Supporting Open Source Projects at Docker

At Docker, we are committed to building a platform that enables collaboration and innovation within the development community. Last month, we announced the launch of a special program to expand our support for Open Source projects that use Docker. The eligible projects that meet the program’s requirements (ie. they must be open source and non-commercial) can request to have their respective OSS namespaces whitelisted and see their data-storage and data-egress restrictions lifted.

The projects we’re supporting, and the organizations behind them, are as diverse as they are numerous, ranging from independent researchers developing frameworks for machine learning, to academic consortia collecting environmental data to human rights NGOs building encryption tools. To date, we’re thrilled to see that more than 80 non-profit organizations, large and small, from the four corners of the world have joined the program. 

Here are but a few diverse projects we’re supporting: 

The Vaccine Impact Modelling Consortium aims to deliver more sustainable, efficient, and transparent approach to generating disease burden and vaccine impact estimates. 

farmOS is a web-based application for farm management, planning, and record keeping. It is developed by a community of farmers, developers, researchers, and organizations with the aim of providing a standard platform for agricultural data collection and management.

Serratus is a collaborative Open Science project for ultra-rapid discovery of known and unknown coronaviruses in response to the COVID-19 pandemic.

The Open Library Foundation enables the development, accessibility and sustainability of open source and open access projects for and by libraries. 

More than ever, Docker is committed to building a platform that enables collaboration and innovation within the development community all the while fostering a paradigm where developers working outside of revenue-generating projects can share ideas, techniques and  artifacts.

It is a true privilege to be supporting a rich mosaic of open source projects and testimony to the vibrant ecosystem we are committed to help grow. If you are working for a non-profit developing open source software leverying our platform, we encourage you to apply to Docker’s our Open Source program today.
The post Supporting Open Source Projects at Docker appeared first on Docker Blog.
Quelle: https://blog.docker.com/feed/

Introducing Docker Engine 20.10

We are pleased to announce that we have completed the next major release of the Docker Engine 20.10. This release continues Docker’s investment in our community Engine adding multiple new features including support for cgroups V2, moving multiple features out of experimental including docker run –mount and rootless, along with a ton of other improvements to the API, client and build experience. The full list of changes can be found as part of our change log. 

Docker engine is the underlying tooling/client that enables users to easily build, manage, share and run their container objects on Linux. The Docker engine is made up of 3 core components:

A server with a long-running daemon process dockerd.APIs which specify interfaces that programs can use to talk to and instruct the Docker daemon.A command line interface (CLI) client docker.

For those who are curious about the recent questions about Docker Engine/K8s, please have a look at Dieu’s blog to learn more. 

Along with this I want to give a huge thank you to everyone in the community and all of our maintainers who have also contributed towards this Engine release. Without their contribution, hard work and support we would not be where we are nor have this Engine release. When I say ‘we’ throughout this article I don’t just mean the (awesome) engineers at Docker, I mean the (awesome) engineers outside of Docker and the wider community that have helped shape this release  

You can get started with the 20.10 version of Docker Engine either by getting the packages from here or this will be available in this week’s community release of Docker Desktop, for those of your can’t wait on Mac and Windows you can try out the RC of 20.10 using the latest Docker Desktop. Now let’s jump in and have a closer look at some of the 20.10 changes. 

Initial support for cgroups V2

Just as a reminder on how Docker works; Docker uses several foundational Linux kernel features to provide isolation to your running processes and the files associated with them. One of these features that we make use of is cgroups. Cgroups in Linux limits the resource usage (CPU, memory, disk, etc.) of a process. Docker combines these with the use of Linux namespaces to isolate your processes in containers. 

V2 of Cgroups was first introduced to the Linux kernel in 2016 bringing with it changes to how groups are managed and support for imposing resource limitations on rootless containers. For a bit more background on where this came from and some background on why these changes have taken a while to come along check out Akihiro’s blog.

Now that support for this in runc has been introduced we have added it to Docker. This change in turn has allowed Docker to graduate rootless from experimental to a fully supported feature.Support for reading docker logs with all logging drivers

Prior to Docker Engine 20.10, the jsonfile and journald log drivers supported reading container logs using docker logs. However, many third party logging drivers had no support for locally reading logs using docker logs, including:

sysloggelffluentdawslogssplunketwlogsgcplogslogentries

This created multiple problems when attempting to gather log data in an automated and standard way. Log information could only be accessed and viewed through the third-party solution in the format specified by that third-party tool.

