Metadata-Version: 1.2
Name: dask-drmaa
Version: 0.2.1
Summary: Dask on DRMAA
Home-page: http://github.com/dask/dask-drmaa/
Maintainer: Matthew Rocklin
Maintainer-email: mrocklin@gmail.com
License: BSD
Description: Dask on DRMAA
        =============
        
        |Build Status| |PyPI Release| |conda-forge Release|
        
        Deploy a Dask.distributed_ cluster on top of a cluster running a
        DRMAA_-compliant job scheduler.
        
        
        Example
        -------
        
        Launch from Python
        
        .. code-block:: python
        
           from dask_drmaa import DRMAACluster
           cluster = DRMAACluster()
        
           from dask.distributed import Client
           client = Client(cluster)
           cluster.start_workers(2)
        
           >>> future = client.submit(lambda x: x + 1, 10)
           >>> future.result()
           11
        
        Or launch from the command line::
        
            $ dask-drmaa 10  # starts local scheduler and ten remote workers
        
        
        Install
        -------
        
        Python packages are available from PyPI and can be installed with ``pip``::
        
            pip install dask-drmaa
        
        Also ``conda`` packages are available from conda-forge::
        
            conda install -c conda-forge dask-drmaa
        
        Additionally the package can be installed from GitHub with the latest changes::
        
            pip install git+https://github.com/dask/dask-drmaa.git --upgrade
        
        or::
        
            git clone git@github.com:dask/dask-drmaa.git
            cd dask-drmaa
            pip install .
        
        You must have the DRMAA system library installed and be able to submit jobs
        from your local machine. Please make sure to set the environment variable
        ``DRMAA_LIBRARY_PATH`` to point to the location of ``libdrmaa.so`` for your
        system.
        
        
        Testing
        -------
        
        This repository contains a Docker-compose testing harness for a Son of Grid
        Engine cluster with a master and two slaves.  You can initialize this system
        as follows:
        
        .. code-block:: bash
        
           docker-compose build
           ./start-sge.sh
        
        If you have done this previously and need to refresh your solution you can do
        the following
        
        .. code-block:: bash
        
           docker-compose stop
           docker-compose build --no-cache
           ./start-sge.sh
        
        And run tests with py.test in the master docker container
        
        .. code-block:: bash
        
          docker exec -it sge_master /bin/bash -c "cd /dask-drmaa; python setup.py develop"
          docker exec -it sge_master /bin/bash -c "cd /dask-drmaa; py.test dask_drmaa --verbose"
        
        
        Adaptive Load
        -------------
        
        Dask-drmaa can adapt to scheduler load, deploying more workers on the grid when
        it has more work, and cleaning up these workers when they are no longer
        necessary.  This can simplify setup (you can just leave a cluster running) and
        it can reduce load on the cluster, making IT happy.
        
        To enable this, call the ``adapt`` method of a ``DRMAACluster``.  You can
        submit computations to the cluster without ever explicitly creating workers.
        
        .. code-block:: python
        
           from dask_drmaa import DRMAACluster
           from dask.distributed import Client
        
           cluster = DRMAACluster()
           cluster.adapt()
           client = Client(cluster)
        
           futures = client.map(func, seq)  # workers will be created as necessary
        
        
        Extensible
        ----------
        
        The DRMAA interface is the lowest common denominator among many different job
        schedulers like SGE, SLURM, LSF, Torque, and others.  However, sometimes users
        need to specify parameters particular to their cluster, such as resource
        queues, wall times, memory constraints, etc..
        
        DRMAA allows users to pass native specifications either when constructing the
        cluster or when starting new workers:
        
        .. code-block:: python
        
           cluster = DRMAACluster(template={'nativeSpecification': '-l h_rt=01:00:00'})
           # or
           cluster.start_workers(10, nativeSpecification='-l h_rt=01:00:00')
        
        
        Related Work
        ------------
        
        * DRMAA_: The Distributed Resource Management Application API, a high level
          API for general use on traditional job schedulers
        * drmaa-python_: The Python bindings for DRMAA
        * DaskSGE_: An earlier dask-drmaa implementation
        * `Son of Grid Engine`_: The default implementation used in testing
        * Dask.distributed_: The actual distributed computing library this launches
        
        .. _DRMAA: https://www.drmaa.org/
        .. _drmaa-python: http://drmaa-python.readthedocs.io/en/latest/
        .. _`Son of Grid Engine`: https://arc.liv.ac.uk/trac/SGE
        .. _dasksge: https://github.com/mfouesneau/dasksge
        .. _Dask.distributed: http://distributed.readthedocs.io/en/latest/
        .. _DRMAA: https://www.drmaa.org/
        
        
        .. |Build Status| image:: https://travis-ci.org/dask/dask-drmaa.svg?branch=master
           :target: https://travis-ci.org/dask/dask-drmaa
        
        .. |PyPI Release| image:: https://img.shields.io/pypi/v/dask-drmaa.svg
           :target: https://pypi.python.org/pypi/dask-drmaa
        
        .. |conda-forge Release| image:: https://img.shields.io/conda/vn/conda-forge/dask-drmaa.svg
           :target: https://github.com/conda-forge/dask-drmaa-feedstock
        
Platform: UNKNOWN
