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articles/batch/batch-docker-container-workloads.md

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ms.topic: how-to
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ms.date: 01/19/2024
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ms.devlang: csharp
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# ms.devlang: csharp, python
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# Use Azure Batch to run container workloads
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> This article references CentOS, a Linux distribution that is nearing End Of Life (EOL) status. Please consider your use and planning accordingly.
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Azure Batch lets you run and scale large numbers of batch computing jobs on Azure. Batch tasks can run directly on virtual machines (nodes) in a Batch pool, but you can also set up a Batch pool to run tasks in Docker-compatible containers on the nodes. This article shows you how to create a pool of compute nodes that support running container tasks, and then run container tasks on the pool.
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The code examples here use the Batch .NET and Python SDKs. You can also use other Batch SDKs and tools, including the Azure portal, to create container-enabled Batch pools and to run container tasks.

articles/batch/batch-pool-compute-intensive-sizes.md

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# Use RDMA or GPU instances in Batch pools
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To run certain Batch jobs, you can take advantage of Azure VM sizes designed for large-scale computation. For example:
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* To run multi-instance [MPI workloads](batch-mpi.md), choose H-series or other sizes that have a network interface for Remote Direct Memory Access (RDMA). These sizes connect to an InfiniBand network for inter-node communication, which can accelerate MPI applications.

articles/batch/batch-pool-node-error-checking.md

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# Azure Batch pool and node errors
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Some Azure Batch pool creation and management operations happen immediately. Detecting failures for these operations is straightforward, because errors usually return immediately from the API, command line, or user interface. However, some operations are asynchronous, run in the background, and take several minutes to complete. This article describes ways to detect and avoid failures that can occur in the background operations for pools and nodes.
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Make sure to set your applications to implement comprehensive error checking, especially for asynchronous operations. Comprehensive error checking can help you promptly identify and diagnose issues.

articles/batch/batch-rendering-applications.md

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# Pre-installed applications on Batch rendering VM images
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It's possible to use any rendering applications with Azure Batch. However, Azure Marketplace VM images are available with common applications pre-installed.
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Where applicable, pay-for-use licensing is available for the pre-installed rendering applications. When a Batch pool is created, the required applications can be specified and both the cost of VM and applications will be billed per minute. Application prices are listed on the [Azure Batch pricing page](https://azure.microsoft.com/pricing/details/batch/#graphic-rendering).

articles/migrate/tutorial-app-containerization-java-kubernetes.md

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# Java web app containerization and migration to Azure Kubernetes Service
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In this article, you'll learn how to containerize Java web applications (running on Apache Tomcat) and migrate them to [Azure Kubernetes Service (AKS)](https://azure.microsoft.com/services/kubernetes-service/) using the Azure Migrate: App Containerization tool. The containerization process doesn’t require access to your codebase and provides an easy way to containerize existing applications. The tool works by using the running state of the applications on a server to determine the application components and helps you package them in a container image. The containerized application can then be deployed on Azure Kubernetes Service (AKS).
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The Azure Migrate: App Containerization tool currently supports -

articles/migrate/tutorial-discover-import.md

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# Tutorial: Build a business case or assess servers using an imported CSV file
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As part of your migration journey to Azure, you discover your on-premises inventory and workloads.
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This tutorial shows you how to build a business case or assess on-premises machines with the Azure Migrate: Discovery and Assessment tool, using an imported comma-separate values (CSV) file.

articles/migrate/tutorial-discover-spring-boot.md

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# Tutorial: Discover Spring Boot applications running in your datacenter (preview)
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This article describes how to discover Spring Boot applications running on servers in your datacenter, using Azure Migrate: Discovery and assessment tool. The discovery process is completely agentless; no agents are installed on the target servers.
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In this tutorial, you learn how to:

articles/migrate/tutorial-migrate-aws-virtual-machines.md

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# Discover, assess, and migrate Amazon Web Services (AWS) VMs to Azure
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This tutorial shows you how to discover, assess, and migrate Amazon Web Services (AWS) virtual machines (VMs) to Azure VMs, using Azure Migrate: Server Assessment and Migration and modernization tools.
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articles/openshift/howto-restrict-egress.md

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# Control egress traffic for your Azure Red Hat OpenShift (ARO) cluster
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This article provides the necessary details that allow you to secure outbound traffic from your Azure Red Hat OpenShift cluster (ARO). With the release of the [Egress Lockdown Feature](./concepts-egress-lockdown.md), all of the required connections for an ARO cluster are proxied through the service. There are additional destinations that you may want to allow to use features such as Operator Hub or Red Hat telemetry.
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> [!IMPORTANT]

articles/orbital/satellite-imagery-with-orbital-ground-station.md

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# Tutorial: Process Aqua satellite data using NASA-provided tools
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> [!NOTE]
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> NASA has deprecated support of the DRL software used to process Aqua satellite imagery. Please see: [DRL Current Status](https://directreadout.sci.gsfc.nasa.gov/home.html). Steps 2, 3, and 4 of this tutorial are no longer relevant but presented for informational purposes only.
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