Environment Modules¶
Usage of software on HPC systems is managed by a modules system.
Module
A module is a user interface that provides utilities for the dynamic modification of a user's environment, e.g. prepending paths to
PATHLD_LIBRARY_PATHMANPATH- and more
to help you to access compilers, loader, libraries and utilities.
By using modules, you can smoothly switch between different versions of installed software packages and libraries.
Module Commands¶
Using modules is quite straightforward and the following table lists the basic commands.
| Command | Description |
|---|---|
module help |
Show all module options |
module list |
List active modules in the user environment |
module purge |
Remove modules from the user environment |
module avail [modname] |
List all available modules |
module spider [modname] |
Search for modules across all environments |
module load <modname> |
Load module modname in the user environment |
module unload <modname> |
Remove module modname from the user environment |
module switch <mod1> <mod2> |
Replace module mod1 with module mod2 |
module show <modname> |
Show the commands in the module file |
Module files are ordered by their topic on ZIH systems. By default, with module avail you will
see all topics and their available module files. If you just wish to see the installed versions of a
certain module, you can use module avail softwarename and it will display the available versions of
softwarename only.
Examples¶
Searching for software
The process of searching for a particular software you want to use on an HPC system consits of
two steps: Login to the target HPC system and invoke module spider command to search for the
software and list available versions.
For example, if you want to search for available MATLAB versions on Barnard, the steps might
be:
marie@login.barnard$ module spider matlab
---------------------------------------------------------------------------------------------------------------------------------------------------------
MATLAB: MATLAB/2022b
---------------------------------------------------------------------------------------------------------------------------------------------------------
Description:
MATLAB is a high-level language and interactive environment that enables you to perform computationally intensive tasks faster than with
traditional programming languages such as C, C++, and Fortran.
You will need to load all module(s) on any one of the lines below before the "MATLAB/2022b" module is available to load.
release/23.04
release/23.10
[...]
As you can see, MATLAB in version 2022b is available on Barnard within the releases 23.04
and23.10. Additionally, the output provides the information how to load it:
marie@login.barnard$ module load release/23.10 MATLAB/2022b
Module MATLAB/2022b and 1 dependency loaded.
Finding available software
This examples illustrates the usage of the command module avail to search for available MATLAB
installations.
marie@compute$ module avail matlab
------------------------------ /sw/modules/scs5/math ------------------------------
MATLAB/2017a MATLAB/2018b MATLAB/2020a
MATLAB/2018a MATLAB/2019b MATLAB/2021a (D)
Wo:
D: Standard Modul.
Verwenden Sie "module spider" um alle verfügbaren Module anzuzeigen.
Verwenden Sie "module keyword key1 key2 ...", um alle verfügbaren Module
anzuzeigen, die mindestens eines der Schlüsselworte enthält.
Loading and removing modules
A particular module or several modules are loaded into your environment using the module load
command. The counter part to remove a module or several modules is module unload.
marie@compute$ module load Python/3.8.6
Module Python/3.8.6-GCCcore-10.2.0 and 11 dependencies loaded.
Removing all modules
To remove all loaded modules from your environment with one keystroke, invoke
marie@compute$ module purge
Die folgenden Module wurden nicht entladen:
(Benutzen Sie "module --force purge" um alle Module zu entladen):
1) release/23.04
Module Python/3.8.6-GCCcore-10.2.0 and 11 dependencies unloaded.
Show the command in module file
The option show <modname> will output the commands in the module file. Using this command,
you can find out what paths are prepended and what environment variables are set.
```console marie@login$ module show GCCcore
/sw/modules/scs5/compiler/GCCcore/11.2.0.lua: --------------------------------------------------------------------------------------------------------------------------------------------------------- help([[ Description =========== The GNU Compiler Collection includes front ends for C, C++, Objective-C, Fortran, Java, and Ada, as well as libraries for these languages (libstdc++, libgcj,...).
[...]
conflict("GCCcore")
prepend_path("CMAKE_LIBRARY_PATH","/sw/installed/GCCcore/11.2.0/lib64")
prepend_path("CMAKE_PREFIX_PATH","/sw/installed/GCCcore/11.2.0")
prepend_path("LD_LIBRARY_PATH","/sw/installed/GCCcore/11.2.0/lib64")
prepend_path("MANPATH","/sw/installed/GCCcore/11.2.0/share/man")
prepend_path("PATH","/sw/installed/GCCcore/11.2.0/bin")
prepend_path("XDG_DATA_DIRS","/sw/installed/GCCcore/11.2.0/share")
setenv("EBROOTGCCCORE","/sw/installed/GCCcore/11.2.0")
setenv("EBVERSIONGCCCORE","11.2.0")
setenv("EBDEVELGCCCORE","/sw/installed/GCCcore/11.2.0/easybuild/GCCcore-11.2.0-easybuild-devel")
```
Front-End ml¶
There is a front end for the module command, which helps you to type less. It is ml.
