VMware has launched a training portal that allows you to improve your skills and knowledge of Tanzu and Kubernetes. On the portal ModernAppsNinja there are many free trainings that will bring you closer to the ModernApps topic. You will find a variety of courses, labs, tutorials, learning materials and handson tutorials. For example, if you want to prepare for the Certified Kubernetes Administrator (CKA), or the VCP ModernApps, you can easily find the necessary resources and tools there. But also useful tutorials such as how to use VSCode.
Using GPU in container workloads is an important demand by developers who work with machine learning and artificial intelligence.
You can create a custom VM class where a VI admin can define a vGPU specification for that class. Developers can use this class to assign GPU resources to the workload. The vm class will define node placement an vGPU profile.
This not only available to GPU enabled TKG clusters, but also for standalone VMs. The use of custom classes will simplify the consumption of GPU resources in ML/AI applications.
See a sample class below
kind: TanzuKubernetesCluster apiVersion: run.tanzu.vmware.com/v1 metadata: name: GPU-Cluster spec: topology: workers: count: 3 class: gpu-vmclass distribution: v1.20.2
This class can be consumed for example in a VM
kind: VirtualMachine metadata: name: gpu-vm namespace: tkg-dev spec: networkInterfaces: - networkName: "dev-network" networkType: vsphere-distributed classname: gpu-vmclass imageName: ubuntu-custom-gpu storageClass: GPU-vm-policy
This blogpost used to be part of my recent vSphere7 Update3 What’s new artice, but has been withdrawn at VMware’s request with an extended embargo until October 5 2021.
This blogpost was under embargo until 28th of September 2021 8:00am (PT) / 17:00 (CEST). The fact that you can read this now means that vSphere 7 Update 3 has (probably) already been released.
[Update 29th Sept 2021]: Download is not yet available. Maybe we need to wait until VMworld2021 next week.
VMware vSphere 7 Update3 comes with a wide range of innovations. They can be categorized into the sections below:
- Tanzu with Kubernetes
- Lifecycle, Upgrade and Patching
- Artificial Intelligence & Machine Learning
- Resource Management
- Availability & Resiliency
- Security & Compliance
- Guest OS and Workloads
- vSphere Management & APIs
Another bunch of features goes into vSAN. But these features will be covered in an extra post.Continue reading “vSphere 7 Update 3 – What’s New”
This will be a multi-part post focused on the VMware Bitfusion product. I will give an introduction to the technology, how to set up a Bitfusion server and how to use its services from Kubernetes pods.
- Part 1 : A primer to Bitfusion
- Part 2 : Bitfusion server setup
- Part 3 : Using Bitfusion from Kubernetes pods and TKGS. (this article)
We saw in parts 1 and 2 what Bitfusion is and how to set up a Bitfusion Server cluster. The challenging part is to make this Bitfusion cluster usable from Kubernetes pods.
In order for containers to access Bitfusion GPU resources, a few general conditions must be met.
I assume in this tutorial that we have a configured vSphere-Tanzu cluster available, as well as a namespace, a user, a storage class and the Kubernetes CLI tools. The network can be organized with either NSX-T or distributed vSwitches and a load balancer such as the AVI load balancer.
In the PoC described, Tanzu on vSphere was used without NSX-T for simplicity. The AVI load balancer, now officially called NSX-Advanced load balancer, was used.
We also need a Linux system with access to Github or a mirror to prepare the cluster.
The procedure in a nutshell:
- Create TKGS cluster
- Get Bitfusion baremetal token laden and create K8s secret
- Load Git project and modify makefile
- Deploy device-plugin to TKGS-cluster
- Pod deployment