Azure Instance Types and Sizes for Cloud Deployment

Azure offers a wide range of instance types and sizes to choose from, each with its own unique characteristics and use cases.

The B-Series is a cost-effective option, designed for batch and queue workloads. It's ideal for applications that don't require high CPU performance.

The D-Series is a general-purpose instance type, suitable for a variety of workloads, including web servers and microservices. It's available in different sizes, ranging from 2 to 16 vCPUs.

The F-Series is designed for high-performance computing, featuring up to 64 vCPUs and 448 GB of RAM. It's perfect for applications that require intense processing power.

Azure Instance Types offer a range of options to suit different workload needs.

The A-family is ideal for entry-level economical workloads, with the Av2-series being a notable example.

For burstable workloads, the B-family VMs are a good choice, as they don't require constant full CPU performance.

The D-family is geared towards enterprise-grade applications, relational databases, in-memory caching, and data analytics.

Here's a breakdown of the different D-family series:

The DC-family offers D-family VMs with confidential computing capabilities.

Azure's instance series are categorized into several types, including Basic, Compute Optimized, Memory Optimized, Storage Optimized, GPU Accelerated, and FPGA Accelerated.

These categories are further broken down into series, which group sizes with similar hardware and features. For example, the Bs-series offers economy VMs tailored to burst demands, while the E-series is optimized for in-memory hyperthreaded applications.

Azure provides several other instance types, including the A-series, which is suitable for tasks such as software development and testing, low-traffic web servers or small databases. The E-series, on the other hand, uses a high memory-to-processor ratio to optimize it for memory-intensive tasks.

Here's a list of the main instance series categories:

Azure's VM size and series naming conventions can be a bit tricky to understand at first, but once you get the hang of it, it's actually quite straightforward.

The VM family is determined by the intended workload, with categories like General purpose, Compute optimized, Memory optimized, Storage optimized, GPU accelerated, and FPGA accelerated.

Azure VM sizes follow a specific naming convention, where each character in the name represents different aspects of the VM, such as the VM family, number of vCPUs, and extra features like premium storage or included accelerators.

The size of the virtual machine is determined by the workload you want to run, and the size you choose determines factors like processing power, memory, storage capacity, and network bandwidth.

Here's a breakdown of the VM size families by type:

To learn more about a specific size family or series, click the tab for that family and scroll to find your desired size series.

Azure instance types can be thought of as single virtual machines that have specific roles to perform and meet the user's needs.

The first created instances have specifications given by the user that define the work roles or web roles.

Instances are broadly divided into shared and reserved instances, with reserved instances offering privileges on the plan according to the user's subscription.

In shared instances, multiple tenants' hosts are on a single virtual machine, making it a shared resource.

The variation of instances depends on their storage, memory, size, GPU, and computing capacity.

For general purposes like testing, development, and management of minimum databases, users can choose A, B, and D series.

For memory and compute-optimized features, F, M, and E-series serve the best.

The L and N series has remarkable features in GPU and storage optimization.

Users can opt for instance series according to their business requirements and infrastructure workloads.

To select an Azure instance type, you should consider the native computing requirements of your workload and translate those to cloud capacity demands. This means thinking in terms of processor cores, memory, disk storage, disk I/O, and network bandwidth.

Start by evaluating the application in a local environment, such as an on-premises data center, and monitor workload performance to detect possible bottlenecks. If possible, try to match the instance size to local server instances in terms of network interface card (NIC) count, disk size, and other metrics.

For model deployments, you'll need to specify the instance type in the deployment YAML or the KubernetesOnlineDeployment class. If you don't specify an instance_type property, the system will use defaultinstancetype.

For most enterprise workloads, instance types like the Dav4-series or Dv3-series provide good performance at a reasonable cost. However, low-priority workloads can run on less expensive A-series or B-series instances if performance demands are low.

If your workload requires high-performance instances, such as H-series for HPC or N-series with GPUs, it's essential to consider whether the investment is worth the cost. For example, a workload designed to perform visualization using Nvidia Tesla V100 GPUs will benefit from an instance like the NCsv3.

To ensure the instance type meets your workload's requirements, use a tool like Azure Diagnostics to measure the application's performance within the instance. This will help you identify bottlenecks and adjust the deployment accordingly.

Azure offers a wide variety of sizes to support many types of uses, with the size you choose determining factors such as processing power, memory, storage capacity, and network bandwidth.

The size of the virtual machine that you use is determined by the workload that you want to run. Azure charges an hourly price based on the virtual machine's size and operating system.

For partial hours, Azure charges only for the minutes used. Storage is priced and charged separately.

Azure VM sizes follow specific naming conventions to denote varying features and specifications. Each character in the name represents different aspects of the VM, including the VM family, number of vCPUs, and extra features like premium storage or included accelerators.

VM naming is further broken down into the 'Series' name and the 'Size' name. Size names include extra characters representing the number of vCPUs, type of storage, etc.

Azure VM sizes are categorized into several types, including general purpose, compute optimized, memory optimized, storage optimized, GPU accelerated, and FPGA accelerated.

Here's a summary of the different VM types:

Azure VM sizes are grouped into families based on their characteristics and features. Each family has its own series of sizes, and each size has its own unique configuration, including vCPUs, memory, and accelerators.

High-performance compute workloads can be handled by Azure's H-series instance types, which are designed to meet the demands of processor and memory bandwidth. These instances are tailored for fluid dynamics, finite element analysis, and weather modeling.

