Performance, cluster environments and reproducibility¶
If you plan to run OGGM on more than a handful of glaciers, you might be interested in using all processors available to you, whether you are working on your laptop or on a cluster: see Parallel computations for how to do this.
For regional or global computations you will need to run OGGM in Cluster environments. Here we provide a couple of guidelines based on our own experience with operational runs.
In Reproducibility with OGGM, we discuss certain aspects of scientific reproducibility with OGGM, and how we try to ensure that our results are reproducible (it’s not easy).
OGGM is designed to use the available resources as well as possible. For single nodes machines but with more than one processor (e.g. for personal computers) OGGM ships with a multiprocessing approach which is fairly simple to use. For cluster environments with more than one machine, you can use MPI.
Most OGGM computations are embarrassingly parallel: they are standalone operations to be realized on one single glacier entity and therefore independent from each other (they are called entity tasks, as opposed to the non-parallelizable global tasks).
When given a list of Glacier directories on which to apply a given task,
workflow.execute_entity_task() will distribute the operations on
the available processors using Python’s multiprocessing module.
You can control this behavior with the
parameter and the number of processors with
The default in OGGM is:
In : from oggm import cfg In : cfg.initialize() In : cfg.PARAMS['use_multiprocessing'] # whether to use multiprocessing Out: False In : cfg.PARAMS['mp_processes'] # number of processors to use Out: 1
-1 means that all available processors will be used.
OGGM can be run in a cluster environment, using standard mpi features.
In our own cluster deployment (see below), we chose not to use MPI, for simplicity. Therefore, our MPI support is currently untested: it should work, but let us know if you encounter any issue.
OGGM depends on mpi4py in that case, which can be installed either via conda:
conda install -c conda-forge mpi4py
pip install mpi4py
mpi4py itself depends on a working mpi environment, which is usually
supplied by the maintainers of your cluster.
On conda, it comes with its own copy of
mpich, which is nice and easy for
quick testing, but maybe undesirable for the performance of actual runs.
For an actual run, invoke any script using oggm via
mpiexec, and pass the
--mpi parameter to the script itself:
mpiexec -n 10 python ./run_rgi_region.py --mpi
Be aware that the first process with rank 0 is the manager process, that by itself does not do any calculations and is only used to distribute tasks. So the actual number of working processes is one lower than the number passed to mpiexec/your clusters scheduler.
Here we describe some of the ways to use OGGM in a cluster environment. We provide examples of our own set-up, but your use case might vary depending on the cluster type you are working with, who is administrating the cluster, etc.
The installation procedure explained in Installing OGGM should also
work in cluster environments. If you don’t have admin rights,
installing with conda in your
$HOME probably is the easiest option.
Once OGGM is installed, you can use your scripts (like the ones provided in
Set-up an OGGM run). But you probably want to check if the tests pass and our
Data storage section below first!
If you are lucky, your cluster might support singularity containers, in which case we highly recommend their usage.
Singularity and docker containers¶
For those not familiar with this concept, containers can be seen as a lightweight, downloadable operating system which can run programs for you. They are highly configurable, and come in many flavors.
Containers may be unfamiliar to some of you, but they are the best way to ensure traceable, reproducible results with any numerical model. We highly recommend their use.
- untested_base is a container based on Ubuntu 18.04 and shipping with all OGGM dependencies installed on it. OGGM is not guaranteed to run on these, but we use them for our tests on Travis.
- base is built upon
untested_base, but is pushed online only after the OGGM tests have run successfully on it. Therefore, is provides a a more secure base for the model, although we cannot guarantee that past or future version of the model will always work on it.
- oggm is built upon
baseeach time that a new change is made to the OGGM codebase. They have OGGM installed, and are guaranteed to run the OGGM version they ship with. We cannot guarantee that past or future version of the model will always work on it.
