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Exclude abipy/gw from tests if nose
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Matteo Giantomassi
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README.rst
28
README.rst
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@ -57,16 +57,12 @@ that can be installed with::
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pip install abipy
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Note that you may need to install pymatgen and other critical dependencies manually.
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For this reason, we strongly suggest to install the required python packages through one
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of the following python distributions:
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Note that you may need to install pymatgen and other critical dependencies manually.
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In this case, please consult the detailed installation instructions provided by the
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`pymatgen howto <http://pymatgen.org/index.html#standard-install>`_ to install pymatgen
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and then follow the instructions in `ourhowto <http://pythonhosted.org/abipy/installation.html>`_.
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and then follow the instructions in `our howto <http://pythonhosted.org/abipy/installation.html>`_.
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Note, however, that the installation process is greatly simplified if you install the required
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The installation process is greatly simplified if you install the required
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python packages through one of the following python distributions:
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* `Anaconda <https://continuum.io/downloads>`_
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@ -94,7 +90,7 @@ Optional libraries that are required if you need certain features:
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``ipython``
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Required to interact with the AbiPy/Pymatgen objects in the ipython shell
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(strongly recommended, already provided by conda).
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(strongly recommended, already provided by ``conda``).
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``jupyter`` and ``nbformat``
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@ -102,12 +98,6 @@ Optional libraries that are required if you need certain features:
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Install these two packages with ``conda install jupyter nbformat`` or use ``pip``.
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Recommended but you will also need a web browser to open the notebook.
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``seaborn``
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Python visualization library based on matplotlib.
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It provides a high-level interface for drawing attractive statistical graphics.
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Used in AbiPy for particular plots.
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``wxPython`` and ``wxmplot`` for the GUI
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Use ``conda install wxpython``
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@ -135,7 +125,7 @@ or alternately::
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to install the package in developmental mode
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(this is the recommended approach, especially if you are planning to implement new features).
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The documentation of the developmental version is hosted on `github pages <http://abinit.github.io/abipy>`_.
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The documentation of the **developmental** version is hosted on `github pages <http://abinit.github.io/abipy>`_.
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The Github version include test files for complete unit testing.
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To run the suite of unit tests, make sure you have ``py.test`` (recommended)
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@ -152,6 +142,16 @@ Unit tests require two additional packages that can be installed with::
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$ pip install nose-exclude scripttest
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Note that several unit tests check the integration between AbiPy and Abinit.
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In order to run the tests, you need a working set of Abinit executables and
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a ``manager.yml`` configuration file.
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A pre-compiled sequential version of Abinit for Linux and OSx can be installed directly from the anaconda cloud with::
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$ conda install abinit -c gmatteo
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For further information on the syntax of the configuration file, please consult the
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`workflows <http://pythonhosted.org/abipy/workflows.html>`_ section.
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Contributing to AbiPy is relatively easy.
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Just send us a `pull request <https://help.github.com/articles/using-pull-requests/>`_.
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When you send your request, make ``develop`` the destination branch on the repository
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@ -23,7 +23,7 @@ AbiPy supports both Python 2.7 as well as Python >= 3.4.
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Note however that Python 2.7 is more intensively tested than py3k especially at the level of workflows
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so we still recommend py2.7 if you plan to run automatic calculations with AbiPy.
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Note that the majority of the post-processing tools available in AbiPy require output files in
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Note also that the majority of the post-processing tools available in AbiPy require output files in
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``netcdf`` format so we strongly suggest to compile Abinit with netcdf support
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(use ``--with_trio_flavor="netcdf-fallback"`` at configure time to activate the internal netcdf library,
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to link Abinit against an external netcdf library please consult the configuration examples
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@ -16,7 +16,7 @@ In this case, please consult the detailed installation instructions provided by
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`pymatgen howto <http://pymatgen.org/index.html#standard-install>`_ to install pymatgen
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and then follow the instructions in the :ref:`netcdf4_installation` section.
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Note, however, that the installation process is greatly simplified if you install the required
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The installation process is greatly simplified if you install the required
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python packages through one of the following python distributions:
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* `Anaconda <https://continuum.io/downloads>`_
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@ -49,7 +49,7 @@ Optional libraries that are required if you need certain features:
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``ipython``
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Required to interact with the AbiPy/Pymatgen objects in the ipython shell
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(strongly recommended, already provided by conda).
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(strongly recommended, already provided by ``conda``).
