Azure AI Foundry - Part1 - Create project

Azure AI Foundry is a unified platform that helps you design, customize, build, test, deploy, and manage generative AI applications. In this article, I will show you how to create a project and a hub, which are the first steps to building your AI solution. Let's get started!

Create project using the portal

• Sign in to the Azure AI Foundry portal https://ai.azure.com.

• Click Create project.
• The project will have an auto generated name or you can provide one. 
• You can also notice that it creates a new Hub, and Storage account, Key Vault and AI Services under a new resource group.
• Click Create.
• The project is getting created now. This may take a minute or two.

• Once it's done, it will take you to this overview page.

• On the Azure AI Foundry portal, you can use the Management center to configure/ get more details about your project, connected resources, models, endpoints etc.

• Under the Hub or Project properties, if you select the Resource Group, it will open a new browser tab and navigate to the Azure portal where you can see all the Azure resources that have been created to support your hub and project.

Create project using Azure CLI

Note: Remove any existing installation of the ml and azure-cli-ml extensions and install new.

• az extension remove -n azure-cli-ml
• az extension remove -n ml
• az extension add -n ml
• az extension update -n ml
  
  
• az login
• az account set --subscription "subscription_id"
• az group create --name "resource_group_name" --location "location_name"
  
  
• az ml workspace create --kind hub --resource-group "resource_group_name" --name "hub_name"

• $hub_id = "you will get this id from the output of previous step"

• az ml workspace create --kind project --hub-id $hub_id --resource-group "resource_group_name" --name "project_name"

In the next part we will explore the AI Services that are available within your Azure AI Foundry project. Hope this was useful. Cheers!

References

• Create an Azure AI Foundry project in Azure AI Foundry portal - Azure AI Foundry | Microsoft Learn
• Install and set up the CLI (v2) - Azure Machine Learning | Microsoft Learn
• How to create a hub using the Azure Machine Learning SDK/CLI - Azure AI Foundry | Microsoft Learn

VMware PowerCLI 101 - part10 - Verify vSphere cluster state

To verify the most common selected status attributes of the cluster:

Get-Cluster | Get-VMHost | Select Name,@{N='HAState';E={$_.ExtensionData.Runtime.DasHostState.State}},ConnectionState,PowerState,@{N='OverallStatus';E={$_.ExtensionData.OverallStatus}},@{N='ConfigStatus';E={$_.ExtensionData.ConfigStatus}},@{N='InMaintenanceMode';E={$_.ExtensionData.Runtime.InMaintenanceMode}},@{N='RebootRequired';E={$_.ExtensionData.Summary.RebootRequired}},@{N='BootTime';E={$_.ExtensionData.Runtime.BootTime}} | ft

> Get-Cluster | Get-VMHost | Select Name,@{N='HAState';E={$_.ExtensionData.Runtime.DasHostState.State}},ConnectionState,PowerState,@{N='OverallStatus';E={$_.ExtensionData.OverallStatus}},@{N='ConfigStatus';E={$_.ExtensionData.ConfigStatus}},@{N='InMaintenanceMode';E={$_.ExtensionData.Runtime.InMaintenanceMode}},@{N='RebootRequired';E={$_.ExtensionData.Summary.RebootRequired}},@{N='BootTime';E={$_.ExtensionData.Runtime.BootTime}} | ft

Name      HAState           ConnectionState PowerState OverallStatus ConfigStatus InMaintenanceMode RebootRequired BootTime
----      -------           --------------- ---------- ------------- ------------ ----------------- -------------- --------
10.90.1.4 connectedToMaster       Connected  PoweredOn        yellow       yellow             False          False 8/27/2024 7:31:10 AM
10.90.1.5 master                  Connected  PoweredOn        yellow       yellow             False          False 9/6/2024 9:08:09 PM

Hope it was useful. Cheers!

