Getting Started (Command Line)

1. Get Duo

You must use Duo authentication to log on to Lighthouse.  Get more details on the Safe Computing Two-Factor page and enroll here.

2. Get a Lighthouse user login

You must establish a user login on Lighthouse by filling out this form.

3. Get an SSH Client & Connect to Lighthouse

You must be on campus or on the VPN to connect to Lighthouse.  If you are trying to log in from off campus, or using an unauthenticated wireless network such as MGuest, you have a couple of options:

Mac or Linux:

Open Terminal and type:

ssh uniqname@lighthouse.arc-ts.umich.edu

Windows (using PuTTY):

Download and install PuTTY here.

Launch PuTTY and enter lighthouse.arc-ts.umich.edu as the host name, then click open.

All Operating Systems:

At the “Enter a passcode or select one of the following options:” prompt, type the number of your preferred choice for Duo authentication.

4. Get files

You can use SFTP (best for simple transfers of small files) or Globus (best for large files or a commonly used endpoint) to transfer data to your /home directory.

SFTP: Mac or Windows using FileZilla
  1. Open FileZilla and click the “Site Manager” button
  2. Create a New Site, which you can name “Lighthouse” or something similar
  3. Select the “SFTP (SSH File Transfer Protocol)” option
  4. In the Host field, type lighthouse-xfer.arc-ts.umich.edu
  5. Select “Interactive” for Logon Type
  6. In the User field, type your uniqname
  7. Click “Connect”
  8. Enter your Kerberos password
  9. Select your Duo method (1-3) and complete authentication
  10. Drag and drop files between the two systems
  11. Click “Disconnect” when finished

On Windows, you can also use WinSCP with similar settings, available alongside PuTTY here.

SFTP: Mac or Linux using Terminal

To copy a single file, type:

scp localfile uniqname@lighthouse-xfer.arc-ts.umich.edu:~/remotefile

To copy an entire directory, type:

scp -r localdir uniqname@lighthouse-xfer.arc-ts.umich.edu:~/remotedir

These commands can also be reversed in order to copy files from Lighthouse to your machine:

scp -r uniqname@lighthouse-xfer.arc-ts.umich.edu:~/remotedir localdir

You will need to authenticate via Duo to complete the file transfer.

Globus: Windows, Mac, or Linux

Globus is a reliable high performance parallel file transfer service provided by many HPC sites around the world. It enables easy transfer of files from one system to another, as long as they are Globus endpoints.

  • The Globus endpoint for Lighthouse is “umich#lighthouse”.
How to use Globus

Globus Online is a web front end to the Globus transfer service. Globus Online accounts are free and you can create an account with your University identity.

  • Set up your Globus account and learn how to transfer files using the Globus documentation.  Select “University of Michigan” from the dropdown box to get started.
  • Once you are ready to transfer files, enter “umich#lighthouse” as one of your endpoints.
Globus Connect Personal

Globus Online also allows for simple installation of a Globus endpoint for Windows, Mac, and Linux desktops and laptops.

  • Follow the Globus instructions to download the Globus Connect Personal installer and set up an endpoint on your desktop or laptop.
Batch File Copies

A non-standard use of Globus Online is that you can use it to copy files from one location to another on the same cluster. To do this use the same endpoint (umich#lighthouse as an example) for both the sending and receiving machines. Setup the transfer and Globus will make sure the rest happens. The service will email you when the copy is finished.

Command Line Globus

There are Command line tools for Globus that are intended for advanced users. If you wish to use these, contact HPC support.

5. Submit a job

This is a simple guide to get your jobs up and running. For more advanced Slurm features, see the Slurm User Guide for Lighthouse. If you are familiar with using the resource manager Torque, you may find the migrating from Torque to Slurm guide useful.

Resources

On Lighthouse, like the Flux Operating Environment, you can run on hosts owned by a researcher who has granted you access.    Unless the researcher has placed additional limits, the only default limit is a 2-week maximum runtime (wallclock).  You will submit your jobs to a partition (queue) named after the PI’s uniqname.

