1_DevOps'ish

Twitter Inc's head of trust and safety told Reuters the company manually reviewed "any and all accounts" that violated its new privacy policy by posting links to a Twitter account called ElonJet that tracked Elon Musk's private jet using information in the public domain.

U.S. cybersecurity agency CISA has added two critical vulnerabilities in Veeam Backup & Replication software to its list of known exploited flaws.

From entertainment franchise games to the precision delivery of medicines, the a16z team highlights over 40 builder-worthy pursuits for the coming year.

The next generation of Steam Decks will likely focus on better displays and battery life, say designers.

Tesla's request for a retrial following a Black worker's racism verdict has been denied. Tesla's attempts for a retail were denied by District Judge William Orrick.

We should look for ways to make reading, publishing, and community services all play nicely together. I'm calling this model "the unbundled web," and I think RSS should be the primary method of interop.

Step-by-step guide to installing, configuring and using a Ventura 13.1 virtual machine on an Apple silicon Mac.

Alpine Linux is a distribution that is designed to be lightweight. In particular, it’s seen a lot of use in Docker images because the resulting image bundles are considerably smaller than those generated by other minimal distros. However, in the context of building a Docker image for a Python application, it’s worth thinking carefully before using Alpine, as it can often result in slower builds and counterintuitively it can even result in larger images occasionally.

Authors: Patrick Ohly (Intel), Kevin Klues (NVIDIA) Dynamic resource allocation is a new API for requesting resources. It is a generalization of the persistent volumes API for generic resources, making it possible to: access the same resource instance in different pods and containers, attach arbitrary constraints to a resource request to get the exact resource you are looking for, initialize a resource according to parameters provided by the user. Third-party resource drivers are responsible for interpreting these parameters as well as tracking and allocating resources as requests come in. Dynamic resource allocation is an alpha feature and only enabled when the DynamicResourceAllocation feature gate and the resource.k8s.io/v1alpha1 API group are enabled. For details, see the --feature-gates and --runtime-config kube-apiserver parameters . The kube-scheduler, kube-controller-manager and kubelet components all need the feature gate enabled as well. The default configuration of kube-scheduler enables the DynamicResources plugin if and only if the feature gate is enabled. Custom configurations may have to be modified to include it. Once dynamic resource allocation is enabled, resource drivers can be installed to manage certain kinds of hardware. Kubernetes has a test driver that is used for end-to-end testing, but also can be run manually. See below for step-by-step instructions. API The new resource.k8s.io/v1alpha1 API group provides four new types: ResourceClass Defines which resource driver handles a certain kind of resource and provides common parameters for it. ResourceClasses are created by a cluster administrator when installing a resource driver. ResourceClaim Defines a particular resource instances that is required by a workload. Created by a user (lifecycle managed manually, can be shared between different Pods) or for individual Pods by the control plane based on a ResourceClaimTemplate (automatic lifecycle, typically used by just one Pod). ResourceClaimTemplate Defines the spec and some meta data for creating ResourceClaims. Created by a user when deploying a workload. PodScheduling Used internally by the control plane and resource drivers to coordinate pod scheduling when ResourceClaims need to be allocated for a Pod. Parameters for ResourceClass and ResourceClaim are stored in separate objects, typically using the type defined by a CRD that was created when installing a resource driver. With this alpha feature enabled, the spec of Pod defines ResourceClaims that are needed for a Pod to run: this information goes into a new resourceClaims field. Entries in that list reference either a ResourceClaim or a ResourceClaimTemplate. When referencing a ResourceClaim, all Pods using this .spec (for example, inside a Deployment or StatefulSet) share the same ResourceClaim instance. When referencing a ResourceClaimTemplate, each Pod gets its own ResourceClaim instance. For a container defined within a Pod, the resources.claims list defines whether that container gets access to these resource instances, which makes it possible to share resources between one or more containers inside the same Pod. For example, an init container could set up the resource before the application uses it. Here is an example of a fictional resource driver. Two ResourceClaim objects will get created for this Pod and each container gets access to one of them. Assuming a resource driver called resource-driver.example.com was installed together with the following resource class: apiVersion: resource.k8s.io/v1alpha1 kind: ResourceClass name: resource.example.com driverName: resource-driver.example.com An end-user could then allocate two specific resources of type resource.example.com as follows: --- apiVersion : cats.resource.example.com/v1 kind : ClaimParameters name : large-black-cats spec : color : black size : large --- apiVersion : resource.k8s.io/v1alpha1 kind : ResourceClaimTemplate metadata : name : large-black-cats spec : spec : resourceClassName : resource.example.com parametersRef : apiGroup : cats.resource.example.com kind : ClaimParameters name : large-black-cats –-- apiVersion : v1 kind : Pod metadata : name : pod-with-cats spec : containers : # two example containers; each container claims one cat resource - name : first-example image : ubuntu:22.04 command : ["sleep" , "9999" ] resources : claims : - name : cat-0 - name : second-example image : ubuntu:22.04 command : ["sleep" , "9999" ] resources : claims : - name : cat-1 resourceClaims : - name : cat-0 source : resourceClaimTemplateName : large-black-cats - name : cat-1 source : resourceClaimTemplateName : large-black-cats Scheduling In contrast to native resources (such as CPU or RAM) and extended resources (managed by a device plugin, advertised by kubelet), the scheduler has no knowledge of what dynamic resources are available in a cluster or how they could be split up to satisfy the requirements of a specific ResourceClaim. Resource drivers are responsible for that. Drivers mark ResourceClaims as allocated once resources for it are reserved. This also then tells the scheduler where in the cluster a claimed resource is actually available. ResourceClaims can get resources allocated as soon as the ResourceClaim is created (immediate allocation ), without considering which Pods will use the resource. The default (wait for first consumer ) is to delay allocation until a Pod that relies on the ResourceClaim becomes eligible for scheduling. This design with two allocation options is similar to how Kubernetes handles storage provisioning with PersistentVolumes and PersistentVolumeClaims. In the wait for first consumer mode, the scheduler checks all ResourceClaims needed by a Pod. If the Pods has any ResourceClaims, the scheduler creates a PodScheduling (a special object that requests scheduling details on behalf of the Pod). The PodScheduling has the same name and namespace as the Pod and the Pod as its as owner. Using its PodScheduling, the scheduler informs the resource drivers responsible for those ResourceClaims about nodes that the scheduler considers suitable for the Pod. The resource drivers respond by excluding nodes that don't have enough of the driver's resources left. Once the scheduler has that resource information, it selects one node and stores that choice in the PodScheduling object. The resource drivers then allocate resources based on the relevant ResourceClaims so that the resources will be available on that selected node. Once that resource allocation is complete, the scheduler attempts to schedule the Pod to a suitable node. Scheduling can still fail at this point; for example, a different Pod could be scheduled to the same node in the meantime. If this happens, already allocated ResourceClaims may get deallocated to enable scheduling onto a different node. As part of this process, ResourceClaims also get reserved for the Pod. Currently ResourceClaims can either be used exclusively by a single Pod or an unlimited number of Pods. One key feature is that Pods do not get scheduled to a node unless all of their resources are allocated and reserved. This avoids the scenario where a Pod gets scheduled onto one node and then cannot run there, which is bad because such a pending Pod also blocks all other resources like RAM or CPU that were set aside for it. Limitations The scheduler plugin must be involved in scheduling Pods which use ResourceClaims. Bypassing the scheduler by setting the nodeName field leads to Pods that the kubelet refuses to start because the ResourceClaims are not reserved or not even allocated. It may be possible to remove this limitation in the future. Writing a resource driver A dynamic resource allocation driver typically consists of two separate-but-coordinating components: a centralized controller, and a DaemonSet of node-local kubelet plugins. Most of the work required by the centralized controller to coordinate with the scheduler can be handled by boilerplate code. Only the business logic required to actually allocate ResourceClaims against the ResourceClasses owned by the plugin needs to be customized. As such, Kubernetes provides the following package, including APIs for invoking this boilerplate code as well as a Driver interface that you can implement to provide their custom business logic: k8s.io/dynamic-resource-allocation/controller Likewise, boilerplate code can be used to register the node-local plugin with the kubelet, as well as start a gRPC server to implement the kubelet plugin API. For drivers written in Go, the following package is recommended: k8s.io/dynamic-resource-allocation/kubeletplugin It is up to the driver developer to decide how these two components communicate. The KEP outlines an approach using CRDs . Within SIG Node, we also plan to provide a complete example driver that can serve as a template for other drivers. Running the test driver The following steps bring up a local, one-node cluster directly from the Kubernetes source code. As a prerequisite, your cluster must have nodes with a container runtime that supports the Container Device Interface (CDI). For example, you can run CRI-O v1.23.2 or later. Once containerd v1.7.0 is released, we expect that you can run that or any later version. In the example below, we use CRI-O. First, clone the Kubernetes source code. Inside that directory, run: $ hack/install-etcd.sh ... $ RUNTIME_CONFIG = resource.k8s.io/v1alpha1 \ FEATURE_GATES=DynamicResourceAllocation=true \ DNS_ADDON="coredns" \ CGROUP_DRIVER=systemd \ CONTAINER_RUNTIME_ENDPOINT=unix:///var/run/crio/crio.sock \ LOG_LEVEL=6 \ ENABLE_CSI_SNAPSHOTTER=false \ API_SECURE_PORT=6444 \ ALLOW_PRIVILEGED=1 \ PATH=$(pwd)/third_party/etcd:$PATH \ ./hack/local-up-cluster.sh -O ... To start using your cluster, you...

