## CPUs and GPUs: What to use when for AI/ML workloads This is a multi-series blog on GPUs, how they intersect with Kubernetes and containers. In this blog, we will discuss how CPUs and GPUs are architecturally similar and different. We will also review when it is ideal to use a CPU vs a GPU. ## Kubernetes v1.30 for Rafay MKS Our [upcoming release scheduled for June](https://docs.rafay.co/releasenotes/preview/upcoming/) to our Preview environment adds support for a number of new features and enhancements. We will write about these in separate blogs. This blog is focused on support for [Kubernetes v1.30](https://kubernetes.io/blog/2024/04/17/kubernetes-v1-30-release/) with [Rafay MKS](https://docs.rafay.co/clusters/upstream/baremetal_vm/overview/) (i.e. upstream Kubernetes for bare metal and VM based environments). Both new cluster provisioning and in-place upgrades of existing clusters are supported. As with most Kubernetes releases, this version also deprecates and removes a number of features. To ensure there is zero impact to our customers, we have made sure that **every feature** in the Rafay Kubernetes Operations Platform has been validated on this Kubernetes version. This will be promoted from Preview to Production in a few days and will be made available to all customers. ## Introduction to JupyterHub This is part of a blog series on AI/Machine Learning. In the [previous blog](https://docs.rafay.co/blog/2024/03/24/introduction-to-jupyter-notebooks/), we discussed Jupyter Notebooks, how they are different and the challenges organizations run into at scale with it. In this blog, we will look at how organizations can use [JupyterHub](https://jupyter.org/hub) to provide access to Jupyter notebooks **as a centralized service** for their data scientists. ## Introduction to Jupyter Notebooks Jupyter Notebook is open-source software created and maintained by the [Jupyter community](https://discourse.jupyter.org/). A Jupyter notebook allows for the creation and sharing of **documents with code and rich text elements**. It works with over 40 programming languages including Python, R, and Ruby making it versatile and flexible for data scientists. In this introductory blog to Jupyter notebooks, we will look at "why it exists" and "what it looks like". ## Choosing between Amazon ECS and EKS We frequently get asked by users that are currently on AWS whether they should be using Amazon ECS or EKS to deploy and operate their **containerized applications**. Since this is such a common question and the answers are somewhat nuanced, we wanted to share our thoughts and recommendations for the benefit of all users. ## Resize and Right Size Applications on Kubernetes It is a well understood fact on Kubernetes that there is a significant amount of "wastage" of expensive cloud/infrastructure because of **over provisioned** applications. In this blog, we will look at how app developers and platform teams can save their organizations **millions of dollars** by right sizing their applications using a [free, open-source tool](https://github.com/RafaySystems/getstarted/tree/master/tools/resize) called **resize** that we recently developed for our customers. Important Note that this is just **one tool** in a comprehensive **Cost Control** solution that Rafay provides our customers. Please [contact](mailto:sales@rafay.co) us if you are interested in this. ## Kubernetes v1.29 for Rafay MKS Our [recent release update in Feb](https://docs.rafay.co/releasenotes/2024/feb/) to our Preview environment adds support for a number of new features and enhancements. We will write about the other new features in separate blogs. This blog is focused on support for [Kubernetes v1.29](https://kubernetes.io/blog/2023/12/13/kubernetes-v1-29-release/) with [Rafay MKS](https://docs.rafay.co/clusters/upstream/baremetal_vm/overview/) (i.e. upstream Kubernetes for bare metal and VM based environments). > This release will be promoted from Preview to Production in a few days and will be made available to all customers. Both new cluster provisioning and in-place upgrades of existing clusters are supported. As with most Kubernetes releases, this version also deprecates and removes a number of features. To ensure there is zero impact to our customers, we have made sure that **every feature** in the Rafay Kubernetes Operations Platform has been validated on this Kubernetes version. ## Amazon EKS v1.29 using Rafay Our [recent release update in Feb](https://docs.rafay.co/releasenotes/2024/feb/) to our Preview environment adds support for a number of new features and enhancements. We will write about the other new features in separate blogs. This blog is focused on our turnkey support for [Amazon EKS v1.29](https://docs.rafay.co/clusters/eks/overview/). Both **new cluster provisioning** and **in-place upgrades** of existing EKS clusters are supported. As with most Kubernetes releases, this version also deprecates and removes a number of features. To ensure there is zero impact to our customers, we have made sure that **every feature** in the Rafay Kubernetes Operations Platform has been validated on this Kubernetes version. This release will be promoted from Preview to Production in a few days and will be made available to all customers. > Note that **no action** is needed on the part of our SaaS customers with the new release. Once the rollout is completed, all they need to do is learn about the new features and determine **how** and **when** they would like to use them. ## Google GKE v1.28 Clusters using Rafay Our recent release update in Jan to our Preview environment adds support for a number of new features and enhancements. We will write about the other new features in separate blogs. This blog is focused on our turnkey support for GKE v1.28. This version of GKE was Generally Available (GA) starting **Jan 2024** and goes end of life in **Nov 2024**. Both **new cluster provisioning** and **in-place upgrades** of existing GKE clusters are supported. > This release will be promoted from Preview to Production in a few days and will be made available to all customers. ## Challenges of Container Vulnerability Management In the dynamic landscape of modern application development, containers have emerged as the cornerstone of microservices, revolutionizing the way software is deployed and managed. However, as we celebrate the agility and efficiency brought by containers, a critical concern looms large in the background — the chaotic state of vulnerability management within the container ecosystem. Several noteworthy challenges persist : - Current-generation container vulnerability scanners lack contextual considerations and actionable suggestions, posing difficulties in effectively addressing and resolving vulnerabilities. - Security teams, accustomed to traditional methods, face challenges in adapting to container security, where the absence of clear context and mitigation measures complicates the resolution of issues. - Open source container projects exhibit inconsistent practices in disclosing vulnerabilities and providing information about fixed versions, further complicating the task of maintaining a secure environment.