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Boosting Performance with the Latest Generations of Virtual Machines in Azure
Microsoft Azure recently announced the availability of the new generation of VMs (v6)—including the Dl/Dv6 (general purpose) and El/Ev6 (memory-optimized) series. These VMs are powered by the latest Intel Xeon processors and are engineered to deliver: Up to 30% higher per-core performance compared to previous generations. Greater scalability, with options of up to 128 vCPUs (Dv6) and 192 vCPUs (Ev6). Significant enhancements in CPU cache (up to 5× larger), memory bandwidth, and NVMe-enabled storage. Improved security with features like Intel® Total Memory Encryption (TME) and enhanced networking via the new Microsoft Azure Network Adaptor (MANA). By Microsoft Evaluated Virtual Machines and Geekbench Results The table below summarizes the configuration and Geekbench results for the two VMs we tested. VM1 represents a previous-generation machine with more vCPUs and memory, while VM2 is from the new Dld e6 series, showing superior performance despite having fewer vCPUs. VM1 features VM1 - D16S V5 (16 Vcpus - 64GB RAM) VM1 - D16S V5 (16 Vcpus - 64GB RAM) VM2 features VM2 - D16ls v6 (16 Vcpus - 32GB RAM) VM2 - D16ls v6 (16 Vcpus - 32GB RAM) Key Observations: Single-Core Performance: VM2 scores 2013 compared to VM1’s 1570, a 28.2% improvement. This demonstrates that even with half the vCPUs, the new Dld e6 series provides significantly better performance per core. Multi-Core Performance: Despite having fewer cores, VM2 achieves a multi-core score of 12,566 versus 9,454 for VM1, showing a 32.9% increase in performance. VM 1 VM 2 Enhanced Throughput in Specific Workloads: File Compression: 1909 MB/s (VM2) vs. 1654 MB/s (VM1) – a 15.4% improvement. Object Detection: 2851 images/s (VM2) vs. 1592 images/s (VM1) – a remarkable 79.2% improvement. Ray Tracing: 1798 Kpixels/s (VM2) vs. 1512 Kpixels/s (VM1) – an 18.9% boost. These results reflect the significant advancements enabled by the new generation of Intel processors. Score VM 1 VM 1 VM 1 Score VM 2 VM 2 VM 2 Evolution of Hardware in Azure: From Ice Lake-SP to Emerald Rapids Technical Specifications of the Processors Evaluated Understanding the dramatic performance improvements begins with a look at the processor specifications: Intel Xeon Platinum 8370C (Ice Lake-SP) Architecture: Ice Lake-SP Base Frequency: 2.79 GHz Max Frequency: 3.5 GHz L3 Cache: 48 MB Supported Instructions: AVX-512, VNNI, DL Boost VM 1 Intel Xeon Platinum 8573C (Emerald Rapids) Architecture: Emerald Rapids Base Frequency: 2.3 GHz Max Frequency: 4.2 GHz L3 Cache: 260 MB Supported Instructions: AVX-512, AMX, VNNI, DL Boost VM 2 Impact on Performance Cache Size Increase: The jump from 48 MB to 260 MB of L3 cache is a key factor. A larger cache reduces dependency on RAM accesses, thereby lowering latency and significantly boosting performance in memory-intensive workloads such as AI, big data, and scientific simulations. Enhanced Frequency Dynamics: While the base frequency of the Emerald Rapids processor is slightly lower, its higher maximum frequency (4.2 GHz vs. 3.5 GHz) means that under load, performance-critical tasks can benefit from this burst capability. Advanced Instruction Support: The introduction of AMX (Advanced Matrix Extensions) in Emerald Rapids, along with the robust AVX-512 support, optimizes the execution of complex mathematical and AI workloads. Efficiency Gains: These processors also offer improved energy efficiency, reducing the energy consumed per compute unit. This efficiency translates into lower operational costs and a more sustainable cloud environment. Beyond Our Tests: Overview of the New v6 Series While our tests focused on the Dld e6 series, Azure’s new v6 generation includes several families designed for different workloads: 1. Dlsv6 and Dldsv6-series Segment: General purpose with NVMe local storage (where applicable) vCPUs Range: 2 – 128 Memory: 4 – 256 GiB Local Disk: Up to 7,040 GiB (Dldsv6) Highlights: 5× increased CPU cache (up to 300 MB) and higher network bandwidth (up to 54 Gbps) 2. Dsv6 and Ddsv6-series Segment: General purpose vCPUs Range: 2 – 128 Memory: Up to 512 GiB Local Disk: Up to 7,040 GiB in Ddsv6 Highlights: Up to 30% improved performance over the previous Dv5 generation and Azure Boost for enhanced IOPS and network performance 3. Esv6 and Edsv6-series Segment: Memory-optimized vCPUs Range: 2 – 192* (with larger sizes available in Q2) Memory: Up to 1.8 TiB (1832 GiB) Local Disk: Up to 10,560 GiB in Edsv6 Highlights: Ideal for in-memory analytics, relational