#CloudMonitoring

[govindhtech]

New GKE Ray Operator on Kubernetes Engine Boost Ray Output

GKE Ray Operator

The field of AI is always changing. Larger and more complicated models are the result of recent advances in generative AI in particular, which forces businesses to efficiently divide work among more machines. Utilizing Google Kubernetes Engine (GKE), Google Cloud’s managed container orchestration service, in conjunction with ray.io, an open-source platform for distributed AI/ML workloads, is one effective strategy. You can now enable declarative APIs to manage Ray clusters on GKE with a single configuration option, making that pattern incredibly simple to implement!

Ray offers a straightforward API for smoothly distributing and parallelizing machine learning activities, while GKE offers an adaptable and scalable infrastructure platform that streamlines resource management and application management. For creating, implementing, and maintaining Ray applications, GKE and Ray work together to provide scalability, fault tolerance, and user-friendliness. Moreover, the integrated Ray Operator on GKE streamlines the initial configuration and directs customers toward optimal procedures for utilizing Ray in a production setting. Its integrated support for cloud logging and cloud monitoring improves the observability of your Ray applications on GKE, and it is designed with day-2 operations in mind.

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Getting started

When establishing a new GKE Cluster in the Google Cloud dashboard, make sure to check the “Enable Ray Operator” function. This is located under “AI and Machine Learning” under “Advanced Settings” on a GKE Autopilot Cluster.

The Enable Ray Operator feature checkbox is located under “AI and Machine Learning” in the “Features” menu of a Standard Cluster.

You can set an addons flag in the following ways to utilize the gcloud CLI:

gcloud container clusters create CLUSTER_NAME \ — cluster-version=VERSION \ — addons=RayOperator

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GKE hosts and controls the Ray Operator on your behalf after it is enabled. After a cluster is created, your cluster will be prepared to run Ray applications and build other Ray clusters.

Record-keeping and observation

When implementing Ray in a production environment, efficient logging and metrics are crucial. Optional capabilities of the GKE Ray Operator allow for the automated gathering of logs and data, which are then seamlessly stored in Cloud Logging and Cloud Monitoring for convenient access and analysis.

When log collection is enabled, all logs from the Ray cluster Head node and Worker nodes are automatically collected and saved in Cloud Logging. The generated logs are kept safe and easily accessible even in the event of an unintentional or intentional shutdown of the Ray cluster thanks to this functionality, which centralizes log aggregation across all of your Ray clusters.

By using Managed Service for Prometheus, GKE may enable metrics collection and capture all system metrics exported by Ray. System metrics are essential for tracking the effectiveness of your resources and promptly finding problems. This thorough visibility is especially important when working with costly hardware like GPUs. You can easily construct dashboards and set up alerts with Cloud Monitoring, which will keep you updated on the condition of your Ray resources.

TPU assistance

Large machine learning model training and inference are significantly accelerated using Tensor Processing Units (TPUs), which are custom-built hardware accelerators. Ray and TPUs may be easily used with its AI Hypercomputer architecture to scale your high-performance ML applications with ease.

By adding the required TPU environment variables for frameworks like JAX and controlling admission webhooks for TPU Pod scheduling, the GKE Ray Operator simplifies TPU integration. Additionally, autoscaling for Ray clusters with one host or many hosts is supported.

Reduce the delay at startup

When operating AI workloads in production, it is imperative to minimize start-up delay in order to maximize the utilization of expensive hardware accelerators and ensure availability. When used with other GKE functions, the GKE Ray Operator can significantly shorten this startup time.

You can achieve significant speed gains in pulling images for your Ray clusters by hosting your Ray images on Artifact Registry and turning on image streaming. Huge dependencies, which are frequently required for machine learning, can lead to large, cumbersome container images that take a long time to pull. For additional information, see Use Image streaming to pull container images. Image streaming can drastically reduce this image pull time.

Moreover, model weights or container images can be preloaded onto new nodes using GKE secondary boot drives. When paired with picture streaming, this feature can let your Ray apps launch up to 29 times faster, making better use of your hardware accelerators.

