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Almost two years ago, Tinder decided to move its platform to Kubernetes. Kubernetes afforded us an opportunity to drive Tinder Engineering toward containerization and low-touch operation through immutable deployment. Application build, deployment, and infrastructure would be defined as code. We were also looking to address challenges of scale and stability. When scaling became critical, we often suffered through several minutes of waiting for new EC2 instances to come online. The idea of containers scheduling and serving traffic within seconds as opposed to minutes was appealing to us.

One afternoon in August 2010, in a conference hall perched on the edge of San Francisco Bay, a 34-year-old Londoner called Demis Hassabis took to the stage. Walking to the podium with the deliberate gait of a man trying to control his nerves, he pursed his lips into a brief smile and began to speak: “So today I’m going to be talking about different approaches to building…” He stalled, as though just realising that he was stating his momentous ambition out loud.

Kubernetes 1.14 consists of 31 enhancements: 10 moving to stable, 12 in beta, and 7 net new. The main themes of this release are extensibility and supporting more workloads on Kubernetes with three major features moving to general availability, and an important security feature moving to beta. More enhancements graduated to stable in this release than any prior Kubernetes release. This represents an important milestone for users and operators in terms of setting support expectations.

What’s new for Kubernetes 1.14. A preview of the enhancements and new features that Kubernetes 1.14 will contain when released on March 25 2019. A colossal effort spanning almost 3 years, SIG-Windows is promoting the support of Windows Containers to stable. With the introduction of Windows nodes, developers will be able to schedule Windows Server containers and run Windows-based applications on Kubernetes. There is a complete document detailing the features that are “working today”, “going to get included in the roadmap after GA”, or “never going to work on a Windows node”, going over all this will be far too extensive for this post, but you can keep reading here!

The computer industry has been busy in recent years trying to figure out how to speed up the calculations needed for artificial neural networks—either for their training or for what’s known as inference, when the network is performing its function. In particular, much effort has gone into designing special-purpose hardware to run such computations. Google, for example, developed its Tensor Processing Unit, or TPU, first described publicly in 2016.

The DNA computer works by layers of DNA logic gates attaching to a DNA origami seed (grey). Adapter strands [red] attach at specific locations on the seed, encoding the 6-bit input. DNA single-stranded logic gates [blue, brown, yellow] attach in locations that match the input, solving the algorithm as the system grows. Probably the most masterful and mysterious act of chemical computation is when a single cell usesits DNA to divide, multiply, and specialize to produce a fully developed organism.

It would be easy to argue that Natural Language Toolkit (NLTK) is the most full-featured tool of the ones I surveyed. It implements pretty much any component of NLP you would need, like classification, tokenization, stemming, tagging, parsing, and semantic reasoning. And there’s often more than one implementation for each, so you can choose theexact algorithm or methodology you’d like to use. It also supports many languages. However, it represents all data in the form of strings, which is fine for simple constructsbut makes it hard to use some advanced functionality.

Implementing a test suite. This is the main focus of this blog post. The Kubernetes E2E framework is written in Go. It relies on Ginkgo for managing tests and Gomega for assertions. These tools support “behavior driven development”, which describes expected behavior in “specs”. In this blog post, “test” is used to reference an individual Ginkgo. It spec. Tests interact with the Kubernetes cluster using client-go. Bringing up a test cluster.

Kubernetes has greatly improved the speed and manageability of backend clusters in production today. Kubernetes has emerged as the de facto standard in container orchestrators thanks to its flexibility, scalability, and ease of use. Kubernetes also provides a range of features that secure production workloads. A more recent introduction in security features is a set of plugins called “admission controllers.” Admission controllers must be enabled to use some of the more advanced security features of Kubernetes, such as pod security policies that enforce a security configuration baseline across an entire namespace.

Recently, a new Kubernetes related vulnerability was announced that affected the kube-apiserver. This was a denial of service vulnerability where authorized users with write permissions could overload the API server as it is handling requests. The issue is categorized as a medium severity (CVSS score of 6.5) and can be resolved by upgrading the kube-apiserver to v1.11.8, v1.12.6, or v1.13.4. In Kubernetes, the control plane on the master node consists of the API Server, the Controller Manager and Scheduler(s).