:
If you have learned Linux in the past but want a quick reference to the syntax of commands, then read this:
Intel® Modern Code Training
Intel brought a workshop to campus in 2014 and the material is covered here. If you want to do any work on the Intel® Xeon Phi™ Coprocessors we have 40 of them installed in ElGato. You can obtain "standard" queue access and can request access to the nodes with them installed.
Created by Colfax International and Intel, and based on the book, Parallel Programming and Optimization with Intel® Xeon Phi™ Coprocessors, this short video series provides an overview of practical parallel programming and optimization with a focus on using the Intel® Many Integrated Core Architecture (Intel® MIC Architecture).
Length: 5 hours
Parallel Programming and Optimization with Intel Xeon Phi Coprocessors
Intel® Software Tools
Intel offers the Cluster Studio XE. On Ocelote we have installed modules (module avail intel ) as:
We have installed the Intel high performance libraries (module avail intel ):
- Intel® Threading Building Blocks
- Intel® Integrated Performance Primitives
- Intel® Math Kernel Library
- Intel® Data Analytics Acceleration Library
The University is licensed and has access to this toolset separate from HPC. Portions of it are FREE for use in teaching/instruction and to students.
Singularity containers let users run applications in a Linux environment of their choosing. This is different from Docker which is not appropriate for HPC due to security concerns. Singularity is like a container for Docker images, but is not just for Docker.
For an overview and more detailed information refer to:
Here are some of the use cases we support using Singularity:
- You already use Docker and want to run your jobs on HPC
- You want to preserve your environment so that a system change will not affect your work
- You need newer or different libraries than are offered on HPC systems
- Someone else developed the workflow using a different version of linux
- You prefer to use something other than Red Hat / CentOS, like Ubuntu
Depending on your environment and the type of Singularity container you want to build, you may need to install some dependencies before installing and/or using Singularity. For instance, the following may need to be installed on Ubuntu for Singularity to build and run properly. (user input in bold)
[user@someUbuntu ~]$ sudo apt-get install build-essential debootstrap yum dh-autoreconf
On Centos, these commands will provide some needed dependencies for Singularity:
[user@someCentos ~]$ sudo yum groupinstall 'Development Tools' [user@someCentos ~]$ sudo yum install wget [user@someCentos ~]$ wget http://dl.fedoraproject.org/pub/epel/7/x86_64/e/epel-release-7-8.noarch.rpm [user@someCentos ~]$ sudo rpm -Uvh epel-release-7-8.noarch.rpm [user@someCentos ~]$ sudo yum install debootstrap.noarch
You can find more information about installing Singularity on your Linux build system here. Because Singularity is being rapidly developed, we recommend downloading and installing the latest release from Github.
A limitation is that all edits to the image have to be on the originating workstation. So if for example, a Python module needs to be added or updated, the image needs to be modified and then recopied back to HPC.
Binding a directory to your Singularity container allows you to access files in a host system directory from within your container. By default, a Singularity will bind your /home/$USER directory and your current working directory (along with a few other directories such as /tmp and /dev). The examples below include a bind to /extra.
If you need more detailed information, follow this link:
Centos with Tensorflow Example
This is an example of creating a singularity image to run code that is not supported on HPC. This example uses Tensorflow but any application could be installed in its place. It also uses CentOS but it could just as easily be Ubuntu.
Install Singularity on linux workstation - http://singularity.lbl.gov/install-linux
Create the container using a size of 1500MB on a Centos workstation / VM with root privileges
Create the definition file, in this example called centosTFlow.def
Bootstrap process creates the installation following the definition file
Copy the new image file to your space on HPC. /extra might be a good location as the image might use up your remaining home. There is a line in the definition file that will create the mount for /extra. Any time you run from a location other than /home on ElGato you are likely to see a warning which you can ignore:
Test with a simple command
Or slightly more complex create a simple python script called hello.py:
And now test tensorflow with this example from their web site, TFlow_example.py:
This example is taken from the Singularity documentation and modified for our HPC. The example taken is tensorflow again but it could be PHP or any other Docker image. Note that you will be creating a container that is running Ubuntu on top of the Red Hat or CentOS clusters.
Create the Singularity container on the workstation or VM where you have root authority:
Import the Docker Tensorflow workflow from the Docker hub:
Move the image to HPC and test it:
Singularity is not to be run on login nodes. That is a general policy for any application.
To run a Singularity container image on ElGato or Ocelote interactively, you need to allocate an interactive session, and load the Singularity module. In this sample session, the Tensorflow Singularity container from above is started, and python is run. Note that in this example, you would be running the version of python that is installed within the Singularity container, not the version on the cluster.
ElGato Interactive Example
Ocelote Interactive Example
The process is the same except that the command to initiate the interactive session will look more like:
ElGato Job Submission
Running a job with Singularity is as easy as running other jobs. The LSF script might look like this, and the results will be found in lsf_tf.out
Ocelote Job Submission
The PBS script might look like this, and the results will be found in singularity-job.ojobid.
Docker Without Singularity
But what if I have a collection of Docker containers and would rather have a simpler process to have them run on HPC? I can create the singularity image without installing Singularity, and then run the image on HPC.
Let's say I have a Blast workflow set up in Docker;
Run docker2singularity to convert the docker image. This tool will run inside a docker container.
Now you have a singularity image file which can be copied to your HPC filespace. You can either run it from /home or from /extra. No other choices are available at the moment. If you need to operate from /rsgrps, then you need to create your singularity image using "import" or "bootstrap" which means installing Singularity where you create the singularity image.
Sample job - extract this in your work directory:
- blast.tar.gz: contains query FASTA file (proteins.fasta) and BLASTable database (pdbaa).
The result of running the job below should be an output file called proteins_blastp.txt.
Note that you have to include the full path name of the blast files even if that is your local directory because singularity predicates that you are in /home.
If you want to build a singularity container image that can run applications on ElGato GPU nodes, you must prepare your container:
- Download the .run installer for the same NVIDIA driver that is currently running on our GPU nodes
- Extract the contents (don't actually need to install the driver)
- Move all of the libraries to a central location like /usr/local/nvidia and make all necessary symbolic links
- Download and install the .run file for the same CUDA libraries that are currently running on the GPU node
- Download, extract, and copy the correct cuDNN libraries
- Edit and export $PATH, $LD_LIBRARY_PATH, and $CUDA_HOME to point to the correct libraries and binaries
For your convenience, the staff at the National Institute of Health maintains an installation script (called cuda4singularity) that automates this process. It has been tested with Ubuntu 16.4 and Centos 7. You can either copy or download cuda4singularity into an existing container and execute it with root privileges, or you can add the lines of code into your .def file and install the NVIDIA/CUDA libraries during the bootstrap procedure. (Note that your container will probably need ~10GB of empty space for the CUDA libraries to install properly. This is because the installer checks for minimum space requirements before running.)
Be patient. This bootstrap procedure will take a long time to run and may look like it has stalled at several points. If you watch the output you may see CUDA issuing warnings and complaining about an incomplete installation. Don't worry. The drivers are running on the GPU nodes so they don't need to be installed within the container.
After creating a container with one of these files, you can copy it to ElGato and test it like so:
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