Scale Ray is currently being produced

A platform that grows with your workloads and provides a simplified Pythonic experience that your AI developers are accustomed to is necessary to stay up with the quick advances in AI. This potent trifecta of usability, scalability, and dependability is delivered by Ray on GKE. It’s now simpler than ever to get started and put best practices for growing Ray in production into reality with the GKE Ray Operator.

Read more on govindhtech.com

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Cloud Monitoring and Management

[bizessenceaustralia]

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[virtualizationhowto]

Netdata VMware vSphere Monitoring Configuration

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Netdata has come onto the scene as a very easy-to-configure and quick time-to-value monitoring solution. It is cloud-based, and you can easily use it to monitor endpoints on-premises, including virtualization and hypervisor environments like VMware vSphere. Table of contentsWhat is Netdata?1. Install a Netdata collector system2. Configure Netdata to monitor VMware vSphere3. Using the edit-config…

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[atlantisitgroup]

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[govindhtech]

Google Cloud Composer Airflow DAG And Task Concurrency

Google Cloud Composer

One well-liked solution for coordinating data operations is Apache Airflow. Authoring, scheduling, and monitoring pipelines is made possible with Google Cloud’s fully managed workflow orchestration solution, Google Cloud Composer, which is based on Apache Airflow.

Apache Airflow DAG

The subtleties of DAG (Directed Acyclic Graph) and task concurrency can be frightening, despite Airflow’s widespread use and ease of use. This is because an Airflow installation involves several different components and configuration settings. Your data pipelines’ fault-tolerance, scalability, and resource utilisation are all improved by comprehending and putting concurrent methods into practice. The goal of this guide is to cover all the ground on Airflow concurrency at four different levels:

The Composer Environment

Installation of Airflow

DAG

Task

You can see which parameters need to be changed to make sure your Airflow tasks function exactly as you intended by referring to the visualisations in each section. Now let’s get going!

The environment level parameters for Cloud Composer 2

This represents the complete Google Cloud service. The managed infrastructure needed to run Airflow is entirely included, and it also integrates with other Google Cloud services like Cloud Monitoring and Cloud Logging. The DAGs, Tasks, and Airflow installation will inherit the configurations at this level.

Minimum and maximum number of workers

You will define the minimum and maximum numbers of Airflow Workers as well as the Worker size (processor, memory, and storage) while creating a Google Cloud Composer environment. The value of worker_concurrency by default will be set by these configurations.

Concurrency among workers

Usually, a worker with one CPU can manage twelve tasks at once. The default worker concurrency value on Cloud Composer 2 is equivalent to:

A minimal value out of 32, 12 * worker_CPU and 8 * worker_memory in Airflow 2.3.3 and later versions.

Versions of Airflow prior to 2.3.3 have 12 * worker_CPU.

For example:

Small Composer environment:

worker_cpu = 0.5

worker_mem = 2

worker_concurrency = min(32, 12*0.5cpu, 8*2gb) = 6

Medium Composer environment:

worker_cpu = 2

worker_mem = 7.5

worker_concurrency = min(32, 12*2cpu, 8*7.5gb) = 24

Large Composer environment:

worker_cpu = 4

worker_mem = 15

worker_concurrency = min(32, 12*4cpu, 8*15gb) = 32

Autoscaling of workers

Two options are related to concurrency performance and the autoscaling capabilities of your environment:

The bare minimum of Airflow employees

The parameter [celery]worker_concurrency

In order to take up any waiting tasks, Google Cloud Composer keeps an eye on the task queue and creates more workers. When [celery]worker_concurrency is set to a high value, each worker can accept a large number of tasks; hence, in some cases, the queue may never fill and autoscaling may never occur.

Each worker would pick up 100 tasks, for instance, in a Google Cloud Composer setup with two Airflow workers, [celery]worker_concurrency set to 100, and 200 tasks in the queue. This doesn’t start autoscaling and leaves the queue empty. Results may be delayed if certain jobs take a long time to finish since other tasks may have to wait for available worker slots.

