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4 merge requests!253include current develop,!252Resolve "release v1.3.0",!248modified docs,!247Resolve "BUG: update reqirements"
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...@@ -6,130 +6,6 @@ Getting started with MLAir ...@@ -6,130 +6,6 @@ Getting started with MLAir
:language: python :language: python
Install MLAir
-------------
MLAir is based on several python frameworks. To work properly, you have to install all packages from the
:py:`requirements.txt` file. Additionally to support the geographical plotting part it is required to install geo
packages built for your operating system. Unfortunately, the names of these package may differ for different systems.
In this instruction, we try to address users of different operating systems namely openSUSE Leap, Ubuntu and macOS.
If the installation is still not working, we recommend skipping the geographical plot. We have put together a small
workaround :ref:`here<Workaround to skip geographical plot>`. For special instructions to install MLAir on the Juelich
HPC systems, see section :ref:`Installation on Jülich HPC systems`.
Pre-requirements
~~~~~~~~~~~~~~~~
* Make sure to have the **python3.6** version installed.
* (geo) A **c++ compiler** is required for the installation of the program **cartopy**
* (geo) Install **proj** and **GEOS** on your machine using the console.
* Install the **python3.6 develop** libraries.
Installation of MLAir
~~~~~~~~~~~~~~~~~~~~~
* Install all requirements from `requirements.txt <https://gitlab.version.fz-juelich.de/toar/machinelearningtools/-/blob/master/requirements.txt>`_
preferably in a virtual environment
* Either clone MLAir from the `gitlab repository <https://gitlab.version.fz-juelich.de/toar/machinelearningtools.git>`_
* or download the distribution file (`current version <https://gitlab.version.fz-juelich.de/toar/mlair/-/blob/master/dist/mlair-1.2.0-py3-none-any.whl>`_)
and install it via :py:`pip install <dist_file>.whl`. In this case, you can simply
import MLAir in any python script inside your virtual environment using :py:`import mlair`.
* (tf) Currently, TensorFlow-1.13 is mentioned in the requirements. We already tested the TensorFlow-1.15 version and couldn't
find any compatibility errors. Please note, that tf-1.13 and 1.15 have two distinct branches each, the default branch
for CPU support, and the "-gpu" branch for GPU support. If the GPU version is installed, MLAir will make use of the GPU
device.
Special Instructions for Installation
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
openSUSE Leap 15.1
""""""""""""""""""
* c++ compiler
:py:`sudo zypper install gcc-c++`
* geo packages
:py:`sudo zypper install proj geos-devel`
* depending on the pre-installed packages it could be required to install further packages
:py:`sudo zypper install libproj-devel binutils gdal-devel graphviz graphviz-gnome`
* python develop libraries
:py:`sudo zypper install python3-devel`
Ubuntu 20.04.1
""""""""""""""
* c++ compiler
:py:`sudo apt install build-essential`
* geo packages
:py:`sudo apt install proj-bin libgeos-dev libproj-dev`
* depending on the pre-installed packages it could be required to install further packages
:py:`sudo apt install graphviz libgeos++-dev`
* python develop libraries
:py:`sudo apt install python3.6-dev`
macOS & windows
"""""""""""""""
The installation on macOS is not tested yet. The following commands are possibly needed:
:py:`brew install geos`
:py:`sudo port install graphviz`
The installation on Windows is not tested yet.
Installation on Jülich HPC systems
""""""""""""""""""""""""""""""""""
*Please note, that the HPC setup is customised for JUWELS and HDFML. When using another HPC system, you can use the HPC
setup files as a skeleton and customise it to your needs.*
The following instruction guide you through the installation on JUWELS and HDFML.
* Clone the repo to HPC system (we recommend to place it in :py:`/p/projects/<project name>`).
* Setup venv by executing :py:`source setupHPC.sh`. This script loads all pre-installed modules and creates a venv for
all other packages. Furthermore, it creates slurm/batch scripts to execute code on compute nodes.
You have to enter the HPC project's budget name (--account flag).
* The default external data path on JUWELS and HDFML is set to :py:`/p/project/deepacf/intelliaq/<user>/DATA/toar_<sampling>`.
* To choose a different location open :py:`run.py` and add the following keyword argument to :py:`ExperimentSetup`:
:py:`data_path=<your>/<custom>/<path>`.
* Execute :py:`python run.py` on a login node to download example data. The program will throw an OSerror after downloading.
* Execute either :py:`sbatch run_juwels_develgpus.bash` or :py:`sbatch run_hdfml_batch.bash` to verify that the setup
went well.
* Currently cartopy is not working on our HPC system, therefore PlotStations does not create any output.
Note: The method :py:`PartitionCheck` currently only checks if the hostname starts with :py:`ju` or :py:`hdfmll`.
Therefore, it might be necessary to adopt the :py:`if` statement in :py:`PartitionCheck._run`.
Workaround to skip geographical plot
""""""""""""""""""""""""""""""""""""
If it is not possible to install all required geo libraries on your system, a good compromise is to skip the creation
of the geographical plot. Therefore, it is required to remove the plot from the :py:`plot_list` manually. We recommend
to use this code snippet as a starting point.
.. code-block:: python
from mlair.helpers import remove_items
from mlair.configuration.defaults import DEFAULT_PLOT_LIST
mlair.run(plot_list=remove_items(DEFAULT_PLOT_LIST, "PlotStationMap"))
How to start with MLAir How to start with MLAir
----------------------- -----------------------
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docs/_source/index.rst
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...@@ -8,10 +8,18 @@ Welcome to MLAir's documentation! ...@@ -8,10 +8,18 @@ Welcome to MLAir's documentation!
