Triton quick reference

In this page, you have all important reference information

Quick reference guide for the Triton cluster at Aalto University, but also useful for many other Slurm clusters.

Connecting

See also: Connecting to Triton.

Method	Description	From where?
ssh	Standard way of connecting via command line. Hostname is triton.aalto.fi >Linux/Mac: ssh USERNAME@triton.aalto.fi >Windows: WSL+Linux/mac command or use Powershell, which works very similar to linux shell.	Connections only from VPN and Aalto networks. kosh.aalto.fi is a good proxy host if you are not there: ssh -J USERNAME@kosh.aalto.fi USERNAME@triton.aalto.fi
VDI	“Virtual desktop interface”, https://vdi.aalto.fi, from there you have to ssh to Triton (previous row) and can run graphical programs via SSH.	Whole internet
Jupyter	https://jupyter.triton.aalto.fi provides the Jupyter interface directly on Triton (including command line).	Whole internet
Open OnDemand	https://ood.triton.aalto.fi, Web-based interface to the cluster. Includes shell access and data transfer.	VPN and Aalto networks

Modules

See also: Software modules.

Command	Description
module load NAME	load module
module avail	list all modules
module spider NAME	search modules
module list	list currently loaded modules
module show NAME	details on a module
module help NAME	details on a module
module unload NAME	unload a module
module save ALIAS	save module collection to this alias (saved in ~/.lmod.d/)
module savelist	list all saved collections
module describe ALIAS	details on a collection
module restore ALIAS	load saved module collection (faster than loading individually)
module purge	unload all loaded modules (faster than unloading individually)

Common software

See also: Applications.

• Python: module load anaconda for the Anaconda distribution of Python 3, including a lot of useful packages. More info.

• R: module load r for a basic R package. More info.

• Matlab: module load matlab for the latest Matlab version. More info.

• Julia: module load julia for the latest Julia version. More info.

Storage

See also: Data storage

Name	Path	Quota	Backup	Locality	Purpose
Home	$HOME or /home/USERNAME/	hard quota 10GB	Nightly	all nodes	Small user specific files, no calculation data.
Work	$WRKDIR or /scratch/work/USERNAME/	200GB and 1 million files	x	all nodes	Personal working space for every user. Calculation data etc. Quota can be increased on request.
Scratch	/scratch/DEPT/PROJECT/	on request	x	all nodes	Department/group specific project directories.
Local temp	/tmp/	limited by disk size	x	single-node	Primary (and usually fastest) place for single-node calculation data. Removed once user’s jobs are finished on the node.
Local persistent	/l/	varies	x	dedicated group servers only	Local disk persistent storage. On servers purchased for a specific group. Not backed up.
ramfs (login nodes only)	$XDG_RUNTIME_DIR	limited by memory	x	single-node	Ramfs on the login node only, in-memory filesystem

Remote data access

See also: Remote access to data.

Method	Description
rsync transfers	Transfer back and forth via command line. Set up ssh first. rsync triton.aalto.fi:/path/to/file.txt file.txt rsync file.txt triton.aalto.fi:/path/to/file.txt
SFTP transfers	Operates over SSH. sftp://triton.aalto.fi in file browsers (Linux at least), FileZilla (to triton.aalto.fi).
SMB mounting	Mount (make remote viewable locally) to your own computer. Linux: File browser, smb://data.triton.aalto.fi/scratch/ MacOS: File browser, same URL as Linux Windows: \\data.triton.aalto.fi\scratch\

Partitions

Partition	Max job size	Mem/core (GB)	Tot mem (GB)	Cores/node	Limits	Use
<default>						If you leave off all possible partitions will be used (based on time/mem)
debug	2 nodes	2.66 - 12	32-256	12,20,24	15 min	testing and debugging short interactive. work. 1 node of each arch.
batch	16 nodes	2.66 - 12	32-256	12, 20,24	5d	primary partition, all serial & parallel jobs
short	8 nodes	4 - 12	48-256	12, 20,24	4h	short serial & parallel jobs, +96 dedicated CPU cores
hugemem	1 node	43	1024	24	3d	huge memory jobs, 1 node only
gpu	1 node, 2-8GPUs	2 - 10	24-128	12	5d	Long gpu jobs
gpushort	4 nodes, 2-8 GPUs	2 - 10	24-128	12	4h	Short GPU jobs
interactive	2 nodes	5	128	24	1d	for sinteractive command, longer interactive work

Use slurm partitions to see more details.