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``jupyter`` and ``nbformat``
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@ -57,12 +57,6 @@ Optional libraries that are required if you need certain features:
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Install these two packages with ``conda install jupyter nbformat`` or use ``pip``.
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Recommended but you will also need a web browser to open the notebook.
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``seaborn``
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Python visualization library based on matplotlib.
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It provides a high-level interface for drawing attractive statistical graphics.
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Used in AbiPy for particular plots.
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``wxPython`` and ``wxmplot`` for the GUI
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Use ``conda install wxpython``
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@ -149,6 +143,8 @@ or alternately::
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to install the package in developmental mode
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(this is the recommended approach, especially if you are planning to implement new features).
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The documentation of the **developmental** version is hosted on `github pages <http://abinit.github.io/abipy>`_.
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The Github version include test files for complete unit testing.
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To run the suite of unit tests, make sure you have ``py.test`` (recommended)
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or ``nose`` installed and then just type::
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@ -164,6 +160,15 @@ Unit tests require two additional packages that can be installed with::
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$ pip install nose-exclude scripttest
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Note that several unit tests check the integration between AbiPy and Abinit.
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In order to run the tests, you need a working set of Abinit executables and
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a ``manager.yml`` configuration file.
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For further information on the syntax of the configuration file, please consult the :ref:`workflows` section.
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A pre-compiled sequential version of Abinit for Linux and OSx can be installed directly from the anaconda cloud with::
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$ conda install abinit -c gmatteo
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Contributing to AbiPy is relatively easy.
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Just send us a `pull request <https://help.github.com/articles/using-pull-requests/>`_.
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When you send your request, make ``develop`` the destination branch on the repository
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@ -16,4 +16,4 @@ Mar 10 2017
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This is the first stable release in which we have reached a relatively stable API
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and a well-defined interface with the netcdf files produced by Abinit.
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We recommend Abinit >= 8.0.8b, version 8.2.2 is required to analyze the electronic fatbands
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saved in ``FATBANDS.nc``.
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saved in the ``FATBANDS.nc`` file.
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@ -20,6 +20,12 @@ environment variables, load modules with ``module load``, run MPI applications w
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It's also a very good idea to run the Abinit test suite with the `runtest.py script <https://asciinema.org/a/40324>`_
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before running production calculations.
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.. TIP::
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A pre-compiled sequential version of Abinit for Linux and OSx can be installed directly from the anaconda cloud with::
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$ conda install abinit -c gmatteo
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.. _task_manager:
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--------------------------------
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@ -243,7 +249,7 @@ to have a summary with the status of the different tasks and::
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$ abirun.py flow_si_ebands deps
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to print the interconnection among the tasks in textual format.
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to print the dependencies of the tasks in textual format.
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.. code-block:: console
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@ -268,7 +274,7 @@ There are two commands that can be used to launch tasks: ``single`` and ``rapid`
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The ``single`` command executes the first task in the flow that is in the ``READY`` state that is a task
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whose dependencies have been fulfilled.
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``rapid``, on the other hand, submits **all tasks** of the flow that are in the ``READY`` state.
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Let's try to run the flow with the ``rapid`` command to see what happens.
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Let's try to run the flow with the ``rapid`` command...
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.. code-block:: console
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@ -381,7 +387,7 @@ At this point, AbiPy starts to look at the output files produced by the task to
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When the first task completes, the status of the second task is automatically changed to ``READY``,
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the ``irdden`` input variable is added to the input file of the second task and a symbolic link to
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the ``DEN`` file produced by ``w0/t0`` is created in the ``indata`` directory of ``w0/t1``.
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Another autoparallel run is executed for the NSCF calculation and the second task is finally submitted.
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Another auto-parallel run is executed for the NSCF calculation and the second task is finally submitted.
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The command line interface is very flexible and sometimes it's the only tool available.
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However, there are cases in which we would like to have a global view of what's happening.
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@ -487,11 +493,11 @@ You should see the following output on the terminal
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PyFlowScheduler, Pid: 72038
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Scheduler options: {'seconds': 10, 'hours': 0, 'weeks': 0, 'minutes': 0, 'days': 0}
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``Pid`` is the process identifier of the scheduler (also reported in the ... file)
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We will see that the scheduler pid is extremely important when we start to run large flows on clusters.
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``Pid`` is the process identifier associated the scheduler (also saved in in the ``_PyFlowScheduler.pid`` file).
