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    "info": {
        "author": "Graham Williams",
        "author_email": "mlhub@togaware.com",
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        "classifiers": [
            "Development Status :: 5 - Production/Stable",
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            "License :: OSI Approved :: MIT License",
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        "description": "The Machine Learning Hub\n========================\n\n**UNDER DEVELOPMENT**\n\n[![MLHub Status](http://badge.kloud51.com/pypi/s/mlhub.svg)](https://mlhub.ai)\n[![PyPi version](https://img.shields.io/pypi/v/mlhub.svg)](https://mlhub.ai)\n[![PyPi downloads](https://pypip.in/d/mlhub/badge.png)](https://mlhub.ai)\n\nA *Command Line* platform for presenting Machine Learning, Artificial\nIntelligence, and Data Science capabilities.\n\nIntroduction\n------------\n\nThe [machine learning hub](https://mlhub.ai) is an open source project\nhosted on [github](https://github.com/mlhubber/mlhub) aimed at easily\nsharing [pre-built machine learning\nmodels](https://github.com/mlhubber/mlmodels). The models are accessed\nand managed using the *ml* command from the *mlhub* package designed\nto install the model and run a demonstration within 5 minutes. Each\nmodel has been tested on Ubuntu (GNU/Linux).\n\nVisit the [repository index](https://mlhub.ai/Packages.html) on\n[mlhub.ai](https://mlhub.ai/) where the models themselves can be browsed.\n\nQuick Start\n-----------\n\nThe command line interface can be installed using\n[PyPi](https://pypi.org/project/mlhub/):\n\n    $ pip3 install mlhub\n\nOnce installed you will be able to run the sample \\'rain\\' model\nassuming that you have the free and open source [R statistical software\npackage](https://cran.r-project.org) installed. The TL;DR version is\nbelow. Note that you type the command `ml ...` and that everything from\nthe `#` to the end of the line is ignored (it\\'s a comment):\n\n    $ ml install   rain # Install the model named 'rain'.\n    $ ml demo      rain # Run the demonstration of the model\n    $ ml display   rain # Graphical display of pre-built model.\n\nThe following commands are available and below is a brief description of\neach command:\n\n    $ ml                # Show a usage message.\n    $ ml available      # List of pre-buld models on the MLHub.\n    $ ml installed      # List of pre-built models installed locally\n    $ ml install   rain # Install the model named 'rain'.\n    $ ml readme    rain # View background information about the model.\n    $ ml configure rain # Install required dependencies.\n    $ ml commands  rain # List of commands supported by the model.\n    $ ml demo      rain # Run the demonstration of the model\n    $ ml print     rain # Textual summary of the model.\n    $ ml display   rain # Graphical display of pre-built model.\n    $ ml score     rain # Run model on your own data.\n\nDifferent model packages will have different dependencies and these will\nbe installed by the *configure* command.\n\nQuick Start: Azure DSVM\n-----------------------\n\nA particularly attractive and simple way to get started with exploring\nthe mlhub functionality is to fire up a [Ubuntu Data Science Virtual\nMachine](https://aka.ms/dsvm) (DSVM) on Azure for as little as USD10 per\nmonth for quite a small server or USD90 for a reasonable one. You can\nget free credit (USD200) from Microsoft to [trial the\nDSVM](https://aka.ms/free).\n\nUsing this virtual machine will save a lot of time compared with setting\nup your own machine with the required dependencies, which of course you\ncan do if you wish as all the dependencies are open source.\n\nTo set up the virtual machine, with an Azure subscription log in to the\n[portal](https://portal.azure.com/) and add a new Data Science Virtual\nMachine for Linux (Ubuntu). You need to provide a name (for the virtual\nmachine), a user name and a password, and then create a new resource\ngroup and give it a name, and finally choose a location. Go with all the\ndefaults for everything else, except choose a size to suit the budget\n(B1s is cheap though a D2s is a better interactive experience). Note\nthat you are only charged whilst the machine is fired up so USD90 per\nmonth is no where near what you will spend if you only fire up the\nserver when you need.