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        "author": "Stefan Peterson",
        "author_email": "stefan.peterson@rubico.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: GNU General Public License (GPL)",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Utilities"
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        "description": "Ampeg\n=====\n\nAmpeg is a simple and lightweight package for parallel computation. It provides\nsimple functions for scheduling and execution of a set of dependent or\nindependent computational tasks over multiple processes using the \n``multiprocessing`` package.\n\nRequirements\n------------\n\nPython 2.7 or later\n\nPython 3.4 or later\n\nInstallation\n------------\n\npip install ampeg\n\nUsage\n-----\n\nAmpeg exposes a scheduling function ``earliest_finish_time``, an execution\nfunction ``execute_task_lists`` and a ``Dependency`` class. The former takes a\ndirected acyclic graph (DAG) and a number of processes and produces a set of\ntask lists for each process and a corresponding set of task IDs for translating\nthe execution result. These two form the input to ``execute_task_lists``, which\nreturns a dict with the result of each task in the original graph.\n\nThe DAG is represented by a python dict of vertices where each key is the ID of\na task and each value is a triple of (function, args or kwargs, computation\ncost). Edges are implicitly defined by instances of the ``Dependency`` class in\nthe args or kwargs.\n\nA simple usage example computing (3^2 + 4^2) - (3^2 * 10/2):\n\n```\n>>> import ampeg as ag\n>>> n_processes = 3\n>>> my_graph = {0: (lambda x: x**2, 3, 10.8),\n                1: (lambda x: x**2, 4, 10.8),\n                2: (lambda x: x/2, 10, 11),\n                3: (lambda x, y: x + y, (ag.Dependency(0, None, 1),\n                                         ag.Dependency(1, None, 1), 10.7),\n                4: (lambda x, y: x*y, (ag.Dependency(0, None, 1),\n                                       ag.Dependency(2, None, 1)), 10.8),\n                5: (lambda x, y: x - y, (ag.Dependency(3, None, 1),\n                                         ag.Dependency(4, None, 1)), 10.9)}\n>>> task_lists, task_ids = ag.earliest_finish_time(my_graph, n_processes)\n>>> ag.execute_task_lists(task_lists, task_ids)\n{0: 9, 1: 16, 2: 5, 3: 25, 4: 45, 5: -20}\n```\n\nThe Dependency class\n--------------------\n\nA dependency is a triple of (task ID or index, key (if any) and communication\ncost). The key may be a single key, index or slice, or it may be an iterable of\nsuch values to be applied in sequence. For example, the key ``('values', 2)``\nextracts the value 5 from the dict ``{'values': [1, 3, 5]}``. Dependency\ninstances are created by ``ampeg.Dependency(task, key, cost)`` where cost is\noptional and defaults to 0.\n\nExceptions\n----------\n\nAmpeg catches exceptions raised by individual tasks, returning them as results\nencapsulated in the ``Err`` class. When an ``Err`` instance is found among the\ndependencies for a task, the result for this task will be an ``Err`` instance\nencapsulating a ``DependencyError``.\n\nWindows\n-------\nNote that under Windows, the functions and their arguments must all be \npicklable.\n\n\n",
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        "keywords": "machine learning data mining out-of-memory",
        "license": "GPL-3.0",
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        "name": "ampeg",
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