Merge pull request #280853 from midesa/main

GPU docs are now marked as deprecated

Author: AnnaMHuff

articles/synapse-analytics/machine-learning/concept-deep-learning.md

@@ -1,29 +1,33 @@
 ---
-title: 'Deep learning'
+title: 'Deep learning (deprecated)'
 description: This article provides a conceptual overview of the deep learning and data science capabilities available through Apache Spark on Azure Synapse Analytics.
 author: midesa
 ms.service: synapse-analytics
 ms.topic: conceptual
 ms.subservice: machine-learning
-ms.date: 05/02/2024
+ms.date: 07/15/2024
 ms.author: midesa
 ---
 
-# Deep learning (Preview)
+# Deep learning (deprecated)
 
 Apache Spark in Azure Synapse Analytics enables machine learning with big data, providing the ability to obtain valuable insight from large amounts of structured, unstructured, and fast-moving data. There are several options when training machine learning models using Azure Spark in Azure Synapse Analytics: Apache Spark MLlib, Azure Machine Learning, and various other open-source libraries.
 
-> [!WARNING]
-> - The GPU accelerated preview is limited to the [Apache Spark 3.2 (End of Support announced)](../spark/apache-spark-32-runtime.md) runtime. End of Support announced for Azure Synapse Runtime for Apache Spark 3.2 has been announced July 8, 2023. End of Support announced runtimes will not have bug and feature fixes. Security fixes will be backported based on risk assessment. This runtime and the corresponding GPU accelerated preview on Spark 3.2 will be retired and disabled as of July 8, 2024.
-> - The GPU accelerated preview is now unsupported on the [Azure Synapse 3.1 (unsupported) runtime](../spark/apache-spark-3-runtime.md). Azure Synapse Runtime for Apache Spark 3.1 has reached its End of Support as of January 26, 2023, with official support discontinued effective January 26, 2024, and no further addressing of support tickets, bug fixes, or security updates beyond this date.
+> [!NOTE]
+> The Preview for Azure Synapse GPU-enabled pools has now been deprecated.
+
+> [!CAUTION]
+> Deprecation and disablement notification for GPUs on the Azure Synapse Runtime for Apache Spark 3.1 and 3.2
+> - The GPU accelerated preview is now deprecated on the [Apache Spark 3.2 (deprecated) runtime](../spark/apache-spark-32-runtime.md). Deprecated runtimes will not have bug and feature fixes. This runtime and the corresponding GPU accelerated preview on Spark 3.2 has been retired and disabled as of July 8, 2024.
+> - The GPU accelerated preview is now deprecated on the [Azure Synapse 3.1 (deprecated) runtime](../spark/apache-spark-3-runtime.md). Azure Synapse Runtime for Apache Spark 3.1 has reached its end of support as of January 26, 2023, with official support discontinued effective January 26, 2024, and no further addressing of support tickets, bug fixes, or security updates beyond this date.
 
 ## GPU-enabled Apache Spark pools
 
 To simplify the process for creating and managing pools, Azure Synapse takes care of pre-installing low-level libraries and setting up all the complex networking requirements between compute nodes. This integration allows users to get started with GPU- accelerated pools within just a few minutes. 
 
 > [!NOTE]
 >  - GPU-accelerated pools can be created in workspaces located in East US, Australia East, and North Europe.
->  - GPU-accelerated pools are only available with the Apache Spark 3.1 (unsupported) and 3.2 runtime.
+>  - GPU-accelerated pools are only available with the Apache Spark 3.1 (deprecated) and 3.2 runtime (deprecated).
 >  - You might need to request a [limit increase](../spark/apache-spark-rapids-gpu.md#quotas-and-resource-constraints-in-azure-synapse-gpu-enabled-pools) in order to create GPU-enabled clusters.
 
 ## GPU ML Environment
@@ -63,4 +67,4 @@ For more information about Petastorm, you can visit the [Petastorm GitHub page](
 This article provides an overview of the various options to train machine learning models within Apache Spark pools in Azure Synapse Analytics. You can learn more about model training by following the tutorial below:
 
 - Run SparkML experiments: [Apache SparkML Tutorial](../spark/apache-spark-machine-learning-mllib-notebook.md)
-- Accelerate ETL workloads with RAPIDS: [Apache Spark Rapids](../spark/apache-spark-rapids-gpu.md)
+- Accelerate ETL workloads with RAPIDS: [Apache Spark Rapids](../spark/apache-spark-rapids-gpu.md)

articles/synapse-analytics/machine-learning/tutorial-horovod-pytorch.md

@@ -1,15 +1,15 @@
 ---
-title: 'Tutorial: Distributed training with Horovod and PyTorch'
+title: 'Tutorial: Distributed training with Horovod and PyTorch (deprecated)'
 description: Tutorial on how to run distributed training with the Horovod Estimator and PyTorch
 ms.service: synapse-analytics 
 ms.subservice: machine-learning
 ms.topic: tutorial
-ms.date: 05/02/2024
+ms.date: 07/15/2024
 author: midesa
 ms.author: midesa
 ---
 