To ensure reproducibility over time or different machines (and avoid
dependency update problems), we recommend to use
for your own purposes. Use
if you want to install your own OGGM version (don’t forget to test it
afterwards!), and use
oggm if you know which OGGM version you want.
As an example, here is how we run a given fixed version of OGGM on our own cluster, using singularity to pull from docker hub:
# All commands in the EOF block run inside of the container singularity exec docker://oggm/oggm:20200708 bash -s <<EOF set -e # Setup a fake home dir inside of our workdir, so we don't clutter the # actual shared homedir with potentially incompatible stuff export HOME="$OGGM_WORKDIR/fake_home" mkdir "\$HOME" # Create a venv that _does_ use system-site-packages, since everything is # already installed on the container. We cannot work on the container # itself, as the base system is immutable. python3 -m venv --system-site-packages "$OGGM_WORKDIR/oggm_env" source "$OGGM_WORKDIR/oggm_env/bin/activate" # OPTIONAL: make sure latest pip is installed pip install --upgrade pip setuptools # OPTIONAL: install another OGGM version (here provided by its git commit hash) pip install "git+https://github.com/OGGM/oggm.git@ce22ceb77f3f6ffc865be65964b568835617db0d" # Finally, you can test OGGM with `pytest --pyargs oggm`, or run your script: YOUR_RUN_SCRIPT_HERE EOF
singularity execuses Singularity to execute a series of commands in a singularity container, which here simply is taken from our Docker container base (singularity can run docker containers). Singularity is preferred over Docker in cluster environments, mostly for security and performance reasons. On our cluster, we use the SLURM manager, so we specify the number of nodes and CPU’s we’d like to use and run singularity with srun -n 1 -c X singularity exec docker://…. This might vary on your cluster.
- we fix the container version we want to use to a certain tag. With this, we are guaranteed to always use the same software versions across runs.
- it follows a number of commands to make sure we don’t mess around with the system settings. Here we use an $OGGM_WORKDIR variable which is probably not available in your case: it points to a directory you can write to, and where OGGM will work (for example, it might also be the directory you are working on with OGGM (cfg.PATHS[‘working_dir’]). We suggest to replace this variable with what works for you.
- the oggm docker images ship whith an OGGM version guaranteed to work on this container. Sometimes, you may want to use another OGGM version, for example whith newer developments on it. You might also add your own flavor or parameterization to OGGM into the environment. For this you can use pip and install the version you want. Here we show an example where we install a specific OGGM version, here specified by its git hash (you can use a git tag as well). If you do that, you might want to run the tests once first to make sure that it works as expected. You can do that by replacing YOUR_RUN_SCRIPT_HERE with pytest –pyargs oggm –run-slow!
- Finally, the YOUR_RUN_SCRIPT_HERE is the actual command you want to run from this container! Most of the time, it will be a call to your python script.
We recommend to keep these scripts alongside our code and data, so that you can trace them later on.
OGGM needs a certain amount of data to run (see Input data). Regardless
if you are using pre-processed directories or raw data, you will need to have
access to them from your environment. The default in OGGM is to download
the data and store it in a folder, specified in the
dl_cache_dir in System settings).
The structure of this folder is following the URLs from which the data
is obtained. You can either let OGGM fill it up at run time by downloading the
data (recommended if you do regional runs, i.e. don’t need the entire data
set), but you might also want to pre-download everything using
equivalent. OGGM will use the data as long as the url structure is OK.
System administrators can mark this folder as being “read only”, in which case OGGM will run only if the data is already there and exit with an error otherwise.
An OGGM run can write a significant amount of data. In particular, it writes a very large number of folder and files. This makes certain operations like copying or even deleting working directory folders quite slow.
Therefore, there are two ways to reduce the amount of data (and data files) you have to deal with:
- the easiest way is to simply delete the glacier directories after a run
and keep only the aggregated statistics files generated with the
compile_tasks (see Workflow). A typical workflow would be to start from pre-processed directories, do the run, aggregate the results, copy the aggregated files for long term storage, and delete the working directory.
