Machine learning model repository manager
The machine learning hub is an [open source project](https://github.com/mlhubber/mlhub) aimed at easily sharing [pre-built machine learning models](https://github.com/mlhubber/mlmodels). The models are accessed and managed using the ml command from the mlhub package designed to install the model and run a demonstration within 5 minutes.
The command line interface can be installed using [PyPi](https://pypi.org/project/mlhub/):
$ pip3 install mlhub
Once installed you will be able to run the sample rain-tomorrow model assuming that you have the free and open source [R statistical software package](https://cran.r-project.org) installed. The TL;DR 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 rain-tomorrow # Install the model named rain-tomorrow. $ ml demo rain-tomorrow # Run the demonstration of the model $ ml display rain-tomorrow # Graphical display of pre-built model.
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 rain-tomorrow # Install the model named rain-tomorrow. $ ml readme rain-tomorrow # View background information about the model. $ ml commands rain-tomorrow # List of commands supported by the model. $ ml configure rain-tomorrow # Install required dependencies. $ ml demo rain-tomorrow # Run the demonstration of the model $ ml print rain-tomorrow # Textual summary of the model. $ ml display rain-tomorrow # Graphical display of pre-built model. $ ml score rain-tomorrow # 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](https://aka.ms/dsvm) (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](https://aka.ms/free).
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](https://portal.azure.com/) 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](http://x2go.org/) 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
Pre-Built Model Archives
A model is a zip file archived as .mlm files and hosted in a repository. The public repository is <https://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](https://github.com/mlhubber/mlmodels).
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:
A tar.gz containing the mlhub package and the command line interface is available as [mlhub_1.3.5.tar.gz](https://mlhub.ai/dist/mlhub_1.3.5.tar.gz) within the [distribution](https://mlhub.ai/dist/) folder of the MLHub.
To install from the tar.gz file:
$ wget https://mlhub.ai/dist/mlhub_1.3.5.tar.gz $ pip install mlhub_1.3.5.tar.gz $ ml
Or extract the above downloaded .tar.gz and install:
$ wget https://mlhub.ai/dist/mlhub_1.3.5.tar.gz $ tar xvf mlhub_1.3.5.tar.gz $ cd mlhub $ python3 setup.py install –user
The open source mlhub command line tool (ml) and sample models are being hosted on [GitHub](https://github.com/mlhubber) 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.