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.. IMPORTANT::
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A ``_PyFlowScheduler.pid`` file in ``FLOWDIR`` means that there's a scheduler running the flow.
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Note that there must be only one scheduler associated to a given flow.
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As you can easily understand the scheduler brings additional power to the AbiPy flow because
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@ -517,92 +523,127 @@ Configuring AbiPy on a cluster
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------------------------------
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In this section we discuss how to configure the manager to run flows on a cluster.
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The configuration depends on specific queue management system (Slurm, PBS, etc) so
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The configuration depends on specific queue management system (Slurm, PBS, etc) hence
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we assume that you are already familiar with job submissions and you know the options
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that mush be specified in the submission script in order to have your job accepted
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and executed by the management system (username, name of the queue, memory ...)
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Let's assume that your computing center uses Slurm and your jobs must be submitted to the `Oban` partition
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A `manager.yml` with a single `qadapter` looks like:
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Let's assume that our computing center uses ``Slurm`` and our jobs must be submitted to the ``default_queue`` partition.
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Hopefully, the system administrator of our cluster already provides an ``Abinit module`` that can be loaded
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directly with ``module load`` before invoking the code.
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To make thinks a little bit more difficult, however, we assume the we had to compile our own version of Abinit
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inside the build directory ``${HOME}/git_repos/abinit/build_impi`` using the following two modules
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already installed by the system administrator::
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compiler/intel/composerxe/2013_sp1.1.106
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intelmpi
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In this case, we have to be careful with the configuration of our environment because the Slurm submission
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script should load the modules and modify our ``$PATH`` so that our version of Abinit can be found.
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A ``manager.yml`` with a single ``qadapter`` looks like:
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.. code-block:: yaml
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# Resource manager e.g slurm, pbs, shell
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qtype: slurm
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qadapters:
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- priority: 1
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# Options passed to the resource manager
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# (the syntax depends on qtype, consult the manual of your resource manager)
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qparams:
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ntasks: 2
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time: 0:20:00
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partition: Oban
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queue:
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qtype: slurm
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qname: default_queue
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qparams: # Slurm options added to job.sh
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mail_type: FAIL
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mail_user: john@doe
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# List of modules to import before running the calculation
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modules:
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- intel/compilerpro/13.0.1.117
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- fftw3/intel/3.3
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job:
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modules:
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- compiler/intel/composerxe/2013_sp1.1.106
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- intelmpi
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shell_env:
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PATH: ${HOME}/git_repos/abinit/build_impi/src/98_main:$PATH
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pre_run:
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- ulimit -s unlimited
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mpi_runner: mpirun
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mpi_runner: /path/to/mpirun
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limits:
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timelimit: 0:20:0
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max_cores: 16
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min_mem_per_proc: 1Gb
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# Shell environment
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shell_env:
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PATH: /home/user/local/bin/:$PATH
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LD_LIBRARY_PATH: /home/user/local/lib:$LD_LIBRARY_PATH
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hardware:
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num_nodes: 120
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sockets_per_node: 2
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cores_per_socket: 8
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mem_per_node: 64Gb
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# Options for the automatic parallelization (Abinit autoparal feature)
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policy:
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autoparal: 1
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max_ncpus: 2
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.. TIP::
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$ abirun.py FLOWDIR doc_manager script
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Description:
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prints to screen the submission script that will be generated by AbiPy at runtime.
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Let's discuss the different options in more detail. Let's start from the ``queue`` section:
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``qtype``
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string specifying the resource manager. This option tells AbiPy how to generate the submission
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String specifying the resource manager. This option tells AbiPy how to generate the submission
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script, submit them, kill jobs in the queue and how to interpret the other options passed by the user.
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``qname``
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Name of the submission queue (string, MANDATORY)
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``qparams``
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Dictionary with the parameters passed to the resource manager.
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We use the *normalized* version of the options i.e dashes in the official name of the parameter
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are replaced by underscores (for the list of supported options see ...)
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are replaced by underscores e.g. ``--mail-type`` becomes ``mail_type``.
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For the list of supported options use the ``doc_manager`` command.
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Use ``qverbatim`` to pass additional options that are not included in the template.
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``modules``
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List of modules to load.
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Note that we are not specifying the number of cores in ``qparams`` because AbiPy will find an appropriate value
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at run-time.
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``shell_env``
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Allows the user to specify or to modify the values of the environment variables.
