Red Hat now offers an extensive portfolio of training and certifications to teach the skills required for advanced Red Hat OpenShift administration. Check out some course highlights!
Quelle: CloudForms
Let’s meet at WCUS 2021! WordPress.com will be there and we hope to see you there as well!
Although we’d love to be at an in-real-life WordCamp right now, we’re still excited about attending the online version of WordCamp US this year.
What is WordCamp US 2021?
WordCamps are informal events that are organized and hosted by the WordPress community. WordCamp US 2021 is a one-day online event for the US WordPress community to attend sessions, network with one another, participate in WordPress-related workshops, and more. Of course, it’s not only for US residents – everyone is invited to attend!
Reminder: It’s free to attend, but you need a ticket so get yours now.
WordCamps are welcoming places for all WordPress businesses, users, bloggers, and enthusiasts to gather. No matter where you host your WordPress site, no matter how big or small your site might be, WCUS 2021 is the place to be.
How is WordPress.com Involved?
As a hosted version of the open source software, WordPress.com is also a part of the WordPress community, so we wanted to participate in this event in a big way. We are proud to be one of several sponsors of WordCamp US 2021 (WCUS 2021). Be sure to visit our sponsor page here, which includes WordPress.com-related facts you may not be aware of. For example, did you know that WordPress.com is a hosted version of the open-source software WordPress, delivered on a fast, secure managed WordPress hosting platform?
What Can You Expect at WCUS 2021?
There are sessions throughout the day, all carefully selected to appeal to the varied interests of WordCamp attendees. Take a look at the schedule and plan ahead so you don’t miss the sessions that interest you the most.
Maybe you’re interested in eCommerce or accessibility. Perhaps you are curious about the future direction of WordPress. Or are you hoping to learn more about community-building? You can delve into these topics and more at this event.
There will also be plenty of time to network with other attendees, and even hang out and enjoy some music during the day.
So will we see you there tomorrow? If you see us around, be sure to say hello.
Psst… We may even have some swag available.
Quelle: RedHat Stack
Last year, we announced the general-purpose N2D Compute Engine machine type based on the 2nd Generation AMD EPYC™ processor. Today, we are excited to announce that the N2D family now supports the latest 3rd Generation AMD EPYC processor.N2D VMs powered by 3rd Generation AMD EPYC processors deliver, on average, over 30% price-performance improvement across a variety of workloads as compared to prior 2nd Generation AMD EPYC processors. If you already use N2D machines, you can use the new hardware simply by selecting “AMD Milan or later” as the CPU platform for your N2D VMs. Further, if you’re using our first-generation N1 VM family, you’ll see a substantial price-performance1 improvement with the new N2D family.N2D VMs based on 3rd Generation AMD EPYC processors offer a broad set of features and options. N2D supports VMs with up to 224 vCPUs and up to 896 GB of memory, for workloads that require a higher number of threads. Google Cloud offers the highest number of vCPUs per VM across all general-purpose machine types available from a public cloud provider. N2D also includes a wide array of VM shapes (spanning standard, high-CPU and high-memory options) and Custom Machine Types, allowing you to pick custom sizes based on your workload needs. N2D VMs also support our recently introduced 100 Gbps high-bandwidth network to meet the demands of high-throughput workloads. In addition, N2D also supports high storage performance with persistent disk and up to 9 TB of local SSD. Combining high-throughput VMs with high-performance Local SSD is beneficial for I/O-intensive workloads. N2D is also available as a sole tenant node for workloads that require isolation to meet regulatory requirements or dedicated hardware for licensing requirements.New innovationCustomers using N2D VMs powered by 3rd generation AMD EPYC processors get access to the latest features in the AMD EPYC processor family including up to 256 MB of L3 cache and ‘Zen 3’ cores, which provide higher instructions per clock (IPC) compared to ‘Zen 2’. These processors include the same features offered in 2nd generation AMD EPYC processors including PCIe 4 support, high levels of memory bandwidth and access to AMD Infinity Guard for advanced security features. All of this means customers using the latest version of N2D with 3rd generation AMD EPYC can take advantage of its high performance for a variety of general-purpose workloads. “With exceptional performance and features, our AMD EPYC processors will