I spent a lot of the past week hacking with containers, AWS, and database code, making things work.
After finally getting permission from AWS to spin EC2 instances with GPUs, I've been able to test the Docker container I made for the classifier and its dependencies, including the Flask server for URL classification. To do this, I created a Dockerfile that starts with an Ubuntu 16 image (with CUDA 10) that installs all the necessary dependencies for running the classifier. In AWS, I spun up an EC2 instance with a GPU where I ran an image of the container I made. The communication between that EC2 and the container works fine, as does the communication between the Flask server and the classifier, but I'm having an issue loading and using the model on the EC2's GPU.
I also refactored the Flask application code itself, modifying the format for HTTP calls and improving how the JSON objects were structured, along with cleaning up the classifier code base substantially. Another feature I added to the system is a super simple DynamoDB database to keep track of all of the URLs each container receives, their predicted label, and whether a human has labeled them. I used Python to write and test basic scripts to read, query, add to, and update the database.
Tasks for next week include successfully loading and running the classifier on AWS and forging ahead with developing the dynamic web server.
My priority this week has been implementing the system architecture for my EDanalysis senior project/research on Amazon Web Services (AWS). First, I'll briefly introduce the project then dive into what I've been up to this week with AWS.
For this project, we trained an instance of the ResNet convolutional neural network to recognize pro-eating disorder images, with the aim of developing software tools (called EDanalysis) to improve eating disorder treatment and patient health outcomes. For more information, check out this video I made describing the project's vision, featuring a sneak peek of some of the software we're building!
This week, we had a 70% Project Demo for GW's CS Senior Design class (see more about the Senior Design aspects of my project here!). My 70% demo goals involved setting up my project on AWS, which is a first for me. My rationale for choosing AWS as a cloud service provider was simple: our project's goal is to publicly deploy the EDanalysis tools; hence, whatever system we make needs room to grow. To my knowledge, AWS offers unparalleled design flexibility--especially for machine learning systems--at web scale (wow, buzzword alert). Disclaimer: my current AWS system is optimized for cost-efficiency (for Senior design purposes ;-)), but I plan to someday use an AWS ECS instance and other beefier architectures/features.
The EDanalysis system has 3 main parts: the R&D server, the website architecture/ backend, and the frontend components, which I refer to as parts 1, 2, and 3 below.
EDanalysis AWS System
This week, I completed the following:
part 1: communication from the R&D server to the S3 bucket
part 2: communication from the R&D server to the S3 bucket triggers a lambda function that forwards the passed data to the EC2 instance
part 2: a modification of the classifier testing script to download a single image from an input URL, run it through the classifier, and return its classification
part 2: a proof-of-concept script for the pytorch EC2 instance that creates a Flask server that adheres to the REST API, communicates with the classifier and passes it an image url in JSON format, runs the classifier on that url, and passes back its classification to the server
