Metadata-Version: 1.1
Name: memory-profiler
Version: 0.56.0
Summary: A module for monitoring memory usage of a python program
Home-page: https://github.com/pythonprofilers/memory_profiler
Author: Fabian Pedregosa
Author-email: f@bianp.net
License: BSD
Description: .. image:: https://travis-ci.org/pythonprofilers/memory_profiler.svg?branch=master
            :target: https://travis-ci.org/pythonprofilers/memory_profiler
        
        =================
         Memory Profiler
        =================
        
        This is a python module for monitoring memory consumption of a process
        as well as line-by-line analysis of memory consumption for python
        programs. It is a pure python module which depends on the `psutil
        <http://pypi.python.org/pypi/psutil>`_ module.
        
        
        ==============
         Installation
        ==============
        Install via pip::
        
            $ pip install -U memory_profiler
        
        The package is also available on `conda-forge
        <https://github.com/conda-forge/memory_profiler-feedstock>`_.
        
        To install from source, download the package, extract and type::
        
            $ python setup.py install
        
        
        =======
         Usage
        =======
        
        
        line-by-line memory usage
        =========================
        
        The line-by-line memory usage mode is used much in the same way of the
        `line_profiler <https://pypi.python.org/pypi/line_profiler/>`_: first
        decorate the function you would like to profile with ``@profile`` and
        then run the script with a special script (in this case with specific
        arguments to the Python interpreter).
        
        In the following example, we create a simple function ``my_func`` that
        allocates lists ``a``, ``b`` and then deletes ``b``::
        
        
            @profile
            def my_func():
                a = [1] * (10 ** 6)
                b = [2] * (2 * 10 ** 7)
                del b
                return a
        
            if __name__ == '__main__':
                my_func()
        
        
        Execute the code passing the option ``-m memory_profiler`` to the
        python interpreter to load the memory_profiler module and print to
        stdout the line-by-line analysis. If the file name was example.py,
        this would result in::
        
            $ python -m memory_profiler example.py
        
        Output will follow::
        
            Line #    Mem usage  Increment   Line Contents
            ==============================================
                 3                           @profile
                 4      5.97 MB    0.00 MB   def my_func():
                 5     13.61 MB    7.64 MB       a = [1] * (10 ** 6)
                 6    166.20 MB  152.59 MB       b = [2] * (2 * 10 ** 7)
                 7     13.61 MB -152.59 MB       del b
                 8     13.61 MB    0.00 MB       return a
        
        
        The first column represents the line number of the code that has been
        profiled, the second column (*Mem usage*) the memory usage of the
        Python interpreter after that line has been executed. The third column
        (*Increment*) represents the difference in memory of the current line
        with respect to the last one. The last column (*Line Contents*) prints
        the code that has been profiled.
        
        Decorator
        =========
        A function decorator is also available.  Use as follows::
        
            from memory_profiler import profile
        
            @profile
            def my_func():
                a = [1] * (10 ** 6)
                b = [2] * (2 * 10 ** 7)
                del b
                return a
        
        In this case the script can be run without specifying ``-m
        memory_profiler`` in the command line.
        
        In function decorator, you can specify the precision as an argument to the
        decorator function.  Use as follows::
        
            from memory_profiler import profile
        
            @profile(precision=4)
            def my_func():
                a = [1] * (10 ** 6)
                b = [2] * (2 * 10 ** 7)
                del b
                return a
        
        If a python script with decorator ``@profile`` is called using ``-m
        memory_profiler`` in the command line, the ``precision`` parameter is ignored.
        
        Time-based memory usage
        ==========================
        Sometimes it is useful to have full memory usage reports as a function of
        time (not line-by-line) of external processes (be it Python scripts or not).
        In this case the executable ``mprof`` might be useful. Use it like::
        
            mprof run <executable>
            mprof plot
        
        The first line run the executable and record memory usage along time,
        in a file written in the current directory.
        Once it's done, a graph plot can be obtained using the second line.
        The recorded file contains a timestamps, that allows for several
        profiles to be kept at the same time.
        
        Help on each `mprof` subcommand can be obtained with the `-h` flag,
        e.g. `mprof run -h`.
        
        In the case of a Python script, using the previous command does not
        give you any information on which function is executed at a given
        time. Depending on the case, it can be difficult to identify the part
        of the code that is causing the highest memory usage.
        