provide future and existing Google Cloud N2D customers high performance capabilities for a variety of workloads,” said Lynn Comp, corporate vice president, Cloud Business Group, AMD. “This is another exciting extension of our relationship with Google, adding to the existing 2nd Gen EPYC based N2D VMs and the new T2D VMs, and our team is proud to continue to work together with Google Cloud on this.”What customers are sayingFullstory, a digital experience intelligence platform provider, was an early user of the new N2D VM family. “At FullStory, we are constantly looking for ways to improve database performance and reduce query latencies, especially as data sizes are always increasing,” said Jaime Yap, Director of Engineering at FullStory. “In our testing with Google Cloud’s latest N2D instances based on the AMD Milan CPU, we were pleased to see some query workloads achieve ~29% performance gains on average when compared to previous generation N2D VMs. We expect this to translate to dramatically improved utilization and better experiences for our customers.”Vimeo, the world’s leading all-in-one video software solution, tested the new N2D VMs. “At Vimeo, we have always believed in providing a best in class video quality experience to our users,” said Joe Peled, Director, hosting and delivery operations at Vimeo. “The bulk of our video content is CPU-processed running encoding workloads such as x264 (H.264), x265 (HEVC), and rav1e (AV1) to achieve optimal video fidelity with minimum artifacts. Google Cloud’s new AMD Milan based N2D VMs unlock a major improvement to our users by significantly reducing time spent in our transcoding pipelines on the order of 20%, and allow us to reduce costs by a similar factor.”Google Kubernetes Engine supportGoogle Kubernetes Engine (GKE) is the leading platform for organizations looking for advanced container orchestration, delivering the highest levels of reliability, security, and scalability. GKE supports N2D nodes based on 3rd Generation AMD EPYC Processors, helping you get the most out of your containerized workloads. You can add nodes based on N2D 3rd Gen EPYC VMs to your GKE clusters by choosing the N2D machine type in your GKE node pools and specifying the minimum CPU platform “AMD Milan”.100 Gbps NetworkingWe’ve optimized Google Cloud’s unique Andromeda network to support hardware offloads such as zero-copy, TSO, and encryption and are able to offer N2D VMs with 100 Gbps networking out-of-the-box.N2D VMs will be able to take full advantage of Google Cloud’s high-performance network infrastructure with bandwidth configurations that enable 100 or 50 Gbps speeds for VM shapes with 48 or more vCPUs. These networking configurations are offered as an add-on feature for N2D VMs and impose no additional inventory constraints on N2D deployments—you’ll be able to upgrade your N2D VMs’ network bandwidth in any zone with N2D availability. Confidential Computing (coming soon)Confidential Computing is an industry-wide effort to protect data in-use including encryption of data in-memory—while it’s being processed. With Confidential Computing, you can run your most sensitive applications and services on N2D VMs.We’re committed to delivering a portfolio of Confidential Computing VM instances and services such as GKE and Dataproc using the Secure Encrypted Virtualization (SEV) extension. You’ll be pleased to know that we’ll support SEV using this latest generation of AMD EPYC™ processors in the near-term and more advanced capabilities in the future.Target workloads N2D VMs are suitable for a wide variety of general-purpose workloads such as web serving, app serving, databases, and enterprise applications. With machine types that include up to 224 vCPUs, N2D machines are ideal for high-throughput workloads that can benefit from having a large number of threads. N2D machines are also ideal for workloads that may benefit from confidential computing features. N2D machines based on 3rd Generation AMD EPYC processors provide significant performance gains over current generation N2D VMs for various benchmarks and general purpose workloads as shown in the graph below.PricingN2D VMs with 3rd Generation AMD EPYC processors are offered at the same price as the previous generation N2D VMs. AvailabilityN2D VMs with 3rd Generation AMD EPYC processors are currently in preview in several Google Cloud regions: us-central (Iowa), us-east1 (S. Carolina), europe-west4 (Netherlands), and asia-southeast1 (Singapore) and will be available in other Google Cloud regions globally in the coming months. Please sign-up here and contact your Google Cloud sales representative if you are interested in the Preview. 1. Based on price-performance improvements measured on the new N2D Milan VMs vs. N1 VMs for the following: VP9 Transcode (51%), Nginx (72%), Server side Java throughput under SLA (72%), AES-256 Encryption (273%).