Job submission

See also: Serial Jobs, Array jobs, Parallel computing, Serial Jobs.

Command	Description
sbatch	submit a job to queue (see standard options below)
srun	Within a running job script/environment: Run code using the allocated resources (see options below)
srun	On frontend: submit to queue, wait until done, show output. (see options below)
sinteractive	Submit job, wait, provide shell on node for interactive playing (X forwarding works, default partition interactive). Exit shell when done. (see options below)
srun --pty bash	(advanced) Another way to run interactive jobs, no X forwarding but simpler. Exit shell when done.
scancel JOBID	Cancel a job in queue
salloc	(advanced) Allocate resources from frontend node. Use srun to run using those resources, exit to close shell when done (see options below)
scontrol	View/modify job and slurm configuration

Command	Option	Description
sbatch/srun/etc	-t, --time=HH:MM:SS	time limit
	-t, --time=DD-HH	time limit, days-hours
	-p, --partition=PARTITION	job partition. Usually leave off and things are auto-detected.
	--mem-per-cpu=N	request n MB of memory per core
	--mem=N	request n MB memory per node
	-c, --cpus-per-task=N	Allocate *n* CPU’s for each task. For multithreaded jobs. (compare ``–ntasks``: ``-c`` means the number of cores for each process started.)
	-N, --nodes=N-M	allocate minimum of n, maximum of m nodes.
	-n, --ntasks=N	allocate resources for and start n tasks (one task=one process started, it is up to you to make them communicate. However the main script runs only on first node, the sub-processes run with “srun” are run this many times.)
	-J, --job-name=NAME	short job name
	-o OUTPUTFILE	print output into file output
	-e ERRORFILE	print errors into file error
	--exclusive	allocate exclusive access to nodes. For large parallel jobs.
	--constraint=FEATURE	request feature (see slurm features for the current list of configured features, or Arch under the hardware list). Multiple with --constraint="hsw|skl".
	--array=0-5,7,10-15	Run job multiple times, use variable $SLURM_ARRAY_TASK_ID to adjust parameters.
	--gres=gpu	request a GPU, or --gres=gpu:n for multiple
	--gres=spindle	request nodes that have disks, spindle:n, for a certain number of RAID0 disks
	--mail-type=TYPE	notify of events: BEGIN, END, FAIL, ALL, REQUEUE (not on triton) or ALL. MUST BE used with --mail-user= only
	--mail-user=YOUR@EMAIL	whome to send the email
srun	-N N_NODES hostname	Print allocated nodes (from within script)

Command	Description
slurm q ; slurm qq	Status of your queued jobs (long/short)
slurm partitions	Overview of partitions (A/I/O/T=active,idle,other,total)
slurm cpus PARTITION	list free CPUs in a partition
slurm history [1day,2hour,…]	Show status of recent jobs
seff JOBID	Show percent of mem/CPU used in job. See Monitoring.
sacct -o comment -p -j JOBID	Show GPU efficiency
slurm j JOBID	Job details (only while running)
slurm s ; slurm ss PARTITION	Show status of all jobs
sacct	Full history information (advanced, needs args)

Full slurm command help:

$ slurm

Show or watch job queue:
 slurm [watch] queue     show own jobs
 slurm [watch] q   show user's jobs
 slurm [watch] quick     show quick overview of own jobs
 slurm [watch] shorter   sort and compact entire queue by job size
 slurm [watch] short     sort and compact entire queue by priority
 slurm [watch] full      show everything
 slurm [w] [q|qq|ss|s|f] shorthands for above!
 slurm qos               show job service classes
 slurm top [queue|all]   show summary of active users
Show detailed information about jobs:
 slurm prio [all|short]  show priority components
 slurm j|job      show everything else
 slurm steps      show memory usage of running srun job steps
Show usage and fair-share values from accounting database:
 slurm h|history   show jobs finished since, e.g. "1day" (default)
 slurm shares
Show nodes and resources in the cluster:
 slurm p|partitions      all partitions
 slurm n|nodes           all cluster nodes
 slurm c|cpus            total cpu cores in use
 slurm cpus   cores available to partition, allocated and free
 slurm cpus jobs         cores/memory reserved by running jobs
 slurm cpus queue        cores/memory required by pending jobs
 slurm features          List features and GRES

Examples:
 slurm q
 slurm watch shorter
 slurm cpus batch
 slurm history 3hours

Other advanced commands (many require lots of parameters to be useful):

Command	Description
squeue	Full info on queues
sinfo	Advanced info on partitions
slurm nodes	List all nodes

Slurm examples

See also: Serial Jobs, Array jobs.