Any module command can be given after ml:
| ml Command | module Command |
|---|---|
ml |
module list |
ml foo bar |
module load foo bar |
ml -foo -bar baz |
module unload foo bar; module load baz |
ml purge |
module purge |
ml show foo |
module show foo |
Usage of front-end ml
marie@compute$ ml +Python/3.8.6
Module Python/3.8.6-GCCcore-10.2.0 and 11 dependencies loaded.
marie@compute$ ml
Derzeit geladene Module:
1) release/23.04 (S) 5) bzip2/1.0.8-GCCcore-10.2.0 9) SQLite/3.33.0-GCCcore-10.2.0 13) Python/3.8.6-GCCcore-10.2.0
2) GCCcore/10.2.0 6) ncurses/6.2-GCCcore-10.2.0 10) XZ/5.2.5-GCCcore-10.2.0
3) zlib/1.2.11-GCCcore-10.2.0 7) libreadline/8.0-GCCcore-10.2.0 11) GMP/6.2.0-GCCcore-10.2.0
4) binutils/2.35-GCCcore-10.2.0 8) Tcl/8.6.10-GCCcore-10.2.0 12) libffi/3.3-GCCcore-10.2.0
Wo:
S: Das Modul ist angeheftet. Verwenden Sie "--force", um das Modul zu entladen.
marie@compute$ ml -Python/3.8.6 +ANSYS/2020R2
Module Python/3.8.6-GCCcore-10.2.0 and 11 dependencies unloaded.
Module ANSYS/2020R2 loaded.
Module Environments¶
On ZIH systems, there exist different module environments, each containing a set of software
modules.
They are activated via the meta module release which has different versions,
one of which is loaded by default.
You can switch between them by simply loading the desired version, e.g.
marie@compute$ module load release/23.10
Searching for Software¶
The command module spider <modname> allows searching for a specific software across all module
environments.
It will also display information on how to load a particular module when giving a
precise module (with version) as the parameter.
Spider command
marie@login$ module spider p7zip
----------------------------------------------------------------------------------------------------------------------------------------------------------
p7zip:
----------------------------------------------------------------------------------------------------------------------------------------------------------
Beschreibung:
p7zip is a quick port of 7z.exe and 7za.exe (command line version of 7zip) for Unix. 7-Zip is a file archiver with highest compression ratio.
Versionen:
p7zip/9.38.1
p7zip/17.03-GCCcore-10.2.0
p7zip/17.03
----------------------------------------------------------------------------------------------------------------------------------------------------------
Um detaillierte Informationen über ein bestimmtes "p7zip"-Modul zu erhalten (auch wie das Modul zu laden ist), verwenden sie den vollständigen Namen des Moduls.
Zum Beispiel:
$ module spider p7zip/17.03
----------------------------------------------------------------------------------------------------------------------------------------------------------
In some cases a desired software is available as an extension of a module.
Extension module
marie@login$ module spider tensorboard
--------------------------------------------------------------------------------------------------------------------------------
tensorboard:
--------------------------------------------------------------------------------------------------------------------------------
Versions:
tensorboard/2.4.1 (E)
Names marked by a trailing (E) are extensions provided by another module.
[...]
You retrieve further information using the spider command.
marie@login$ module spider tensorboard/2.4.1
--------------------------------------------------------------------------------------------------------------------------------
tensorboard: tensorboard/2.4.1 (E)
--------------------------------------------------------------------------------------------------------------------------------
This extension is provided by the following modules. To access the extension you must load one of the following modules. Note that any module names in parentheses show the module location in the software hierarchy.
TensorFlow/2.4.1 (release/23.04 GCC/10.2.0 CUDA/11.1.1 OpenMPI/4.0.5)
Names marked by a trailing (E) are extensions provided by another module.
Finaly, you can load the dependencies and tensorboard/2.4.1 and check the version.
marie@login$ module load release/23.04 GCC/11.3.0 OpenMPI/4.1.4
Modules GCC/10.2.0, CUDA/11.1.1, OpenMPI/4.0.5 and 15 dependencies loaded.
marie@login$ module load TensorFlow/2.11.0-CUDA-11.7.0
Aktiviere Module:
1) CUDA/11.7.0 2) GDRCopy/2.3
Module TensorFlow/2.11.0-CUDA-11.7.0 and 39 dependencies loaded.
marie@login$ tensorboard --version
2.11.1
Toolchains¶
A program or library may break in various ways (e.g. not starting, crashing or producing wrong results) when it is used with a software of a different version than it expects.So each module specifies the exact other modules it depends on. They get loaded automatically when the dependent module is loaded.