Taking a different approach, we can see that Composer’s scaling is based on adding up all of the Queued Tasks and Running Tasks, dividing that total by [celery]worker_concurrency, and then ceiling()ing the result. The target number of workers is ceiling((11+8)/6) = 4 if there are 11 tasks in the Running state and 8 tasks in the Queued state with [celery]worker_concurrency set to 6. The composer aims to reduce the workforce to four employees.

Airflow installation level settings

This is the Google Cloud Composer-managed Airflow installation. It consists of every Airflow component, including the workers, web server, scheduler, DAG processor, and metadata database. If they are not already configured, this level will inherit the Composer level configurations.

Worker concurrency ([celery]): For most use scenarios, Google Cloud Composer‘s default defaults are ideal, but you may want to make unique tweaks based on your environment.

core.parallelism: the most jobs that can be executed simultaneously within an Airflow installation. Infinite parallelism=0 is indicated.

Maximum number of active DAG runs per DAG is indicated by core.max_active_runs_per_dag.

Maximum number of active DAG tasks per DAG is indicated by core.max_active_tasks_per_dag.

Queues

It is possible to specify which Celery queues jobs are sent to when utilising the CeleryExecutor. Since queue is a BaseOperator attribute, any job can be assigned to any queue. The celery -> default_queue section of the airflow.cfg defines the environment’s default queue. This specifies which queue Airflow employees listen to when they start as well as the queue to which jobs are assigned in the absence of a specification.

Airflow Pools

It is possible to restrict the amount of simultaneous execution on any given collection of tasks by using airflow pools. Using the UI (Menu -> Admin -> Pools), you can manage the list of pools by giving each one a name and a number of worker slots. Additionally, you may choose there whether the pool’s computation of occupied slots should take postponed tasks into account.

Configuring the DAG level

The fundamental idea behind Airflow is a DAG, which groups tasks together and arranges them according to linkages and dependencies to specify how they should operate.

max_active_runs: the DAG’s maximum number of active runs. Once this limit is reached, the scheduler will stop creating new active DAG runs. backs to the core.If not configured, max_active_runs_per_dag

max_active_tasks: This is the maximum number of task instances that are permitted to run concurrently throughout all active DAG runs. The value of the environment-level option max_active_tasks_per_dag is assumed if this variable is not defined.

Configuring the task level

Concerning Airflow Tasks

A Task Instance may be in any of the following states:

none: Because its dependencies have not yet been satisfied, the task has not yet been queued for execution.

scheduled: The task should proceed because the scheduler has concluded that its dependencies are satisfied.

queued: An Executor has been given the task, and it is awaiting a worker.

running: A worker (or a local/synchronous executor) is performing the task.

success: There were no mistakes in the task’s completion.

restarting: While the job was operating, an external request was made for it to restart.

failed: A task-related fault prevented it from completing.

skipped: Branching, LatestOnly, or a similar reason led to the job being skipped.

upstream_failed: The Trigger Rule indicates that we needed it, but an upstream task failed.

up_for_retry: The job failed, but there are still retries available, and a new date will be set.

up_for_reschedule: A sensor that is in reschedule mode is the task.

deferred: A trigger has been assigned to complete the task.

removed: Since the run began, the task has disappeared from the DAG.

A task should ideally go from being unplanned to being scheduled, queued, running, and ultimately successful. Unless otherwise indicated, tasks will inherit concurrency configurations established at the DAG or Airflow level. Configurations particular to a task comprise:

Pool: the area where the task will be carried out. Pools can be used to restrict the amount of work that can be done in parallel.

The maximum number of concurrently executing task instances across dag_runs per task is controlled by max_active_tis_per_dag.

Deferrable Triggers and Operators

Even when they are idle, Standard Operators and Sensors occupy a full worker slot. For instance, if you have 100 worker slots available for Task execution and 100 DAGs are waiting on an idle but running Sensor, you will not be able to run any other tasks, even though your entire Airflow cluster is effectively idle.

Deferrable operators can help in this situation.

When an operator is constructed to be deferrable, it can recognise when it needs to wait, suspend itself, free up the worker, and assign the task of resuming to something known as a trigger. Because of this, it is not consuming a worker slot while it is suspended (delayed), which means that your cluster will use a lot fewer resources on inactive Operators and Sensors. It should be noted that delayed tasks do not automatically eat up pool slots; however, you can modify the pool in question to make them do so if desired.

Triggers are short, asynchronous Python code segments that are intended to execute concurrently within a single Python session; their asynchrony allows them to coexist effectively. Here’s a rundown of how this procedure operates:

When a task instance, also known as a running operator, reaches a waiting point, it defers itself using a trigger connected to the event that should resume it. This allows the employee to focus on other tasks.

A triggerer process detects and registers the new Trigger instance within Airflow.

The source task of the trigger gets rescheduled after it fires. The trigger is triggered.

The task is queued by the scheduler to be completed on a worker node.

Sensor Modes

Sensors can operate in two separate modes as they are mostly idle, which allows you to use them more effectively:

Poke (default): Throughout its whole duration, the Sensor occupies a worker slot.

Reschedule: The Sensor sleeps for a predetermined amount of time in between checks, only using a worker slot when it is checking. Part of problem is resolved when Sensors are run in rescheduled mode, which restricts their operation to predetermined intervals. However, this mode is rigid and only permits the use of time as justification for resuming operations.

As an alternative, some sensors let you specify deferrable=True, which transfers tasks to a different Triggerer component and enhances resource efficiency even more.

Distinction between deferrable=True and mode=’reschedule’ in sensors

Sensors in Airflow wait for certain requirements to be satisfied before moving on to downstream operations. When it comes to controlling idle times, sensors have two options: mode=’reschedule’ and deferrable=True. If the condition is not met, the sensor can reschedule itself thanks to the mode=’reschedule’ parameter specific to the BaseSensorOperator in Airflow. In contrast, deferrable=True is a convention used by some operators to indicate that the task can be retried (or deferred), but it is not a built-in parameter or mode in the Airflow. The individual operator implementation may cause variations in the behaviour of the task’s retry.

Read more on govindhtech.com

[marilynsmith521]

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Cloud Monitoring and Management

[crafsol]

At Crafsol, we help you develop an effective Cloud modernization strategy that meets your business needs along with quality and cost optimization. We are a Pune, India-based IT service that offers Cloud Migration Consulting Services across the globe.

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Cloud Monitoring: About, Benifits, Cost and Precaustions

Today, 'Cloud Monitoring' is used to refer to a set of web-based applications that are designed and implemented to provide real-time information on the performance of servers. Cloud computing helps in managing the huge IT infrastructure while offering improved control and visibility. The main advantages of using this technology are: better enterprise security reduced IT costs, easier reporting, easy updates, and integration with other applications and tools. Cloud-based services let you easily scale up and down your requirements. They are easy to use, flexible, and can be installed and configured by any IT professional.

Cloud services make it easy for organizations to gain access to real-time data from any location. They are ideal for financial and customer care companies that need to continuously analyze and monitor their clients' activity. In the enterprise, many applications can be accessed simultaneously by different users. So, each department can run various applications in parallel without slowing down the overall network performance. The cloud monitoring services offered by various companies offer more robust and flexible functionality compared to what is available in on-premises software.

Companies in the web development, marketing, and eCommerce industry can take advantage of advanced Cloud Monitoring analytics applications to monitor all their websites. They can find out current visitor activity, detect errors and troubleshoot the site in real-time. Companies also benefit from improved deployment procedures, increased visibility and control, increased efficiency and scalability, reduced cost, improved visibility and control, simplified recovery, and improved flexibility.

Read more @ https://latestbizjournal.com/2021/02/19/cloud-monitoring-enable-easy-access-to-obtain-real-time-information-from-any-location/

[siennastewart2020]

Google capped off a busy 2020 by announcing some of its cloud plans for 2021. Google Cloud is adding 3 new global regions to its network, including Chile, Saudi Arabia, and a 2nd region in Germany.