Merge pull request #267848 from Padmalathas/BatchRenderingAndPythonRevision

Batch Rendering and Python Revision

Author: prmerger-automator[bot]

articles/batch/batch-rendering-functionality.md

@@ -31,18 +31,6 @@ Most rendering applications will require licenses obtained from a license server
 * Resource files:
   * Application files are uploaded to Azure blob storage, and you specify file references in the [pool start task](/rest/api/batchservice/pool/add#starttask). When pool VMs are created, the resource files are downloaded onto each VM.
 
-### Environment variables for pre-installed applications
-
-To be able to create the command line for rendering tasks, the installation location of the rendering application executables must be specified.  System environment variables have been created on the Azure Marketplace VM images, which can be used instead of having to specify actual paths.  These environment variables are in addition to the [standard Batch environment variables](./batch-compute-node-environment-variables.md) created for each task.
-
-|Application|Application Executable|Environment Variable|
-|---------|---------|---------|
-|Autodesk 3ds Max 2021|3dsmaxcmdio.exe|3DSMAX_2021_EXEC|
-|Autodesk Maya 2020|render.exe|MAYA_2020_EXEC|
-|Chaos Group V-Ray Standalone|vray.exe|VRAY_4.10.03_EXEC|
-|Arnold 2020 command line|kick.exe|ARNOLD_2020_EXEC|
-|Blender|blender.exe|BLENDER_2018_EXEC|
-
 ## Azure VM families
 
 As with other workloads, rendering application system requirements vary, and performance requirements vary for jobs and projects.  A large variety of VM families are available in Azure depending on your requirements – lowest cost, best price/performance, best performance, and so on.

articles/batch/quick-run-python.md

@@ -1,7 +1,7 @@
 ---
 title: 'Quickstart: Use Python to create a pool and run a job'
 description: Follow this quickstart to run an app that uses the Azure Batch client library for Python to create and run Batch pools, nodes, jobs, and tasks.
-ms.date: 04/13/2023
+ms.date: 03/01/2024
 ms.topic: quickstart
 ms.devlang: python
 ms.custom: mvc, devx-track-python, mode-api
@@ -25,7 +25,7 @@ After you complete this quickstart, you understand the [key concepts of the Batc
 
 - A Batch account with a linked Azure Storage account. You can create the accounts by using any of the following methods: [Azure CLI](quick-create-cli.md) | [Azure portal](quick-create-portal.md) | [Bicep](quick-create-bicep.md) | [ARM template](quick-create-template.md) | [Terraform](quick-create-terraform.md).
 
-- [Python](https://python.org/downloads) version 3.6 or later, which includes the [pip](https://pip.pypa.io/en/stable/installing) package manager.
+- [Python](https://python.org/downloads) version 3.8 or later, which includes the [pip](https://pip.pypa.io/en/stable/installing) package manager.
 
 ## Run the app
 

articles/batch/tutorial-parallel-python.md

@@ -3,7 +3,7 @@ title: "Tutorial: Run a parallel workload using the Python API"
 description: Learn how to process media files in parallel using ffmpeg in Azure Batch with the Batch Python client library.
 ms.devlang: python
 ms.topic: tutorial
-ms.date: 05/25/2023
+ms.date: 03/01/2024
 ms.custom: mvc, devx-track-python, linux-related-content
 ---
 
@@ -25,7 +25,7 @@ In this tutorial, you convert MP4 media files to MP3 format, in parallel, by usi
 
 ## Prerequisites
 
-* [Python version 3.7 or later](https://www.python.org/downloads/)
+* [Python version 3.8 or later](https://www.python.org/downloads/)
 
 * [pip package manager](https://pip.pypa.io/en/stable/installation/)
 

articles/batch/tutorial-run-python-batch-azure-data-factory.md

@@ -3,7 +3,7 @@ title: 'Tutorial: Run a Batch job through Azure Data Factory'
 description: Learn how to use Batch Explorer, Azure Storage Explorer, and a Python script to run a Batch workload through an Azure Data Factory pipeline.
 ms.devlang: python
 ms.topic: tutorial
-ms.date: 04/20/2023
+ms.date: 03/01/2024
 ms.custom: mvc, devx-track-python
 ---
 
@@ -27,7 +27,7 @@ In this tutorial, you learn how to:
 - A Data Factory instance. To create the data factory, follow the instructions in [Create a data factory](/azure/data-factory/quickstart-create-data-factory-portal#create-a-data-factory).
 - [Batch Explorer](https://azure.github.io/BatchExplorer) downloaded and installed.
 - [Storage Explorer](https://azure.microsoft.com/products/storage/storage-explorer) downloaded and installed.
-- [Python 3.7 or above](https://www.python.org/downloads), with the [azure-storage-blob](https://pypi.org/project/azure-storage-blob) package installed by using `pip`.
+- [Python 3.8 or above](https://www.python.org/downloads), with the [azure-storage-blob](https://pypi.org/project/azure-storage-blob) package installed by using `pip`.
 - The [iris.csv input dataset](https://github.com/Azure-Samples/batch-adf-pipeline-tutorial/blob/master/iris.csv) downloaded from GitHub.
 
 ## Use Batch Explorer to create a Batch pool and nodes