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The ``job`` section is the most critical one because it defines how to configure the environment
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before executing the application and how to run the code.
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The ``modules`` entry specifies the list of modules to load, ``shell_env`` allows us to modify the
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``$PATH`` environment variables so that the OS can find our Abinit executable.
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We also increase the size of the stack with ``ulimit`` before running the code and we run Abinit
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with the ``mpirun`` provided by the modules.
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``policy``
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This section governs the automatic parallelization of the run: in this case AbiPy will use
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the ``autoparal`` capabilities of Abinit to determine an optimal configuration with
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**maximum** ``max_ncpus`` MPI nodes. Setting ``autoparal`` to 0 disables the automatic parallelization.
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Other values of autoparal are not supported*
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The ``limits`` section defines the constraints that must be fulfilled in order to run on this queue
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while ``hardware`` is a dictionary with info on the hardware available on this queue.
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Every job will have a ``timelimit`` of 20 minutes, cannot use more that ``max_cores`` cores,
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and the first job submission will request 1 Gb of memory.
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Note that the actual number of cores will be determined at runtime by calling Abinit in ``autoparal`` mode
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to get all parallel configurations up to ``max_cores``.
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If the job is killed due to insufficient memory, AbiPy will resubmit the task with increased resources
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and it will stop when it reaches the maximum amount given by ``mem_per_node``.
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The complete list of ``qparams`` options supported with Slurm is obtained with
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Note that there are more advances options supported by ``limits`` and other options
|
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will be added as time goes by
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The get the complete list of options supported by the Slurm ``qadapter`` use:
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.. command-output:: abirun.py . doc_manager slurm
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If for some reason you need to cancel all tasks that have been submitted to the resource manager, use::
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.. IMPORTANT::
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If you need to cancel all tasks that have been submitted to the resource manager, use::
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|
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$ abirun.py FLOWDIR cancel
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Note that the script will ask for confirmation before killing all the jobs belonging to the flow.
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.. TODO: Section about QPARAMS
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Once you have a ``manager.yml`` properly configured for your cluster, you can start
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to use the scheduler to automate job submission.
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Very likely your flows will require hours or even days to complete and, in principle,
|
||||
you should maintain an active connection to the machine in order to keep your scheduler alive
|
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(if your session expires, all subprocesses launched within your terminal,
|
||||
including the python scheduler, will be automatically killed).
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Fortunately there's a standard Unix tool called ``nohup`` that comes to our rescue.
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Fortunately there is a standard Unix tool called ``nohup`` that comes to our rescue.
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||||
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For long-running jobs, we strongly suggest to start the scheduler with::
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$ nohup abirun.py FLOWDIR scheduler > sched.stdout 2> sched.stderr &
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||||
This command executes the scheduler in background and redirects the stdout and stderr
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This command executes the scheduler in background and redirects the ``stdout`` and ``stderr``
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to ``sched.log`` and ``sched.err``, respectively.
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The process identifier of the scheduler is saved in the ``_PyFlowScheduler.pid`` file inside ``FLOWDIR``
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and this file is removed automatically when the scheduler completes its execution.
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@ -744,44 +785,53 @@ by the event handlers.
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New error handlers will be added in the new versions of Abipy/Abinit.
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Please, let us know if you need handlers for errors commonly occuring in your calculations.
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||||
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||||
.. _task_policy:
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..
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||||
-----------
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TaskPolicy
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-----------
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.. _task_policy:
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||||
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||||
At this point, you may wonder why we need to specify all these parameters in the configuration file.
|
||||
The reason is that, before submitting a job to a resource manager, AbiPy will use the autoparal
|
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feature of ABINIT to get all the possible parallel configurations with ``ncpus <= max_cores``.
|
||||
On the basis of these results, AbiPy selects the "optimal" one, and changes the ABINIT input file
|
||||
and the submission script accordingly .
|
||||
(this is a very useful feature, especially for calculations done with ``paral_kgb=1`` that require
|
||||
the specification of ``npkpt``, ``npfft``, ``npband``, etc).
|
||||
If more than one `QueueAdapter` is specified, AbiPy will first compute all the possible
|
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configuration and then select the "optimal" `QueueAdapter` according to some kind of policy
|
||||
-----------
|
||||
TaskPolicy
|
||||
-----------
|
||||
|
||||
In some cases, you may want to enforce some constraint on the "optimal" configuration.