        Adding the `profile` decorator to a function and running the Python
        script with
        
            mprof run <script>
        
        will record timestamps when entering/leaving the profiled function. Running
        
            mprof plot
        
        afterward will plot the result, making plots (using matplotlib) similar to these:
        
        .. image:: https://camo.githubusercontent.com/3a584c7cfbae38c9220a755aa21b5ef926c1031d/68747470733a2f2f662e636c6f75642e6769746875622e636f6d2f6173736574732f313930383631382f3836313332302f63623865376337382d663563632d313165322d386531652d3539373237623636663462322e706e67
           :target: https://github.com/scikit-learn/scikit-learn/pull/2248
           :height: 350px
        
        or, with ``mprof plot --flame`` (the function and timestamp names will appear on hover):
        
        .. image:: ./images/flamegraph.png
           :height: 350px
        
        A discussion of these capabilities can be found `here <http://fa.bianp.net/blog/2014/plot-memory-usage-as-a-function-of-time/>`_.
        
        .. warning:: If your Python file imports the memory profiler `from memory_profiler import profile` these timestamps will not be recorded. Comment out the import, leave your functions decorated, and re-run.
        
        The available commands for `mprof` are:
        
          - ``mprof run``: running an executable, recording memory usage
          - ``mprof plot``: plotting one the recorded memory usage (by default,
            the last one)
          - ``mprof list``: listing all recorded memory usage files in a
            user-friendly way.
          - ``mprof clean``: removing all recorded memory usage files.
          - ``mprof rm``: removing specific recorded memory usage files
        
        Tracking forked child processes
        ===============================
        In a multiprocessing context the main process will spawn child processes whose
        system resources are allocated separately from the parent process. This can
        lead to an inaccurate report of memory usage since by default only the parent
        process is being tracked. The ``mprof`` utility provides two mechanisms to
        track the usage of child processes: sum the memory of all children to the
        parent's usage and track each child individual.
        
        To create a report that combines memory usage of all the children and the
        parent, use the ``include_children`` flag in either the ``profile`` decorator or
        as a command line argument to ``mprof``::
        
            mprof run --include-children <script>
        
        The second method tracks each child independently of the main process,
        serializing child rows by index to the output stream. Use the ``multiprocess``
        flag and plot as follows::
        
            mprof run --multiprocess <script>
            mprof plot
        
        This will create a plot using matplotlib similar to this:
        
        .. image:: https://cloud.githubusercontent.com/assets/745966/24075879/2e85b43a-0bfa-11e7-8dfe-654320dbd2ce.png
            :target: https://github.com/pythonprofilers/memory_profiler/pull/134
            :height: 350px
        
        You can combine both the ``include_children`` and ``multiprocess`` flags to show
        the total memory of the program as well as each child individually. If using
        the API directly, note that the return from ``memory_usage`` will include the
        child memory in a nested list along with the main process memory.
        
        Plot settings
        ===============================
        
        By default, the command line call is set as the graph title. If you wish to customize it, you can use the ``-t`` option to manually set the figure title.
        
        
            mprof plot -t 'Recorded memory usage'
        
        You can also hide the function timestamps using the ``n`` flag, such as
        
            mprof plot -n
        
        
        Setting debugger breakpoints
        =============================
        It is possible to set breakpoints depending on the amount of memory used.
        That is, you can specify a threshold and as soon as the program uses more
        memory than what is specified in the threshold it will stop execution
        and run into the pdb debugger. To use it, you will have to decorate
        the function as done in the previous section with ``@profile`` and then
        run your script with the option ``-m memory_profiler --pdb-mmem=X``,
        where X is a number representing the memory threshold in MB. For example::
        
            $ python -m memory_profiler --pdb-mmem=100 my_script.py
        
        will run ``my_script.py`` and step into the pdb debugger as soon as the code
        uses more than 100 MB in the decorated function.
        
        .. TODO: alternatives to decoration (for example when you don't want to modify
            the file where your function lives).
        
        =====
         API
        =====
        memory_profiler exposes a number of functions to be used in third-party
        code.
        
