Getting started


Using Anaconda (multi-platform)

  1. Download and install Anaconda. Make sure to grab the 64-bit version of the Python 2.7 interpreter.

  2. Install some dependencies:

    $ conda install hdf5 libnetcdf
    $ conda install -c conda-forge git-lfs

    and optionally:

    $ conda install -c bohrium bohrium
  3. Clone our repository:

    $ git clone
  4. Install Veros via:

    $ pip install -e ./veros

Using apt-get (Ubuntu / Debian)

  1. Install some dependencies:

    $ sudo apt-get install git python-dev python-pip libhdf5-dev libnetcdf-dev

    and optionally:

    $ sudo add-apt-repository ppa:bohrium/nightly
    $ sudo apt-get update
    $ sudo apt-get install bohrium
If you want to clone the input files needed for running the larger setups, you will also need to install git lfs.
  1. Clone our repository:

    $ git clone
  2. Install Veros via:

    $ pip install -e ./veros

Setting up a model

To run Veros, you need to set up a model - i.e., specify which settings and model domain you want to use. This is done by subclassing the Veros base class in a setup script that is written in Python. You should have a look at the pre-implemented model setups in the repository’s setup folder, or use the veros copy-setup command to copy one into your current folder. A good place to start is the ACC model:

$ veros copy-setup acc

By working through the existing models, you should quickly be able to figure out how to write your own simulation. Just keep in mind this genral advice:

  • You can (and should) use any (external) Python tools you want in your model setup. Before implementing a certain functionality, you should check whether it is already provided by a common library. Especially the SciPy module family provides countless implementations of common scientific functions (and SciPy is installed along with Veros).

  • If you decorate your methods with @veros_method, the variable np inside that function will point to the currently used backend (i.e., NumPy or Bohrium). Thus, if you want your setup to be able to dynamically switch between backends, you should write your methods like this:

    from veros import Veros, veros_method
    class MyVerosSetup(Veros):
        def my_function(self):
            arr = np.array([1,2,3,4]) # "np" uses either NumPy or Bohrium
  • If you are curious about the general procedure in which a model is set up and ran, you should read the source code of veros.Veros (especially the setup() and run() methods). This is also the best way to find out about the order in which methods and routines are called.

  • Out of all functions that need to be imlpemented by your subclass of veros.Veros, the only one that is called in every time step is set_forcing() (at the beginning of each iteration). This implies that, to achive optimal performance, you should consider moving calculations that are constant in time to other functions.

If you want to learn more about setting up advanced configurations, you should check out our tutorial that walks you through the creation of a realistic configuration with an idealized Atlantic.

Running Veros

After adapting your setup script, you are ready to run your first simulation. It is advisable to include something like:

if __name__ == "__main__":
   simulation = MyVerosSetup()

in your setup file, so you can run it as a script:

$ python

However, you are not required to do so, and you are welcome to write include your simulation class into other Python files and call it dynamically or interactively (e.g. in an IPython session).

All Veros setups accept additional options via the command line when called as a script or as arguments to their __init__() function when called from another Python module. You can check the available commands through

$ python --help

Reading Veros output

All output is handled by the available diagnostics. The most basic diagnostic, snapshot, writes some model variables to netCDF files in regular intervals (and puts them into your current working directory).

NetCDF is a binary format that is widely adopted in the geophysical modeling community. There are various packages for reading, visualizing and processing netCDF files (such as ncview and ferret), and bindings for many programming languages (such as C, Fortran, MATLAB, and Python).

In fact, after installing Veros, you will already have installed the netCDF bindings for Python, so reading data from an output file and plotting it is as easy as:

import matplotlib.pyplot as plt
from netCDF4 import Dataset

with Dataset("", "r") as datafile:
    # read variable "u" and save it to a NumPy array
    u = datafile.variables["u"][...]

# plot surface velocity at the last time step included in the file
plt.imshow(u[-1, -1, ...])

For further reference refer to the netcdf4-python documentation.

Using Bohrium


While Bohrium yields significant speed-ups for large to very large setups, the overhead introduced by Bohrium often leads to (sometimes considerably) slower execution for problems below a certain threshold size (see also Which backend should I choose to run my model (NumPy / Bohrium)?). You are thus advised to test carefully whether Bohrium is beneficial in your particular use case.