Revisiting Storage Performance Benchmarking

Few years ago, I had the opportunity to explore the intricacies of storage performance benchmarking using tools like FIO, DISKSPD, and Iometer. Those studies provided valuable insights into the performance characteristics of various storage solutions, shaping my understanding and approach to storage performance analysis. As I prepare for an upcoming project in this domain, I find it essential to revisit my previous work, reflect on the lessons learned, and share my experiences. This blog post aims to provide a comprehensive overview of my benchmarking journey and the evolving landscape of storage performance studies.

Recent advancements 

The field of storage technology has seen significant advancements in recent years. The rise of NVMe and storage-class memory technologies has also redefined high-end storage performance, offering unprecedented speed and efficiency. These advancements highlight the dynamic nature of storage performance benchmarking and underscore the importance of staying updated with the latest tools and methodologies.

Challenges

Benchmarking storage performance is not without its challenges. One of the primary difficulties is ensuring a consistent and controlled testing environment, as variations in hardware, software, and network conditions can significantly impact results. Another challenge is the selection of appropriate benchmarks that accurately reflect real-world workloads, which requires a deep understanding of the specific use cases and performance metrics. Additionally, interpreting the results can be complex, as it involves analyzing multiple metrics such as IOPS, throughput, and latency, and understanding their interplay. These challenges necessitate meticulous planning and a thorough understanding of both the benchmarking tools and the storage systems being tested.

Prior works

Following are some of the articles on storage benchmarking that I’ve published in the past:

• Benchmarking vSphere environment using HCIBench
• vSAN performance benchmarking considerations
• Stress test your storage system using Iometer
• Storage performance benchmarking of Kubernetes using FIO StatefulSet
• Benchmarking Kubernetes using K-Bench

Custom storage benchmarking framework

While there are numerous storage benchmarking tools available, such as VMFleet and HCIBench, I wanted to highlight a custom framework I developed a few years ago. Here are some reasons why we created this custom tool:

• Great learning experience: It provided valuable insights into how things work.
• Customization: Being a custom framework, it allows you to add or remove features as needed.
• Flexibility: You can modify multiple parameters to suit your requirements.
• Custom test profiles: You can create tailored storage test profiles.
• No IP assignment needed: There’s no need for IP assignment or DHCP for the stress test VMs.
• Centralized log collection: It offers centralized log collection for detailed analysis.

You can access the scripts and readme on my GitHub repository:

https://github.com/vineethac/vsan_cluster_storage_benchmarking_with_diskspd

Here is an overview.

• Profile Manifest: All storage test profiles are listed in profile_manifest.psd1. You can define as many profiles as you want.

• VM Template: A Windows VM template should be present in the vCenter server.

• Benchmarking Manifest: Details of vCenter, cluster name, VM template, number of stress test VMs per host, etc., are provided in benchmarking_manifest.psd1.

• Deploy Test VMs: deploy_test_vms.ps1 will deploy all the test VMs with pre-configured parameters.

• Start Stress Test: start_stress_test.ps1 will initiate the storage stress test process for all the profiles mentioned in profile_manifest.psd1 one by one.

• Log Collection: All log files will be automatically copied to a central location on the host from where these scripts are running.

• Cleanup: Use delete_test_vms.ps1 to clean up the stress test VMs from the cluster.

Note: These scripts were created about five years ago, and I haven’t had the opportunity to refactor them according to current best practices and new PowerShell scripting standards. I plan to enhance them in the coming months!

This overview should provide you with a clear understanding of the overall process and workflow involved in the storage benchmarking process. I hope it was useful. Cheers!

A decade of tech - My professional journey so far

Laying the Groundwork

My professional career commenced in February 2014, as a Trainee IT Services Engineer at Alamy Images. During my initial days, I was tasked with daily maintenance activities such as running tape backups, setting up Active Directory user accounts, mailboxes, and desktops for new employees. I also handled general IT support, troubleshooting various user issues within the organization.

