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for the current user:\n\n.. code:: shell\n\n   pip install --user https://github.com/matz-e/sparkmanager/tarball/master\n\nUsage\n-----\n\nThe module itself acts as a mediator to Spark:\n\n.. code:: python\n\n   import sparkmanager as sm\n\n   # Create a new application\n   sm.create(\"My fancy name\",\n             [(\"spark.executor.cores\", 4), (\"spark.executor.memory\", \"8g\")])\n\n   data = sm.spark.range(5)\n   # Will show up in the UI with the name \"broadcasting some data\"\n   with sm.jobgroup(\"broadcasting some data\"):\n       data = sm.broadcast(data.collect())\n\nThe Spark session can be accessed via ``sm.spark``, the Spark context via\n``sm.sc``. Both attributes are instantiated once the ``create`` method is\ncalled, with the option to call unambiguous methods from both directly via\nthe :py:class:`SparkManager` object:\n\n.. code:: python\n\n   # The following two calls are equivalent\n   c = sm.parallelize(range(5))\n   d = sm.sc.parallelize(range(5))\n   assert c.collect() == d.collect()\n\nCluster support scripts\n-----------------------\n\n.. note::\n\n   Scripts to run on the cluster are still somewhat experimental and should\n   be used with caution!\n\nEnvironment setup\n~~~~~~~~~~~~~~~~~\n\nTo create a self-contained Spark environment, the script provided in\n``examples/env.sh`` can be used. It is currently tuned to the requirements of\nthe `bbpviz` cluster.  A usage example:\n\n.. code:: shell\n\n   SPARK_ROOT=/path/to/my/spark/installation SM_WORKDIR=/path/to/a/work/directory examples/env.sh\n\nThe working directory will contain:\n\n* A Python virtual environment\n* A basic Spark configuration pointing to directories within the working\n  directory\n* An environment script to establish the setup\n\nTo use the resulting working environment:\n\n.. code:: shell\n\n   . /path/to/a/work/directory/env.sh\n\nSpark deployment on allocations\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nWithin a cluster allocation, the script ``sm_cluster`` can be used to start\na Spark cluster.  The script will be automatically installed by `pip`.  To\nuse it, pass either a working directory containing an environment or\nspecify them separately:\n\n.. code:: shell\n\n   sm_cluster startup $WORKDIR\n   sm_cluster startup $WORKDIR /path/to/some/env.sh\n\nSimilar, to stop a cluster (not necessary with slurm):\n\n.. code:: shell\n\n   sm_cluster shutdown $WORKDIR\n   sm_cluster shutdown $WORKDIR /path/to/some/env.sh\n\nSpark applications then can connect to a master found via:\n\n.. code:: shell\n\n   cat $WORKDIR/spark_master\n\nTL;DR on BlueBrain 5\n~~~~~~~~~~~~~~~~~~~~\n\nSetup a Spark environment in your current shell, and point `WORKDIR` to a\nshared directory. `SPARK_HOME` needs to be in your environment and point to\nyour Spark installation.  By default, only a file with the Spark master and\nthe cluster launch script will be copied to `WORKDIR`. Then submit a\ncluster with:\n\n.. code:: shell\n\n   sbatch -A proj16 -t 24:00:00 -N4 --exclusive -C nvme $(which sm_cluster) startup $WORKDIR\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmatz-e%2Fsparkmanager","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmatz-e%2Fsparkmanager","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmatz-e%2Fsparkmanager/lists"}