Loading a single module is easy as there can't be any conflicts between dependencies. However when loading multiple modules they can require different versions of the same software. This conflict is currently handled in that loading the same software with a different version automatically unloads the earlier loaded module. As the dependents of that module are not automatically unloaded this means they now have a wrong dependency (version) which can be a problem (see above).
To avoid this there are (versioned) toolchains and for each toolchain there is (usually) at most
one version of each software.
A "toolchain" is a set of modules used to build the software for other modules.
The most common one is the foss-toolchain consisting of GCC, OpenMPI, OpenBLAS & FFTW.
This toolchain can be broken down into a sub-toolchain called gompi consisting of only
GCC & OpenMPI, or further to GCC (the compiler and linker)
and even further to GCCcore which is only the runtime libraries required to run programs built
with the GCC standard library.
Hint
As toolchains are regular modules you can display their parts via module show foss/2019a.
This way the toolchains form a hierarchy and adding more modules makes them "higher" than another.
Examples:
| Toolchain | Components |
|---|---|
foss |
GCC OpenMPI OpenBLAS FFTW |
gompi |
GCC OpenMPI |
GCC |
GCCcore binutils |
GCCcore |
none |
intel |
intel-compilers impi imkl |
iimpi |
intel-compilers impi |
intel-compilers |
GCCcore binutils |
As you can see GCC and intel-compilers are on the same level, as are gompi and iimpi,
although they are one level higher than the former.
You can load and use modules from a lower toolchain with modules from
one of its parent toolchains.
For example Python/3.6.6-foss-2019a can be used with Boost/1.70.0-gompi-2019a.
But you cannot combine toolchains or toolchain versions.
So QuantumESPRESSO/6.5-intel-2019a and OpenFOAM/8-foss-2020a
are both incompatible with Python/3.6.6-foss-2019a.
However LLVM/7.0.1-GCCcore-8.2.0 can be used with either
QuantumESPRESSO/6.5-intel-2019a or Python/3.6.6-foss-2019a
because GCCcore-8.2.0 is a sub-toolchain of intel-2019a and foss-2019a.
With the hierarchical module scheme we use at ZIH modules from other toolchains cannot be directly
loaded and don't show up in module av which avoids loading incompatible modules.
So the concept if this hierarchical toolchains is already built into this module environment.
Info
The toolchains usually have a year and letter as their version corresponding to their release.
So 2019a and 2020b refer to the first half of 2019 and the 2nd half of 2020 respectively.
Per-Architecture Builds¶
Since we have a heterogeneous cluster, we do individual builds of the software for each architecture present. This ensures that, no matter what partition/cluster the software runs on, a build optimized for the host architecture is used automatically.
However, not every module will be available on all clusters.
Use ml av or ml spider to search for modules available on the sub-cluster you are on.
Advanced Usage¶
For writing your own module files please have a look at the Guide for writing project and private module files.
Troubleshooting¶
When I log in, the wrong modules are loaded by default¶
Reset your currently loaded modules with module purge.
Then run module save to overwrite the
list of modules you load by default when logging in.
I can't load module TensorFlow¶
Check the dependencies by e.g. calling module spider TensorFlow/2.4.1
it will list a number of modules that need to be loaded
before the TensorFlow module can be loaded.
Loading the dependencies
marie@compute$ module load TensorFlow/2.4.1
Lmod hat den folgenden Fehler erkannt: Diese Module existieren, aber
können nicht wie gewünscht geladen werden: "TensorFlow/2.4.1"
Versuchen Sie: "module spider TensorFlow/2.4.1" um anzuzeigen, wie die Module
geladen werden.
marie@compute$ module spider TensorFlow/2.4.1
----------------------------------------------------------------------------------
TensorFlow: TensorFlow/2.4.1
----------------------------------------------------------------------------------
Beschreibung:
An open-source software library for Machine Intelligence
Sie müssen alle Module in einer der nachfolgenden Zeilen laden bevor Sie das Modul "TensorFlow/2.4.1" laden können.
release/23.04 GCC/10.2.0 CUDA/11.1.1 OpenMPI/4.0.5
This extension is provided by the following modules. To access the extension you must load one of the following modules. Note that any module names in parentheses show the module location in the software hierarchy.