To help in the transition, we have included 2 test nodes in your partition.  These 2 test nodes are shared with all FOE/Lighthouse researchers, so availability will depend on current demand.    This is similar to the environment you previously tested on the Beta cluster (there is a short limit on run times for jobs). You can submit jobs to that partition to validate your workflows.  Once the PI has moved their nodes from FOE to Lighthouse, the test nodes will be removed from the PI’s partition and the maximum runtime will be set to 2 weeks.

Batch Jobs

Most work will be queued to be run on Lighthouse and is described through a batch script. The sbatch command is used to submit a batch script to Slurm. To submit a batch script simply run the following from a shared file system; those include your home directory, /scratch, and any directory under /nfs that you can normally use in a job on Flux. Output will be sent to this working directory (jobName-jobID.log). Do not submit jobs from /tmp or any of its subdirectories.

$ sbatch myJob.sh

The batch job script is composed of three main components:

  • The interpreter used to execute the script
  • #SBATCH directives that convey submission options
  • The application(s) to execute along with its input arguments and options

Example:

#!/bin/bash
# The interpreter used to execute the script

#“#SBATCH” directives that convey submission options:

#SBATCH --job-name=example_job
#SBATCH --mail-type=BEGIN,END
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --mem-per-cpu=1000m 
#SBATCH --time=10:00
#SBATCH --account=test
#SBATCH --partition=standard

# The application(s) to execute along with its input arguments and options:

/bin/hostname
sleep 60

How many nodes and processors you request will depend on the capability of your software and what it can do. There are four common scenarios:

Example: One Node, One Processor

This is the simplest case and is shown in the example above. The majority of software cannot use more than this. Some examples of software for which this would be the right configuration are SAS, Stata, R, many Python programs, most Perl programs.

#!/bin/bash
#SBATCH --job-name JOBNAME
#SBATCH --nodes=1
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=1g
#SBATCH --time=00:15:00
#SBATCH --account=test
#SBATCH --partition=standard
#SBATCH --mail-type=NONE

srun hostname -s

Example: One Node, Multiple Processors

This is similar to what a modern desktop or laptop is likely to have. Software that can use more than one processor may be described as multicore, multiprocessor, or multithreaded. Some examples of software that can benefit from this are MATLAB and Stata/MP. You should read the documentation for your software to see if this is one of its capabilities.

#!/bin/bash
#SBATCH --job-name JOBNAME
#SBATCH --nodes=1
#SBATCH --cpus-per-task=4
#SBATCH --mem-per-cpu=1g
#SBATCH --time=00:15:00
#SBATCH --account=test
#SBATCH --partition=standard
#SBATCH --mail-type=NONE

srun hostname -s

Example: Multiple Nodes, One Process per CPU

This is the classic MPI approach, where multiple machines are requested, one process per processor on each node is started using MPI. This is the way most MPI-enabled software is written to work.

#!/bin/bash
#SBATCH --job-name JOBNAME
#SBATCH --nodes=2
#SBATCH --ntasks-per-node=4
#SBATCH --mem-per-cpu=1g
#SBATCH --time=00:15:00
#SBATCH --account=test
#SBATCH --partition=standard
#SBATCH --mail-type=NONE

srun hostname -s

Example: Multiple Nodes, Multiple CPUs per Process

This is often referred to as the “hybrid mode” MPI approach, where multiple machines are requested and multiple processes are requested. MPI will start a parent process or processes on each node, and those in turn will be able to use more than one processor for threaded calculations.