After a few months, I had the opportunity to set up a lab infrastructure project using old decommissioned servers as part of a continuous learning initiative. This hands-on experience involved racking, stacking, and cabling physical servers, installing and configuring ESXi and Hyper-V hypervisors, FreeNAS storage servers, and deploying highly available clusters. Additionally, I gained exposure to configuring L2 network switches. This project significantly contributed to building my IT infrastructure foundation.

A year later, I was promoted to Junior IT Services Engineer, where I focused on virtualization projects. I spearheaded the migration of over 20 Dev/ Test/ UAT virtual machines from VMware to a Hyper-V cluster, enhancing system flexibility and cost-efficiency. I deployed a high-availability Hyper-V failover cluster in production and contributed to the planning and execution of a iSCSI storage server migration project.

Beyond virtualization, I worked on network infrastructure by a seamless L2 switch replacement and upgrade project with minimal operational disruption. Furthermore, I assisted in capacity planning initiatives for optimized resource utilization for both physical and virtual environments. These experiences refined my technical skills and problem-solving abilities. During this time, I developed a passion for infrastructure management and optimization, shaping my future career path.

From Junior IT Services Engineer to Storage Solutions Engineer

In January 2017, I transitioned to a Systems Development Engineer role at Dell EMC, specializing in Solutions Engineering. This marked a significant career shift as I immersed myself in the world of storage and virtualization solutions integration/development.

My daily responsibilities encompassed the installation and testing of various components, progressing from integration to validating system reliability and performance at scale. I designed and deployed multiple PowerFlex software-defined storage clusters for customer demos and proof-of-concepts, showcasing the product's performance and auto rebuild capabilities. A notable achievement was automating the storage performance benchmarking using PowerShell, FIO, and ELK stack, reducing process time from weeks to days.

I led the engineering efforts for developing a vROps management pack for PowerFlex, ensuring seamless integration and visibility. Additionally, I mastered vSphere Virtual Volumes (vVols), successfully executing integration projects between Dell storage solutions and VMware environments.

To streamline operations, I created a PowerShell module for managing PowerFlex using REST APIs and developed Ansible playbook for automated deployment of Kubernetes cluster with PowerFlex CSI driver. My expertise extended beyond systems engineering and automation as I authored and published whitepapers on disaster recovery using VMware SRM and hardware lifecycle management with Dell OME.

This period solidified my reputation as a virtualization and storage solutions expert, providing me with a deep understanding of storage architecture, performance optimization, and automation. I developed a passion for building scalable and reliable hyperconverged solutions.

From Storage Solutions Engineer to Site Reliability Engineer

In July 2021, I transitioned to a Site Reliability Engineer (SRE) role at VMware, focusing on ensuring the reliability and scalability of Kubernetes-as-a-Service project based on the vSphere with Tanzu platform.

Managing a vast infrastructure of Kubernetes clusters, I honed my skills in incident response, GitOps pipelines, automation, and monitoring. I played a crucial role in maintaining platform availability, collaborating closely with multiple internal teams and stakeholders to resolve issues and enhance service delivery. My proficiency in Python and PowerShell was instrumental in automating tasks and building custom monitoring solutions. During this time, I prepared diligently, practiced extensively, and successfully qualified for the CKA exam.

Beyond core SRE responsibilities, I explored emerging technologies. I successfully deployed and evaluated open-source language models on Kubernetes using Python, Ollama, and LangChain. In addition, I contributed to developing custom metrics for the Kubernetes-as-a-Service platform using Python, Prometheus, Grafana, and Helm.

This role deepened my expertise and ability to bridge the gap between development and operations, fostering a culture of reliability and efficiency. It has been an exciting journey of learning and growth, positioning me as a versatile IT professional with a strong foundation in both infrastructure and cloud-native technologies.

Gratitude

"This journey has been immensely fulfilling, made possible by the support and encouragement of exceptional organisations, inspiring managers, talented colleagues, friends, and family. I am truly grateful for the opportunities to learn, grow, and contribute meaningfully to driving success and making a positive impact."