Quelle: Google Cloud Platform
Google Cloud Next and VMworld 2021 are less than two weeks away, and the partnership between Google Cloud and VMware is entering a new chapter. Over the past year, our close partnership with VMware and mutual dedication to customer success has inspired us to deliver several innovative capabilities, including expanding the service to 12 regions worldwide along with our industry-leading 99.99% availability, multi-region networking, and improved scalability to make it easy for customers to rapidly migrate to the cloud. “Our collaboration with Google is noteworthy because of the value it brings to our joint customers. The mutual success we have had partnering with Google on solutions that enable VMware workloads to run natively in the cloud with Google Cloud VMware Engine and digital workspace with Android and Chrome Enterprise is a testament to the quality of our joint offerings,” said Gregory Lehrer, VP Strategic Technology Partnerships, VMware. “As a result of our ongoing collaboration, we continue to see customers adopting our joint solution, indicating a strong and effective partnership. Our joint roadmap for the future points to an upward trajectory as we scale to meet anticipated demand.Across industries, customers are increasingly looking to accelerate their digital transformation due to the need for app modernization, aging infrastructure on-premises, and the need to meet customer needs in an always-on, digital environment. Customers such as Carrefour, a global retailer across 30 countries, quickly moved their on-premises environment to Google Cloud VMware Engine, while reducing operating costs by 40% and energy consumption by 45%. Furthermore, they were able to simultaneously improve the experience for shoppers and employees, and bolstered sales and shopper engagement with personalized offers. Companies are also looking to migrate and modernize their business with Google Cloud VMware Engine. LIQ, a CRM software company, migrated 80% of business applications and 50% of databases in just three months, and now plan to modernize their applications with microservices to lower maintenance time and costs. Looking forward, we’re focused on helping customers derive greater ROI from their investments in three ways:Flexibility – Single node Private Cloud SDDC to enable trials or proof-of-concept validations at a much lower cost.Availability – New geographic zones and expanded capacity within zones to better serve local business needs and continue to maintain data sovereignty within local regions.Ecosystem integrations – Building on our leading open platform, we’ve developed even more integrations with solutions across the ecosystem. VMware has validated it’s Disaster Recovery tool (Site Recovery Manager), Virtualization management tool (vRealize Cloud Management), as well as Virtual Desktop Infrastructure tool (Horizon Desktop) to ensure you can bring your mission critical applications to the cloud without disruption.We continue to focus on making migrations simpler as well. The recently announced Catalyst Program now provides even greater financial flexibility as eligible customers can get one-time Google Cloud credits to help offset existing VMware license investments. The program is consumption-based and designed to provide even more value as you accelerate your migration to the cloud. Furthermore, programs such as our Rapid Assessment and Migration Program (RAMP) provide free assessment and planning tools to reduce complexity, enable choice, and increase flexibility throughout the migration process.There’s much more to come from VMware and Google Cloud. We’re proud to be a Platinum Sponsor at VMworld 2021 and invite you to join us to learn more about our commitment to enabling digital transformation. Be sure to catch the fireside chat with Google Cloud CEO Thomas Kurian and VMware CEO Raghu Raghuram as they discuss industry trends and customer success. You’ll also hear more from our joint-customers and our product leaders about what’s to come. We can’t wait to connect with you virtually at the event.Related ArticleNew in Google Cloud VMware Engine: autoscaling, Mumbai expansion, etc.A review of the latest updates to Google Cloud VMware Engine.Read Article
Quelle: Google Cloud Platform
Who would have known that today technology would enable us with the ability to use machine learning to track vessel activity, and make pattern inferences to help address IUU (illegal, unreported, and unregulated) fishing activities. What’s even more noteworthy is that we now have the computing power to share this information publicly in order to enable fair and sustainable use of our ocean. An amazing group of humans at the nonprofit Global Fishing Watch took on this massive big data challenge and succeeded. You can immediately access their dynamic map on their website globalfishingwatch.org/map that is bringing greater transparency to fishing activity and supporting the creation and management of marine protected areas throughout the world.Time lapse of Global Fishing Watch’s global fishing map powered by MLIn our second episode of our People and Planet AI series we were inspired by their ML solution to this challenge, and we built a short video and sample with all the relevant code you need to get started with building a basic time-series classification model in Google Cloud, and visualize it in an interactive