\n\nOnce the DSVM is set up go to its Overview page and click on DNS name\nConfigure and provide a name by which to refer to the server publicly\n(e.g., myml.westus2.cloudapp.azure.com).\n\nWe now have a server ready to showcase the pre-built Machine Learning\nmodels. There are several options to connect to the server but a\nrecommended one is to use [X2Go](https://x2go.org/) which supports\nLinux, Windows, and Mac. Install it and point it to your server (e.g.,\nmyml.westus2.cloudapp.azure.com) in the setup.\n\nConnect to the DSVM. Close the Firefox window that pops up. Click on the\nterminal icon down the bottom, and you are ready to go:\n\n    $ pip install mlhub\n    $ ml\n    $ ml available\n\netc.\n\nPre-Built Model Archives\n------------------------\n\nA model is a zip file archived as .mlm files and hosted in a repository.\nThe public repository is [mlhub.ai](https://mlhub.ai/). The *ml* command\ncan install the pre-built model locally, ready to run a demo, to print\nand display the model, and to score new data using the model. Some\nmodels provide ability to retrain the model with user provided data.\n\nContributing Models to ML Hub\n-----------------------------\n\nAnyone is welcome to contribute a pre-built model package to ML Hub.\nPlease submit a pull request through\n[github](https://github.com/mlhubber).\n\nInstalling Pip3\n---------------\n\nOn Ubuntu this is as simple as:\n\n    $ sudo apt install python3-pip\n\nAlternative pip Install\n-----------------------\n\nDepending on your setup of pip, you may need to use:\n\n    $ pip3 install mlhub\n\nThe executable may be placed into `~/.local/bin` which will need to be\non your path. Edit your shell startup which is either `.profile` or\n`.bashrc`, etc:\n\n    PATH=\"$HOME/.local/bin:$PATH\"\n\nAlternative Install\n-------------------\n\nA tar.gz containing the mlhub package and the command line interface is\navailable as\n[mlhub_3.5.5.tar.gz](https://mlhub.ai/dist/mlhub_3.5.5.tar.gz) within\nthe [distribution folder](https://mlhub.ai/dist/) of the MLHub.\n\nTo install from the tar.gz file:\n\n    $ wget https://mlhub.ai/dist/mlhub_3.5.5.tar.gz\n    $ pip install mlhub_3.5.5.tar.gz\n    $ ml\n\nOr extract the above downloaded .tar.gz and install:\n\n    $ wget https://mlhub.ai/dist/mlhub_3.5.5.tar.gz\n    $ tar xvf mlhub_3.5.5.tar.gz\n    $ cd mlhub\n    $ python3 setup.py install --user\n\nUnder Development\n-----------------\n\nAn interactive MLHub session that is initiated through the demo\ncommand is quite similar to a Jupyter Notebook presentation running on\ntop of a Jupyter interpreter. Notebooks can be automatically\ntransformed into a MLHub package so that the notebook becomes the\nsource for the interactive demo.py or demo.R script required by\nMLHub. In this way users have the choice to either run the Notebook\ninteractively within Jupyter or from the command line as an\ninteractive script.\n\nContributions\n-------------\n\nThe open source mlhub command line tool (ml) and sample models are being\nhosted on [github](https://github.com/mlhubber) and contributions to\nboth the command line tool and contributions of new open source\npre-built machine learning models are most welcome. Feel free to submit\npull requests.\n\nMetrics\n-------\n\nMLHub PyPI download statistics: https://pepy.tech/project/mlhub\n\n[![Downloads](https://pepy.tech/badge/mlhub)](https://pepy.tech/project/mlhub)\n[![Downloads](https://pepy.tech/badge/mlhub/month)](https://pepy.tech/project/mlhub)\n[![Downloads](https://pepy.tech/badge/mlhub/week)](https://pepy.tech/project/mlhub)\n\nMLHub Dev PyPI download statistics: https://pepy.tech/project/mlhubdev\n\n[![Downloads](https://pepy.tech/badge/mlhubdev)](https://pepy.tech/project/mlhubdev)\n[![Downloads](https://pepy.tech/badge/mlhubdev/month)](https://pepy.tech/project/mlhubdev)\n[![Downloads](https://pepy.tech/badge/mlhubdev/week)](https://pepy.tech/project/mlhubdev)",
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