Simple batch script, submit with sbatch the_script.sh:

#!/bin/bash -l
#SBATCH --time=01:00:00
#SBATCH --mem-per-cpu=1G

module load anaconda
python my_script.py

Simple batch script with array (can also submit with sbatch --array=1-10 the_script.sh):

#!/bin/bash -l
#SBATCH --array=1-10

python my_script.py --seed=$SLURM_ARRAY_TASK_ID

Toolchains

Toolchain	Compiler version	MPI version	BLAS version	ScaLAPACK version	FFTW version	CUDA version
GOOLF Toolchains:
goolf/triton-2016a	GCC/4.9.3	OpenMPI/1.10.2	OpenBLAS/0.2.15	ScaLAPACK/2.0.2	FFTW/3.3.4
goolf/triton-2016b	GCC/5.4.0	OpenMPI/1.10.3	OpenBLAS/0.2.18	ScaLAPACK/2.0.2	FFTW/3.3.4
goolfc/triton-2016a	GCC/4.9.3	OpenMPI/1.10.2	OpenBLAS/0.2.15	ScaLAPACK/2.0.2	FFTW/3.3.4	7.5.18
goolfc/triton-2017a	GCC/5.4.0	OpenMPI/2.0.1	OpenBLAS/0.2.19	ScaLAPACK/2.0.2	FFTW/3.3.4	8.0.61
GMPOLF Toolchains:
gmpolf/triton-2016a	GCC/4.9.3	MPICH/3.0.4	OpenBLAS/0.2.15	ScaLAPACK/2.0.2	FFTW/3.3.4
gmpolfc/triton-2016a	GCC/4.9.3	MPICH/3.0.4	OpenBLAS/0.2.15	ScaLAPACK/2.0.2	FFTW/3.3.4	7.5.18
GMVOLF Toolchains:
gmvolf/triton-2016a	GCC/4.9.3	MVAPICH2/2.0.1	OpenBLAS/0.2.15	ScaLAPACK/2.0.2	FFTW/3.3.4
gmvolfc/triton-2016a	GCC/4.9.3	MVAPICH2/2.0.1	OpenBLAS/0.2.15	ScaLAPACK/2.0.2	FFTW/3.3.4	7.5.18
IOOLF Toolchains:
ioolf/triton-2016a	icc/2015.3.187	OpenMPI/1.10.2	OpenBLAS/0.2.15	ScaLAPACK/2.0.2	FFTW/3.3.4
IOMKL Toolchains:
iomkl/triton-2016a	icc/2015.3.187	OpenMPI/1.10.2	imkl/11.3.1.150	imkl/11.3.1.150	imkl/11.3.1.150
iomkl/triton-2016b	icc/2015.3.187	OpenMPI/1.10.3	imkl/11.3.1.150	imkl/11.3.1.150	imkl/11.3.1.150
iompi/triton-2017a	icc/2017.1.132	OpenMPI/2.0.1	imkl/2017.1.132	imkl/2017.1.132	imkl/2017.1.132

Hardware

See also: Cluster technical overview.

