Interactive jobs¶
Video
Watch this in our courses: 2022 February, 2021 January
Abstract
We use the standard (and dominant) Slurm batch queuing system, all standard commands will work.
See the quick reference for the reference you need if you know Slurm or batch systems already.
Interactive jobs allow you to quickly test code (before scaling up) or getting more resources for manual analysis.
To run a single command interactively
srun [SLURM OPTIONS] COMMAND ...
to run before any COMMAND to run it in Slurm
To get an interactive shell
srun [SLURM OPTIONS] --pty bash
(general Slurm)sinteractive
(Triton specific)
Why interactive jobs?¶
There are two ways you can submit your jobs to Slurm queue system:
either interactively using srun
or by submitting a script
using sbatch
. This tutorial walks you through running your jobs
interactively, and the next tutorial on serial jobs
will go through serial jobs.
Some people say “the cluster is for batch computing”, but really it is to help you get your work done. Interactive jobs let you:
Run a single job in the Slurm environment to test parameters and make sure it works (which is easier than constantly modifying batch scripts).
Get a large amount of resources for some manual data analysis.
Interactive jobs¶
Let’s say you want to run the following command:
$ python3 slurm/pi.py 10000
You can submit this program to Triton using srun
. All input/output still goes to your terminal
(but note that graphical applications don’t work this way - see
below):
$ srun --mem=100M --time=0:10:00 python3 slurm/pi.py
srun: job 52204499 queued and waiting for resources
Here, we are asking for 100 Megabytes of memory (--mem=100M
) for a
duration of ten minutes (--time=0:10:00
) (See the quick
reference or below for more options).
While your job - with jobid 52204499 - is waiting to be allocated resources, your shell
blocks while it is waiting to continue.
You can open a new shell (ssh again) on triton and run the command
squeue -u $USER
or slurm q
to see all the jobs
you currently have waiting in queue:
$ slurm q
JOBID PARTITION NAME TIME START_TIME STATE NODELIST(REASON)
52204499 short-ivb python3 0:00 N/A PENDING (None)
You can see information such as the state, which partition the requested node reside in, etc.
Once resources are allocated to your job, you see the name of the machine in the Triton cluster your program ran on, output to your terminal:
srun: job 52204499 has been allocated resources
{"pi_estimate": 3.126, "iterations": 10000, "successes": 7815}
To show it’s running on a diferent computer, you can srun
hostname
(in this case, it runs on csl42
):
$ hostname
login3.triton.aalto.fi
$ srun hostname
srun: job 19039411 queued and waiting for resources
srun: job 19039411 has been allocated resources
csl42.int.triton.aalto.fi
Disadvantages
Interactive jobs are useful for debugging purposes, to test your setup and configurations before you put your tasks in a batch script for later execution.
The major disadvantages include:
It blocks your shell until it finishes
If your connection to Triton gets interrupted, you lose the job and its output.
Keep in mind that you shouldn’t open 20 shells to run 20 srun
jobs at once.
Please have a look at the next tutorial about serial jobs.
Interactive shell¶
What if you want an actual shell to do things interactively?
Put more precisely, you want access to a node in the cluster
through an interactive bash shell, with many resources available, that
will let you run commands such as Python and let do some basic work.
For this, you just need srun’s --pty
option coupled with the shell
you want:
$ srun -p interactive --time=2:00:00 --mem=600M --pty bash
The command prompt will appear when the job starts.
And you will have a bash shell runnnig on one of the
computation nodes with at least 600 Megabytes of memory,
for a duration of 2 hours, where you can run your programs in.
The option -p interactive
requests a node in the interactive
partition (group of nodes) which is dedicated to interactive usage
(more on this later).
Warning
Remember to exit the shell when you are done! The shell will be running if you don’t and it will count towards your usage. This wastes resources and effectively means your priority will degrade in the future.
Note
you can use sinfo
to see information such as the available partitions,
number of nodes in each, their time limits, etc.
Interactive shell with graphics¶
sinteractive
is very similar to srun
, but more clever and thus
allows you to do X forwarding. It starts a screen session on the node,
then sshes to there and connects to the shell:
$ sinteractive --time=1:00:00 --mem=1000M
Warning
Just like with srun --pty bash
, remember to exit the shell.
Since there is a separate screen session running, just closing the terminal isn’t enough.
Exit all shells in the screen session on the node (C-d or exit
) or cancel
the job.
Use remote desktop if off campus
If you are off-campus, you might want to use https://vdi.aalto.fi as a
virtual desktop to connect to Triton to run graphical programs: ssh
from there to Triton with ssh -XY
. Graphical programs run very
slowly when sent across the general Internet.
Checking your jobs¶
When your jobs enter the queue, you need to be able to get information on how much time, memory, etc. your jobs are using in order to know what requirements to ask for. We’ll see this later in Monitoring job progress and job efficiency.
The command slurm history
(or sacct --long | less
) gives you
information such as the actual memory used by your recent jobs, total
CPU time, etc. You will learn more about these commands later on.
As shown in a previous example, the command slurm queue
(or
squeue -u $USER
) will tell you the currently running processes,
which is a good way to make sure you have stopped everything.
Setting resource parameters¶
Remember to set the resources you need well, otherwise your are wasting resources and lowering your priority. We went over this in Slurm: the queuing system.
Exercises¶
The scripts you need for the following exercises can be found in this git
repository: hpc-examples.
You can clone the repository by running
git clone https://github.com/AaltoSciComp/hpc-examples.git
. This repository
will be used for most of the tutorial exercises.
Interactive-2: Time scaling
The program hpc-examples/slurm/pi.py
calculates pi using a simple stochastic algorithm. The program takes
one positional argument: the number of trials.
The time
program allows you to time any program, e.g. you can
time python x.py
to print the amount of time it takes.
Run the program, timing it with
time
, a few times, increasing the number of trials, until it takes about 10 seconds:time python hpc-examples/slurm/pi.py 500
, then 5000, then 50000, and so on.Add
srun
in front (srun python ...
). Use theseff JOBID
command to see how much time the program took to run. (If you’d like to use thetime
command, you can runsrun --mem=MEM --time=TIME time python hpc-examples/slurm/pi.py ITERS
)Look at the job history using
slurm history
- can you see how much time each process used? What’s the relation between TotalCPUTime and WallTime?
Interactive-3: Info commands
Run squeue -a
to see what is running, and then run slurm job
JOBID
on some running job - does anything look interesting?
Interactive-4: Showing node information
Run scontrol show node csl1
What is this? (csl1
is the
name of a node on Triton - if you are not on Triton, look at the
sinfo -N
command and try one of those names).
Interactive-5: Why not script srun
Some people are clever and use shell scripting to run
srun
many times in a loop (using&
to background it so that they all run at the same time). Can you list some advantages and disadvantages to this?
Solution
In does work, but it’s fragile: if the login node dies, everything
gets lost. It’s actually more work than doing it properly
(Array jobs: embarassingly parallel execution). And Slurm knows all array jobs are the same, so
it takes less resources to manage them - if someone scripts too
many srun
s, it can actually block other jobs from running
when they could otherwise.
What’s next?¶
In the next tutorial on serial batch jobs, you will learn how to put the above-mentioned commands in a script, namely a batch script (a.k.a submission script) that allows for a multitude of jobs to run unattended.