Starting with Docker Engine 20.10, you can use docker logs to read container logs regardless of the configured logging driver or plugin. This capability, sometimes referred to as dual logging, allows you to use docker logs to read container logs locally in a consistent format, regardless of the remote log driver used, because the Engine is configured to log information to the “local” logging driver. Refer to “Configure the default logging driver “ in the Docker documentation for additional information

OS support changes

With the 20.10 release of Engine we are updating the OS support we have, this means we are adding support for both Ubuntu 20.10 and Fedora 33 along with continuing the support for CentOS8 – giving users on these OS’s access to Docker’s latest features.  

CLI improvements

Along with all of this we have made multiple changes to improve the CLI experience to provide you with access to the functionality you need and the configurability to automate this. We have been looking at making the experience across the CLI more consistent, removing older and unused commands to make it simpler and adding new options  to make it easier to get started and easier to script using Docker. 

Taking a look at a few of these:

Docker push – we have changed the default behavior to be in line with pull, now if you push an image name without a tag we will only push the :latest tag rather than all tags. To support this we have also now added a -a/all-tags flag to push all the tags of an image. 

–pull=missing|always|never – have been added to the run and create commands, giving you more fine grain control over when to pull images

docker exec – we have added a new -env-file flag. This allows you to run docker exec with the –env-file flag and a file containing environment variables. And subsequently print/use any of the variables inside the file in the command.

 To learn more about Docker Engine 20.10:

Visit the Docker Engine website or check out the release notesDownload Docker Desktop Edge to try this out today Download Docker Engine – Community for your preferred operating system
The post Introducing Docker Engine 20.10 appeared first on Docker Blog.
Quelle: https://blog.docker.com/feed/

AI, Kubernetes, multi-cloud, and more: Free training to take advantage of before 2021

Google Cloud is continuing to offer no-cost learning opportunities until the end of this year to help you build in-demand cloud skills. Read on to find out how you can further your knowledge of AI, Kubernetes, multi-cloud, and more. AI and machine learning Join Lak Lakshmanan, Google Cloud’s Director of Data Analytics and AI Solutions, on December 15 to find out how to use Google Cloud’s new Document AI. Lak will walk you through how to extract structured data from scanned PDF documents so you can increase operational efficiency, improve customer experience, and make faster, smarter decisions. You’ll also have an opportunity to get your questions answered live. Reserve your seathereto learn about Document AI.You can also learn how to use AI and machine learning to help power businesses in different industries such as finance and retail in sessions from our Let’s Get Solving series. Google experts will provide case studies, teach you how to use BigQuery, demo technology like Open Banking, and more. Registerherefor industry-specific learning opportunities.  Kubernetes If you sign up by December 31, you’ll get access to unlimited Kubernetes training and the opportunity to earn Google Cloud skill badges on Qwiklabs at no-cost for 30 days. We recommend you begin with the following quests on Qwiklabs: “Deploy to Kubernetes in Google Cloud” and “Kubernetes Solutions.” “Deploy to Kubernetes in Google Cloud” will teach you how to configure Docker images and containers and deploy fully fledged Kubernetes Engine applications. By taking the “Kubernetes Solutions” quest, you’ll have a chance to practice with a wide variety of Kubernetes use cases such as building Slackbots with NodeJS, deploying game servers on clusters, and running the Cloud Vision API. Sign uphereto get started with Kubernetes. Hybrid and multi-cloud  Instructors will lead you through hands-on labs and you’ll have a chance to test your skills throughout with quizzes during our two-day Cloud OnBoard series on architecting with Google Kubernetes Engine (GKE) and hybrid architecture with Anthos. Register for the introduction to hybrid and multi-cloud technologyhere. Data analytics Interested in taking your data analytics skills to the next level? If you register by December 31, you can get access to a 6-week learning path which will outline recommended Google Cloud training to prepare experienced cloud professionals for the Google Cloud Professional Data Engineer certification. Google Cloud certifications validate your cloud expertise and help you elevate your career. Through our training partner, Coursera1, the first month of training will be available at no cost as well as 30 days of unlimited Qwiklabs access at no cost. You can get access to the certification preparation learning pathhere. We hope these training resources help you strengthen your cloud skills this year. Stay tuned for new learning opportunities in January. 1. Coursera first month free promotion is only available to learners who have not previously paid for training on Coursera. A credit card is required to activate your free month. After the first month is over, your subscription will auto-renew to a $49 monthly charge until you cancel your Coursera subscription.
Quelle: Google Cloud Platform