The journey continues...

vSphere with Tanzu using NSX-T - Part35 - Monitoring supervisor cluster health with Python and vCenter APIs

vSphere with Tanzu Supervisor cluster is a Kubernetes platform that simplifies the deployment, management, and scaling of Kubernetes clusters. Monitoring the health of your WCP/ Supervisor clusters is crucial to ensure the smooth running of your Tanzu Kubernetes Clusters (TKCs) and applications. In this blog post, we'll explore how to use Python and vCenter APIs to verify the health of your Supervisor clusters.

You can access the Python script from my GitHub repository: https://github.com/vineethac/VMware/tree/main/vSphere_with_Tanzu/wcp_cluster_health

This script connects to the vCenter server, retrieves the cluster summary, and checks the Tanzu Supervisor cluster configuration info and prints the status of the cluster. By using this Python script, you can easily monitor the health of your Tanzu Supervisor clusters through vCenter APIs.

Hope it was useful. Cheers!

vSphere with Tanzu using NSX-T - Part34 - CPU and Memory utilization of a supervisor cluster

vSphere with Tanzu is a Kubernetes-based platform for deploying and managing containerized applications. As with any cloud-native platform, it's essential to monitor the performance and utilization of the underlying infrastructure to ensure optimal resource allocation and avoid any potential issues. In this blog post, we'll explore a Python script that can be used to check the CPU and memory allocation/ usage of a WCP Supervisor cluster.

You can access the Python script from my GitHub repository: https://github.com/vineethac/VMware/tree/main/vSphere_with_Tanzu/wcp_cluster_util

Sample screenshot of the output

The script uses the Kubernetes Python client library (kubernetes) to connect to the Supervisor cluster using the admin kubeconfig and retrieve information about the nodes and their resource utilization. The script then calculates the average CPU and memory utilization across all nodes and prints the results to the console.

Note: In my case instead of running it as a script every time, I made it an executable plugin and copied it to the system executable path. I placed it in $HOME/.krew/bin in my laptop.

Hope it was useful. Cheers!

vSphere with Tanzu using NSX-T - Part33 - Troubleshooting intermittent connection timeouts to apiserver and workloads

In the realm of managing Tanzu Kubernetes clusters (TKCs), we have encountered several challenges that hindered the smooth functioning of our applications. In this blog post, we will discuss three such cases and the workarounds we employed to resolve them.

Case 1: TKC Control Plane Node Connectivity Issues

Symptoms:

• TKC apiserver connection timeouts when attempting to connect using the kubeconfig.
• Traffic was not flowing to two of the control plane nodes.
• NSX-T web UI LB VS stats indicated this issue.

Case 2: TKC Worker Node Connectivity Issues

Symptoms:

• Workload (example: PostgreSQL cluster) connection timeouts.
• Traffic was not flowing to two of the worker nodes in the TKC.
• NSX-T web UI LB VS stats indicated this issue.

Case 3: Load Balancer Connectivity Issues

Symptoms:

• Connection timeouts when attempting to connect to a PostgreSQL workload through the load balancer VS IP.
• This issue was observed only when creating new services of type LoadBalancer in the TKC.
• We noticed datapath mempool usage for the edge nodes was above the threshold value.

Resolution/ work around

• Find the T1 router that is attached to the TKC which has connectivity issues. 
• In an Active - Standby HA configuration, you will see that there will be one Edge node that will be Active and another one in Standby status. 
• First place the Standby Edge node in NSX MM, reboot it, and then exit it from NSX MM. 
• Now, place the Active Edge node in NSX MM, there will be a slight network disruption during this failover, once it is in NSX MM, reboot it, and then exit NSX MM. 
• This should resolved the issue.

In conclusion, these cases illustrate the importance of verifying NSX-T components when managing Tanzu Kubernetes clusters. By identifying the root cause of the issues and employing effective workarounds, we were able to restore functionality and maintain the health of our applications. Stay tuned for more insights and best practices in managing Kubernetes clusters.

Hope it was useful. Cheers!