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Radiology and DeepLearningDetecting pneumonia opacities from chest X-Ray images using deep learning. One day back in August, I was catching up with my best friend from high school who is now a radiology resident. One thing led to another, and we started talking about our interests in artificial intelligence and machine learning and its possible applications in radiology. A couple of months after our talk, I stumbled upon a Kaggle challenge hosted by the Radiological Society of North American (RSNA).

TL;DR—As far as I can tell, there’s currently no way of providing documentation for specific containers that we could fairly call canonical, “best practice,” or even all that widely used. This blog post suggests some currently available (but sadly not-great) workarounds but also points to what I think could be a fundamentally better path. Source: medium.com

By now, all of you have probably followed deep learning research for quite a while. In 1998, LeCun et al. proposed the very simple MNIST data set of handwritten digits and showed with their LeNet-5 that we can achieve a high validation accuracy on it. The data sets subsequently proposed became more complex (e.g., ImageNet or Atari games), hence the models performing well on them became more sophisticated, i.e. complex, as well.

Kubernetes is a great orchestator for containers. But it does not manage network for Pod-to-Pod communication. This is the mission of Container Network Interfaces (CNI) plugins which are a standardized way to achieve network abstraction for container clustering tools (Kubernetes, Mesos, OpenShift, etc.) But here is the point: what are the differences between those CNIs? Which one has the best performance? Which one is the leanest? This article is showing the results of a benchmark I’ve conducted on 10Gbit/s network.

MicroProfile is a fast-growing open community. It is a warm and friendly platform for developers to come together to evolve programming model for cloud-native microservices. Since it was established in June 2016, it has released 6 overall releases and 16 individual specification releases in less than 2 years. Thispage shows which application servers support MicroProfile at which version. Open Libertyis seenas one of the leading implementations of MicroProfile and determined to implement MicroProfile’s latest releases rapidly.

As people continue to adopt CRI-O as a new container runtime for Kubernetes I am hearing questions from administrators who are confused whether they should use Crictl or Podman to diagnose and understand what is going on in a Kubernetes node. This is not one or the other — these tools are complementary, and this article attempts to explain the tools and examine when it is best to use each of these tools.

Doing Continuous Integration is a lot easier if you have the right tools. In our project at a german car manufacturer, we were tasked with developing new services and bringing them to the cloud. We had a centralized Jenkins instance, shared by all the teams in the department. It didn’t fit our needs and made it harder for us to deliver software quickly and reliably. Source: thoughtworks.com

We are excited to announce the release of HashiCorp Consul 1.2. This release supports a major new feature called Connect that automatically turns any existing Consul cluster into a service mesh solution. Connect enables secure service-to-service communication with automatic TLS encryption and identity-based authorization. Consul is currently deployed on millions of machines worldwide. After upgrading to Consul 1.2 and enabling Connect, any existing cluster will instantly become a service mesh solution that works on any platform: physical machines, cloud, containers, schedulers, and more.

Today we are thrilled to announce the release of SuperGloo, an open-source project to manage and orchestrate service meshes at scale. SuperGloo is an opinionated abstraction layer that will simplify the installation, management, and operation of your service mesh, whether you use (or plan to use) a single mesh or multiple mesh technologies, on-site, in the cloud, or on any topology that best fits you. A service mesh is an infrastructure layer that handles service-to-service communication, freeing applications from being aware of the complex communication network.

I am excited to announce the public-release of modules.tf, which allows a conversion from visual AWS diagrams using cloudcraft.co to infrastructure code using Terraform. Resources with arguments once specified during creation of diagrams are present in infrastructure code. Most popular EC2 types of resources are supported, more will be added in the future. VPC, Security group, Autoscaling Group, RDS, ELB, ALB, SNS, SQS, EC2 instances are implemented already. Many of relations between resources and their dependencies are respected in infrastructure code.