This is the documentation of the `MLAir package <https://gitlab.version.fz-juelich.de/toar/mlair>`_. This is the documentation of the `MLAir package <https://gitlab.version.fz-juelich.de/toar/mlair>`_.
.. figure:: ../logo/MLAir_Logo.png
MLAir Logo
MLAir (Machine Learning on Air data) is an environment that simplifies and accelerates the creation of new machine
learning (ML) models for the analysis and forecasting of meteorological and air quality time series.
.. toctree:: .. toctree::
:maxdepth: 2 :maxdepth: 2
:caption: Contents: :caption: Contents:
installation
get-started get-started
customise customise
defaults defaults
......
docs/_source/installation.rst 0 → 100644
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.. role:: py(code)
:language: python
Install MLAir
-------------
MLAir is based on several python frameworks. To work properly, you have to install all packages from the
:py:`requirements.txt` file. Additionally to support the geographical plotting part it is required to install geo
packages built for your operating system. Unfortunately, the names of these package may differ for different systems.
In this instruction, we try to address users of different operating systems namely openSUSE Leap, Ubuntu and macOS.
If the installation is still not working, we recommend skipping the geographical plot. We have put together a small
workaround :ref:`here<Workaround to skip geographical plot>`. For special instructions to install MLAir on the Juelich
HPC systems, see section :ref:`Installation on Jülich HPC systems`.
Pre-requirements
~~~~~~~~~~~~~~~~
* Make sure to have the **python3.6** version installed.
* (geo) A **c++ compiler** is required for the installation of the program **cartopy**
* (geo) Install **proj** and **GEOS** on your machine using the console.
* Install the **python3.6 develop** libraries.
Installation of MLAir
~~~~~~~~~~~~~~~~~~~~~
* Install all requirements from `requirements.txt <https://gitlab.version.fz-juelich.de/toar/machinelearningtools/-/blob/master/requirements.txt>`_
preferably in a virtual environment
* Either clone MLAir from the `gitlab repository <https://gitlab.version.fz-juelich.de/toar/machinelearningtools.git>`_
* or download the distribution file (`current version <https://gitlab.version.fz-juelich.de/toar/mlair/-/blob/master/dist/mlair-1.2.0-py3-none-any.whl>`_)
and install it via :py:`pip install <dist_file>.whl`. In this case, you can simply
import MLAir in any python script inside your virtual environment using :py:`import mlair`.
* (tf) Currently, TensorFlow-1.13 is mentioned in the requirements. We already tested the TensorFlow-1.15 version and couldn't
find any compatibility errors. Please note, that tf-1.13 and 1.15 have two distinct branches each, the default branch
for CPU support, and the "-gpu" branch for GPU support. If the GPU version is installed, MLAir will make use of the GPU
device.
Special Instructions for Installation
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
openSUSE Leap 15.1
""""""""""""""""""
* c++ compiler
:py:`sudo zypper install gcc-c++`
* geo packages
:py:`sudo zypper install proj geos-devel`
* depending on the pre-installed packages it could be required to install further packages
:py:`sudo zypper install libproj-devel binutils gdal-devel graphviz graphviz-gnome`
* python develop libraries
:py:`sudo zypper install python3-devel`
Ubuntu 20.04.1
""""""""""""""
* c++ compiler
:py:`sudo apt install build-essential`
* geo packages
:py:`sudo apt install proj-bin libgeos-dev libproj-dev`
* depending on the pre-installed packages it could be required to install further packages
:py:`sudo apt install graphviz libgeos++-dev`
* python develop libraries
:py:`sudo apt install python3.6-dev`
macOS & windows
"""""""""""""""
The installation on macOS is not tested yet. The following commands are possibly needed:
:py:`brew install geos`
:py:`sudo port install graphviz`
The installation on Windows is not tested yet.
Installation on Jülich HPC systems
""""""""""""""""""""""""""""""""""
*Please note, that the HPC setup is customised for JUWELS and HDFML. When using another HPC system, you can use the HPC
setup files as a skeleton and customise it to your needs.*
The following instruction guide you through the installation on JUWELS and HDFML.
* Clone the repo to HPC system (we recommend to place it in :py:`/p/projects/<project name>`).
* Setup venv by executing :py:`source setupHPC.sh`. This script loads all pre-installed modules and creates a venv for
all other packages. Furthermore, it creates slurm/batch scripts to execute code on compute nodes.
You have to enter the HPC project's budget name (--account flag).
* The default external data path on JUWELS and HDFML is set to :py:`/p/project/deepacf/intelliaq/<user>/DATA/toar_<sampling>`.
* To choose a different location open :py:`run.py` and add the following keyword argument to :py:`ExperimentSetup`:
:py:`data_path=<your>/<custom>/<path>`.
* Execute :py:`python run.py` on a login node to download example data. The program will throw an OSerror after downloading.
* Execute either :py:`sbatch run_juwels_develgpus.bash` or :py:`sbatch run_hdfml_batch.bash` to verify that the setup
went well.
* Currently cartopy is not working on our HPC system, therefore PlotStations does not create any output.
Note: The method :py:`PartitionCheck` currently only checks if the hostname starts with :py:`ju` or :py:`hdfmll`.
Therefore, it might be necessary to adopt the :py:`if` statement in :py:`PartitionCheck._run`.
Workaround to skip geographical plot
""""""""""""""""""""""""""""""""""""
If it is not possible to install all required geo libraries on your system, a good compromise is to skip the creation
of the geographical plot. Therefore, it is required to remove the plot from the :py:`plot_list` manually. We recommend
to use this code snippet as a starting point.
.. code-block:: python
from mlair.helpers import remove_items
from mlair.configuration.defaults import DEFAULT_PLOT_LIST
mlair.run(plot_list=remove_items(DEFAULT_PLOT_LIST, "PlotStationMap"))
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