WebAssembly, or Wasm, was shown to be a very practical way to run code on a  web browser, serving as a compiler of sorts. Eventually, it dawned on developers that Wasm could run on server operations systems as well and its use now extends across hardware platforms, leading some to attempt to view it as an alternative to Kubernetes.

Developer News

"Flexing geopolitical muscles in space to harm others has already happened."

NEW: Twitter currently does not have admin access to some of its GitHub repos. These repos contain Twitter source code (much of it is open source; some is not). This includes code for companies Twitter acquired, like Smyte. 1/— Zoë Schiffer (@ZoeSchiffer) December 13, 2022

The regulatory agency charged them in what it says was a $100 million securities fraud scheme run by people who portrayed themselves as successful stock traders.

The Twitter CEO's selective release of internal communications largely corroborate what is already known about the messy business of policing a large social network.

ChatGPT is changing the game, and I want to share real things you can do with this AI system today.Please save this thread and start testing this technology NOW so you’re ahead of the curve.— Allie K. Miller (@alliekmiller) December 13, 2022

Nature - Researchers at the US National Ignition Facility created a reaction that made more energy than they put in.

The Southfield-based auto supplier will exclusively supply the battery disconnect units on all GM trucks and SUVs using the Ultium platform.

Zero-day vulnerability alert! Apple has released security updates to prevent malicious code execution on your devices.

Developer platforms won't eliminate DevOps but will prioritize developer experience.