- the method above does not allow to go back to a single glacier
for plotting or restarting a run, or to have a more detailed look at the
glacier geometry evolution. If you want to do these things, you’ll need to
store the glacier directories as well. In order to reduce the number of files
you’ll have to deal with in this case, you can use the
utils.base_dir_to_tar()functions to create compressed, aggregated files of your directories. You can later initialize new directories from these tar files with the from_tar keyword argument in
Run per RGI region, not globally¶
For performance and data handling reasons, we recommend to run the model on single RGI regions independently (or smaller regional entities). This is a good compromise between performance (parallelism) and output file size as well as other workflow considerations.
On our cluster, we use the following parallelization strategy: we use an array of jobs to submit as many jobs as RGI regions (or experiments, if you are running experiments on a single region for example), and each job is run on one node only. This way, we avoid using MPI and do not require communication between nodes, while still using our cluster at near 100%.
Reproducibility with OGGM¶
Within OGGM, we do our best to follow the FAIR principles.
Source code and version control¶
The source code of OGGM is located on GitHub. All the history of the codebase (and the tests and documentation) are documented in the form of git commits.
When certain development milestones are reached, we release a new version of the model using a so-called “tag” (version number). We will try to follow our own semantic versioning convention for release numbers. We use MAJOR.MINOR.PATCH, with:
- PATCH version number increase when the changes to the codebase are small increments or harmless bug fixes, and when we are confident that the model output is not affected by these changes.
- MINOR version number increase when we add functionality or bug fixes which are not affecting the model behavior in a significant way. However, it is possible that the model results are affected in some unpredictable ways, that we estimated to be “small enough” to justify a minor release instead of major one. Unlike the original convention, we cannot always guarantee backwards compatibility in the OGGM syntax yet, because it is too costly. We’ll try not to brake things at each release, though
- MAJOR version number increase when we significantly change the OGGM syntax and/or the model results, for example by relying on a new default parametrization.
The current OGGM model version is:
In : import oggm In : oggm.__version__ Out: '0.1.dev50+gc05d330'
We document the changes we make to the model on GitHub, and in the Version history.
OGGM relies on a large number of external python packages (dependencies). Many of them have complex dependencies themselves, often compiled binaries (for example rasterio, which relies on a C package: GDAL).
The complexity of this dependency tree as well as the permanent updates of both OGGM and its dependencies has lead to several unfortunate situations in the past: this involved a lot of maintenance work for the OGGM developers that had little or nothing to do with the model itself.
Furthermore, while the vast majority of the dependency updates are without consequences, some might change the model results. As an example, updates in the interpolation routines of GDAL/rasterio can change the glacier topography in a non-traceable way for OGGM. This is an obstacle to reproducible science, and we should try to avoid these situations.
Therefore, we have written a “roadmap” as a tool to guide our decision regarding software dependencies in OGGM. This document also lists some example situations affecting model users and developers.
The short answer is: use our docker/singularity containers for the most reproducible workflows. Refer to Singularity and docker containers for how to do that.
Dependence on hardware and input data¶
The OGGM model will always be dependant on the input data (topography, climate, outlines…). Be aware that while certain results are robust (like interannual variability of surface mass-balance), other results are highly sensitive to small changes in the boundary conditions. Some examples include:
- the ice thickness inversion at a specific location is highly sensitive to the local slope
- the equilibrium volume of a glacier under a constant climate is highly sensitive to small changes in the ELA or the bed topography
- more generally: growing large glaciers on longer periods are “more sensitive” to boundary conditions than shrinking small glaciers on shorter periods.
We haven’t really tested the dependency of OGGM on hardware, but we expect it to be low, as glaciers are not chaotic systems like the atmosphere.
Tools to monitor OGGM results¶
We have developed a series of checks to monitor the changes in OGGM. They are not perfect, but we constantly seek to improve them: