Deep dive into Kubeflow Pipelines, and details about Tekton backend implementation for KFP, including compiler, logging, artifacts and lineage tracking
End to end Machine Learning using Kubeflow - Build, Train, Deploy and ManageAnimesh Singh
With the breadth of sheer functionalities which need to be addressed in the Machine Learning world around building, training, serving and managing models, getting it done in a consistent, composable, portable, and scalable manner is hard. The Kubernetes framework is well suited to address these issues, which is why it's a great foundation for deploying ML workloads. Kubeflow is designed to take advantage of these benefits. In this talk, we are going to address how to make it easy for everyone to develop, deploy, and manage portable, scalable ML everywhere and support the full lifecycle Machine Learning using open source technologies like Kubeflow, Tensorflow, PyTorch,Tekton, Knative, Istio and others. We are going to discuss how to enable distributed training of models, model serving, canary rollouts, drift detection, model explainability, metadata management, pipelines and others. Additionally we will discuss Watson productization in progress based on Kubeflow Pipelines and Tekton, and point to Kubeflow Dojo materials and follow-on workshops.
Deep dive into Kubeflow Pipelines, and details about Tekton backend implementation for KFP, including compiler, logging, artifacts and lineage tracking
End to end Machine Learning using Kubeflow - Build, Train, Deploy and ManageAnimesh Singh
With the breadth of sheer functionalities which need to be addressed in the Machine Learning world around building, training, serving and managing models, getting it done in a consistent, composable, portable, and scalable manner is hard. The Kubernetes framework is well suited to address these issues, which is why it's a great foundation for deploying ML workloads. Kubeflow is designed to take advantage of these benefits. In this talk, we are going to address how to make it easy for everyone to develop, deploy, and manage portable, scalable ML everywhere and support the full lifecycle Machine Learning using open source technologies like Kubeflow, Tensorflow, PyTorch,Tekton, Knative, Istio and others. We are going to discuss how to enable distributed training of models, model serving, canary rollouts, drift detection, model explainability, metadata management, pipelines and others. Additionally we will discuss Watson productization in progress based on Kubeflow Pipelines and Tekton, and point to Kubeflow Dojo materials and follow-on workshops.
KFServing - Serverless Model InferencingAnimesh Singh
Deep dive into KFServing: Serverless Model Inferencing Platform built on top of KNative and Istio. Part of the Kubeflow project, and deployed in production across organizations.
The slides used during the mlops.community meetup on KFServing. We looked inside some popular model formats like the SavedModel of Tensorflow and the Model Archiver of PyTorch, to understand how the weights of the NN are saved there, the graph and the signature concepts. We discussed the relevant resources of the deployment stack of Istio (the ingress gateway, the sidecar and the virtual service) and Knative (the service and revisions), as well as Kubeflow and KFServing. Then we got into the design details of KFServing, its custom resources, its controller. Then we spent some time to discuss the monitoring stack, the metrics of the servable as well as the model metrics which end up to Prometheus. We looked at the inference payload and prediction logging to observe drifts and trigger the retraining of the pipeline. Finally, a few words about the awesome community and the roadmap of the project on multi-model serving and inference routing graph.
Kubernetes Helm (Boulder Kubernetes Meetup, June 2016)Matt Butcher
Kubernetes Helm is the package manager for Kubernetes. In this presentation, we walk through the basics of Helm, Tiller, and the Helm Charts file format.
Serverless Workflow: New approach to Kubernetes service orchestration | DevNa...Red Hat Developers
With the rise of Serverless Architectures, Workflows have gained a renewed interest and usefulness. Typically thought of as centralized and monolithic, they now play a key role in service orchestration and coordination as well as modular processing. With many different architecture approaches already in place, the Cloud Native Computing Foundation (CNCF) has started an initiative to specify serverless workflows to ensure portability and vendor neutrality. In this talk, we introduce the CNCF Serverless Workflow specification and provide examples and demos on top of Kogito, Red Hat's business automation toolkit. You will learn: 1- The what, why, and how of the CNCF Serverless Workflow specification 2- Why using the Serverless Workflow specification and orchestration can improve your serverless architecture 3- When to use CNCF Serverless Workflow and Kogito together and the benefits derived.
Introduction to Helm, the package manager for Kubernetes: Create and use Kubernetes charts. Deploy releases on a cluster ... and rollback your releases. Get for instance Prometheus up and running with just a single command.
Kubernetes Application Deployment with Helm - A beginner Guide!Krishna-Kumar
Google DevFest2019 Presentation at Infosys Campus Bangalore. Application deployment in Kubernetes with Helm is demo'ed in Google Kubernetes Engine (GKE). This is an introductory session on Helm. Several references are given in it to further explore helm3 as it is in Beta state now.
Flink Forward Berlin 2017: Dominik Bruhn - Deploying Flink Jobs as Docker Con...Flink Forward
This talk will focus on how to package, distribute and deploy Flink Jobs by leveraging existing docker technology: Previously deploying of Flink Jobs has been a manual job which leads into errors. In this talk, we present an approach which works well in an CI/CD environment by automating most steps: From the code of a Flink Job in a repository to a running Job on an YARN cluster.
Future of Apache Flink Deployments: Containers, Kubernetes and More - Flink F...Till Rohrmann
Container technology experiences an ever increasing adoption throughout many industries. Not only does this technology make your applications portable across different machines and operating systems, it also allows to scale applications in a matter of seconds. Moreover, it significantly simplifies and speeds up deployments which decreases development and operation costs. Consequently, more and more Flink deployments run in containerized environments which poses new challenges for Flink.
In this talk, we will take a look at Flink's current and future container support which will make it a first class citizen of the container world. First of all, we will explain how the new reactive execution mode will solve the problem of seamless application scaling and how it blends in with any environment. Complementary to the reactive mode, the active execution mode demonstrates its strengths when it comes to changing workloads such as batch jobs. Last but not least, we will take a look beyond Flink's own nose and investigate how Flink can be used together with Kubernetes operators or data Artisans' Application Manager. We will conclude the talk with a short demo of Flink's native Kubernetes support and giving an outlook on future developments in the container realm.
Fission: Serverless Functions for KubernetesSoam Vasani
"Serverless" functions allow users to easily create services from source code without dealing with the packaging, deployment, scaling, etc.
Fission is a serverless function framework built on Kubernetes. Users write functions and map them to HTTP routes. They don't have to deal with container images, registries or even learn Kubernetes in much detail.
Functions can be associated with HTTP routes, events, or timers. Functions consume CPU and memory resources only when running; they are started on-demand and killed when idle. Fission makes on-demand function loading very fast, by keeping an idle pool of containers running, in effect creating a distributed "threadpool".
Fission is useful for:
* Creating web app backends or REST APIs
* Implementing webhooks
* Writing event handlers
We'll demo the creation of a simple web app using Fission functions in Python.
We'll also show how tying together Kubernetes Watches and Fission functions make it very easy to write custom behavior triggered by changes to arbitrary resources on Kubernetes.
Flink Forward Berlin 2017: Patrick Lucas - Flink in ContainerlandFlink Forward
Apache Flink, a powerful distributed stateful stream processing framework, is an especially good fit for deployment on a containerization platform: its storage requirement is primarily external (e.g. HDFS or S3), clusters often share the lifetime of the jobs that run on them, and the flexibility of allocating resources on such a platform allows for scaling jobs up and down as necessary. In this talk I will give a brief introduction to Apache Flink, then describe the journey to making it a first-class citizen of the container world. I will cover my experience preparing to publish the “official repository” of Flink images on Docker Hub, the challenges of fitting a Flink deployment in a Kubernetes-shaped box, and the rough edges of Flink itself that were exposed by this process.
Streaming your Lyft Ride Prices - Flink Forward SF 2019Thomas Weise
At Lyft we dynamically price our rides with a combination of various data sources, machine learning models, and streaming infrastructure for low latency, reliability and scalability. Dynamic pricing allows us to quickly adapt to real world changes and be fair to drivers (by say raising rates when there's a lot of demand) and fair to passengers (by let’s say offering to return 10 mins later for a cheaper rate). The streaming platform powers pricing by bringing together the best of two worlds using Apache Beam; ML algorithms in Python and Apache Flink as the streaming engine.
https://sf-2019.flink-forward.org/conference-program#streaming-your-lyft-ride-prices
These slides were used during a technical session for the Cloud-Native El Salvador community. It covers the basic Kubernetes components, some installers and main Kubernetes resources. For the demo, it was used the capabilites provided by the Horizontal Pod Autoscaler.
A practical look at the different strategies to deploy an application to Kubernetes. We list the pros and cons of each strategy and define which one to adopt depending on real world examples and use cases.
Machine learning with Apache Spark on Kubernetes | DevNation Tech TalkRed Hat Developers
The first challenge for an AI/ML practitioner is to gather the data inputs needed to feed a learning model. This is where a solution such as Apache Spark’s unified DataFrame API and a scale-out compute model allows you to execute parallelized queries against SQL, Kafka, and S3. In this session, we are going to explore the use of https://radanalytics.io/ and https://opendatahub.io/ on top of Kubernetes/OpenShift to demonstrate a dynamically scalable ETL pipeline for federated data ingestion.
Altoros Cloud Foundry Training: hands-on workshop for DevOps, Architects and ...Manuel Garcia
Dealing with high-load services of all kinds makes us to seek for new generation tools to build reliable, scalable, and 100% available systems. At this workshop, you will have chance to dive deep into how Cloud Foundry solves the issues of portability, scalability, reliability and extensibility.
Hands-on agenda:
- Application lifecycle: from development to production
- Deep dive into Cloud Foundry architecture
- Where to deploy Cloud Foundry
- How to Deploy Cloud Foundry: from small evaluation to hundreds VMs High Availability production environments
- Scale up and down your infrastructure. Can you auto scale?
- Zero downtime upgrades
- Auto Healing deployments
- Cloud Foundry system logging and monitoring
- Services: types, current restrictions and expectations
KFServing - Serverless Model InferencingAnimesh Singh
Deep dive into KFServing: Serverless Model Inferencing Platform built on top of KNative and Istio. Part of the Kubeflow project, and deployed in production across organizations.
The slides used during the mlops.community meetup on KFServing. We looked inside some popular model formats like the SavedModel of Tensorflow and the Model Archiver of PyTorch, to understand how the weights of the NN are saved there, the graph and the signature concepts. We discussed the relevant resources of the deployment stack of Istio (the ingress gateway, the sidecar and the virtual service) and Knative (the service and revisions), as well as Kubeflow and KFServing. Then we got into the design details of KFServing, its custom resources, its controller. Then we spent some time to discuss the monitoring stack, the metrics of the servable as well as the model metrics which end up to Prometheus. We looked at the inference payload and prediction logging to observe drifts and trigger the retraining of the pipeline. Finally, a few words about the awesome community and the roadmap of the project on multi-model serving and inference routing graph.
Kubernetes Helm (Boulder Kubernetes Meetup, June 2016)Matt Butcher
Kubernetes Helm is the package manager for Kubernetes. In this presentation, we walk through the basics of Helm, Tiller, and the Helm Charts file format.
Serverless Workflow: New approach to Kubernetes service orchestration | DevNa...Red Hat Developers
With the rise of Serverless Architectures, Workflows have gained a renewed interest and usefulness. Typically thought of as centralized and monolithic, they now play a key role in service orchestration and coordination as well as modular processing. With many different architecture approaches already in place, the Cloud Native Computing Foundation (CNCF) has started an initiative to specify serverless workflows to ensure portability and vendor neutrality. In this talk, we introduce the CNCF Serverless Workflow specification and provide examples and demos on top of Kogito, Red Hat's business automation toolkit. You will learn: 1- The what, why, and how of the CNCF Serverless Workflow specification 2- Why using the Serverless Workflow specification and orchestration can improve your serverless architecture 3- When to use CNCF Serverless Workflow and Kogito together and the benefits derived.
Introduction to Helm, the package manager for Kubernetes: Create and use Kubernetes charts. Deploy releases on a cluster ... and rollback your releases. Get for instance Prometheus up and running with just a single command.
Kubernetes Application Deployment with Helm - A beginner Guide!Krishna-Kumar
Google DevFest2019 Presentation at Infosys Campus Bangalore. Application deployment in Kubernetes with Helm is demo'ed in Google Kubernetes Engine (GKE). This is an introductory session on Helm. Several references are given in it to further explore helm3 as it is in Beta state now.
Flink Forward Berlin 2017: Dominik Bruhn - Deploying Flink Jobs as Docker Con...Flink Forward
This talk will focus on how to package, distribute and deploy Flink Jobs by leveraging existing docker technology: Previously deploying of Flink Jobs has been a manual job which leads into errors. In this talk, we present an approach which works well in an CI/CD environment by automating most steps: From the code of a Flink Job in a repository to a running Job on an YARN cluster.
Future of Apache Flink Deployments: Containers, Kubernetes and More - Flink F...Till Rohrmann
Container technology experiences an ever increasing adoption throughout many industries. Not only does this technology make your applications portable across different machines and operating systems, it also allows to scale applications in a matter of seconds. Moreover, it significantly simplifies and speeds up deployments which decreases development and operation costs. Consequently, more and more Flink deployments run in containerized environments which poses new challenges for Flink.
In this talk, we will take a look at Flink's current and future container support which will make it a first class citizen of the container world. First of all, we will explain how the new reactive execution mode will solve the problem of seamless application scaling and how it blends in with any environment. Complementary to the reactive mode, the active execution mode demonstrates its strengths when it comes to changing workloads such as batch jobs. Last but not least, we will take a look beyond Flink's own nose and investigate how Flink can be used together with Kubernetes operators or data Artisans' Application Manager. We will conclude the talk with a short demo of Flink's native Kubernetes support and giving an outlook on future developments in the container realm.
Fission: Serverless Functions for KubernetesSoam Vasani
"Serverless" functions allow users to easily create services from source code without dealing with the packaging, deployment, scaling, etc.
Fission is a serverless function framework built on Kubernetes. Users write functions and map them to HTTP routes. They don't have to deal with container images, registries or even learn Kubernetes in much detail.
Functions can be associated with HTTP routes, events, or timers. Functions consume CPU and memory resources only when running; they are started on-demand and killed when idle. Fission makes on-demand function loading very fast, by keeping an idle pool of containers running, in effect creating a distributed "threadpool".
Fission is useful for:
* Creating web app backends or REST APIs
* Implementing webhooks
* Writing event handlers
We'll demo the creation of a simple web app using Fission functions in Python.
We'll also show how tying together Kubernetes Watches and Fission functions make it very easy to write custom behavior triggered by changes to arbitrary resources on Kubernetes.
Flink Forward Berlin 2017: Patrick Lucas - Flink in ContainerlandFlink Forward
Apache Flink, a powerful distributed stateful stream processing framework, is an especially good fit for deployment on a containerization platform: its storage requirement is primarily external (e.g. HDFS or S3), clusters often share the lifetime of the jobs that run on them, and the flexibility of allocating resources on such a platform allows for scaling jobs up and down as necessary. In this talk I will give a brief introduction to Apache Flink, then describe the journey to making it a first-class citizen of the container world. I will cover my experience preparing to publish the “official repository” of Flink images on Docker Hub, the challenges of fitting a Flink deployment in a Kubernetes-shaped box, and the rough edges of Flink itself that were exposed by this process.
Streaming your Lyft Ride Prices - Flink Forward SF 2019Thomas Weise
At Lyft we dynamically price our rides with a combination of various data sources, machine learning models, and streaming infrastructure for low latency, reliability and scalability. Dynamic pricing allows us to quickly adapt to real world changes and be fair to drivers (by say raising rates when there's a lot of demand) and fair to passengers (by let’s say offering to return 10 mins later for a cheaper rate). The streaming platform powers pricing by bringing together the best of two worlds using Apache Beam; ML algorithms in Python and Apache Flink as the streaming engine.
https://sf-2019.flink-forward.org/conference-program#streaming-your-lyft-ride-prices
These slides were used during a technical session for the Cloud-Native El Salvador community. It covers the basic Kubernetes components, some installers and main Kubernetes resources. For the demo, it was used the capabilites provided by the Horizontal Pod Autoscaler.
A practical look at the different strategies to deploy an application to Kubernetes. We list the pros and cons of each strategy and define which one to adopt depending on real world examples and use cases.
Machine learning with Apache Spark on Kubernetes | DevNation Tech TalkRed Hat Developers
The first challenge for an AI/ML practitioner is to gather the data inputs needed to feed a learning model. This is where a solution such as Apache Spark’s unified DataFrame API and a scale-out compute model allows you to execute parallelized queries against SQL, Kafka, and S3. In this session, we are going to explore the use of https://radanalytics.io/ and https://opendatahub.io/ on top of Kubernetes/OpenShift to demonstrate a dynamically scalable ETL pipeline for federated data ingestion.
Altoros Cloud Foundry Training: hands-on workshop for DevOps, Architects and ...Manuel Garcia
Dealing with high-load services of all kinds makes us to seek for new generation tools to build reliable, scalable, and 100% available systems. At this workshop, you will have chance to dive deep into how Cloud Foundry solves the issues of portability, scalability, reliability and extensibility.
Hands-on agenda:
- Application lifecycle: from development to production
- Deep dive into Cloud Foundry architecture
- Where to deploy Cloud Foundry
- How to Deploy Cloud Foundry: from small evaluation to hundreds VMs High Availability production environments
- Scale up and down your infrastructure. Can you auto scale?
- Zero downtime upgrades
- Auto Healing deployments
- Cloud Foundry system logging and monitoring
- Services: types, current restrictions and expectations
Putting Kafka In Jail – Best Practices To Run Kafka On Kubernetes & DC/OSLightbend
Apache Kafka–part of Lightbend Fast Data Platform–is a distributed streaming platform that is best suited to run close to the metal on dedicated machines in statically defined clusters. For most enterprises, however, these fixed clusters are quickly becoming extinct in favor of mixed-use clusters that take advantage of all infrastructure resources available.
In this webinar by Sean Glover, Fast Data Engineer at Lightbend, we will review leading Kafka implementations on DC/OS and Kubernetes to see how they reliably run Kafka in container orchestrated clusters and reduce the overhead for a number of common operational tasks with standard cluster resource manager features. You will learn specifically about concerns like:
* The need for greater operational knowhow to do common tasks with Kafka in static clusters, such as applying broker configuration updates, upgrading to a new version, and adding or decommissioning brokers.
* The best way to provide resources to stateful technologies while in a mixed-use cluster, noting the importance of disk space as one of Kafka’s most important resource requirements.
* How to address the particular needs of stateful services in a model that natively favors stateless, transient services.
Stas Ivaschenko (Senior Operations Analyst/DevOps engineer at Provectus, Inc)
Senior DevOps engineer, more than 10 years in IT.
AWS, Chef, Ansible, Kubernetes, Docker, Hadoop.
Best customers: Symantec, CloudMade
Kubernetes is up and running, what's next? We will talk about recent experience with Kubernetes-centric Serverless technologies. Concepts, overview of 2 frameworks: funktion from RedHat's Fabric8 and Kubeless. How they stand against AWS Lambda and how they rely on Kubernetes internals to do what they are doing.
Learn how to take advantage of the Pebble build system by creating customized wscripts that let you concatenate JS files, automatically run linters, and internationalize your apps with Cherie Williams (Developer Evangelist).
Get you Java application ready for Kubernetes !Anthony Dahanne
In this demos loaded talk we’ll explore the best practices to create a Docker image for a Java app (it’s 2019 and new comers such as Jib, CNCF buildpacks are interesting alternatives to Docker builds !) - and how to integrate best with the Kubernetes ecosystem : after explaining main Kubernetes objects and notions, we’ll discuss Helm charts and productivity tools such as Skaffold, Draft and Telepresence.
Build Your Own CaaS (Container as a Service)HungWei Chiu
In this slide, I introduce the kubernetes and show an example what is CaaS and what it can provides.
Besides, I also introduce how to setup a continuous integration and continuous deployment for the CaaS platform.
Docker and Puppet for Continuous IntegrationGiacomo Vacca
Today developers want to change the code, build and deploy often, even several times per day.
New versions of software may need to be tested on different distributions, and with different configurations.
Achieving this with Virtual Machines it’s possible, but it’s very resource and time consuming. Docker provides an incredibly good solution for this, in particular if combined with Continuous Integration tools like Jenkins and Configuration Management tools like Puppet.
This presentation focuses on the opportunities to configure automatically Docker images, use Docker containers as disposable workers during your tests, and even running your Continuous Integration system inside Docker.
Building Portable Applications with KubernetesKublr
Containers and Kubernetes enable code portability across on-premise VMs, bare metal, or multiple clouds. However, many developers may include configuration and application definitions that constrain or even eliminate application portability.
We'll outline best practices for “configuration as code” in a Kubernetes environment. He'll demonstrate how a properly constructed containerized app can be deployed to both Amazon and Azure using the Kublr platform, and how Kubernetes objects, such as persistent volumes, ingress rules, and services, can be leveraged to abstract from the infrastructure.
Running Kafka On Kubernetes With Strimzi For Real-Time Streaming ApplicationsLightbend
In this talk by Sean Glover, Principal Engineer at Lightbend, we will review how the Strimzi Kafka Operator, a supported technology in Lightbend Platform, makes many operational tasks in Kafka easy, such as the initial deployment and updates of a Kafka and ZooKeeper cluster.
See the blog post containing the YouTube video here: https://www.lightbend.com/blog/running-kafka-on-kubernetes-with-strimzi-for-real-time-streaming-applications
Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process?
In this session we will cover:
- The Art of Effective Code Reviews
- Streamlining the Review Process
- Elevating Reviews with Automated Tools
By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces
A Comprehensive Look at Generative AI in Retail App Testing.pdfkalichargn70th171
Traditional software testing methods are being challenged in retail, where customer expectations and technological advancements continually shape the landscape. Enter generative AI—a transformative subset of artificial intelligence technologies poised to revolutionize software testing.
Enterprise Resource Planning System includes various modules that reduce any business's workload. Additionally, it organizes the workflows, which drives towards enhancing productivity. Here are a detailed explanation of the ERP modules. Going through the points will help you understand how the software is changing the work dynamics.
To know more details here: https://blogs.nyggs.com/nyggs/enterprise-resource-planning-erp-system-modules/
Field Employee Tracking System| MiTrack App| Best Employee Tracking Solution|...informapgpstrackings
Keep tabs on your field staff effortlessly with Informap Technology Centre LLC. Real-time tracking, task assignment, and smart features for efficient management. Request a live demo today!
For more details, visit us : https://informapuae.com/field-staff-tracking/
In software engineering, the right architecture is essential for robust, scalable platforms. Wix has undergone a pivotal shift from event sourcing to a CRUD-based model for its microservices. This talk will chart the course of this pivotal journey.
Event sourcing, which records state changes as immutable events, provided robust auditing and "time travel" debugging for Wix Stores' microservices. Despite its benefits, the complexity it introduced in state management slowed development. Wix responded by adopting a simpler, unified CRUD model. This talk will explore the challenges of event sourcing and the advantages of Wix's new "CRUD on steroids" approach, which streamlines API integration and domain event management while preserving data integrity and system resilience.
Participants will gain valuable insights into Wix's strategies for ensuring atomicity in database updates and event production, as well as caching, materialization, and performance optimization techniques within a distributed system.
Join us to discover how Wix has mastered the art of balancing simplicity and extensibility, and learn how the re-adoption of the modest CRUD has turbocharged their development velocity, resilience, and scalability in a high-growth environment.
SOCRadar Research Team: Latest Activities of IntelBrokerSOCRadar
The European Union Agency for Law Enforcement Cooperation (Europol) has suffered an alleged data breach after a notorious threat actor claimed to have exfiltrated data from its systems. Infamous data leaker IntelBroker posted on the even more infamous BreachForums hacking forum, saying that Europol suffered a data breach this month.
The alleged breach affected Europol agencies CCSE, EC3, Europol Platform for Experts, Law Enforcement Forum, and SIRIUS. Infiltration of these entities can disrupt ongoing investigations and compromise sensitive intelligence shared among international law enforcement agencies.
However, this is neither the first nor the last activity of IntekBroker. We have compiled for you what happened in the last few days. To track such hacker activities on dark web sources like hacker forums, private Telegram channels, and other hidden platforms where cyber threats often originate, you can check SOCRadar’s Dark Web News.
Stay Informed on Threat Actors’ Activity on the Dark Web with SOCRadar!
OpenFOAM solver for Helmholtz equation, helmholtzFoam / helmholtzBubbleFoamtakuyayamamoto1800
In this slide, we show the simulation example and the way to compile this solver.
In this solver, the Helmholtz equation can be solved by helmholtzFoam. Also, the Helmholtz equation with uniformly dispersed bubbles can be simulated by helmholtzBubbleFoam.
Quarkus Hidden and Forbidden ExtensionsMax Andersen
Quarkus has a vast extension ecosystem and is known for its subsonic and subatomic feature set. Some of these features are not as well known, and some extensions are less talked about, but that does not make them less interesting - quite the opposite.
Come join this talk to see some tips and tricks for using Quarkus and some of the lesser known features, extensions and development techniques.
How Recreation Management Software Can Streamline Your Operations.pptxwottaspaceseo
Recreation management software streamlines operations by automating key tasks such as scheduling, registration, and payment processing, reducing manual workload and errors. It provides centralized management of facilities, classes, and events, ensuring efficient resource allocation and facility usage. The software offers user-friendly online portals for easy access to bookings and program information, enhancing customer experience. Real-time reporting and data analytics deliver insights into attendance and preferences, aiding in strategic decision-making. Additionally, effective communication tools keep participants and staff informed with timely updates. Overall, recreation management software enhances efficiency, improves service delivery, and boosts customer satisfaction.
We describe the deployment and use of Globus Compute for remote computation. This content is aimed at researchers who wish to compute on remote resources using a unified programming interface, as well as system administrators who will deploy and operate Globus Compute services on their research computing infrastructure.
Software Engineering, Software Consulting, Tech Lead.
Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Security,
Spring Transaction, Spring MVC,
Log4j, REST/SOAP WEB-SERVICES.
Check out the webinar slides to learn more about how XfilesPro transforms Salesforce document management by leveraging its world-class applications. For more details, please connect with sales@xfilespro.com
If you want to watch the on-demand webinar, please click here: https://www.xfilespro.com/webinars/salesforce-document-management-2-0-smarter-faster-better/
Top Features to Include in Your Winzo Clone App for Business Growth (4).pptxrickgrimesss22
Discover the essential features to incorporate in your Winzo clone app to boost business growth, enhance user engagement, and drive revenue. Learn how to create a compelling gaming experience that stands out in the competitive market.
Experience our free, in-depth three-part Tendenci Platform Corporate Membership Management workshop series! In Session 1 on May 14th, 2024, we began with an Introduction and Setup, mastering the configuration of your Corporate Membership Module settings to establish membership types, applications, and more. Then, on May 16th, 2024, in Session 2, we focused on binding individual members to a Corporate Membership and Corporate Reps, teaching you how to add individual members and assign Corporate Representatives to manage dues, renewals, and associated members. Finally, on May 28th, 2024, in Session 3, we covered questions and concerns, addressing any queries or issues you may have.
For more Tendenci AMS events, check out www.tendenci.com/events
First Steps with Globus Compute Multi-User EndpointsGlobus
In this presentation we will share our experiences around getting started with the Globus Compute multi-user endpoint. Working with the Pharmacology group at the University of Auckland, we have previously written an application using Globus Compute that can offload computationally expensive steps in the researcher's workflows, which they wish to manage from their familiar Windows environments, onto the NeSI (New Zealand eScience Infrastructure) cluster. Some of the challenges we have encountered were that each researcher had to set up and manage their own single-user globus compute endpoint and that the workloads had varying resource requirements (CPUs, memory and wall time) between different runs. We hope that the multi-user endpoint will help to address these challenges and share an update on our progress here.
Unleash Unlimited Potential with One-Time Purchase
BoxLang is more than just a language; it's a community. By choosing a Visionary License, you're not just investing in your success, you're actively contributing to the ongoing development and support of BoxLang.
Accelerate Enterprise Software Engineering with PlatformlessWSO2
Key takeaways:
Challenges of building platforms and the benefits of platformless.
Key principles of platformless, including API-first, cloud-native middleware, platform engineering, and developer experience.
How Choreo enables the platformless experience.
How key concepts like application architecture, domain-driven design, zero trust, and cell-based architecture are inherently a part of Choreo.
Demo of an end-to-end app built and deployed on Choreo.
AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
AI Pilot Review: The World’s First Virtual Assistant Marketing Suite
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-pilot-review/
AI Pilot Review: Key Features
✅Deploy AI expert bots in Any Niche With Just A Click
✅With one keyword, generate complete funnels, websites, landing pages, and more.
✅More than 85 AI features are included in the AI pilot.
✅No setup or configuration; use your voice (like Siri) to do whatever you want.
✅You Can Use AI Pilot To Create your version of AI Pilot And Charge People For It…
✅ZERO Manual Work With AI Pilot. Never write, Design, Or Code Again.
✅ZERO Limits On Features Or Usages
✅Use Our AI-powered Traffic To Get Hundreds Of Customers
✅No Complicated Setup: Get Up And Running In 2 Minutes
✅99.99% Up-Time Guaranteed
✅30 Days Money-Back Guarantee
✅ZERO Upfront Cost
See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
2. Kubeflow control plane
• 安装,管理和监视(Deploy, manage
and monitor)Kubeflow
• 文档(Document)
• https://www.kubeflow.org/docs/started/getting-started/
• 多样环境运行(On various environments)
• GCP/AWS/IKS/OpenShift
• Other K8S
• On-prem Linux/MacOS/Windows
• minikube/miniKF
• 命令行或Operator安装(Deployed through
command line or operator)
• 部件和应用可配置(Configuration for the
collection of components/applications)
• Use one from manifests repo, or
• Create your own
• 源码仓库(Two repos)
• kfctl https://github.com/kubeflow/kfctl
• manifests https://github.com/kubeflow/manifests https://www.kubeflow.org/docs/images/kubeflow-getting-started-diagram.svg
3. kfctl
• kfctl 控制器(the control plane for deploying and managing Kubeflow)
• Run kfctl as a CLI with KfDef configurations for different Kubernetes flavors
• kubeflow/kfctl also incubates an operator to deploy and monitor Kubeflow
• KfDef 配置文件(configurations)
• are manifests specifying a set of applications to be deployed by their kustomization and resources by
kustomize
• resources of each application are organized in the layout for kustomize to process
• kubeflow/manifests is the repo for the collection of KfDef configurations
• kustomize 资源配置生成器
• customizes raw, template-free YAML files. It patches Kubernetes resources files with a kustomization
file and various overlays.
• kustomization is also a Kubernetes resource (kind: Kustomization). It contains the generators and
transformers to be applied on the resources.
4. KfDef
• KfDef
• yaml格式配置文件(Configuration
through yaml)
• 源码(Code)
https://github.com/kubeflow/kfctl/blob/master/pkg/ap
is/apps/kfdef/v1/application_types.go
• 应用(applications)are in kustomize
form
• starting from v1.1 supports kustomize v3 in
stacks form (a kubeflow-apps application is
required)
• Also support plugins for certain platforms
(ie. Aws, Gcp)
• 支持远程或本地资源配置文件仓库
(Manifest repo can be either remote
archive or local directory)
• The directory structure for manifests follows
kustomize requirement
• Eg. Argo
Configuration in yaml Directory structure
6. Kubeflow control plane
• kfctl
• 显示所有命令(List all commands)
• $> kfctl help
• 安装和删除命令(Command line to install/uninstall Kubeflow)
• $> kfctl build –V –f <config_uri>
• $> kfctl apply –V –f <config_uri>
• $> kfctl delete –V –f <config_uri>
• <config_uri> can be remote or local
• 基本工作流程(High-level flow)
• Downloads the manifests for applications (if remote) from the repo:uri defined in the configuration
file, and caches in the local disk
• Loops through all applications’ kustomization configuration and build/apply
• Runs platform special handling if the configuration contains plugins section
7. Kubeflow control plane
• Kubeflow 应用资源配置文件仓库( manifests repo)
• Maintains the manifests for Kubeflow’s common applications
• Argo, centraldashboard, admission-webhook, basic-auth, metadata, profiles and more
• Other applications
• Each application can be built with kustomize tool
• $> kustomize build
• $> kubectl apply -k
8. Kubeflow control plane
• Kubeflow Operator
• 定制资源定义+API(CRD+API)
• Configuration file is the custom resource (CR)
• 管理Kubeflow及各应用生命周期(Operator
helps deploy, monitor and manage the
lifecycle of applications deployed on
Kubernetes and OpenShift clusters)
• Built with operator-sdk
• Learn more about operators - link
• 共享apply程序源码(Shares the same apply
function with kfctl command)
• delete function diffs from kfctl command
• 文档(Document)
• https://github.com/kubeflow/kfctl/blob/master/op
erator.md
https://miro.medium.com/max/2116/1*GYLAUB7KGCysjPgwek-pPA.jpeg
9. Kubeflow control plane
• Kubeflow Operator
• 源码结构(Code structure)
• /deploy: Contains all the k8s resources for deploying the operator image and crd
• /build: Operator image build script
• /pkg/controller: main package for operator controller logic
• /cmd/manager: main.go file for the operator go program
• 监视相关资源(Kubeflow operator watches the KfDef and other related resources)
• 两步安装Kubeflow(Two steps to install Kubeflow)
• Deploy the Kubeflow operator, then
• Install the Kubeflow by creating the KfDef CR
• 监视和管理功能(Kubeflow operator continues to monitor and manage any KfDef
CR created)
10. Kubeflow control plane
• Kubeflow operator
• 安装 Kubeflow Operator
• Operator can be deployed by command line
• $> export OPERATOR_NAMESPACE=operators
• $> kubectl create ns ${OPERATOR_NAMESPACE}
• $> cd deploy/
• $> kustomize edit set namespace
${OPERATOR_NAMESPACE}
• $> kustomize build | kubectl apply -f -
• Operator is registered on operatorhub.io, can be installed through
OLM console
• OLM discovers the Kubeflow operator from its catalog source
• 安装Kubeflow(installed either by command lines or by
subscription)
• creating a KfDef CR from command line
• download the KfDef configuration file from
kubeflow/manifests
• add metadata.name
• $> kubectl apply –f <kfdef_configuration.yaml>
• creating a subscription to the operator from the OLM console