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    jupyter-lab-neural-compressor
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    1.0.8 • Public • Published

    Intel® Neural Compressor as JupyterLab Extension

    A JupyterLab Extension library supporting Neural Coder, a novel feature powered by Intel® Neural Compressor providing automatic quantization to further simplify computing performance optimizations of Deep Learning models.

    Installation

    By Extension Manager in JupyterLab (Recommended)

    Search for jupyter-lab-neural-compressor in the Extension Manager in JupyterLab.

    By Linux Terminal

    npm i jupyter-lab-neural-compressor
    jupyter labextension install jupyter-lab-neural-compressor

    Getting Started!

    As shown in the drop-down list, the supported features include "INT8 (Static Quantization)", "INT8 (Dynamic Quantization)", "BF16", and "Auto Enable & Benchmark". Each of the first three options enables a specific quantization feature into your Deep Learning scripts. The last option automatically enables all quantization features on a Deep Learning script and automatically evaluates the best performance on the model. It is a code-free solution that can help users enable quantization algorithms on a Deep Learning model with no manual coding needed.

    Architecture

    Auto-enable a feature

    Click the run button on the left side of the drop-down list to start. After finishing, you can see the code changes for the specific optimization enabling as shown in the figure below:

    Architecture

    Or let us help you auto-select the best feature

    The last option automatically enables each quantization feature on your Deep Learning script and automatically evaluates for the best performance among all features on your Deep Learning model. Since it will automatically run the Python script for benchmark, it requires you to enter additional parameters needed to run your Python script. If there is no additional parameter needed, you can just leave it blank:

    Architecture

    In the new cell box appeared below your Code cell boxes, you can see the execution progress, and at the end you can see which one turns out to be the best optimization and how much performance gain can it bring to your Deep Learning model:

    Architecture

    When it is finished, you can also see that the code changes for the best optimization are automatically enabled into your script:

    Architecture

    Pre-requisites

    apt-get update && apt-get install bc numactl
    conda install mkl mkl-include jemalloc
    pip3 install neural-compressor opencv-python-headless

    Install

    npm i jupyter-lab-neural-compressor

    DownloadsWeekly Downloads

    27

    Version

    1.0.8

    License

    BSD-3-Clause

    Unpacked Size

    87.5 kB

    Total Files

    17

    Last publish

    Collaborators

    • liangy2