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and Communication","monthly_downloads":516,"total_dependent_repos":1,"total_dependent_packages":1,"readme":".. image:: https://raw.githubusercontent.com/Helmholtz-AI-Energy/perun/main/docs/images/full_logo.svg\n\n| |fair-software| |openssf| |zenodo| |license| |docs|\n| |pypi-version| |python-version| |pypi-downloads| |black| |codecov|\n\n.. |fair-software| image:: https://img.shields.io/badge/fair--software.eu-%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F-green\n   :target: https://fair-software.eu\n.. |openssf| image:: https://bestpractices.coreinfrastructure.org/projects/7253/badge\n   :target: https://bestpractices.coreinfrastructure.org/projects/7253\n.. |zenodo| image:: https://zenodo.org/badge/523363424.svg\n   :target: https://zenodo.org/badge/latestdoi/523363424\n.. |pypi-version| image:: https://img.shields.io/pypi/v/perun\n.. |pypi-downloads| image:: https://img.shields.io/pypi/dm/perun\n.. |black| image:: https://img.shields.io/badge/code%20style-black-000000.svg\n   :target: https://github.com/psf/black\n.. |codecov| image:: https://codecov.io/gh/Helmholtz-AI-Energy/perun/graph/badge.svg?token=9O6FSJ6I3G\n   :target: https://codecov.io/gh/Helmholtz-AI-Energy/perun\n.. |python-version| image:: https://img.shields.io/badge/Python-3.9+-blue.svg\n   :target: https://www.python.org/downloads/\n.. |license| image:: https://img.shields.io/badge/License-BSD_3--Clause-blue.svg\n   :target: https://opensource.org/licenses/BSD-3-Clause\n.. |docs| image:: https://readthedocs.org/projects/perun/badge/?version=latest\n   :target: https://perun.readthedocs.io/en/latest/?badge=latest\n\n===============================================================\n\n*perun* is a Python package that calculates the energy consumption of Python scripts by sampling usage statistics from your Intel, Nvidia or AMD hardware components. It can handle MPI applications, gather data from hundreds of nodes, and accumulate it efficiently. *perun* can be used as a command-line tool or as a function decorator in Python scripts.\n\nCheck out the `docs \u003chttps://perun.readthedocs.io/en/latest/\u003e`_ or a working `example \u003chttps://github.com/Helmholtz-AI-Energy/perun/blob/main/examples/torch_mnist/README.md\u003e`_!\n\nKey Features\n------------\n\n- Measures energy consumption of Python scripts and binaries, supporting different hardware configurations\n- Capable of handling MPI applications, gathering data from hundreds of nodes efficiently\n- Monitor individual functions using decorators\n- Tracks energy usage of the application over multiple executions\n- Easy to benchmark applications and functions\n- Experimental!: Can monitor any non-distributed command line application\n\n-----------------------------------------------------------------\n\n\nQuick Start\n-----------\n\nInstallation\n^^^^^^^^^^^^\n\nFrom PyPI:\n\n.. code:: console\n\n    $ pip install perun\n\nExtra dependencies like *nvidia-smi*, *rocm-smi* and *mpi4py* can be installed using pip as well:\n\n.. code:: console\n\n    $ pip install perun[nvidia, rocm, mpi]\n\nFrom Github:\n\n.. code:: console\n\n    $ pip install git+https://github.com/Helmholtz-AI-Energy/perun\n\nCommand Line\n^^^^^^^^^^^^\n\nTo use *perun* as a command-line tool:\n\n.. code:: console\n\n    $ perun monitor path/to/your/script.py [args]\n\n*perun* will output two files, an HDF5 style containing all the raw data that was gathered, and a text file with a summary of the results.\n\n.. code:: text\n\n    PERUN REPORT\n\n    App name: finetune_qa_accelerate\n    First run: 2023-08-15T18:56:11.202060\n    Last run: 2023-08-17T13:29:29.969779\n\n    RUN ID: 2023-08-17T13:29:29.969779\n\n    +-----------+------------------------+-----------+-------------+--------------+-------------+-------------+-------------+---------------+-------------+\n    | Round #   | Host                   | RUNTIME   | ENERGY      | CPU_POWER    | CPU_UTIL    | GPU_POWER   | GPU_MEM     | DRAM_POWER    | MEM_UTIL    |\n    +===========+========================+===========+=============+==============+=============+=============+=============+===============+=============+\n    | 0         | hkn0432.localdomain    | 995.967 s | 960.506 kJ  | 231.819 W    | 3.240 %     | 702.327 W   | 55.258 GB   | 29.315 W      | 0.062 %     |\n    | 0         | hkn0436.localdomain    | 994.847 s | 960.469 kJ  | 235.162 W    | 3.239 %     | 701.588 W   | 56.934 GB   | 27.830 W      | 0.061 %     |\n    | 0         | All                    | 995.967 s | 1.921 MJ    | 466.981 W    | 3.240 %     | 1.404 kW    | 112.192 GB  | 57.145 W      | 0.061 %     |\n\n    The application has been run 7 times. In total, it has used 3.128 kWh, released a total of 1.307 kgCO2e into the atmosphere, and you paid 1.02 € in electricity for it.\n\n\nBinary support (experimental)\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n*perun* is capable of monitoring simple applications written in other languages:\n\n.. code:: console\n\n    $ perun monitor --binary path/to/your/executable [args]\n\nFunction Monitoring\n^^^^^^^^^^^^^^^^^^^\n\nUsing a function decorator\n\n.. code:: python\n\n    import time\n    from perun import monitor\n\n    @monitor()\n    def main(n: int):\n        time.sleep(n)\n\nAfter running with ``perun monitor``, the report will contain:\n\n.. code:: text\n\n    Monitored Functions\n\n    +-----------+----------------------------+---------------------+------------------+--------------------+------------------+-----------------------+\n    | Round #   | Function                   | Avg Calls / Rank    | Avg Runtime      | Avg Power          | Avg CPU Util     | Avg GPU Mem Util      |\n    +===========+============================+=====================+==================+====================+==================+=======================+\n    | 0         | main                       | 1                   | 993.323±0.587 s  | 964.732±0.499 W    | 3.244±0.003 %    | 35.091±0.526 %        |\n    | 0         | prepare_train_features     | 88                  | 0.383±0.048 s    | 262.305±19.251 W   | 4.541±0.320 %    | 3.937±0.013 %         |\n    | 0         | prepare_validation_features| 11                  | 0.372±0.079 s    | 272.161±19.404 W   | 4.524±0.225 %    | 4.490±0.907 %         |\n\n\nMPI\n^^^\n\n*perun* is compatible with MPI applications using ``mpi4py``:\n\n.. code:: console\n\n    $ mpirun -n 8 perun monitor path/to/your/script.py\n\nDocs\n----\n\nSee the `documentation \u003chttps://perun.readthedocs.io/en/latest/\u003e`_ or `examples \u003chttps://github.com/Helmholtz-AI-Energy/perun/tree/main/examples\u003e`_ for more details.\n\nCiting perun\n------------\n\nIf you found *perun* useful, please cite the conference paper:\n\n::\n\n    Gutiérrez Hermosillo Muriedas, J.P., Flügel, K., Debus, C., Obermaier, H., Streit, A., Götz, M.:\n    perun: Benchmarking Energy Consumption of High-Performance Computing Applications.\n    In: Cano, J., Dikaiakos, M.D., Papadopoulos, G.A., Pericàs, M., and Sakellariou, R. (eds.)\n    Euro-Par 2023: Parallel Processing. pp. 17–31. 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