Serdar Yegulalp

About the Author Serdar Yegulalp


What’s new in TensorFlow machine learning

TensorFlow, Google’s contribution to the world of machine learning and data science, is a general framework for quickly developing neural networks. Despite being relatively new, TensorFlow has already found wide adoption as a common platform for such work, thanks to its powerful abstractions and ease of use.

TensorFlow 1.4 API additions

TensorFlow Keras API

The biggest changes in TensorFlow 1.4 involve two key additions to the core TensorFlow API. The tf.keras API allows users to employ the Keras API, a neural network library that predates TensorFlow but is quickly being displaced by it. The tf.keras API allows software using Keras to be transitioned to TensorFlow, either by using the Keras interface permanently, or as a prelude to the software being reworked to use TensorFlow natively.

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Docker will include Kubernetes in the box

Docker announced today it will integrate an “unmodified” version of Google’s Kubernetes container-orchestration tool as a native part of Docker. Docker said the Kubernetes integration will be available as a beta release, but gave no release date.

This integration will be extended to all versions of Docker—the for-pay Enterprise Edition, and the desktop incarnations, Docker for Mac and Docker for Windows, which use the free Community Edition. Both enterprise and desktop versions will have Kubernetes support for all the operating systems they currently support.

Why Docker is adding Kubernetes

One reason Docker is including Kubernetes is to spare developers the effort of standing up a Kubernetes instance, whether for simple dev/test or for actual production use. Historically it’s been a chore to get Kubernetes running, and so a slew of Kubernetes tools and third-party Kubernetes projects have emerged to simplify the process. Most of the time, it’s easier to use a Kubernetes distribution, becayse the distribution’s packaging deals with these problems at a high level.

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Gluon brings AI developers self-tuning machine learning

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What is Grafeas? Better auditing for containers

The software we run has never been more difficult to vouchsafe than it is today. It is scattered between local deployments and cloud services, built with open source components that aren’t always a known quantity, and delivered on a fast-moving schedule, making it a challenge to guarantee safety or quality.

The end result is software that is hard to audit, reason about, secure, and manage. It is difficult not just to know what a VM or container was built with, but what has been added or removed or changed and by whom. Grafeas, originally devised by Google, is intended to make these questions easier to answer.

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What’s new in Kubernetes 1.8: role-based access, for starters

The latest version of the open source container orchestration framework Kubernetes, Kubernetes 1.8, promotes some long-gestating, long-awaited features to beta or even full production release. And it adds more alpha and beta features as well.

The new additions and promotions:

  • Role-based security features.
  • Expanded auditing and logging functions.
  • New and improved ways to run both interactive and batch workloads.
  • Many new alpha-level features, designed to become full-blown additions over the next couple of releases.

Kubernetes 1.8’s new security features

Earlier versions of Kubernetes introduced role-based access control (RBAC) as a beta feature. RBAC lets an admin define access permissions to Kubernetes resources, such as pods or secrets, and then grant (“bind”) them to one or more users. Permissions can be for changing things (“create”, “update”, “patch”) or just obtaining information about them (“get”, “list”, “watch”). Roles can be applied on a single namespace or across an entire cluster, via two distinct APIs.

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What’s new in MySQL 8.0

MySQL, the popular open-source database that’s a standard element in many web application stacks, has unveiled the first release candidate for version 8.0.

Features to be rolled out in MySQL 8.0 include:

  • First-class support for Unicode 9.0 out of the box.
  • Window functions and recursive SQL syntax, for queries that previously weren’t possible or would have been difficult to write.
  • Expanded support for native JSON data and document-store functionality.

With version 8.0, MySQL is jumping several versions in its numbering (from 5.5), due to 6.0 being nixed and 7.0 being reserved for the clustering version of MySQL.

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Cython 0.27 speeds Python by moving away from oddball syntax

Cython, the toolkit that allows Python code to be converted to high-speed C code, has a new 0.27 release that can now use Python’s own native typing syntax to speed up the Python-to-C conversion process.

Previously, Cython users could accelerate Python only by decorating the code with type annotations in a dialect peculiar to Cython. Python has its own optional syntax for variable type annotation, but Cython didn’t use it.

With Cython 0.27, Cython can now recognize PEP 526-style type declarations for native Python types, such as str or list. The same syntax can also be used to explicitly define native C types, using declarations like declaration like var: cython.int = 32.

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ONNX makes machine learning models portable, shareable

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ONNX makes machine learning models portable, shareable

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