-# Tutorial: Distributed Training with Horovod Estimator and PyTorch (Preview)
+# Tutorial: Distributed Training with Horovod Estimator and PyTorch (deprecated)
 
 [Horovod](https://github.com/horovod/horovod) is a distributed training framework for libraries like TensorFlow and PyTorch. With Horovod, users can scale up an existing training script to run on hundreds of GPUs in just a few lines of code.
 
@@ -20,10 +20,13 @@ Within Azure Synapse Analytics, users can quickly get started with Horovod using
 - [Azure Synapse Analytics workspace](../get-started-create-workspace.md) with an Azure Data Lake Storage Gen2 storage account configured as the default storage. You need to be the *Storage Blob Data Contributor* of the Data Lake Storage Gen2 file system that you work with.
 - Create a GPU-enabled Apache Spark pool in your Azure Synapse Analytics workspace. For details, see [Create a GPU-enabled Apache Spark pool in Azure Synapse](../spark/apache-spark-gpu-concept.md). For this tutorial, we suggest using the GPU-Large cluster size with 3 nodes.
 
-> [!WARNING]
-> - The GPU accelerated preview is limited to the [Apache Spark 3.2 (End of Support announced)](../spark/apache-spark-32-runtime.md) runtime. End of Support announced for Azure Synapse Runtime for Apache Spark 3.2 has been announced July 8, 2023. End of Support announced runtimes will not have bug and feature fixes. Security fixes will be backported based on risk assessment. This runtime and the corresponding GPU accelerated preview on Spark 3.2 will be retired and disabled as of July 8, 2024.
-> - The GPU accelerated preview is now unsupported on the [Azure Synapse 3.1 (unsupported) runtime](../spark/apache-spark-3-runtime.md). Azure Synapse Runtime for Apache Spark 3.1 has reached its End of Support as of January 26, 2023, with official support discontinued effective January 26, 2024, and no further addressing of support tickets, bug fixes, or security updates beyond this date.
+> [!NOTE]
+> The Preview for Azure Synapse GPU-enabled pools has now been deprecated.
 
+> [!CAUTION]
+> Deprecation and disablement notification for GPUs on the Azure Synapse Runtime for Apache Spark 3.1 and 3.2
+> - The GPU accelerated preview is now deprecated on the [Apache Spark 3.2 (deprecated) runtime](../spark/apache-spark-32-runtime.md). Deprecated runtimes will not have bug and feature fixes. This runtime and the corresponding GPU accelerated preview on Spark 3.2 has been retired and disabled as of July 8, 2024.
+> - The GPU accelerated preview is now deprecated on the [Azure Synapse 3.1 (deprecated) runtime](../spark/apache-spark-3-runtime.md). Azure Synapse Runtime for Apache Spark 3.1 has reached its end of support as of January 26, 2023, with official support discontinued effective January 26, 2024, and no further addressing of support tickets, bug fixes, or security updates beyond this date.
 
 ## Configure the Apache Spark session
 

articles/synapse-analytics/machine-learning/tutorial-horovod-tensorflow.md

@@ -1,5 +1,5 @@
 ---
-title: 'Tutorial: Distributed training with Horovod and TensorFlow'
+title: 'Tutorial: Distributed training with Horovod and TensorFlow (deprecated)'
 description: Tutorial on how to run distributed training with the Horovod Runner and TensorFlow
 ms.service: synapse-analytics 
 ms.subservice: machine-learning
@@ -9,7 +9,7 @@ author: midesa
 ms.author: midesa
 ---
 
-# Tutorial: Distributed Training with Horovod Runner and TensorFlow (Preview)
+# Tutorial: Distributed Training with Horovod Runner and TensorFlow (deprecated)
 
 [Horovod](https://github.com/horovod/horovod) is a distributed training framework for libraries like TensorFlow and PyTorch. With Horovod, users can scale up an existing training script to run on hundreds of GPUs in just a few lines of code. 
 
@@ -20,10 +20,13 @@ Within Azure Synapse Analytics, users can quickly get started with Horovod using
 - [Azure Synapse Analytics workspace](../get-started-create-workspace.md) with an Azure Data Lake Storage Gen2 storage account configured as the default storage. You need to be the *Storage Blob Data Contributor* of the Data Lake Storage Gen2 file system that you work with.
 - Create a GPU-enabled Apache Spark pool in your Azure Synapse Analytics workspace. For details, see [Create a GPU-enabled Apache Spark pool in Azure Synapse](../spark/apache-spark-gpu-concept.md). For this tutorial, we suggest using the GPU-Large cluster size with 3 nodes.
 