|
||||
For example, you may want to select only those configurations whose parallel efficiency is greater than 0.7
|
||||
and whose number of MPI nodes is divisible by 4.
|
||||
One can easily enforce this constraint via the ``condition`` dictionary whose syntax is similar to the one used in `mongodb`
|
||||
At this point, you may wonder why we need to specify all these parameters in the configuration file.
|
||||
The reason is that, before submitting a job to a resource manager, AbiPy will use the autoparal
|
||||
feature of ABINIT to get all the possible parallel configurations with ``ncpus <= max_cores``.
|
||||
On the basis of these results, AbiPy selects the "optimal" one, and changes the ABINIT input file
|
||||
and the submission script accordingly .
|
||||
(this is a very useful feature, especially for calculations done with ``paral_kgb=1`` that require
|
||||
the specification of ``npkpt``, ``npfft``, ``npband``, etc).
|
||||
If more than one `QueueAdapter` is specified, AbiPy will first compute all the possible
|
||||
configuration and then select the "optimal" `QueueAdapter` according to some kind of policy
|
||||
|
||||
.. code-block:: yaml
|
||||
In some cases, you may want to enforce some constraint on the "optimal" configuration.
|
||||
For example, you may want to select only those configurations whose parallel efficiency is greater than 0.7
|
||||
and whose number of MPI nodes is divisible by 4.
|
||||
One can easily enforce this constraint via the ``condition`` dictionary whose syntax is similar to
|
||||
the one used in ``mongodb``
|
||||
|
||||
policy:
|
||||
autoparal: 1
|
||||
max_ncpus: 10
|
||||
condition: {$and: [ {"efficiency": {$gt: 0.7}}, {"tot_ncpus": {$divisible: 4}} ]}
|
||||
.. code-block:: yaml
|
||||
|
||||
The parallel efficiency is defined as $\epsilon = \dfrac{T_1}{T_N * N}$ where $N$ is the number
|
||||
of MPI processes and $T_j$ is the wall time needed to complete the calculation with $j$ MPI processes.
|
||||
For a perfect scaling implementation $\epsilon$ is equal to one.
|
||||
The parallel speedup with N processors is given by $S = T_N / T_1$.
|
||||
Note that ``autoparal = 1`` will automatically change your ``job.sh`` script as well as the input file
|
||||
so that we can run the job in parallel with the optimal configuration required by the user.
|
||||
For example, you can use ``paral_kgb = 1`` in GS calculations and AbiPy will automatically set the values
|
||||
of ``npband``, ``npfft``, ``npkpt`` ... for you!
|
||||
Note that if no configuration fulfills the given condition, AbiPy will use the optimal configuration
|
||||
that leads to the highest parallel speedup (not necessarily the most efficient one).
|
||||
policy:
|
||||
autoparal: 1
|
||||
max_ncpus: 10
|
||||
condition: {$and: [ {"efficiency": {$gt: 0.7}}, {"tot_ncpus": {$divisible: 4}} ]}
|
||||
|
||||
The parallel efficiency is defined as $\epsilon = \dfrac{T_1}{T_N * N}$ where $N$ is the number
|
||||
of MPI processes and $T_j$ is the wall time needed to complete the calculation with $j$ MPI processes.
|
||||
For a perfect scaling implementation $\epsilon$ is equal to one.
|
||||
The parallel speedup with N processors is given by $S = T_N / T_1$.
|
||||
Note that ``autoparal = 1`` will automatically change your ``job.sh`` script as well as the input file
|
||||
so that we can run the job in parallel with the optimal configuration required by the user.
|
||||
For example, you can use ``paral_kgb = 1`` in GS calculations and AbiPy will automatically set the values
|
||||
of ``npband``, ``npfft``, ``npkpt`` ... for you!
|
||||
Note that if no configuration fulfills the given condition, AbiPy will use the optimal configuration
|
||||
that leads to the highest parallel speedup (not necessarily the most efficient one).
|
||||
|
||||
``policy``
|
||||
This section governs the automatic parallelization of the run: in this case AbiPy will use
|
||||
the ``autoparal`` capabilities of Abinit to determine an optimal configuration with
|
||||
**maximum** ``max_ncpus`` MPI nodes. Setting ``autoparal`` to 0 disables the automatic parallelization.
|
||||
Other values of autoparal are not supported*
|
||||
|
||||
|
||||
.. _flow_troubeshooting:
|
||||
|
|
|
|||
Loading…
Reference in New Issue