        
        
        ``memory_usage(proc=-1, interval=.1, timeout=None)`` returns the memory usage
        over a time interval. The first argument, ``proc`` represents what
        should be monitored.  This can either be the PID of a process (not
        necessarily a Python program), a string containing some python code to
        be evaluated or a tuple ``(f, args, kw)`` containing a function and its
        arguments to be evaluated as ``f(*args, **kw)``. For example,
        
        
            >>> from memory_profiler import memory_usage
            >>> mem_usage = memory_usage(-1, interval=.2, timeout=1)
            >>> print(mem_usage)
        	[7.296875, 7.296875, 7.296875, 7.296875, 7.296875]
        
        
        Here I've told memory_profiler to get the memory consumption of the
        current process over a period of 1 second with a time interval of 0.2
        seconds. As PID I've given it -1, which is a special number (PIDs are
        usually positive) that means current process, that is, I'm getting the
        memory usage of the current Python interpreter. Thus I'm getting
        around 7MB of memory usage from a plain python interpreter. If I try
        the same thing on IPython (console) I get 29MB, and if I try the same
        thing on the IPython notebook it scales up to 44MB.
        
        
        If you'd like to get the memory consumption of a Python function, then
        you should specify the function and its arguments in the tuple ``(f,
        args, kw)``. For example::
        
        
            >>> # define a simple function
            >>> def f(a, n=100):
                ...     import time
                ...     time.sleep(2)
                ...     b = [a] * n
                ...     time.sleep(1)
                ...     return b
                ...
            >>> from memory_profiler import memory_usage
            >>> memory_usage((f, (1,), {'n' : int(1e6)}))
        
        This will execute the code `f(1, n=int(1e6))` and return the memory
        consumption during this execution.
        
        =========
        REPORTING
        =========
        
        The output can be redirected to a log file by passing IO stream as
        parameter to the decorator like @profile(stream=fp)
        
            >>> fp=open('memory_profiler.log','w+')
            >>> @profile(stream=fp)
            >>> def my_func():
                ...     a = [1] * (10 ** 6)
                ...     b = [2] * (2 * 10 ** 7)
                ...     del b
                ...     return a
        
            For details refer: examples/reporting_file.py
        
        ``Reporting via logger Module:``
        
        Sometime it would be very convenient to use logger module specially
        when we need to use RotatingFileHandler.
        
        The output can be redirected to logger module by simply making use of
        LogFile of memory profiler module.
        
            >>> from memory_profiler import LogFile
            >>> import sys
            >>> sys.stdout = LogFile('memory_profile_log')
        
        ``Customized reporting:``
        
        Sending everything to the log file while running the memory_profiler
        could be cumbersome and one can choose only entries with increments
        by passing True to reportIncrementFlag, where reportIncrementFlag is
        a parameter to LogFile class of memory profiler module.
        
            >>> from memory_profiler import LogFile
            >>> import sys
            >>> sys.stdout = LogFile('memory_profile_log', reportIncrementFlag=False)
        
            For details refer: examples/reporting_logger.py
        
        =====================
         IPython integration
        =====================
        After installing the module, if you use IPython, you can use the `%mprun`, `%%mprun`,
        `%memit` and `%%memit` magics.
        
        For IPython 0.11+, you can use the module directly as an extension, with
        ``%load_ext memory_profiler``
        
        To activate it whenever you start IPython, edit the configuration file for your
        IPython profile, ~/.ipython/profile_default/ipython_config.py, to register the
        extension like this (If you already have other extensions, just add this one to
        the list)::
        
            c.InteractiveShellApp.extensions = [
                'memory_profiler',
            ]
        
        (If the config file doesn't already exist, run ``ipython profile create`` in
        a terminal.)
        
        It then can be used directly from IPython to obtain a line-by-line
        report using the `%mprun` or `%%mprun` magic command. In this case, you can skip
        the `@profile` decorator and instead use the `-f` parameter, like
        this. Note however that function my_func must be defined in a file
        (cannot have been defined interactively in the Python interpreter)::
        
            In [1]: from example import my_func, my_func_2
        
            In [2]: %mprun -f my_func my_func()
        
        or in cell mode::
        
            In [3]: %%mprun -f my_func -f my_func_2
               ...: my_func()
               ...: my_func_2()
        
        Another useful magic that we define is `%memit`, which is analogous to
        `%timeit`. It can be used as follows::
        
            In [1]: %memit range(10000)
            peak memory: 21.42 MiB, increment: 0.41 MiB
        
            In [2]: %memit range(1000000)
            peak memory: 52.10 MiB, increment: 31.08 MiB
        
        or in cell mode (with setup code)::
        
            In [3]: %%memit l=range(1000000)
               ...: len(l)
               ...:
            peak memory: 52.14 MiB, increment: 0.08 MiB
        
        For more details, see the docstrings of the magics.
        