For large simulations, it is often beneficial to use the Bohrium backend for computations. When using Bohrium, all number crunching will make full use of your available architecture, i.e., computations are executed in parallel on all of your CPU cores, or even GPU when using BH_STACK=opencl or BH_STACK=cuda (experimental). You may switch between NumPy and Bohrium with a simple command line switch:

$ python -b bohrium

or, when running inside another Python module:

simulation = MyVerosSetup(backend="bohrium")

Re-starting from a previous run

Restart data (in HDF5 format) is written at the end of each simulation or after a regular time interval if the setting restart_frequency is set to a finite value. To use this restart file as initial conditions for another simulation, you will have to point restart_input_filename of the new simulation to the corresponding restart file. This can (as all settings) also be given via command line:

$ python -s restart_input_filename /path/to/restart_file.h5

Enhancing Veros

Veros was written with extensibility in mind. If you already know some Python and have worked with NumPy, you are pretty much ready to write your own extension. The model code is located in the veros subfolder, while all of the numerical routines are located in veros/core.

We believe that the best way to learn how Veros works is to read its source code. Starting from the Veros base class, you should be able to work your way through the flow of the program, and figure out where to add your modifications. If you installed Veros through pip -e or develop, all changes you make will immediately be reflected when running the code.

In case you want to add additional output capabilities or compute additional quantities without changing the main solution of the simulation, you should consider adding a custom diagnostic.

A convenient way to implement your modifications is to create your own fork of Veros on GitHub, and submit a pull request if you think your modifications could be useful for the Veros community.

Code conventions

When contributing to Veros, please adhere to the following general guidelines:

  • Your first guide should be the surrounding Veros code. Look around, and be consistent with your modifications.
  • Unless you have a very good reason not to do so, please stick to the PEP8 style guide throughout your code. One exception we make in Veros is in regard to the maximum line length - since numerical operations can take up quite a lot of horizontal space, you may use longer lines if it increases readability.
  • Please follow the PEP8 naming conventions, and use meaningful, telling names for your variables, functions, and classes. The variable name stretching_factor is infinitely more meaningful than k. This is especially important for settings and generic helper functions.
  • “Private” helper functions that are not meant to be called from outside the current source file should be prefixed with an underscore (_).
  • Use double quotes (") for all strings longer than a single character.
  • Document your functions using Google-style docstrings. This is especially important if you are implementing a user-facing API (such as a diagnostic, a setup, or tools that are meant to be called from setups).

Running tests and benchmarks

If you want to make sure that your changes did not break anything, you can run our test suite that compares the results of each subroutine to pyOM2. To do that, you will need to compile the Python interface of pyOM2 on your machine, and then point the testing suite to the library location, e.g. through:

$ python /path/to/pyOM2/py_src/

from Veros’s test folder.

If you deliberately introduced breaking changes, you can disable them during testing by prefixing them with:

if not vs.pyom_compatibility_mode:
    # your changes

Automated benchmarks are provided in a similar fashion. The benchmarks run some dummy problems with varying problem sizes and all available computational backends: numpy, bohrium-openmp, bohrium-opencl, bohrium-cuda, fortran (pyOM2), and fortran-mpi (parallel pyOM2). For options and further information run:

$ python --help

from the test folder. Timings are written in JSON format.

Performance tweaks

If your changes to Veros turn out to have a negative effect on the runtime of the model, there several ways to investigate and solve performance problems:

  • Run your model with the -v debug option to get additional debugging output (such as timings for each time step, and a timing summary after the run has finished).

  • Run your model with the -p option to profile Veros with pyinstrument. You may have to run pip install pyinstrument before being able to do so. After completion of the run, a file profile.html will be written that can be opened with a web browser and contains timings for the entire call stack.

  • You should try and avoid explict loops over arrays at all cost (even more so when using Bohrium). You should always try to work on the whole array at once.

  • When using Bohrium, it is sometimes beneficial to copy an array to NumPy before passing it to an external module or performing an operation that cannot be vectorized efficiently. Just don’t forget to copy it back to Bohrium after you are finished, e.g. like so:

    if vs.backend_name == "bohrium":
        u_np = vs.u.copy2numpy()
        u_np = vs.u
    vs.u[...] = np.asarray(external_function(u_np))
  • If you are still having trouble, don’t hesitate to ask for help (e.g. on GitHub).