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Documentation for cms SLURM installation

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Abstract

1.0 Installation

The installation takes around an hour on a cluster of four Raspberry Pi 4 Model B computers.

To use the cloudmesh SLURM command, one must have cloudmesh installed by using the following commands.

We assume you are in a venv Python environment. Ours is called (ENV3)

(ENV3) you@yourlaptop $ mkdir ~/cm
(ENV3) you@yourlaptop $ cd ~/cm
(ENV3) you@yourlaptop $ pip install cloudmesh-installer
(ENV3) you@yourlaptop $ cloudmesh-installer get pi

Initialize the cms command:

(ENV3) you@yourlaptop $ cms help

Then clone the cloudmesh-slurm repository:

(ENV3) you@yourlaptop $ cd ~/cm
(ENV3) you@yourlaptop $ cloudmesh-installer get cmd5
(ENV3) you@yourlaptop $ git clone https://github.com/cloudmesh/cloudmesh-slurm.git
(ENV3) you@yourlaptop $ cd cloudmesh-slurm
(ENV3) you@yourlaptop $ pip install -e .
(ENV3) you@yourlaptop $ cms help

You may proceed if slurm shows in the documented commands.

After following the burn tutorial and ensuring that the cluster is online, you have two methods of installing SLURM.

2.0 Method 1 - Install from Host

You can install SLURM on a cluster by executing commands from the host computer. The host computer is the same computer that is previously burned your SD Cards and is referred to as you@yourlaptop. This machine can be used to ssh into each of the Pis.

To install it, use the command:

(ENV3) you@yourlaptop $ cms slurm pi install as host --hosts=red,red0[1-4]

The --hosts parameter needs to include the hostnames of your cluster, including manager and workers, separated by comma using a parameterized naming scheme.

The user can also specify a --partition parameter, as in --partition=mycluster, to personalize the name of the partition.

The command will take a long time to finish. It may appear to not progress at certain points, but please be patient. However they will last hopefully not longer than 45 minutes. The reason this takes such a long time is that at time of writing of this tutorial, the prebuilt SLURM packages did not work, so we compile it from source.

Once the script completes, you can check if SLURM is installed by issuing on the manager:

(ENV3) pi@red:~ $ srun --nodes=4 hostname

and replacing the --nodes parameter with the number of workers.

You will see an output similar to

(ENV3) you@yourlaptop $ ssh red
(ENV3) pi@red:~ $ srun --nodes=4 hostname
red01
red02
red03
red04

The nodes may be out of order. That is okay and normal.

3.0 Method 2 - Install on Manager

3.1 Install cloudmesh

This method involves the user logging into the manager via ssh and first installing cloudmesh in the manager with:

(ENV3) you@yourhostcomputer $ ssh red
pi@red $ curl -Ls http://cloudmesh.github.io/get/pi | sh -

This output is printed upon successful installation.

Please activate with

    source ~/ENV3/bin/activate

Followed by a reboot

After activating venv with the source command and rebooting via sudo reboot, issue the commands:

(ENV3) you@yourhostcomputer $ ssh red
pi@red:~ $ cd ~/cm
pi@red:~/cm $ git clone https://github.com/cloudmesh/cloudmesh-slurm.git
pi@red:~/cm $ cd cloudmesh-slurm
pi@red:~/cm/cloudmesh-slurm $ pip install -e .
pi@red:~/cm/cloudmesh-slurm $ cms help

The slurm command should appear in the list.

3.2 Install SLURM Directly on Pi

Run this command to begin SLURM installation:

pi@red:~/cm/cloudmesh-slurm $ cms slurm pi install --workers=red0[1-4]

The user can also specify a --partition parameter, as in --partition=mycluster, to personalize the name of the partition.

The user must ssh back into the manager after the cluster reboots and perform the last command (cms slurm pi install...) 3 more times. The script will inform the user when this is no longer necessary and SLURM is fully installed.

You can check if SLURM is installed by issuing on the manager:

srun --nodes=4 hostname

and replacing the --nodes parameter with the number of workers.

You will see an output similar to

(ENV3) pi@red:~ $ srun --nodes=4 hostname
red01
red02
red03
red04

The nodes may be out of order. That is okay and normal.

4.0 Install Single-Node

To make job management simple, we can install SLURM on one computer. This one computer has no workers and is a manager to its own self. The user can make and automate jobs for simplicity's sake, and the same computer will carry out those jobs.

Single-node installation, which is a SLURM cluster with only one node, can be easily configured by using the host command with the manager and workers listed as the same hostname. In the following example, red is the single-node.

cms slurm pi install as host --hosts=red,red

5.0 MPI Example

To run a test MPI example, ssh into the manager and then use the example command. This is only possible if cms is installed on the Pi; if you have not done this because you installed SLURM via the host method, then refer to section 3.1 to install cloudmesh on Pi. Then run the following (change the number after --n to the number of nodes):

(ENV3) you@yourhostcomputer $ ssh red
pi@red:~ $ cms slurm pi example --n=4

This cms slurm command runs salloc -N 4 mpiexec python -m mpi4py.bench helloworld but the number after -N is altered to whatever is input for the --n parameter. Do not run the salloc command. It is unnecessary when we have already implemented it within the aforementioned cms slurm pi example command. It is just listed here for reference. The output will be similar to:

pi@red:~ $ cms slurm pi example --n=4
salloc: Granted job allocation 17
Hello, World! I am process 0 of 4 on red01.
Hello, World! I am process 1 of 4 on red02.
Hello, World! I am process 2 of 4 on red03.
Hello, World! I am process 3 of 4 on red04.
salloc: Relinquishing job allocation 17

6.0 Interactive Job

You can start an interactive job by issuing the following:

(ENV3) pi@red:~ $ srun --nodes=1 --ntasks-per-node=1 --time=01:00:00 --pty bash -i
pi@red01:~ $

This works in home dir, but not if you stand in other dir.

7.0 Using sbatch

The cloudmesh-mpi repository contains a Python file that automatically creates job submissions using the sbatch command. sbatch allows for easy customization of job parameters, such as where the output of the commands should reside, how much time should be allotted for the job, how much memory should be allotted per CPU, and others.

The Python program, 100jobs.py, is located at https://github.com/cloudmesh/cloudmesh-mpi/blob/main/examples/slurm/100jobs.py

To use the cloudmesh-mpi Python program, named 100 jobs for its creation of 100 jobs that execute the sleep command for a short amount of time, execute the following commands.

In the case that cloudmesh-mpi is not downloaded:

(ENV3) pi@red:~ $ cd cm
(ENV3) pi@red:~/cm $ git clone https://github.com/cloudmesh/cloudmesh-mpi.git
(ENV3) pi@red:~/cm $ cd cloudmesh-mpi/examples/slurm
(ENV3) pi@red:~/cm/cloudmesh-mpi/examples/slurm $ cp 100jobs.py ~
(ENV3) pi@red:~/cm/cloudmesh-mpi/examples/slurm $ cd
(ENV3) pi@red:~ $ python 100jobs.py

This program only works if run on the home directory of the manager node, and if the nfs shared file system is installed on the cluster. The shared file system should already be installed if the SLURM installation has been run successfully.

The output files of the 100 jobs can be found inside /nfs/tmp/:

(ENV3) pi@red:~ $ cat job-0.slurm
#!/bin/bash
#SBATCH -o job-0.out
#SBATCH -e job-0.err

hostname
echo $SLURM_JOB_NAME
NAME="${SLURM_JOB_NAME%%.*}"
echo $NAME
sleep 5.473056730256757

cp ${NAME}.out /nfs/tmp/
cp ${NAME}.err /nfs/tmp/
(ENV3) pi@red:~ $ cd /nfs/tmp
(ENV3) pi@red:/nfs/tmp $ cat job-0.out
red01
job-0.slurm
job-0

Using Slurm on local PI file space

Often it is time consuming during a slurm run to copy all the files and data to a remote host. This is valid also for accessing NFS. Furthermore, if you have a lareg number of nodes this could be problematic as the nodes compeet with each other. In such cases its is useful to be able to copy the data and potentially programs before you run sbatch. However, you must dod this for all nodes that you expect to be using for the batch job. As we are in full control of the Raspberry Pi cluster, we simply copy it to all of them.

Let us assume we hafe a clutsre burned for red,red0[1-2]. Let us create a simple program and distribute it to the workers.

red$ mkdir ~/tmp

red$ echo "#! /usr/bin/env python" > tmp/hello.py
red$ echo "import os" > tmp/hello.py
red$ echo "os.system('hostname')" > tmp/hello.py

red$ chmod a+x ~/tmp/hello.py
red$ rsync -a ~/tmp red01:tmp
red$ rsync -a ~/tmp red02:tmp

Now we can run it with

red$ srun -n 8 /home/pi/tmp/hello.py

As the PI4 has four threads and we copied the program to each pi, we will see an outut where each thread will execute the hostname command. We will see the following output. Please note that the order can be different.

red02
red02
red02
red02
red01
red01
red01
red01

8.0 Manual Page for the slurm command

Note to execute the command on the command line you have to type in cms slurm and not just slurm.

      slurm pi install [--workers=WORKERS] [--mount=MOUNT]
      slurm pi install as host [--os=OS] [--hosts=HOSTS] [--mount=MOUNT]
      slurm pi example --n=NUMBER

This command installs slurm on the current PI and also worker nodes if you specify them.

The manager can also be a worker by using the single-node method. For example, red can be
a manager and worker, simultaneously, by issuing
cms slurm pi install as host --hosts=red,red --mount=//dev//sda

Arguments:
  COMMAND  the slurm command to be executed [default: salloc]

Options:

Description:

Install:

  pip install cloudmesh-slurm
  cms help
  cms slurm pi install

Example:
  cms slurm pi example --n=4 [COMMAND]

  MODE is one of salloc, srun, sbatch

  will run the command

    salloc -N 4 mpiexec python -m mpi4py.bench helloworld

  API:

    from cloudmesh.slurm.slurm import Slurm
    from cloudmesh.slurm import Slurm

    Slurm.install()

    in case you use self

    slurm = Slurm()    slef instead of Slurm
    slurm.install

Acknowledgments

Continued work was in part funded by the NSF CyberTraining: CIC: CyberTraining for Students and Technologies from Generation Z with the award numbers 1829704 and 2200409.