Interactive jobs

Introduction to Slurm

Triton is a large system that combines many different individual computer nodes. Hundreds of people are using Triton simultaneously. Thus resources (CPU time, memory, etc.) need to be shared among everyone.

This resource sharing is done by a software called a job scheduler or workload manager. Triton’s workload manager is slurm. Triton users submit jobs which are then scheduled and allocated resources by the workload manager.

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 in the next tutorial we will go through the more common and advanced way of submitting jobs, batch scripts.

An analogy: the HPC Diner

You’re eating out at the HPC Diner. What happens when you arrive?

  • A host greets you and takes your party size and estimated dining time.

  • You are given a number and asked to wait a bit.

  • The host looks at who is currently waiting.

  • If you are two people, you might squeeze in soon.

  • If you are a lot of people, the host will try to slowly free up enough tables to join to eat together.

  • If you are a really large party, you might need an advance reservation (or have to wait a really long time).

  • They want everyone to get a fair share of their food. Thus, people that have visited more often are asked to wait slightly longer for their table, as a balancing mechanic.

Thanks to HPC Carpentry / Sabry Razick for the idea.

Your first interactive job

Let’s say you want to run the following command:

$ python3 -c 'import os; print("hi from", os.uname().nodename)'

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=1:00:00 python3 -c 'import os; print("hi from", os.uname().nodename)'
srun: job 52204499 queued and waiting for resources

Here, we are asking for 100 Megabytes of memory (--mem=100M) for a duration of an hour (--time=1:00:00). While your job - with jobid 52204499 - is waiting to be allocated resources, your shell effectively become non-interactive.

You can open a new shell on triton and run the command slurm q to see all the jobs you have submitted to the queue:

$ slurm q
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.


The fact that we had to open another shell can be impractical if you need to run other jobs or just simply use the current shell. Additionally, if your shell quits while waiting (your internet may disconnect), the process cancels and you have to run the srun command again.

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
hi from


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. Or if you need graphical applications - such as Matlab, RStudio, etc. Additionally, if your task is small and not worth writing a batch script for, interactive job is the way to go. 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 that has all of the requested resources available. 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.


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 effectively means your priority will degrade in the future.

The option -p interactive requests a node in the interactive partition which is dedicated to interactive usage (more on this later). A partition is a group of nodes you can run on, with set limits.


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 screen. You can also ssh to this node again and connect to the process again.

sinteractive --time=1:00:00 --mem=1000M


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 process.


If you are off-campus, you might want to use as a virtual desktop to connect to Triton to run graphical programs. Otherwise, programs may run very slowly.

Monitoring your usage

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.

The command slurm history 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 will tell you the currently running processes, which is a good way to make sure you have stopped everything.


Generally, estimating the amount of time or memory you need comes down to monitoring you slurm history and utilizing command-line tools such as time on a few of your jobs and averaging. This is basically a trial and error process.

Setting resource parameters

Slurm comes with a multitude of parameters which you can specify to ensure you will be allocated enough memory, CPU cores, time, etc. You saw two of them in use in the above examples (--mem and --time) and you will learn more in the following tutorials.

Because you are sharing resource with other users, you should always estimate the amount of time, memory, etc. you need and then request them accordingly for efficiency reasons; the default memory and time limits are intentionally set low and may not be sufficient for your jobs to run/finish.

The general rule of thumb is to request the least possible, so that your stuff can run faster. That is because the less you request, the faster you are likely to be allocated resources. If you request something slightly less than a node size (note that we have different size nodes) or partition limit, you are more likely to fit into a spare spot.

For example, we have many nodes with 12 cores, and some with 20 or 24. If you request 24 cores, you have very limited options. However, you are more likely to be allocated a node if you request 10 cores. The same applies to memory: most common cutoffs are 48, 64, 128, 256GB. It’s best to use smaller values when submitting interactive jobs, and more for batch scripts.

See also

This reference page covers the existing resource parameters and options you can use in both your interactive jobs and batch jobs which you will learn about in the next tutorial.


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 This repository will be used for most of the tutorial exercises.

  1. The program hpc-examples/slurm/ uses up a lot of memory to do nothing. Let’s play with it. It’s run as follows: python hpc-examples/slurm/ 50M, where the last argument is however much memory you want to eat. You can use --help to see the options of the program.

    1. Try running the program with 50M.

    2. Run the program with 50M and srun --mem=500M.

    3. Increase the amount of memory the Python process tries to use (not the amount of memory Slurm allocates). How much memory can you use before the job fails?

    4. Look at the job history using slurm history - can you see how much memory it actually used? - Note that Slurm only measures memory every 60 seconds or so. To make the program last longer, so that the memory used can be measured, give the --sleep option to the Python process, like this: python hpc-examples/slurm/ 50M --sleep=60 - keep it available.

  2. The program hpc-examples/slurm/ 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 to print the amount of time it takes.

    1. 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/ 500, then 5000, then 50000, and so on.

    2. Add srun in front (srun python ...). Use the seff <jobid> command to see how much time the program took to run. (If you’d like to use the time command, you can run srun --mem=<m> --time=<t> time python hpc-examples/slurm/ <iters>)

    3. Tell srun to use five CPUs (-c 5). Does it go any faster?

    4. Use the --threads=5 option to the Python program to tell it to also use five threads. ... python .../ --threads=5

    5. 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?

  3. Check out some of these commands: sinfo, sinfo -N, squeue. Run slurm job <jobid> on some running job - does anything look interesting?

  4. 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).

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.