:orphan:

|rss_image|  **benchmark - 1/1** :ref:`Blog <ap-main-0>` :ref:`benchmark (4) <ap-cat-benchmark-0>` :ref:`onnx (8) <ap-cat-onnx-0>`

.. |rss_image| image:: feed-icon-16x16.png
    :target: ../_downloads/rss.xml
    :alt: RSS

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.. index:: benchmark


.. _ap-cat-benchmark-0:

benchmark - 1/1
+++++++++++++++

.. blogpostagg::
    :title: ONNX Backend Scoreboard
    :date: 2022-05-29
    :keywords: onnx,coverage,scoreboard
    :categories: benchmark
    :rawfile: 2022/2022-05-29_onnxcov.rst

    `ONNX Backend Scoreboard
    <http://onnx.ai/backend-scoreboard/index.html>`_
    shows how many operators a runtime supports.
    Page :ref:`l-backend-python-coverage` computes the
    same figure for the Python Runtime implemented
    in this package, more than 90%.




.. blogpostagg::
    :title: Custom C++ TopK
    :date: 2019-12-16
    :keywords: scikit-learn,topk,argpartition
    :categories: benchmark
    :rawfile: 2019/2019-12-16_topk.rst

    It started with the fact the python runtime for
    the AdaBoostRegressor was quite slow. I noticed three
    operators were quite slow even though their implementation
    was based on :epkg:`numpy`: *TopK*, *ArrayFeatureExtractor*
    and *GatherElement*. I made a custom implementation
    of the first two.
    

    ...




.. blogpostagg::
    :title: RandomForestClassifier - prediction for one observation
    :date: 2019-12-04
    :keywords: scikit-learn,py-spy,benchmark,one-off prediction
    :categories: benchmark
    :rawfile: 2019/2019-12-04_py-spy.rst

    I was meeting with Olivier Grisel this morning and
    we were wondering why :epkg:`scikit-learn` was slow
    to compute the prediction of a random forest for
    one observation compare to what :epkg:`onnxruntime` does,
    and more specically some optimized C++ code inspired
    from :epkg:`onnxruntime`.
    We used :epkg:`py-spy` and wrote the following script:
    

    ...




.. blogpostagg::
    :title: The bug which makes you waste time
    :date: 2019-10-04
    :keywords: asv,bug
    :categories: benchmark
    :rawfile: 2019/2019-10-04_asv.rst

    It is not a bug but it is something which makes
    you waste some significant time just to understand
    what's going on. :epkg:`asv` would refuse to detect
    the benchmark I was trying to set up just because
    filenames did contain dots. So, for :epkg:`asv`
    don't add a file ``name.option.py`` but use
    ``name_option.py``.
    A couple of benchmark for tries:
    :epkg:`bench1`, :epkg:`bench2`.





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|rss_image|  **benchmark - 1/1** :ref:`2021-08 (3) <ap-month-2021-08-0>` :ref:`2022-02 (1) <ap-month-2022-02-0>` :ref:`2022-05 (1) <ap-month-2022-05-0>` :ref:`2022-06 (1) <ap-month-2022-06-0>` :ref:`2022-11 (1) <ap-month-2022-11-0>`