TensorFlow/2.4.1 (release/23.04 GCC/10.2.0 CUDA/11.1.1 OpenMPI/4.0.5)
This module provides the following extensions:
absl-py/0.10.0 (E), astunparse/1.6.3 (E), cachetools/4.2.0 (E), dill/0.3.3 (E), gast/0.3.3 (E), google-auth-oauthlib/0.4.2 (E), google-auth/1.24.0 (E), google-pasta/0.2.0 (E), grpcio/1.32.0 (E), gviz-api/1.9.0 (E), h5py/2.10.0 (E), Keras-Preprocessing/1.1.2 (E), Markdown/3.3.3 (E), oauthlib/3.1.0 (E), opt-einsum/3.3.0 (E), portpicker/1.3.1 (E), pyasn1-modules/0.2.8 (E), requests-oauthlib/1.3.0 (E), rsa/4.7 (E), tblib/1.7.0 (E), tensorboard-plugin-profile/2.4.0 (E), tensorboard-plugin-wit/1.8.0 (E), tensorboard/2.4.1 (E), tensorflow-estimator/2.4.0 (E), TensorFlow/2.4.1 (E), termcolor/1.1.0 (E), Werkzeug/1.0.1 (E), wrapt/1.12.1 (E)
Help:
Description
===========
An open-source software library for Machine Intelligence
More information
================
- Homepage: https://www.tensorflow.org/
Included extensions
===================
absl-py-0.10.0, astunparse-1.6.3, cachetools-4.2.0, dill-0.3.3, gast-0.3.3,
google-auth-1.24.0, google-auth-oauthlib-0.4.2, google-pasta-0.2.0,
grpcio-1.32.0, gviz-api-1.9.0, h5py-2.10.0, Keras-Preprocessing-1.1.2,
Markdown-3.3.3, oauthlib-3.1.0, opt-einsum-3.3.0, portpicker-1.3.1,
pyasn1-modules-0.2.8, requests-oauthlib-1.3.0, rsa-4.7, tblib-1.7.0,
tensorboard-2.4.1, tensorboard-plugin-profile-2.4.0, tensorboard-plugin-
wit-1.8.0, TensorFlow-2.4.1, tensorflow-estimator-2.4.0, termcolor-1.1.0,
Werkzeug-1.0.1, wrapt-1.12.1
Names marked by a trailing (E) are extensions provided by another module.
marie@compute$ ml +GCC/10.2.0 +CUDA/11.1.1 +OpenMPI/4.0.5 +TensorFlow/2.4.1
Die folgenden Module wurden in einer anderen Version erneut geladen:
1) GCC/7.3.0-2.30 => GCC/10.2.0 3) binutils/2.30-GCCcore-7.3.0 => binutils/2.35
2) GCCcore/7.3.0 => GCCcore/10.2.0
Module GCCcore/7.3.0, binutils/2.30-GCCcore-7.3.0, GCC/7.3.0-2.30, GCC/7.3.0-2.30 and 3 dependencies unloaded.
Module GCCcore/7.3.0, GCC/7.3.0-2.30, GCC/10.2.0, CUDA/11.1.1, OpenMPI/4.0.5, TensorFlow/2.4.1 and 50 dependencies loaded.
marie@compute$ module list
Derzeit geladene Module:
1) release/23.04 (S) 28) Tcl/8.6.10
2) GCCcore/10.2.0 29) SQLite/3.33.0
3) zlib/1.2.11 30) GMP/6.2.0
4) binutils/2.35 31) libffi/3.3
5) GCC/10.2.0 32) Python/3.8.6
6) CUDAcore/11.1.1 33) pybind11/2.6.0
7) CUDA/11.1.1 34) SciPy-bundle/2020.11
8) numactl/2.0.13 35) Szip/2.1.1
9) XZ/5.2.5 36) HDF5/1.10.7
10) libxml2/2.9.10 37) cURL/7.72.0
11) libpciaccess/0.16 38) double-conversion/3.1.5
12) hwloc/2.2.0 39) flatbuffers/1.12.0
13) libevent/2.1.12 40) giflib/5.2.1
14) Check/0.15.2 41) ICU/67.1
15) GDRCopy/2.1-CUDA-11.1.1 42) JsonCpp/1.9.4
16) UCX/1.9.0-CUDA-11.1.1 43) NASM/2.15.05
17) libfabric/1.11.0 44) libjpeg-turbo/2.0.5
18) PMIx/3.1.5 45) LMDB/0.9.24
19) OpenMPI/4.0.5 46) nsync/1.24.0
20) OpenBLAS/0.3.12 47) PCRE/8.44
21) FFTW/3.3.8 48) protobuf/3.14.0
22) ScaLAPACK/2.1.0 49) protobuf-python/3.14.0
23) cuDNN/8.0.4.30-CUDA-11.1.1 50) flatbuffers-python/1.12
24) NCCL/2.8.3-CUDA-11.1.1 51) typing-extensions/3.7.4.3
25) bzip2/1.0.8 52) libpng/1.6.37
26) ncurses/6.2 53) snappy/1.1.8
27) libreadline/8.0 54) TensorFlow/2.4.1
Wo:
S: Das Modul ist angeheftet. Verwenden Sie "--force", um das Modul zu entladen.