databases, and enterprise applications requiring vast amounts of RAM Note: Sizes with higher vCPUs and memory (e.g., E128/E192) will be generally available in Q2 of this year. Key Innovations in the v6 Generation Increased CPU Cache: Up to 5× more cache (from 60 MB to 300 MB) dramatically improves data access speeds. NVMe for Storage: Enhanced local and remote storage performance, with up to 3× more IOPS locally and the capability to reach 400k IOPS remotely via Azure Boost. Azure Boost: Delivers higher throughput (up to 12 GB/s remote disk throughput) and improved network bandwidth (up to 200 Gbps for larger sizes). Microsoft Azure Network Adaptor (MANA): Provides improved network stability and performance for both Windows and Linux environments. Intel® Total Memory Encryption (TME): Enhances data security by encrypting the system memory. Scalability: Options ranging from 128 vCPUs/512 GiB RAM in the Dv6 family to 192 vCPUs/1.8 TiB RAM in the Ev6 family. Performance Gains: Benchmarks and internal tests (such as SPEC CPU Integer) indicate improvements of 15%–30% across various workloads including web applications, databases, analytics, and generative AI tasks. My personal perspective and point of view The new Azure v6 VMs mark a significant advancement in cloud computing performance, scalability, and security. Our Geekbench tests clearly show that the Dld e6 series—powered by the latest Intel Xeon Platinum 8573C (Emerald Rapids)—delivers up to 30% better performance than previous-generation machines with more resources. Coupled with the hardware evolution from Ice Lake-SP to Emerald Rapids—which brings a dramatic increase in cache size, improved frequency dynamics, and advanced instruction support—the new v6 generation sets a new standard for high-performance workloads. Whether you’re running critical enterprise applications, data-intensive analytics, or next-generation AI models, the enhanced capabilities of these VMs offer significant benefits in performance, efficiency, and cost-effectiveness. References and Further Reading: Microsoft’s official announcement: Azure Dld e6 VMs Internal tests performed with Geekbench 6.4.0 (AVX2) in the Germany West Central Azure region.
Determining sizing requirements for GPU enabled Azure VM
Greetings, We are trying to determine the correct VM sizing requirement for our AI workload, which is used for NLP processing. This workload does not require any training, but will only be used for inference. We have the following software configuration: a C# application that is heavily multithreaded using a lot of socket I/O. The application has concentrated bursts where 10-20 threads are fired concurrently to perform tasks (mostly socket I/O). This app communicates via dedicated sockets to: a Python application which performs various NLP tasks. This app is also multithreaded to handle multiple incoming requests from the .NET app. This app sends queries to a local LLM (model size will vary based on query type). We estimate we will need to support sub-second performance (at the very least) on a 7B parameter model. Ultimately, we may need to go to larger model sizes if accuracy is insufficient. The amount of text passed to the LLM will range from 300-3000 tokens. In short, we need: a) a CPU with sufficient cores to handle multiple concurrent threads on the .NET side. The app will have 5 or 6 background threads running continuously, and sudden bursts of activity which will require a minimum of 10-20 threads to run shorter-lived tasks. b) a GPU with sufficient VRAM to handle at the very least, a 7B parameter model. Ultimately, we may need to support larger models to perform the same task due to insufficient accuracy. We need the ideal configuration of GPU/VRAM and CPU/RAM to handle these tasks, and also, potentially, larger LLM sizes of up to 14B or 70B parameters. We are looking at the NC-series VMs, with a budget of about $1,000/month (see https://azure.microsoft.com/en-us/pricing/details/virtual-machines/windows/#pricing). Any feedback on the optimal configuration in terms of CPU/GPU would be greatly appreciated. Thank you in advance.
Adding VM Instance View Details, e.g. osName, to the VM Resource Object JSON (for Custom Policy Use)
I'm requesting to add more details to the JSON of the VM resource object, particularly from the VM instance view data. This is to include operating system information, such as the name and version (osName and osVersion), for use in a custom Policy. Although these details are visible in the portal, they're not present in the VM's resource object, which is necessary for our custom policy.
