mlhub 3.11.2

Machine learning model repository manager

The Machine Learning Hub

A Command Line framework and platform for presenting and utilising Machine Learning, Artificial Intelligence, and Data Science capabilities.

Introduction

The machine learning hub is a free and open source project hosted on github aimed at easily sharing machine learning, artificial intelligence, and data science models and technologies. The source code for such models will be found on github, gitlab, or bitbucket. The models are installed and managed using the ml command from the mlhub package designed to install the model and run a demonstration within 5 minutes, as well as providing a suite of useful command line tools based on the pre-built models. Each model has been tested on Ubuntu (GNU/Linux).

A number of demonstration models have been packaged and are listed in the repository index on mlhub.ai where the models themselves can be browsed.

In this blog post we first review how to get started with MLHub and then illustrate some of its functionality. See the growing documentation at the GNU/Linux Survival Guide.

Quick Start

The command line interface can be installed using PyPi:

$ pip3 install mlhub

Once installed you will be able to run any curated or other model. The quick start version is below. Note that you type the command ml ... and that everything from the # to the end of the line is ignored (it's a comment):

$ ml install <model> # Install the named model.
$ ml demo    <model> # Run the demonstration of the model.
$ ml gui     <model> # Graphical display to utilise the mode.

The following commands are available and below is a brief description of each command:

$ ml                # Show a usage message.
$ ml available      # List of pre-buld models on the MLHub.
$ ml installed      # List of pre-built models installed locally
$ ml install   <model> # Install the model named 'rain'.
$ ml configure <model> # Install required dependencies.
$ ml readme    <model> # View background information about the model.
$ ml commands  <model> # List of commands supported by the model.
$ ml demo      <model> # Run the demonstration of the model
$ ml gui       <model> # Graphical display of pre-built model.
$ ml score     <model> # Run model on your own data.

Different model packages will have different dependencies and these will be installed by the configure command.

Quick Start: Azure DSVM

A particularly attractive and simple way to get started with exploring the mlhub functionality is to fire up a Ubuntu Data Science Virtual Machine (DSVM) on Azure for as little as USD10 per month for quite a small server or USD90 for a reasonable one. You can get free credit (USD200) from Microsoft to trial the DSVM.

Using this virtual machine will save a lot of time compared with setting up your own machine with the required dependencies, which of course you can do if you wish as all the dependencies are open source.

To set up the virtual machine, with an Azure subscription log in to the portal and add a new Data Science Virtual Machine for Linux (Ubuntu). You need to provide a name (for the virtual machine), a user name and a password, and then create a new resource group and give it a name, and finally choose a location. Go with all the defaults for everything else, except choose a size to suit the budget (B1s is cheap though a D2s is a better interactive experience). Note that you are only charged whilst the machine is fired up so USD90 per month is no where near what you will spend if you only fire up the server when you need.

Once the DSVM is set up go to its Overview page and click on DNS name Configure and provide a name by which to refer to the server publicly (e.g., myml.westus2.cloudapp.azure.com).

We now have a server ready to showcase the pre-built Machine Learning models. There are several options to connect to the server but a recommended one is to use X2Go which supports Linux, Windows, and Mac. Install it and point it to your server (e.g., myml.westus2.cloudapp.azure.com) in the setup.

Connect to the DSVM. Close the Firefox window that pops up. Click on the terminal icon down the bottom, and you are ready to go:

$ pip install mlhub
$ ml
$ ml available

etc.

Pre-Built Model Archives

A model is a zip file archived as .mlm files and hosted in a repository. The public repository is mlhub.ai. The ml command can install the pre-built model locally, ready to run a demo, to print and display the model, and to score new data using the model. Some models provide ability to retrain the model with user provided data.

Contributing Models to ML Hub

Anyone is welcome to contribute a pre-built model package to ML Hub. Please submit a pull request through github.

Installing Pip3

On Ubuntu this is as simple as:

$ sudo apt install python3-pip

Alternative pip Install

Depending on your setup of pip, you may need to use:

$ pip3 install mlhub

The executable may be placed into ~/.local/bin which will need to be on your path. Edit your shell startup which is either .profile or .bashrc, etc:

PATH="$HOME/.local/bin:$PATH"

Alternative Install

A tar.gz containing the mlhub package and the command line interface is available as mlhub_3.7.0.tar.gz within the distribution folder of the MLHub.

To install from the tar.gz file:

$ wget https://mlhub.ai/dist/mlhub_3.7.0.tar.gz
$ pip install mlhub_3.7.0.tar.gz
$ ml

Or extract the above downloaded .tar.gz and install:

$ wget https://mlhub.ai/dist/mlhub_3.7.0.tar.gz
$ tar xvf mlhub_3.7.0.tar.gz
$ cd mlhub
$ python3 setup.py install --user

Under Development

An interactive MLHub session that is initiated through the demo command is quite similar to a Jupyter Notebook presentation running on top of a Jupyter interpreter. Notebooks can be automatically transformed into a MLHub package so that the notebook becomes the source for the interactive demo.py or demo.R script required by MLHub. In this way users have the choice to either run the Notebook interactively within Jupyter or from the command line as an interactive script.

Contributions

The open source mlhub command line tool (ml) and sample models are being hosted on github and contributions to both the command line tool and contributions of new open source pre-built machine learning models are most welcome. Feel free to submit pull requests.

Metrics

MLHub PyPI download statistics: https://pepy.tech/project/mlhub

MLHub Dev PyPI download statistics: https://pepy.tech/project/mlhubdev