map. The model making predictions whether a vessel is fishing or note.ArchitectureThese are the components used to build a model for this sample:Architectural diagram for creating our time-series classification model.Global Fishing Watch GitHub: where we got the dataApache Beam: (open source library) runs on Dataflow. Dataflow: (Google’s data processing service) creates 2 datasets; 1 for training a model and the other to evaluate its results.TensorflowKeras: (high level API library) used to define a machine learning model, which we then train in Vertex AI.Vertex AI: (a platform to build, deploy, and scale ML models) we train and output the model.cost of building this time-series classification model is less than $5 in compute resourcesPricing and stepsThe total cost to run this solution was less than $5. There are seven steps we went through with their approximate time and cost:Why do we use a time series classification model? Vessels in the ocean are constantly moving, which creates distinctive patterns from a satellite view.Different fishing gear in vessels move in distinct spatial patterns and have varying regulations and environmental impacts. We can train a model to recognize the shapes of a vessel’s trajectory. Large vessels are required to use the automatic identification system, or AIS. The GPS-like transponders regularly broadcast a vessel’s maritime mobile service identity, or MMSI, and other critical information to nearby ships, as well as to terrestrial and satellite receivers. While AIS is designed to prevent collisions and boost overall safety at sea, it has turned out to be an invaluable system for monitoring vessels and detecting suspicious fishing behavior globally.GPS-like device called the automatic identification system transmitting positions of vessels.One tricky part is that the MMSI data location signal (which includes a timestamp, latitude, longitude, distance from port, and more) is not emitted at regular intervals. AIS broadcast frequency changes with vessel speed (faster at higher speeds), and not all AIS messages that are broadcast are received – terrestrial receivers require line-of-sight, satellites must be overhead, and high vessel density can cause signal interference. For example, AIS messages might be received frequently as a vessel leaves the docks and operates near shore, then less frequently as they move further offshore until satellite reception improves. This is challenging for a machine learning model to interpret. There are too many gaps in the data, which makes it hard to predict.A way to solve this is to normalize the data and generate fixed-sized hourly windows. Then the model can predict if the vessel is fishing or not fishing for each hour.Split panel where left side shows irregular GPS signals collected. Right side shows how we must normalize the data into hourly windows.It could be hard to know if a ship is fishing or not by just looking at its current position, speed, and direction. So we look at the data from the past as well, looking at the future could also be an option if we don’t need to do real time predictions. For this sample, it seemed reasonable to look 24 hours into the past to make a prediction. This means we need at least 25 hours of data to make a prediction for a single hour (24 hours in the past + 1 current hour). But we could predict longer time sequences as well. In general, to get hourly predictions, we need (n+24) hours of data.Options to deploy and access the modelFor this sample specifically we used Cloud Run to host the model as a web app so that other apps can call it to make predictions on an ongoing basis; this is our favorite in terms of pricing if you need to access your model from the internet over an extended period of time (charged per prediction request). You can also host it directly from Vertex AI where you trained and built the model, just note there is an hourly cost for using those VMs even if they are idle. If you do not need to access the model over the internet, you can make predictions locally or download the model onto a microcontroller if you have an IoT sensor strategy.3 options for hosting modelWant to go deeper?If you found this project interesting and would like to dive deeper either into the specifics of the thought process behind each step of this solution or even run through the code in your own project (or test project); we invite you to check out our interactive sample hosted on Colab, which is a free Jupyter notebook. It serves as a guide with all the steps to run the sample, including visualizing the predictions on a dynamically moving map using an open source Python library called Folium. There’s no prior experience required! Just click “open in Colab” which is linked at the bottom of GitHub.You will need a Google Cloud Platform project. If you do not have a Google Cloud project you can create one with the free $300 Google Cloud credit, you just need to ensure you set up billing, and later delete the project after testing the desired sample.screenshot of interactive notebook in colab notebook
Thanks to all of your positive support of the Docker subscription updates we announced on Aug 31, 2021, we’ve been able to focus on delivering more value to all users, starting with making Volume Management available for users on any subscription tier, including Docker Personal. Just update to Docker Desktop 4.1 to start using it.