-> [!WARNING]
-> - The GPU accelerated preview is limited to the [Apache Spark 3.2 (End of Support announced)](../spark/apache-spark-32-runtime.md) runtime. End of Support announced for Azure Synapse Runtime for Apache Spark 3.2 has been announced July 8, 2023. End of Support announced runtimes will not have bug and feature fixes. Security fixes will be backported based on risk assessment. This runtime and the corresponding GPU accelerated preview on Spark 3.2 will be retired and disabled as of July 8, 2024.
-> - The GPU accelerated preview is now unsupported on the [Azure Synapse 3.1 (unsupported) runtime](../spark/apache-spark-3-runtime.md). Azure Synapse Runtime for Apache Spark 3.1 has reached its End of Support as of January 26, 2023, with official support discontinued effective January 26, 2024, and no further addressing of support tickets, bug fixes, or security updates beyond this date.
+> [!NOTE]
+> The Preview for Azure Synapse GPU-enabled pools has now been deprecated.
 
+> [!CAUTION]
+> Deprecation and disablement notification for GPUs on the Azure Synapse Runtime for Apache Spark 3.1 and 3.2
+> - The GPU accelerated preview is now deprecated on the [Apache Spark 3.2 (deprecated) runtime](../spark/apache-spark-32-runtime.md). Deprecated runtimes will not have bug and feature fixes. This runtime and the corresponding GPU accelerated preview on Spark 3.2 has been retired and disabled as of July 8, 2024.
+> - The GPU accelerated preview is now deprecated on the [Azure Synapse 3.1 (deprecated) runtime](../spark/apache-spark-3-runtime.md). Azure Synapse Runtime for Apache Spark 3.1 has reached its end of support as of January 26, 2023, with official support discontinued effective January 26, 2024, and no further addressing of support tickets, bug fixes, or security updates beyond this date.
 
 ## Configure the Apache Spark session
 

articles/synapse-analytics/machine-learning/tutorial-load-data-petastorm.md

@@ -1,5 +1,5 @@
 ---
-title: 'Load data with Petastorm'
+title: 'Load data with Petastorm (deprecated)'
 description: This article provides a conceptual overview of how to load data with Petastorm.
 author: midesa
 ms.service: synapse-analytics
@@ -9,7 +9,7 @@ ms.date: 05/02/2024
 ms.author: midesa
 ---
 
-# Load data with Petastorm (Preview)
+# Load data with Petastorm (deprecated)
 
 Petastorm is an open source data access library, which enables single-node or distributed training of deep learning models. This library enables training directly from datasets in Apache Parquet format and datasets that are loaded as an Apache Spark DataFrame. Petastorm supports popular training frameworks such as Tensorflow and PyTorch.
 
@@ -20,9 +20,13 @@ For more information about Petastorm, you can visit the [Petastorm GitHub page](
 - [Azure Synapse Analytics workspace](../get-started-create-workspace.md) with an Azure Data Lake Storage Gen2 storage account configured as the default storage. You need to be the *Storage Blob Data Contributor* of the Data Lake Storage Gen2 file system that you work with.
 - Create a GPU-enabled Apache Spark pool in your Azure Synapse Analytics workspace. For details, see [Create a GPU-enabled Apache Spark pool in Azure Synapse](../spark/apache-spark-gpu-concept.md). For this tutorial, we suggest using the GPU-Large cluster size with 3 nodes.
 
-> [!WARNING]
-> - The GPU accelerated preview is limited to the [Apache Spark 3.2 (End of Support announced)](../spark/apache-spark-32-runtime.md) runtime. End of Support announced for Azure Synapse Runtime for Apache Spark 3.2 has been announced July 8, 2023. End of Support announced runtimes will not have bug and feature fixes. Security fixes will be backported based on risk assessment. This runtime and the corresponding GPU accelerated preview on Spark 3.2 will be retired and disabled as of July 8, 2024.
-> - The GPU accelerated preview is now unsupported on the [Azure Synapse 3.1 (unsupported) runtime](../spark/apache-spark-3-runtime.md). Azure Synapse Runtime for Apache Spark 3.1 has reached its End of Support as of January 26, 2023, with official support discontinued effective January 26, 2024, and no further addressing of support tickets, bug fixes, or security updates beyond this date.
+> [!NOTE]
+> The Preview for Azure Synapse GPU-enabled pools has now been deprecated.
+
+> [!CAUTION]
+> Deprecation and disablement notification for GPUs on the Azure Synapse Runtime for Apache Spark 3.1 and 3.2
+> - The GPU accelerated preview is now deprecated on the [Apache Spark 3.2 (deprecated) runtime](../spark/apache-spark-32-runtime.md). Deprecated runtimes will not have bug and feature fixes. This runtime and the corresponding GPU accelerated preview on Spark 3.2 has been retired and disabled as of July 8, 2024.
+> - The GPU accelerated preview is now deprecated on the [Azure Synapse 3.1 (deprecated) runtime](../spark/apache-spark-3-runtime.md). Azure Synapse Runtime for Apache Spark 3.1 has reached its end of support as of January 26, 2023, with official support discontinued effective January 26, 2024, and no further addressing of support tickets, bug fixes, or security updates beyond this date.
 
