An open notebook to my experiments with Apple’s CoreML and other frameworks, as well as Swift, Xcode and more Apple goodies.
Take a look and enjoy.
Hello again! In preparation to training a model to be converted by Core ML to be used in an application, I would like to make sure we have a suitable environment to work on. One of the first things that came to my attention looking at the
coremltools module is the fact that it only supports Python 2! Yes, you read correctly, you will have to make sure you use Python 2.7 if you want to make this work. As you probably know, Python 2 will be retired in 2020, so I hope that Apple is considering in their development cycles. In the meantime you can see the countdown to Python 2's retirement here, and thanks Python 2 for the many years of service...
Anyway, if you are a Python 2 user, then you are good to go. If on the other hand you have moved with the times you may need to make appropriate installations. I am using Anaconda (you may use your favourite distro) and I will be creating a conda environment (I'm calling it
coreml) with Python 2.7 and some of the libraries I will be using:
> conda create --name coreml python=2.7 ipython jupyter scikit-learn > source activate coreml (coreml) > pip install coremltools
I am sure there may be other modules that will be needed, and I will make appropriate installations (and additions to this post) as that becomes clearer.
You can get a look at Apple's coremltools github repo here.
ADDITIONS: As I mentioned, there may have been other modules that needed installing in the new environment here is a list:
In a previous post I mentioned that I will be sharing some notes about my journey with doing data science and machine learning by Apple technology. This is the firsts of those posts and here I will go about what Core ML is...
Core ML is a computer framework. So what is a framework? Well, in computer terms is a software abstraction that enables generic functionality to be modified as required by the user to transform it into software for specific purposes to enable the development of a system or even a humble project.
So Core ML is an Apple provided framework to speed apps that use trained machine learning models. Notice that word in bold - trained - is part of the description of the framework. This means that the model has to be developed externally with appropriate training data for the specific project in mind. For instance if you are interested in building a classifier that distinguishes cats from cars, then you need to train the model with lots of cat and car images.
As it stands Core ML supports a variety of machine learning models, from generalised linear models (GLMs for short) to neural nets. Furthermore it helps with the tests of adding the trained machine learning model to your application by automatically creating a custom programmatic interface that supplies an APU to your model. All this within the comfort of Xcode!
There is an important point to remember. The model has to be developed externally from Core ML, in other words you may want to use your favourite machine learning framework (that word again), computer language and environment to cover the different aspects of the data science workflow. You can read more in that in Chapter 3 of my "Data Science and Analytics with Python" book. So whether you use Scikit-learnm, Keras or Caffe, the model you develop has to be trained (tested and evaluated) beforehand. Once you are ready, then Core ML will support you in bringing it to the masses via your app.
As mentioned in the Core ML documentation:
Core ML is optimized for on-device performance, which minimizes memory footprint and power consumption. Running strictly on the device ensures the privacy of user data and guarantees that your app remains functional and responsive when a network connection is unavailable.
OK, so in the next few posts we will be using Python and
coremltools, to generate a so-called
.mlmodel file that Xcode can use and deploy. Stay tuned!
We all know how cool machine learning, predictive analytics and data science concepts and problems are. There are a number of really interesting technologies and frameworks to use and choose from. I have been a Python and R user for some time now and they seem to be pretty good for a lot of the things I have to do on a day-to-day basis.
As many of you know, I am also a mac user and have been for quite a lot time. I remember using early versions of Mathematica on PowerMacs back at Uni... I digress..
Apple has also been moving into the machine learning arena and has made available a few interesting goodies that help people like me make the most of the models we develop.
I am starting a series of posts that I hope can be seen as an "open notebook" of my experimentation and learning with Apple technology. One that comes to mind is CoreML, a new framework that makes running various machine learning and statistical models on macOS and iOS natively supported. The idea is that the framework helps data scientists and developers bridge the gap between them by integrating trained models into our apps. Sounds cool, don't you think? Ready... Let's go!”Read