Bell Eapen

Physician | HealthIT Developer | Digital Health Consultant

Deploy a fastai image classifier using OpenFaaS for serverless on DigitalOcean in 5 easy steps!

Fastai is a python library that simplifies training neural nets using modern best practices. See the fastai website and view the free online course to get started. Fastai lets you develop and improve various NN models with little effort. Some of the deployment strategies are mentioned in their course, but most are not production-ready.

OpenFaaS® (Functions as a Service) is a framework for building Serverless functions easily with Docker that can be deployed on multiple infrastructures including Docker swarm and Kubernetes with little boilerplate code. Serverless is a cloud-computing model in which the cloud provider runs the server, and dynamically manages the allocation of machine resources and can scale to zero if a service is not being used. It is interesting to note that OpenFaaS has the same requirements as the new Google Cloud Run and is interoperable. Read more about OpenFaaS (and install the CLI) from their website.

DigitalOcean: I host all my websites on DigitalOcean (DO) which offers good (in my opinion) cloud services at a low cost. They have data centres in Canada and India. DO supports Kubernetes and Docker Swarm, but they offer a One-Click install of OpenFaaS for as little as $5 per month (You can remove the droplet after the experiment if you like, and you will only be charged for the time you use it.) If you are new to DO, please sign up and setup OpenFaaS as shown here:

In fastai class, Jeremy creates a dog breed classifier.

As STEP 1, export the model to .pkl as below

This creates the export.pkl file that we will be using later. To deploy we need a base container to run the prediction workflow. I have created one with Python3 along with fastai core and vision dependencies (to keep the size small). It is available here: https://hub.docker.com/r/beapen/fastai-vision But you don’t have to directly use this container. My OpenFaaS template will make this easy for you.

STEP 2: Using the OpenFaaS CLI (How to Install) pull my template as below:

STEP 3: Copy export.pkl to the model folder

STEP 4: Add http://digitaloceanIP:8080 to dog-classifier.yml

and finally in STEP 5:

That’s it! Your predictor is up and running! Access it at http://digitaloceanIP:8080/function/dog-classifier

The template has a builtin image uploader interface! If you get stuck at any stage, give me a shout below. More to follow on using OpenFaaS for deploying machine learning workflows!

Running Tomcat as www-data

For last couple of days I have been working on a web based application for skin color measurement as I blogged here. The core of the application is implemented in php as it is much easier to handle. (May be I am more comfortable with it). But the GD image library for php has limited functionality. So I had to rely on Java for border detection and segmentation. So I implemented part of the functionality as a servlet running on tomcat6.

Today's latte, Apache Tomcat.
Today’s latte, Apache Tomcat. (Photo credit: yukop)

Both php and servlet had to read and write same files. But standard configuration does not allow this as php creates files as www-data (apache user) and tomcat creates files as tomcat6 user. I searched the internet for solutions. One suggestion was to create an appropriate umask for both which I could never completely understand. Finally I realised that the easiest solution is to make tomcat run as user www-data so that all created files will have same ownership.

I found this webpage with detailed instructions on how to do this. I would like to add two more steps:
1. Edit /etc/init.d/tomcat6 also to add the user ID and group ID
2. chown var/cache/tomcat6 directory also.

Nina Jablonski - The Evolution of Human Skin Color
Nina Jablonski – The Evolution of Human Skin Color (Photo credit: wagnerfreeinstitute)

BTW the application is hosted on my laptop. If you want to check it out click on the banner below if you see it. You will see it only when my laptop is on.

Negative N to Unknown U

The identification of disease specific genes is pivotal in clinical informatics. This paper describes an improved algorithm for machine learning in which the negative N is classified more appropriately as Unknown U.

English: Weka Data Mining Open Software in Java
English: Weka Data Mining Open Software in Java (Photo credit: Wikipedia)

Peng Yang, Xiao-Li Li, Jian-Ping Mei, Chee-Keong Kwoh, and See-Kiong Ng. Positive-Unlabeled Learning for Disease Gene Identification
Bioinformatics first published online August 24, 2012 doi:10.1093/bioinformatics/bts504

SVMs are an important tool in bioinformaticians armamentarium. Weka is a collection of machine learning algorithms for data mining tasks.