#!/bin/bash
#SBATCH --job-name JOBNAME
#SBATCH --nodes=2
#SBATCH --ntasks-per-node=4
#SBATCH --cpus-per-task=4
#SBATCH --mem-per-cpu=1g
#SBATCH --time=00:15:00
#SBATCH --account=test
#SBATCH --partition=standard
#SBATCH --mail-type=NONE

srun hostname -s
Common Job Submission Options
Description Slurm directive (#SBATCH option) Lighthouse Usage
Job name --job-name=<name> --job-name=lhjob1
Account --account=<account> --account=test
Queue --partition=<partition_name> --partition=name

The partition name depends on the PI’s requested cluster

Wall time limit --time=<hh:mm:ss> --time=02:00:00
Node count --nodes=<count> --nodes=2
Process count per node --ntasks-per-node=<count> --ntasks-per-node=1
Minimum memory per processor --mem-per-cpu=<memory> --mem-per-cpu=1000m
Request software license(s) --licenses=<application>@slurmdb:<N> --licenses=stata@slurmdb:1
requests one license for Stata
Request event notification

--mail-type=<events>

Note: multiple mail-type requests may be specified in a comma separated list:

--mail-type=BEGIN,END,NONE,FAIL,REQUEUE

--mail-type=BEGIN,END,FAIL

Please note that if your job is set to utilize more than one node, make sure your code is MPI enabled in order to run across these nodes and you must use srun rather then mpirun or mpiexec. More advanced job submission options can be found in the Slurm User Guide for Lighthouse.

Interactive Jobs

If you need to actively interact to complete a task, you can submit an interactive job.  When your interactive job runs, you will have command line access (instead of running a script) on a compute node and can interact with all of the resources you requested. Interactive jobs are useful when debugging or interacting with an application. The srun command is used to submit an interactive job to Slurm. When the job starts, a command line prompt will appear on one of the compute nodes assigned to the job. From here commands can be executed using the resources allocated on the local node.

[user@login ~]$ srun --pty /bin/bash 
srun: job 309 queued and waiting for resources 
srun: job 309 has been allocated resources 
[user@node0001 ~]$ hostname 
node0001.lh.arc-ts.umich.edu 
[user@node0001 ~]$

Jobs submitted with srun –pty /bin/bash will be assigned the cluster default values of 1 CPU and 1024MB of memory. If additional resources are required, they can be requested as options to the srun command. The following example job is assigned 2 nodes with 4 CPUS and 4GB of memory each:

[user@login ~]$ srun --nodes=2 --ntasks-per-node=4 --mem-per-cpu=1GB --pty /bin/bash
srun: job 894 queued and waiting for resources
srun: job 894 has been allocated resources
[user@node0001 ~]$ srun hostname
node0001.lh.arc-ts.umich.edu
node0002.lh.arc-ts.umich.edu
node0001.lh.arc-ts.umich.edu
node0001.lh.arc-ts.umich.edu
node0001.lh.arc-ts.umich.edu
node0002.lh.arc-ts.umich.edu
node0002.lh.arc-ts.umich.edu
node0002.lh.arc-ts.umich.edu

In the above example srun is used within the job from the first compute node to run a command once for every task in the job on the assigned resources. srun can be used to run on a subset of the resources assigned to the job. See the srun man page for more details.

Job Status

You can see the status of a job with `squeue -j <jobID>` . Most of a job’s specifications can be seen by invoking scontrol show job <jobID>.  More details about the job can be written to a file by using  scontrol write batch_script <jobID> output.txt. If no output file is specified, the script will be written to slurm<jobID>.sh.

A job’s record remains in Slurm’s memory for 30 minutes after it completes.  scontrol show job will return “Invalid job id specified” for a job that completed more than 30 minutes ago.  At that point, one must invoke the sacct command to retrieve the job’s record from the Slurm database.

To view TRES (Trackable RESource) utilization by user or account, use the following commands (substitute your values for bolded parts):

Shows TRES usage by all users on account during date range:
sreport cluster UserUtilizationByAccount start=mm/dd/yy end=mm/dd/yy account=test --tres type
Shows TRES usage by specified user(s) on account during date range:
sreport cluster UserUtilizationByAccount start=mm/dd/yy end=mm/dd/yy users=un1,un2 account=test --tres type
Lists users alphabetically along with TRES usage and total during date range:
sreport cluster AccountUtilizationByUser start=mm/dd/yy end=mm/dd/yy tree account=test --tres type
Possible TRES types:

cpu
mem
node
gres/gpu

For more reporting options, see the Slurm sreport documentation.