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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.
Introduction to Parallel Computing
Introduction to OpenMP
This PDF file is a presentation from a series called Xsede * HPC Workshop.
* XSEDE, the Extreme Science and Engineering Discovery Environment, is the most advanced, powerful, and robust collection of integrated digital resources and services in the world. It is a single virtual system that scientists and researchers can use to interactively share computing resources, data, and expertise. XSEDE integrates the resources and services, makes them easier to use, and helps more people use them.
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.
The most important thing to know is that you create the singularity container called an image on a workstation where you have root privileges, and then transfer the image to HPC where you can execute the image. If root authority is an issue then the answer might be a virtual environmen t on your laptop, like Vagrant for MacOS
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.
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
singularity create -s 1500 centosTFlow.img # Create an image file to host the content of the container. # Think of it like creating the virtual hard drive for a VM. # In ext3, an actual file of specified size is created.
Create the definition file, in this example called centosTFlow.def
Bootstrap process creates the installation following the definition file
singularity bootstrap centosTFlow.img centosTFlow.def
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:
WARNING: Not mounting current directory: user bind control is disabled by system administrator
Test with a simple command
$module load singularity $singularity exec centosTFlow.img python --version Python 2.7.5
Or slightly more complex create a simple python script called hello.py:
$singularity exec centosTFlow.img python /extra/netid/hello.py Hello World: The Python version is 2.7.5 $
$singularity shell centosTFlow.img Singularity.centosTFlow.img>python hello.py Hello World: The Python version is 2.7.5 Singularity.centosTFlow.img>
And now test tensorflow with this example from their web site, TFlow_example.py:
$singularity exec centosTFlow.img python /extra/netid/TFlow_example.py (0, array([-0.08299404], dtype=float32), array([ 0.59591037], dtype=float32)) (20, array([ 0.03721666], dtype=float32), array([ 0.3361423], dtype=float32)) (40, array([ 0.08514741], dtype=float32), array([ 0.30855015], dtype=float32)) (60, array([ 0.09648635], dtype=float32), array([ 0.3020227], dtype=float32)) (80, array([ 0.0991688], dtype=float32), array([ 0.30047852], dtype=float32)) (100, array([ 0.09980337], dtype=float32), array([ 0.3001132], dtype=float32)) (120, array([ 0.09995351], dtype=float32), array([ 0.30002677], dtype=float32)) (140, array([ 0.09998903], dtype=float32), array([ 0.30000633], dtype=float32)) (160, array([ 0.0999974], dtype=float32), array([ 0.3000015], dtype=float32)) (180, array([ 0.09999938], dtype=float32), array([ 0.30000037], dtype=float32)) (200, array([ 0.09999986], dtype=float32), array([ 0.3000001], dtype=float32))
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:
$singularity create --size 4000 docker-tf.img
Import the Docker Tensorflow workflow from the Docker hub:
$singularity import docker-tf.img docker://tensorflow/tensorflow:latest Cache folder set to /root/.singularity/docker Downloading layer sha256:a3ed95caeb02ffe68cdd9fd84406680ae93d633cb16422d00e8a7c22955b46d4 Extracting /root/.singularity/docker/sha256:a3ed95caeb02ffe68cdd9fd84406680ae93d633cb16422d00e8a7c22955b46d4.tar.gz Downloading layer sha256:65f3587f2637c17b30887fb0d5dbfad2f10e063a72239d840b015528fd5923cd Extracting ... Extracting /root/.singularity/docker/sha256:56eb14001cebec19f2255d95e125c9f5199c9e1d97dd708e1f3ebda3d32e5da7.tar.gz Bootstrap initialization No bootstrap definition passed, updating container Executing Prebootstrap module Executing Postbootstrap module Done.
Move the image to HPC and test it:
[user@host]$ singularity shell docker-tf.img Singularity: Invoking an interactive shell within container... Singularity.docker-tf.img> python Python 2.7.6 (default, Oct 26 2016, 20:30:19) [GCC 4.8.4] on linux2 Type "help", "copyright", "credits" or "license" for more information. >>> import tensorflow >>> exit() Singularity.docker-tf.img> exit
$singularity exec docker-tf.img lsb_release -a No LSB modules are available. Distributor ID: Ubuntu Description: Ubuntu 14.04.4 LTS Release: 14.04 Codename: trusty
user@host$ singularity exec docker-tf.img python /extra/netid/TFlow_example.py WARNING:tensorflow:From TFlow_example.py:21 in <module>.: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02. Instructions for updating: Use `tf.global_variables_initializer` instead. (0, array([ 0.72233653], dtype=float32), array([-0.00956423], dtype=float32)) (20, array([ 0.24949318], dtype=float32), array([ 0.22735602], dtype=float32)) (40, array([ 0.13574874], dtype=float32), array([ 0.28262845], dtype=float32)) (60, array([ 0.10854871], dtype=float32), array([ 0.2958459], dtype=float32)) (80, array([ 0.1020443], dtype=float32), array([ 0.29900661], dtype=float32)) (100, array([ 0.10048886], dtype=float32), array([ 0.29976246], dtype=float32)) (120, array([ 0.10011692], dtype=float32), array([ 0.29994321], dtype=float32)) (140, array([ 0.10002796], dtype=float32), array([ 0.29998642], dtype=float32)) (160, array([ 0.10000668], dtype=float32), array([ 0.29999676], dtype=float32)) (180, array([ 0.1000016], dtype=float32), array([ 0.29999924], dtype=float32)) (200, array([ 0.10000039], dtype=float32), array([ 0.29999983], dtype=float32)) user@host$
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
[netid@elgato singularity]$ bsub -Is bash Job <633365> is submitted to default queue <windfall>. <<Waiting for dispatch ...>> <<Starting on gpu44>> [netid@gpu44 singularity]$ module load singularity [netid@gpu44 singularity]$ singularity exec docker-tf.img python\ /extra/chrisreidy/singularity/TFlow_example.py WARNING: Not mounting current directory: user bind control is disabled by system administrator Instructions for updating: Use `tf.global_variables_initializer` instead. (0, array([ 0.12366909], dtype=float32), array([ 0.3937912], dtype=float32)) (20, array([ 0.0952933], dtype=float32), array([ 0.30251619], dtype=float32)) ... (200, array([ 0.0999999], dtype=float32), array([ 0.30000007], dtype=float32)) [netid@gpu44 singularity]$ exit
Ocelote Interactive Example
The process is the same except that the command to initiate the interactive session will look more like:
$ qsub -I -N jobname -m bea -M firstname.lastname@example.org -W group_list=hpcteam -q windfall -l select=1:ncpus=28:mem=168gb -l cput=1:0:0 -l walltime=1:0:0
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
###======================================== #!/bin/bash #BSUB -n 1 #BSUB -q "windfall" #BSUB -R "span[ptile=1]" #BSUB -o lsf_tf.out #BSUB -e lsf_tf.err #BSUB -J testtensorflow #--------------------------------------------------------------------- module load singularity cd /extra/netid/data singularity exec docker-tf.img python /extra/chrisreidy/singularity/TFlow_example.py
Ocelote Job Submission