Azure IMDS (Instance Metadata Service) calls to 168.63.129.16 blocked after July 1st, 2025
[ACTION REQUIRED] After 1 July 2025, it will no longer be possible to query Azure IMDS endpoints at the IP address 168.63.129.16. Please begin using 169.254.169.254 to communicate with Azure IMDS as soon as possible. Officially, IMDS APIs can only be queried at 169.254.169.254. However, due to the internal design of Azure, IMDS endpoints can also be queried at the IP address 168.63.129.16 from within a virtual machine. Some customers are using this unofficial pathway to communicate with IMDS. An upcoming change in Azure will permanently block IMDS requests on 168.63.129.16. After 1 July 2025, you won’t be able to access Azure IMDS endpoints with that IP. You can continue to use 168.63.129.16 to call into IMDS APIs until up until that date, but we recommend you begin your transition now. HOW TO CHECK IF YOU ARE IMPACTED Code analysis in your application. IMDS has a reserved IP address of “169.254.169.254" VM’s Private communication channel has reserved IP address of "168.63.129.16". Use code search to evaluate that your client is not using IP address “168.63.129.16” for making metadata requests. All IMDS REST requests starts with “/metadata” and all endpoints can be found at IMDS Public endpoints. REQUIRED ACTION Fix all URLs using 168.63.129.16 to prepare for its decoupling from IMDS. For example, this IMDS token endpoint URL would soon be blocked: curl -s -H Metadata:true --noproxy "*" "http://168.63.129.16/metadata/identity/oauth2/token?api-version=2018-02-01&resource=https://management.azure.com/" To avoid service disruptions, fix URLs to include 169.254.169.254., as in this example: curl -s -H Metadata:true --noproxy "*" "http://169.254.169.254/metadata/identity/oauth2/token?api-version=2018-02-01&resource=https://management.azure.com/”
Automating Azure VM Snapshot Creation Across Subscriptions
Introduction Managing virtual machines in Azure can be time-consuming, especially when creating snapshots across multiple subscriptions. Typically, this involves logging into the Azure portal, manually locating the VM, and creating snapshots for both the OS disk and attached data disks an inefficient and tedious process. To simplify this, I developed a PowerShell script that automates snapshot creation, allowing me to create snapshots by simply inputting the VM name. This script is part of my toolkit for automating repetitive Azure tasks. It iterates through all subscriptions linked to my Azure account, identifies the specified VM, and generates snapshots for both the OS and data disks within the VM’s resource group, adhering to a consistent naming convention. This article describes the script, the rationale behind its design, and how it improves the efficiency of managing Azure resources. Design Considerations When designing this script for automating Azure VM snapshot creation, several key considerations were prioritized to enhance efficiency and user experience: 1. Subscription Handling All-Subscription Search: The script loops through all Azure subscriptions associated with the account. This design ensures that the script can locate the VM across any subscription without manual intervention to switch between them. This is particularly useful for environments with multiple subscriptions. 2. Dynamic VM Search Automatic VM Discovery: Instead of requiring users to manually input resource group and subscription details, the script dynamically searches for the VM by its name across all subscriptions. This automation simplifies the process and reduces the likelihood of errors. 3. Snapshot Naming Convention Consistent Naming Format: Snapshots are named using the format VMname_diskname_dd-MM-yyyy_HH_mm. This approach ensures that snapshots are well-organized and easily identifiable. The script also removes random characters, such as GUIDs, often appended to disk names, resulting in clean and consistent snapshot names. 4. OS and Data Disk Snapshots Comprehensive Backup: The script separately handles snapshots for both the OS disk and data disks. This ensures that all disks attached to the VM are included in the backup process, providing complete coverage. 5. Time Efficiency Streamlined Process: The script is designed to eliminate the need for repeated manual input and navigation within the Azure portal. By simply providing the VM name, users can automate the entire process, from VM identification to snapshot creation. This saves considerable time and effort, particularly in environments with many VMs and subscriptions. By focusing on these design considerations, the script offers a robust and user-friendly solution for automating VM snapshot creation across Azure subscriptions. Prerequisites To use this script, you need: Azure PowerShell module installed (Az module). Active Azure account with sufficient permissions to access VMs and create snapshots across subscriptions. A VM name as input. Why Automate Snapshot Creation? In many organizations, virtual machines (VMs) are critical for running services, and regularly creating snapshots of these VMs is essential for disaster recovery and version control. Traditionally, creating snapshots for Azure VMs involves several manual steps: Log in to the Azure