Node name	Number of nodes	Node type	Year	Arch (constraint)	CPU type	Memory Configuration	Infiniband	GPUs	Disks
pe[1-48,65-81]	65	Dell PowerEdge C4130	2016	hsw avx avx2	2x12 core Xeon E5 2680 v3 2.50GHz	128GB DDR4-2133	FDR		HDD
pe[49-64,82]	17	Dell PowerEdge C4130	2016	hsw avx avx2	2x12 core Xeon E5 2680 v3 2.50GHz	256GB DDR4-2133	FDR		HDD
pe[83-91]	8	Dell PowerEdge C4130	2017	bdw avx avx2	2x14 core Xeon E5 2680 v4 2.40GHz	128GB DDR4-2400	FDR		HDD
c[579-628,639-698]	110	ProLiant XL230a Gen9	2017	hsw avx avx2	2x12 core Xeon E5 2690 v3 2.60GHz	128GB DDR4-2666	FDR
c[629-638]	10	ProLiant XL230a Gen9	2017	hsw avx avx2	2x12 core Xeon E5 2690 v3 2.60GHz	256GB DDR4-2400	FDR
skl[1-48]	48	Dell PowerEdge C6420	2019	skl avx avx2 avx512	2x20 core Xeon Gold 6148 2.40GHz	192GB DDR4-2667	EDR
csl[1-48]	48	Dell PowerEdge C6420	2020	csl avx avx2 avx512	2x20 core Xeon Gold 6248 2.50GHz	192GB DDR4-2667	EDR
fn3	1	Dell PowerEdge R940	2020	avx avx2 avx512	4x20 core Xeon Gold 6148 2.40GHz	2TB DDR4-2666	EDR
gpu[1-10]	10	Dell PowerEdge C4140	2020	skl avx avx2 avx512 volta	2x8 core Intel Xeon Gold 6134 @ 3.2GHz	384GB DDR4-2667	EDR	4x V100 32GB	SSD
gpu[11-17]	7	Dell PowerEdge XE8545	2021	milan avx avx2 a100	2x24 core AMD EPYC 7413 @ 2.65GHz	503GB DDR4-3200	EDR	4x A100 80GB	SSD
gpu[20-22]	3	Dell PowerEdge C4130	2016	hsw avx avx2 kepler	2x6 core Xeon E5 2620 v3 2.50GHz	128GB DDR4-2133	EDR	4x2 GPU K80	SSD
gpu[23-27]	5	Dell PowerEdge C4130	2017	hsw avx avx2 pascal	2x12 core Xeon E5-2680 v3 @ 2.5GHz	256GB DDR4-2400	EDR	4x P100	SSD
gpu[28-37]	10	Dell PowerEdge C4140	2019	skl avx avx2 avx512 volta	2x8 core Intel Xeon Gold 6134 @ 3.2GHz	384GB DDR4-2667	EDR	4x V100 32GB	SSD
dgx[1-2]	2	Nvidia DGX-1	2018	bdw avx avx2 volta	2x20 core Xeon E5-2698 v4 @ 2.2GHz	512GB DDR4-2133	EDR	8x V100 16GB	SSD
dgx[3-7]	5	Nvidia DGX-1	2018	bdw avx avx2 volta	2x20 core Xeon E5-2698 v4 @ 2.2GHz	512GB DDR4-2133	EDR	8x V100 32GB	SSD
gpuamd1	1	Dell PowerEdge R7525	2021	rome avx avx2 mi100	2x8 core AMD EPYC 7262 @3.2GHz	250GB DDR4-3200	EDR	3x MI100 32GB	SSD

Node type	CPU count
48GB Xeon Westmere (2012)	1404
24GB Xeon Westmere + 2x GPU (2012)	120
96GB Xeon Westmere (2012)	288
1TB Xeon Westmere (2012)	48
256GB Xeon Ivy Bridge (2014)	480
64GB Xeon Ivy Bridge (2014)	480
128GB Xeon Haswell (2016)	1224
256GB Xeon Haswell (2016)	360
128GB Xeon Haswell + 4x GPU (2016)	36

GPUs

See also: GPU computing.

Card	Slurm feature name (--constraint=)	Slurm gres name (--gres=gpu:NAME:n)	total amount	nodes	architecture	compute threads per GPU	memory per card	CUDA compute capability
Tesla K80*	kepler	teslak80	12	gpu[20-22]	Kepler	2x2496	2x12GB	3.7
Tesla P100	pascal	teslap100	20	gpu[23-27]	Pascal	3854	16GB	6.0
Tesla V100	volta	v100	40	gpu[1-10]	Volta	5120	32GB	7.0
Tesla V100	volta	v100	40	gpu[28-37]	Volta	5120	32GB	7.0
Tesla V100	volta	v100	16	dgx[1-7]	Volta	5120	16GB	7.0
Tesla A100	ampere	a100	28	gpu[11-17]	Ampere	7936	80GB	8.0
AMD MI100 (testing)	mi100	Use -p gpu-amd only, no --gres		gpuamd[1]