 
 ## Configure the Apache Spark session
@@ -265,4 +269,4 @@ for epoch in range(1, loop_epochs + 1):
 ## Next steps
 
 * [Check out Synapse sample notebooks](https://github.com/Azure-Samples/Synapse/tree/main/MachineLearning)
-* [Learn more about GPU-enabled Apache Spark pools](../spark/apache-spark-gpu-concept.md)
+* [Learn more about GPU-enabled Apache Spark pools](../spark/apache-spark-gpu-concept.md)

articles/synapse-analytics/spark/apache-spark-gpu-concept.md

@@ -1,5 +1,5 @@
 ---
-title: GPU-accelerated pools
+title: GPU-accelerated pools (deprecated)
 description: Introduction to GPUs inside Synapse Analytics.
 author: midesa
 ms.service: synapse-analytics
@@ -9,19 +9,19 @@ ms.date: 05/02/2024
 ms.author: midesa
 ---
 
-# GPU-accelerated Apache Spark pools in Azure Synapse Analytics (Preview)
+# GPU-accelerated Apache Spark pools in Azure Synapse Analytics (deprecated)
 
 Azure Synapse Analytics now supports Apache Spark pools accelerated with graphics processing units (GPUs). 
 
 By using NVIDIA GPUs, data scientists and engineers can reduce the time necessary to run data integration pipelines, score machine learning models, and more. This article describes how GPU-accelerated pools can be created and used with Azure Synapse Analytics. This article also details the GPU drivers and libraries that are pre-installed as part of the GPU-accelerated runtime.
 
-> [!WARNING]
-> - The GPU accelerated preview is limited to the [Apache Spark 3.2 (End of Support announced)](../spark/apache-spark-32-runtime.md) runtime. End of Support announced for Azure Synapse Runtime for Apache Spark 3.2 has been announced July 8, 2023. End of Support announced runtimes will not have bug and feature fixes. Security fixes will be backported based on risk assessment. This runtime and the corresponding GPU accelerated preview on Spark 3.2 will be retired and disabled as of July 8, 2024.
-> - The GPU accelerated preview is now unsupported on the [Azure Synapse 3.1 (unsupported) runtime](../spark/apache-spark-3-runtime.md). Azure Synapse Runtime for Apache Spark 3.1 has reached its End of Support as of January 26, 2023, with official support discontinued effective January 26, 2024, and no further addressing of support tickets, bug fixes, or security updates beyond this date.
-
+> [!CAUTION]
+> Deprecation and disablement notification for GPUs on the Azure Synapse Runtime for Apache Spark 3.1 and 3.2
+> - The GPU accelerated preview is now deprecated on the [Apache Spark 3.2 (deprecated) runtime](../spark/apache-spark-32-runtime.md). Deprecated runtimes will not have bug and feature fixes. This runtime and the corresponding GPU accelerated preview on Spark 3.2 has been retired and disabled as of July 8, 2024.
+> - The GPU accelerated preview is now deprecated on the [Azure Synapse 3.1 (deprecated) runtime](../spark/apache-spark-3-runtime.md). Azure Synapse Runtime for Apache Spark 3.1 has reached its end of support as of January 26, 2023, with official support discontinued effective January 26, 2024, and no further addressing of support tickets, bug fixes, or security updates beyond this date.
 
 > [!NOTE]
-> Azure Synapse GPU-enabled pools are currently in Public Preview.
+> Azure Synapse GPU-enabled preview has now been deprecated.
 
 ## Create a GPU-accelerated pool
 
@@ -30,7 +30,6 @@ To simplify the process for creating and managing pools, Azure Synapse takes car
 > [!NOTE]
 >  - GPU-accelerated pools can be created in workspaces located in East US, Australia East, and North Europe.
 >  - GPU-accelerated pools are only availble with the Apache Spark 3 runtime.
->  - You might need to request a [limit increase](./apache-spark-rapids-gpu.md#quotas-and-resource-constraints-in-azure-synapse-gpu-enabled-pools) in order to create GPU-enabled clusters. 
  
 ## GPU-accelerated runtime