Portal: Access the Azure portal to start the snapshot creation process. Navigate Through Subscriptions: Switch between different Azure subscriptions to find the correct VM. Locate the Correct VM: Search for and select the specific VM for which you want to create snapshots. Create Snapshots: Manually create snapshots for both the OS disk and any attached data disks. Repeat the Process: Perform these steps for each disk across multiple VMs or subscriptions. This manual process is not only time-consuming but also prone to errors. Automating snapshot creation simplifies and streamlines the process: Reduces Manual Effort: The entire process can be accomplished with a few clicks. Saves Time: Automation eliminates the need to repeat steps across multiple VMs and subscriptions. Minimizes Errors: By automating the process, you reduce the risk of human error. With the automation script, you only need to provide the VM name, and the script handles the rest, making snapshot management more efficient and reliable. Script Overview Below is the PowerShell script that automates the process of creating snapshots for a VM across multiple subscriptions in Azure: <# .SYNOPSIS This script automates the process of creating snapshots for a virtual machine (VM) in Azure across multiple subscriptions. The script will locate the VM by its name, determine the resource group where it exists, and create snapshots for both the OS disk and any attached data disks. It ensures that the snapshot names follow a specific naming convention while removing any random characters appended to the disk names. .DESCRIPTION - Loops through all Azure subscriptions attached to the account. - Searches for a specified VM by name across all subscriptions. - Identifies the resource group of the VM. - Creates snapshots for the OS disk and all data disks in the same resource group as the VM. - Follows the snapshot naming convention: computername_diskname_dd-mm-yyyy_hh_mm. - Removes random characters (e.g., GUIDs) after the disk name in snapshot naming. .NOTES Author: Vivek Chandran Date Created: 11-09-2023 #> # Login to Azure (if not already logged in) Connect-AzAccount # Prompt the user to enter the VM name $computerName = Read-Host -Prompt "Please enter the name of the VM you want to snapshot" # Get all subscriptions available to the account $subscriptions = Get-AzSubscription # Loop through each subscription to find the specified VM foreach ($subscription in $subscriptions) { # Set the subscription context so that all subsequent commands target this subscription Set-AzContext -SubscriptionId $subscription.Id # Retrieve all VMs in the current subscription $vms = Get-AzVM # Check if a VM with the specified name exists in this subscription $vm = $vms | Where-Object { $_.Name -eq $computerName } if ($vm) { # Output message indicating the VM was found Write-Host "VM '$computerName' found in subscription '$($subscription.Name)'" # Retrieve the resource group where the VM resides $resourceGroup = $vm.ResourceGroupName # Loop through each data disk attached to the VM and create a snapshot foreach ($disk in $vm.StorageProfile.DataDisks) { # Get the name of the data disk $diskName = $disk.Name # Remove any random characters from the disk name after the first underscore (if present) $cleanedDiskName = ($diskName -split '_')[0..1] -join '_' # Get the current date and time in the format 'dd-MM-yyyy_HH_mm' for use in the snapshot name $currentDateTime = Get-Date -Format 'dd-MM-yyyy_HH_mm' # Construct the snapshot name using the cleaned disk name and the date/time $snapshotNameWithDataDisk = "$computerName-$cleanedDiskName-$currentDateTime" # Define the snapshot configuration using the disk's managed disk ID $snapshotConfig = New-AzSnapshotConfig -SourceUri $disk.ManagedDisk.Id -Location $vm.Location -CreateOption Copy -AccountType Standard_LRS # Create the snapshot in the same resource group as the VM New-AzSnapshot -Snapshot $snapshotConfig -ResourceGroupName $resourceGroup -SnapshotName $snapshotNameWithDataDisk # Output message indicating that the snapshot was successfully created for the data disk Write-Host "Snapshot created for data disk: $snapshotNameWithDataDisk" } # Create a snapshot for the OS disk of the VM $osDisk = $vm.StorageProfile.OsDisk # Get the name of the OS disk $osDiskName = $osDisk.Name # Remove any random characters from the OS disk name after the first underscore (if present) $cleanedOsDiskName = ($osDiskName -split '_')[0..1] -join '_' # Get the current date and time in the format 'dd-MM-yyyy_HH_mm' for use in the snapshot name $currentDateTime = Get-Date -Format 'dd-MM-yyyy_HH_mm' # Construct the snapshot name using the cleaned OS disk name and the date/time $snapshotNameWithOSDisk = "$computerName-$cleanedOsDiskName-$currentDateTime" # Define the snapshot configuration using the OS disk's managed disk ID $snapshotConfig = New-AzSnapshotConfig -SourceUri $osDisk.ManagedDisk.Id -Location $vm.Location -CreateOption Copy -AccountType Standard_LRS # Create the snapshot in the same resource group as the VM New-AzSnapshot -Snapshot $snapshotConfig -ResourceGroupName $resourceGroup -SnapshotName $snapshotNameWithOSDisk # Output message indicating that the snapshot was successfully created for the OS disk Write-Host "Snapshot created for OS disk: $snapshotNameWithOSDisk" # Exit the loop since the VM has been found and processed break } else { # Output message indicating that the VM was not found in this subscription Write-Host "VM '$computerName' not found in subscription '$($subscription.Name)'" } } # Output a final message indicating that the snapshot process has completed Write-Host "Snapshots process completed!" How the Script Works 1. Azure Authentication Connect to Azure: The script starts by authenticating the user to Azure using the Connect-AzAccount command. If the user is already logged in, this step is skipped. 