Showing 4 open source projects for "pc benchmark test"

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  • 1
    pcsc-tools

    pcsc-tools

    Some tools to be used with smart cards and PC/SC

    pcsc-tools is a suite of tools used to test smart cards and PC/SC drivers. It provides utilities like pcsc_scan to monitor smart card readers and scriptor to send commands to smart cards, aiding in development and troubleshooting.
    Downloads: 27 This Week
    Last Update:
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  • 2
    GoNB

    GoNB

    GoNB, a Go Notebook Kernel for Jupyter

    Go is a compiled language, but with very fast compilation, that allows one to use it in a REPL (Read-Eval-Print-Loop) fashion, by inserting a "Compile" step in the middle of the loop -- so it's a Read-Compile-Run-Print-Loop — while still feeling very interactive. GoNB leverages that compilation speed to implement a full-featured (at least it's getting there) Jupyter notebook kernel. As a side benefit it works with packages that use CGO — although it won't parse C code in the cells, so it...
    Downloads: 0 This Week
    Last Update:
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  • 3
    MMDeploy

    MMDeploy

    OpenMMLab Model Deployment Framework

    MMDeploy is an open-source deep learning model deployment toolset. It is a part of the OpenMMLab project. Models can be exported and run in several backends, and more will be compatible. All kinds of modules in the SDK can be extended, such as Transform for image processing, Net for Neural Network inference, Module for postprocessing and so on. Install and build your target backend. ONNX Runtime is a cross-platform inference and training accelerator compatible with many popular ML/DNN...
    Downloads: 0 This Week
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  • 4
    benchm-ml

    benchm-ml

    A benchmark of commonly used open source implementations

    This repository is designed to provide a minimal benchmark framework comparing commonly used machine learning libraries in terms of scalability, speed, and classification accuracy. The focus is on binary classification tasks without missing data, where inputs can be numeric or categorical (after one-hot encoding). It targets large scale settings by varying the number of observations (n) up to millions and the number of features (after expansion) to about a thousand, to stress test different implementations. ...
    Downloads: 0 This Week
    Last Update:
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