        For IPython 0.10, you can install it by editing the IPython configuration
        file ~/.ipython/ipy_user_conf.py to add the following lines::
        
            # These two lines are standard and probably already there.
            import IPython.ipapi
            ip = IPython.ipapi.get()
        
            # These two are the important ones.
            import memory_profiler
            memory_profiler.load_ipython_extension(ip)
        
        ============================
         Frequently Asked Questions
        ============================
            * Q: How accurate are the results ?
            * A: This module gets the memory consumption by querying the
              operating system kernel about the amount of memory the current
              process has allocated, which might be slightly different from
              the amount of memory that is actually used by the Python
              interpreter. Also, because of how the garbage collector works in
              Python the result might be different between platforms and even
              between runs.
        
            * Q: Does it work under windows ?
            * A: Yes, thanks to the
              `psutil <http://pypi.python.org/pypi/psutil>`_ module.
        
        
        
        ===========================
         Support, bugs & wish list
        ===========================
        For support, please ask your question on `stack overflow
        <http://stackoverflow.com/>`_ and add the `*memory-profiling* tag <http://stackoverflow.com/questions/tagged/memory-profiling>`_.
        Send issues, proposals, etc. to `github's issue tracker
        <https://github.com/pythonprofilers/memory_profiler/issues>`_ .
        
        If you've got questions regarding development, you can email me
        directly at fabian@fseoane.net
        
        .. image:: http://fseoane.net/static/tux_memory_small.png
        
        
        =============
         Development
        =============
        Latest sources are available from github:
        
            https://github.com/pythonprofilers/memory_profiler
        
        ===============================
        Projects using memory_profiler
        ===============================
        
        `Benchy <https://github.com/python-recsys/benchy>`_
        
        `IPython memory usage <https://github.com/ianozsvald/ipython_memory_usage>`_
        
        `PySpeedIT <https://github.com/peter1000/PySpeedIT>`_ (uses a reduced version of memory_profiler)
        
        `pydio-sync <https://github.com/pydio/pydio-sync>`_ (uses custom wrapper on top of memory_profiler)
        
        =========
         Authors
        =========
        This module was written by `Fabian Pedregosa <http://fseoane.net>`_
        and `Philippe Gervais <https://github.com/pgervais>`_
        inspired by Robert Kern's `line profiler
        <http://packages.python.org/line_profiler/>`_.
        
        `Tom <http://tomforb.es/>`_ added windows support and speed improvements via the
        `psutil <http://pypi.python.org/pypi/psutil>`_ module.
        
        `Victor <https://github.com/octavo>`_ added python3 support, bugfixes and general
        cleanup.
        
        `Vlad Niculae <http://vene.ro/>`_ added the `%mprun` and `%memit` IPython magics.
        
        `Thomas Kluyver <https://github.com/takluyver>`_ added the IPython extension.
        
        `Sagar UDAY KUMAR <https://github.com/sagaru>`_ added Report generation feature and examples.
        
        `Dmitriy Novozhilov <https://github.com/demiurg906>`_ and `Sergei Lebedev <https://github.com/superbobry>`_ added support for `tracemalloc <https://docs.python.org/3/library/tracemalloc.html>`_.
        
        `Benjamin Bengfort <https://github.com/bbengfort>`_ added support for tracking the usage of individual child processes and plotting them.
        
        `Muhammad Haseeb Tariq <https://github.com/mhaseebtariq>`_ fixed issue #152, which made the whole interpreter hang on functions that launched an exception.
        
        `Juan Luis Cano <https://github.com/Juanlu001>`_ modernized the infrastructure and helped with various things.
        
        =========
         License
        =========
        BSD License, see file COPYING for full text.
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.2
Classifier: Programming Language :: Python :: 3.3
Classifier: Topic :: Software Development
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