2. Input the VM Name Prompt for VM Name: After successful authentication, the script prompts you to enter the name of the virtual machine (VM) you want to create snapshots for. 3. Subscription Looping Retrieve Subscriptions: The script retrieves all Azure subscriptions associated with the account using Get-AzSubscription. Check Each Subscription: It iterates through each subscription to check if the specified VM exists. When the VM is found, the script switches the context to that subscription using Set-AzContext. 4. Snapshot Creation Data Disk Snapshots: For each data disk attached to the VM, the script creates a snapshot. It follows a consistent naming convention that includes the VM name, disk name, and timestamp to ensure clarity and organization. OS Disk Snapshot: After handling the data disks, the script creates a snapshot for the OS disk, using the same naming convention. 5. Completion Confirmation Message: Once all snapshots (for both OS and data disks) are created, the script outputs a message confirming the successful completion of the snapshot creation process. Conclusion This PowerShell script has greatly improved my workflow for managing Azure VMs. By automating the snapshot creation process, it eliminates the need to manually log into the Azure portal, locate the VM, and create snapshots for each disk individually. Instead, I can simply run the script, provide the VM name, and let it handle the entire process. For anyone managing multiple Azure subscriptions and seeking a reliable method to automate snapshot creation, this script offers a quick and effective solution. It ensures that backups are created consistently and stored properly, enhancing overall backup management and efficiency.XXX virtual machines should enable Azure Disk Encryption or EncryptionAtHost.
Hello everyone, I'm facing issues related to a policy: Linux virtual machines should enable Azure Disk Encryption or EncryptionAtHost. Windows virtual machines should enable Azure Disk Encryption or EncryptionAtHost. After enabling EncryptionAtHost, it appears as encrypted in the portal. However, the policy does not recognize that it is encrypted and shows it as non-compliant. The same happens when enabling Azure Disk Encryption (ADE): the policy still indicates that it is non-compliant. Has anyone else experienced this?Finding Azure Batch Python client in Conda packaging
I've started working with Azure Batch and use Python, with my Python environment managed by Anaconda. I'd like to install the Azure Batch Client Library and Azure Batch Management Client Library from the Azure SDK for Python in my Anaconda environments, preferably using conda instead of pip. These are the azure.batch and azure.mgmt.batch modules (or "module packages"; whatever they're called), found in the azure-batch and azure-mgmt-batch PyPI packages. But I don't know where to find them in the Conda packaging of Azure SDK for Python. The Azure SDK for Python introduced a Conda packaging of it back in 2021, and its use is described in the "How to install Azure library packages for Python" page. The Conda packaging differs from the PyPI packaging. The Python Azure SDK modules are packaged in to a smaller number of packages in the Conda form, with sometimes different naming. Is the Azure Batch client library available in the Microsoft-supplied Conda packages somewhere (the ones in the microsoft conda channel, instead of the conda-forge channel)? If so, which Conda package? And more generally, if I know what Azure SDK for Python module I want, or what PyPI package it's in, how can I find out which microsoft-channel Conda package it's in? I haven't been able to find a list of which module is in which Conda package anywhere. There's an azure-batch conda package in the conda-forge channel (instead of the microsoft channel). But if I understand correctly, those conda-forge Azure packages are the old ones from before the 2021 introduction of the microsoft conda channel's packaging, and have different dependencies and stuff. I'd prefer to install the Azure Batch client from the microsoft-channel Conda packages, instead of the conda-forge channel package or from PyPI/pip, for consistency with my other Azure Python packages, which are all installed from the microsoft-channel Conda packages. I've read that mixing interdependent packages from different channels can sometimes cause problems, and if you're mixing conda-managed and pip-managed packages in an Anaconda environment, you're supposed to install all the conda packages first, then the pip packages, and then don't go back and install or update any conda packages afterwards, or something like that.