Volume management gives you an easy way to manage and explore your volumes so you can identify which volumes are being used, what containers they are being used by and what data is in the volume. Now, personal users can view, download and delete contents from inside a volume.
You’ll be able to explore the contents of the volumes so that you can more easily get an understanding of what’s taking up space within the volume.
You’ll also be able to easily see which specific containers are using any particular volume.
We’re continuing to enhance Volume Management and would love your input. Have ideas on how we might make managing volumes easier? Interested in sharing your volumes with colleagues? Let us know what you’re interested in using here.
Docker Dashboard Update Settings
We’re continuing to enhance the update process, and now with Docker Desktop 4.1, you will be notified when a new update is available in the updates settings section. You can choose to view more details on what’s new or start the download process straight from the dashboard.
If you use Docker Desktop at work you may need to stay on a specific version or need an IT admin to install an update, you may want to turn off checking for updates all together, which will disable the badge icon in the dashboard as well. Users on a paid team or business plan can do so by unchecking the “Automatically Check for Updates” setting in general.
This is just phase one, we are looking to provide a more flexible experience to users on how they want updates to be handled by providing additional optional settings. Let us know what you would find the most useful here!
We <3 when folks try new versions, staying up to date makes sure you have the latest features (like volume management with Docker Personal!) and helps us provide you the best experience as we are continuously addressing issues identified with each release. We also love to hear your feedback on the new stuff we’re putting out there.
Docker Compose V2
We’ve released Docker Compose v2.0.0, it is now fully supported for users. We’re still working on providing a more standard installation path for Linux users, but all of the feedback you have given us in the past 3 months since we released the beta version has helped us identify issues and made us confident that it’s ready to be fully supported.
You can use this functionality by running the docker compose command, dropping the – in docker compose. We are continuing to roll this out gradually, you’ll be notified if you are using the new docker compose (meaning docker-compose will alias to use docker compose).
You can opt-in to run Compose v2 with docker-compose, by running `docker-compose enable-v2 command` or by updating your Docker Desktop’s settings.
If you run into any issues using Compose V2, simply run docker-compose disable-v2 command, or turn it off using Docker Desktop’s settings. Let us know your feedback on the ‘compose’ command by creating an issue in the Compose GitHub repository.
Self Diagnosis Tool
Most of the time Docker Desktop runs smoothly … but occasionally it doesn’t. And because it interacts with some of the lowest level parts of your OS (virtualization, networking, file system etc.) it’s often hard to figure out exactly what has gone wrong. So we’ve written a new self-diagnosis tool to help diagnose some of the most common problems, and where possible explain how to fix them. We released this quietly in version 3.6.0, so you may have noticed it already, but we’ve been improving the checks based on user feedback in real-world examples, and we’re now ready to launch it officially.
To try it out, use this command in PowerShell:
& C:Program FilesDockerDockerresourcescom.docker.diagnose.exe check
or this command on Mac:
/Applications/Docker.app/Contents/MacOS/com.docker.diagnose check
The tool runs a suite of checks and displays PASS or FAIL next to each one. If there are any failures, it highlights the most relevant ones at the end.
Please let us know your feedback on this new tool in the docker/for-win or docker/for-mac repository. Did it help you, or did it misdiagnose your problem? Are there more checks we could add?
The post Docker Desktop 4.1 Release: Volume Management Now Included with Docker Personal appeared first on Docker Blog.
Quelle: https://blog.docker.com/feed/
Elastic Load Balancing unterstützt jetzt die direkte Weiterleitung von Datenverkehr vom Network Load Balancer (NLB) zum Application Load Balancer (ALB). Mit dieser Funktion können Sie jetzt AWS PrivateLink verwenden und statische IP-Adressen für Anwendungen bereitstellen, die auf ALB basieren.
Quelle: aws.amazon.com
Nach der Einführung von Red Hat Enterprise Linux mit Microsoft SQL Server für Amazon EC2 können Sie jetzt Verfügbarkeitsgruppen von RHEL SQL Server Always On einfach mit dem AWS Launch Wizard bereitstellen.
Quelle: aws.amazon.com
Quelle: aws.amazon.com
