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        "author": "Max Zheng",
        "author_email": "maxzheng.os @t gmail.com",
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        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.6",
            "Programming Language :: Python :: 2.7",
            "Topic :: Software Development"
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        "description": "memory-tools\n========================\n\nA set of simple yet effective tools to troubleshoot memory leaks.\n\nWhen debugging memory issues in Python 2.6, the author had tried memory_profiler_ and heapy_,\nunfortunately neither worked. And so memory-tools was born with the goal of being simple - it\nshould always work, yet effective - it is good at helping you find memory leaks.\n\n.. _memory_profiler: https://pypi.python.org/pypi/memory_profiler\n.. _heapy: https://pypi.python.org/pypi/guppy\n\nQuick Start Tutorial\n====================\n\nInstallation\n------------\n\n.. code::\n\n    $ pip install memory-tools\n\nShow Memory Usage / Delta\n-------------------------\n\nUse the `show-mem` command to show system or process memory usage. When paired with watch_, this becomes even more useful.\n\nShow system memory::\n\n    $ show-mem\n\n    Commit Mem (MB):       27,852.80 total   17,278.42 used\n    Physical Mem (MB):     16,384.00 total   13,128.05 used\n\nRe-run to show delta from last run::\n\n    $ show-mem\n\n    Commit Mem (MB):       27,852.80 total   17,888.59 used (delta: 310.15)\n    Physical Mem (MB):     16,384.00 total   13,126.40 used (delta: -1.65)\n\nShow memory for process::\n\n    $ show-mem -p python\n\n    1 process matching \"python\":\n      PID 26143 (MB):           4.79 rss          1.23 private\n\n    $ show-mem -p 26143\n\n      PID 26143 (MB):           4.80 rss          1.24 private\n\nWatch system/process memory using watch_::\n\n    $ watch show-mem -s -p python\n\n    Commit Mem (MB):       27,852.80 total   17,888.59 used (delta: 310.15)\n    Physical Mem (MB):     16,384.00 total   13,126.40 used (delta: -1.65)\n\n    2 processes matching \"python\" (showing 1st & last):\n      PID 26143 (MB):          40.79 rss         30.23 private\n      PID 24118 (MB):           4.79 rss          1.23 private\n\nSummarize / Save GC Objects\n---------------------------\n\nAfter running your program, view summary of gc.get_objects()::\n\n    from memorytools import summarize_objects\n\n    summarize_objects()\n\nAnd here is a sample output::\n\n    Objects count 3,790\n    Objects size 833,344\n\n          Size Count Type\n       476,864   296 <type 'dict'>\n        76,320   954 <type 'wrapper_descriptor'>\n        64,920   541 <type 'function'>\n        ...\n\n    Count       Size Type\n      954     76,320 <type 'wrapper_descriptor'>\n      541     64,920 <type 'function'>\n      515     37,080 <type 'builtin_function_or_method'>\n      ...\n\nSave all objects (along with the above summary) to a file::\n\n    from memorytools import save_objects\n\n    save_objects()\n\n    # Output: Wrote 3887 objects to /var/tmp/objects-45271 (882040 bytes)\n\n\nLooping / Stress Testing\n------------------------\n\nUse the `loop` command to run a command, module:method, or code in a forever loop to perform stress testing, which is\nuseful in finding memory leaks. The command/code should, of course, act against a long running server for this to be useful.\n\nRun a script in a loop::\n\n    $ loop show-mem 1\n\n    Physical Mem (MB):     16,384.00 total    9,415.50 used (delta: -190.67)\n    Physical Mem (MB):     16,384.00 total    9,415.27 used (delta: -0.23)\n    Physical Mem (MB):     16,384.00 total    9,415.85 used (delta: 0.58)\n    ^C                   [ User CTRL-C here as it loops forever by default ]\n    Looped 3 times in 2.80 secs\n\nRun a module:method in a loop - count of 10::\n\n    $ loop memorytools:summarize_objects 10 -c 10\n\n    # Results from summarize_objects() every 10 seconds\n\n    Looped 10 times in 100 secs\n\nRun adhoc code in a loop - count of 2 and concurrency of 3::\n\n    $ loop 'print(\"Hello World!\")' 0.1 -c 2 -cc 3\n    Hello World!\n    ... 5 more times\n\n    Looped 2 times in 0.21 secs with concurrency of 3 (6 runs, 0.10 secs per loop, 0.03 secs per run)\n\n\nLog Stack / Start Debugger on Signal\n------------------------------------\n\nIf you need to get a stacktrace of a running process, or start the debugger in specific situations to look at memory footprint,\nthen a signal handler could help::\n\n    from memorytools import add_debug_handler\n\n    add_debug_handler(start_debugger_password='test')  # remove start_debugger_password to skip rpdb2 debugger\n\nThe above will add a handler to SIGUSR2 that will log a stacktrace on trigger and also start the rpdb2_ debugger.\n\n.. _rpdb2: http://winpdb.org/docs/embedded-debugging/\n\n\nLinks & Contact Info\n====================\n\n| Documentation: http://memory-tools.readthedocs.org\n|\n| PyPI Package: https://pypi.python.org/pypi/memory-tools\n| GitHub Source: https://github.com/maxzheng/memory-tools\n| Report Issues/Bugs: https://github.com/maxzheng/memory-tools/issues\n|\n| Connect: https://www.linkedin.com/in/maxzheng\n| Contact: maxzheng.os @t gmail.com\n\n.. _watch: https://en.wikipedia.org/wiki/Watch_(Unix)",
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