Windows offers four container base images that users can build from. Each base image is a different type of the Windows or Windows Server operating system, has a different on-disk footprint, and has a different set of the Windows API set.
All Windows container base images are discoverable through Docker Hub. The Windows container base images themselves are served from mcr.microsoft.com, the Microsoft Container Registry (MCR). This is why the pull commands for the Windows container base images look like the following:
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Many Windows users want to containerize applications that have a dependency on .NET. In addition to the four base images described here, Microsoft publishes several Windows container images that come pre-configured with popular Microsoft frameworks, such as a the .NET framework image and the ASP .NET image.
Microsoft provides "insider" versions of each container base image. These insider container images carry the latest and greatest feature development in our container images. When you're running a host that is an insider version of Windows (either Windows Insider or Windows Server Insider), it is preferable to use these images. The following insider images are available on Docker Hub:
Windows Server Core and Nanoserver are the most common base images to target. The key difference between these images is that Nanoserver has a significantly smaller API surface. PowerShell, WMI, and the Windows servicing stack are absent from the Nanoserver image.
Nanoserver was built to provide just enough API surface to run apps that have a dependency on .NET core or other modern open source frameworks. As a tradeoff to the smaller API surface, the Nanoserver image has a significantly smaller on-disk footprint than the rest of the Windows base images. Keep in mind that you can always add layers on top of Nano Server as you see fit. For an example of this check out the .NET Core Nano Server Dockerfile.
The VM image operating system VHD must be based on an Azure-approved base image (including Windows Server or SQL Server). When starting, create the VM from an image located on the Azure portal. These images can also be found on the Azure Marketplace Windows Server and SQL Server.
P2 instances use NVIDIA Tesla K80 GPUs and are designed for general purpose GPU computing using the CUDA or OpenCL programming models. P2 instances provide customers with high bandwidth 25 Gbps networking, powerful single and double precision floating-point capabilities, and error-correcting code (ECC) memory, making them ideal for deep learning, high performance databases, computational fluid dynamics, computational finance, seismic analysis, molecular modeling, genomics, rendering, and other server-side GPU compute workloads.
You can deploy deep learning models trained on Trn1 instances on any other Amazon EC2 instance that supports deep learning use cases, including instances based on CPUs, GPUs, or other accelerators. You can also deploy models trained on Trn1 instances outside of AWS, such as on-premises data centers or in embedded devices at the edge. For example, you can train your models on Trn1 instances and deploy them on Inf1 instances, G5 instances, G4 instances, or compute devices at the edge.
Q: What are Amazon EC2 C7g instances? Amazon EC2 C7g instances, powered by the latest generation AWS Graviton3 processors, provide the best price performance in Amazon EC2 for compute-intensive workloads. C7g instances are ideal for high performance computing (HPC), batch processing, electronic design automation (EDA), gaming, video encoding, scientific modeling, distributed analytics, CPU-based machine learning (ML) inference, and ad-serving. They offer up to 25% better performance over the sixth generation AWS Graviton2-based C6g instances.
C6g instances deliver significant price performance benefits for compute-intensive workloads such as high performance computing (HPC), batch processing, ad serving, video encoding, gaming, scientific modelling, distributed analytics, and CPU-based machine learning inference. Customers deploying applications built on open source software across the C instance family will find the C6g instances an appealing option to realize the best price performance within the instance family. Arm developers can also build their applications directly on native Arm hardware as opposed to cross-compilation or emulation.
C6g instances: Amazon EC2 C6g instances are powered by Arm-based AWS Graviton2 processors. They deliver up to 40% better price performance over C5 instances and are ideal for running advanced compute-intensive workloads. This includes workloads such as high performance computing (HPC), batch processing, ad serving, video encoding, gaming, scientific modelling, distributed analytics, and CPU-based machine learning inference.
The AWS Graviton2 processors deliver up to 7x performance, 4x the number of compute cores, 2x larger caches, 5x faster memory, and 50% faster per core encryption performance than first generation AWS Graviton processors. Each core of the AWS Graviton2 processor is a single-threaded vCPU. These processors also offer always-on fully encrypted DRAM memory, hardware acceleration for compression workloads, dedicated engines per vCPU that double the floating-point performance for workloads such as video encoding, and instructions for int8/fp16 CPU-based machine learning inference acceleration. The CPUs are built utilizing 64-bit Arm Neoverse cores and custom silicon designed by AWS on the advanced 7 nm manufacturing technology.
Many Citrix customers must support a significant number of applications and a complex set of user requirements. When using an image-based provisioning technology, meeting these requirements often requires a high number of images to manage. These images have to support different user groups or different sets of conflicting applications. Often there is some overlap in the applications deployed to each image as well. 2ff7e9595c
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