:orphan:

|rss_image|  **runtime - 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:: runtime


.. _ap-cat-runtime-0:

runtime - 1/1
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.. blogpostagg::
    :title: ONNX from C#
    :date: 2021-07-09
    :keywords: ONNX,C#
    :categories: runtime
    :rawfile: 2021/2021-07-09_csharp.rst

    This example shows how to compute the predictions of a model
    using C#.
    

    ...




.. blogpostagg::
    :title: Parallelization of Random Forest predictions
    :date: 2020-11-27
    :keywords: scikit-learn,parallelization,Random Forest
    :categories: runtime
    :rawfile: 2020/2020-11-27_parallelisation.rst

    I've been struggling to understand why the first implementation
    of TreeEnsemble could not get as fast as *scikit-learn* implementation
    for a RandomForest when the number of observations was 100.000 or above,
    100 trees and a depth >= 10. The only difference was that the computation
    was parallelized by trees and not by observations. These
    observations are benchmarked in
    :ref:`l-example-tree-ensemble-reg-bench`
    (:ref:`l-example-tree-ensemble-cls-bench-multi` for the
    multiclass version).
    

    ...




.. blogpostagg::
    :title: x / y != x * (1 / y)
    :date: 2020-06-09
    :keywords: scikit-learn,float inverse,compilation,StandardScaler
    :categories: runtime
    :rawfile: 2020/2020-06-09_float_inverse.rst

    I was recently investigating issue
    `onnxruntime/4130 <https://github.com/microsoft/onnxruntime/issues/4130>`_
    in notebook :ref:`onnxdiscrepenciesrst`.
    While looking into a way to solve it, I finally discovered
    that this is not an easy problem.
    

    ...





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|rss_image|  **runtime - 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>`