The H-series instances offer a mix of large vCPU counts and ample memory, with high memory bandwidth, high vCPU clock speeds, and large vCPU cache per core. Local storage is handled with SSD devices.

For video editing, intense graphical features, and rendering of graphics, Azure's N-series is the best solution. It uses multiple GPUs to perform these tasks, making it ideal for predictive analytics, deep learning interpretations, and top-class visualization.

The N-series is a game-changer for video editing, handling intense graphical features and rendering of graphics with ease.

It's equipped with multiple GPUs, making it the perfect solution for predictive analytics, deep learning interpretations, and top-class visualization.

The N-series comes in three flavors: NC-series for intense computing activities, ND-series for deep learning, and NV-series for streaming, gaming, encoding, and remote operations.

These different flavors cater to specific needs, ensuring you get the right tool for the job.

The GPU acceleration in N-series makes it an ideal choice for applications that require heavy computational power.

With its ability to handle complex tasks, the N-series is a great option for those who need to process large amounts of data quickly.

FPGA accelerated VMs are a game-changer for compute-intensive workloads. They're designed to handle tasks that require high disk throughput and I/O, making them perfect for databases, big data applications, and data warehousing.

The NP-family VM size series is one of Azure's storage-optimized VM instances, specifically designed for workloads that require high disk throughput and I/O. These VMs are equipped with high disk throughput and large local disk storage capacities, supporting applications and services that benefit from low latency and high sequential read and write speeds.

Here are some of the key benefits of NP-family VMs:

NP-family VMs excel in environments where data needs to be processed in real time with minimal latency, such as financial trading, real-time analytics, and network data processing. They can also significantly speed up genomic sequencing tasks and other life sciences applications that benefit from custom hardware acceleration.

High-performance compute (HPC) workloads require enormous demands on processor and memory bandwidth. Examples of these workloads include fluid dynamics, finite element analysis, and weather modeling.

Azure's H-series instance types are tailored to handle HPC workloads, featuring a mix of large vCPU counts and ample memory. Other characteristics include high memory bandwidth, high vCPU clock speeds, large vCPU cache per core, and high-performance SSD storage bandwidth.

The H-series instances provide 8- and 16-core VMs using Intel Haswell E5-2667 v3 processors and fast DDR4 memory. Local storage is handled with SSD devices.

You can deploy a group of Azure instances to create high-performance computing clusters for Hadoop or other big data projects.

For compute-intensive tasks, Azure's H-series provides a better option than the A-series, which has been retired.

Here are some key features of H-series instances:

These features make H-series instances ideal for HPC workloads, such as fluid dynamics, finite element analysis, and weather modeling.

Benchmark scores are a great way to measure compute performance. CoreMark benchmark scores are used for Linux VMs, providing a standardized way to compare performance across different instances.

The CoreMark benchmark measures the raw processing power of a system, giving you an idea of how fast your Linux VM will run. SPECInt benchmark scores, on the other hand, are used for Windows VMs, offering a similar performance comparison.

These benchmark scores can help you choose the right instance for your workload, ensuring you get the best performance for your specific needs.

Azure provides a simple and intuitive way to manage instances, making it easy to scale up or down as needed. This includes the ability to stop and start instances, which can be useful for cost savings during periods of low usage.

To stop an instance, you can use the Azure portal or the Azure CLI, which is a powerful tool for managing Azure resources from the command line. Azure also provides a feature called "deallocate" which allows you to release the resources associated with an instance without deleting it.

By managing instances effectively, you can optimize your Azure costs and ensure that your applications are running smoothly and efficiently.

Creating a default instance type is a great way to get started with instance management. By default, an instance type called defaultinstancetype is created when you attach a Kubernetes cluster to an Azure Machine Learning workspace.

This default instance type has a specific definition, which is worth noting: it doesn't appear as an InstanceType custom resource in the cluster when you run the command kubectl get instancetype, but it does appear in all clients, including the UI, Azure CLI, and SDK.

You can also override the default instance type with a custom instance type that has the same name. This gives you flexibility in managing your instance types.

Resource Section Validation is a crucial aspect of Instance Management. A valid resource definition must meet specific rules to ensure successful model deployment.

You can't specify zero or empty CPU values in the resource section. CPU values should be string values, such as 100m, which represents 100 millicores.

Memory values in the resource section also can't be zero or empty. You can specify memory as a full number plus suffix, like 1024Mi for 1024 MiB.

If you need GPU, the limits section becomes required. You can specify CPU and memory limits in the limits section, just like in the requests section.

However, if you require CPU only, you can omit the entire limits section. The instance type is required for model deployment, and the resource limits must be less than the instance type limits.

Here are the rules for validating the resource section against the instance type:

So you're looking to manage your instances using a REST API. This is a great approach, as it allows you to automate and streamline your instance management tasks.

To get started, you can use the REST API to query for VM sizes. You can do this in three different ways: by listing available virtual machine sizes for resizing, listing available virtual machine sizes for a subscription, or listing available virtual machine sizes in an availability set.

These options are useful for different scenarios. For example, if you need to resize an instance, you'll want to list the available VM sizes for resizing. If you're setting up a new instance, you'll want to list the available VM sizes for a subscription.

Here are the specific REST API methods you can use:

By using these methods, you can easily manage your instances and ensure they're running with the right resources.