“Python wrestling with the docker-compose squid”, by the author. The Python package testcontainers solves two problems common to Python-apps. We develop Python-based applications and deploy them using the AWS ECS-CLI. So we directly deploy a docker-compose configuration into AWS ECS. That configuration wants to be tested locally as well, and I haven’t found a proper solution for that other than the package testcontainers. If you don’t work with docker-compose but k8n or some other docker orchestrator, you for sure encounter the second use case. It’s to spin up a local container with Postgres, NGINX or Redis to run a small integration […]

Network Photo by Alina Grubnyak on Unsplash PyTorch BigGraph is a tool to create and handle large graph embeddings for machine learning. Currently there are two approaches in graph-based neural networks: Directly use the graph structure and feed it to a neural network. The graph structure is then preserved at every layer. graphCNNs use that approach, see for instance my post or this paper on that. But most graphs are too large for that. So it’s also reasonable to create a large embedding of the graph. And then use it as features in a traditional neural network. PyTorch BigGraph handles the […]

GraphCNNs recently got interesting with some easy to use keras implementations. The basic idea of a graph based neural network is that not all data comes in traditional table form. Instead some data comes in well, graph form. Other relevant forms are spherical data or any other type of manifold considered in geometric deep learning. So what does graph data look like if not like a table? Here’s an example: Let’s put some meaning into those variables, and no I’m not gonna use a “citation network” example which would be the default for graph based neural networks. While easy to […]