Conda

See also: Python environments with Conda

Command	Description
module load miniconda	Load module that provides miniconda on Triton - recommended for using conda for your own environments.
First time setup	See link for six commands to run once per user account on Triton (to avoid filling up all space on your home directory).
name: conda-example channels: - conda-forge dependencies: - numpy - pandas	Minimal environment.yml example. By defining our requirements in one place, our environment becomes reproducible and we can solve problems by re-creating it. “Dependencies” lists packages that will be installed.
Environment management:
conda env create --file environment.yml	Create environment from yaml file. Use -n NAME to set or override the name from the .yml file. Environments with -n are stored in conda config --show envs_dirs.
source activate NAME	Activate environment of name NAME. Note we use this and not conda init/conda activate to avoid changing Python for your whole account.
source deactivate	Deactivate conda from this session.
conda env list	List all environments.
conda env remove -n NAME	Remove the environment of that name.
Package management:	Inside the activate environment
conda list	List packages in currently active environment.
conda install --freeze-installed --channel CHANNEL PACKAGE_NAME	Install packages in an environment with minimal changes to what is already installed. Usually you would want to go at add them to environment.yml if it is a dependency. Better: add to environment.yml and then see next line.
conda env update --file environment.yml	Update an environment file based on environment.yml
conda env export	Export an environment.yml that describes the current environment. Add --no-builds to make it more portable across operating systems. Add --from-history to list only what you have explicitly requested in the past.
conda search [--channel conda-forge] NAME	Search for a package. List includes name, version, build version (often including linked libraries like Python/CUDA), and channel.
Other:
mamba ...	Use mamba instead of conda for faster operations. mamba is a drop-in replacement. It should be installed in the environment.
conda clean -a	Clean up cached files to free up space (not environments or packages in them).
CONDA_OVERRIDE_CUDA="11.2" conda ..	Used when making CUDA environment on login node (choose right CUDA version for you). Used with ... env create or ... install to indicate that CUDA will be available when the program runs.
Channel conda-forge Package selection tensorflow=*=*cuda*	Package selection for tensorflow. The first * can be replaced with the Tensorflow version specification
Channels pytorch and conda-forge Package selection pytorch=*=*cuda*	Package selection for pytorch. The first * can be replaced with the pytorch version specification.
CUDA	In channel conda-forge, automatically selected based on software you need. For manual compilation, package cudatoolkit in conda-forge.

Command line

See also: Linux shell crash course.

General notes

The command line has many small programs that when connected, allow you to do many things. Only a little bit of this is shown here.

Programs are generally silent if everything worked, and only print an error if something goes wrong.

ls [DIR]

List current directory (or DIR if given).

pwd

Print current directory.

cd DIR

Change directory. .. is parent directory, / is root, / is also chaining directories, e.g. dir1/dir2 or ../../

nano FILE

Edit a file (there are many other editors, but nano is common, nice, and simple).

mkdir DIR-NAME

Make a new directory.

cat FILE

Print entire contents of file to standard output (the terminal).

less FILE

Less is a “pager”, and lets you scroll through a file (up/down/pageup/pagedown). q to quit, / to search.

mv SOURCE DEST

Move (=rename) a file. mv SOURCE1 SOURCE2 DEST-DIRECTORY/ copies multiple files to a directory.

cp SOURCE DEST

Copy a file. The DEST-DIRECTORY/ syntax of mv works as well.

rm FILE ...

Remove a file. Note, from the command line there is no recovery, so always pause and check before running this command! The -i option will make it confirm before removing each file. Add -r to remove whole directories recursively.

head [FILE]

Print the first 10 (or N lines with -n N) of a file. Can take input from standard input instead of FILE. tail is similar but the end of the file.

grep PATTERN [FILE]

Print lines matching a pattern in a file, suitable as a primitive find feature, or quickly searching for output. Can also use standard input instead of FILE.

du [-ash] [DIR]

Print disk usage of a directory. Default is KiB, rounded up to block sizes (1 or 4 KiB), -h means “human readable” (MB, GB, etc), -s means “only of DIR, not all subdirectories also”. -a means “all files, not only directories”. A common pattern is du -h DIR | sort -h to print all directories and their sizes, sorted by size.

stat

Show detailed information on a file’s properties.

find [DIR]

find can do almost anything, but that means it’s really hard to use it well. Let’s be practical: with only a directory argument, it prints all files and directories recursively, which might be useful itself. Many of us do find DIR | grep NAME to grep for the name we want (even though this isn’t the “right way”, there are find options which do this same thing more efficiently).

| (pipe): COMMAND1 | COMMAND2

The output of COMMAND1 is sent to the input of COMMAND2. Useful for combining simple commands together into complex operations - a core part of the unix philosophy.

> (output redirection): COMMAND > FILE

Write standard output of COMMAND to FILE. < does the opposite, read input from a file.

type COMMAND or which COMMAND

Show exactly what will be run, for a given command (e.g. type python3).

man COMMAND-NAME

Browse on-line help for a command. q will exit, / will search (it uses less as its pager by default).

-h and --help

Common command line options to print help on a command. But, it has to be implemented by each command.