updates

lgayhardt · lgayhardt · commit 5ba3fb3d0a86 · 2023-03-20T14:55:10.000-07:00
diff --git a/articles/machine-learning/how-to-azure-container-for-pytorch-environment.md b/articles/machine-learning/how-to-azure-container-for-pytorch-environment.md
@@ -1,5 +1,5 @@
 ---
-title: Create Azure Container for PyTorch Custom Curated Environment
+title: How to create Azure Container for PyTorch Custom Curated Environment
 titleSuffix: Azure Machine Learning
 description: Create custom curated Azure Container for PyTorch Environments in Azure Machine Learning studio to run your machine learning models and reuse it in different scenarios.
 services: machine-learning
@@ -20,7 +20,7 @@ If you're looking to extend curated environment and add Hugging Face (HF) transf
 
 Before following the steps in this article, make sure you have the following prerequisites:
 
-- An Azure subscription. If you don't have an Azure subscription, create a free account before you begin. Try the [free or paid version of Azure Machine Learning](https://azure.microsoft.com/en-us/free/).
+- An Azure subscription. If you don't have an Azure subscription, create a free account before you begin. Try the [free or paid version of Azure Machine Learning](https://azure.microsoft.com/free/).
 
 - An Azure Machine Learning workspace. If you don't have one, use the steps in the [Quickstart: Create workspace resources](quickstart-create-resources.md) article to create one.
 
diff --git a/articles/machine-learning/how-to-train-distributed-gpu.md b/articles/machine-learning/how-to-train-distributed-gpu.md
@@ -128,7 +128,7 @@ Azure Machine Learning will set the `MASTER_ADDR`, `MASTER_PORT`, `WORLD_SIZE`,
 
 `DeepSpeed` can be enabled using either Pytorch distribution or MPI for running distributed training. Azure Machine Learning supports the `DeepSpeed` launcher to launch distributed training as well as autotuning to get optimal `ds` configuration.
 
-You can use a [curated environment](resource-curated-environments.md#azure-container-for-pytorch-acpt-preview) for an out of the box environment with the latest state of art technologies including `DeepSpeed`, `ORT`, `MSSCCL`, and `Pytorch` for your DeepSpeed training jobs.
+You can use a [curated environment](resource-curated-environments.md#azure-container-for-pytorch-acpt) for an out of the box environment with the latest state of art technologies including `DeepSpeed`, `ORT`, `MSSCCL`, and `Pytorch` for your DeepSpeed training jobs.
 
 ### DeepSpeed example
 
diff --git a/articles/machine-learning/reference-checkpoint-performance-for-large-models.md b/articles/machine-learning/reference-checkpoint-performance-for-large-models.md
@@ -78,7 +78,7 @@ Nebula can
 * An Azure subscription and an Azure Machine Learning workspace. See [Create workspace resources](./quickstart-create-resources.md) for more information about workspace resource creation
 * An Azure Machine Learning compute target. See [Manage training & deploy computes](./how-to-create-attach-compute-studio.md) to learn more about compute target creation
 * A training script that uses **PyTorch**.
-* ACPT-curated (Azure Container for Pytorch) environment. See [Curated environments](resource-curated-environments.md#azure-container-for-pytorch-acpt-preview) to obtain the ACPT image. Learn how to use the curated environment [here](./how-to-use-environments.md)
+* ACPT-curated (Azure Container for Pytorch) environment. See [Curated environments](resource-curated-environments.md#azure-container-for-pytorch-acpt) to obtain the ACPT image. Learn how to [use the curated environment](./how-to-use-environments.md)
 * An Azure Machine Learning script run configuration file. If you don’t have one, you can follow [this resource](./how-to-set-up-training-targets.md)
 
 ## How to Use Nebula
@@ -90,7 +90,7 @@ Nebula use involves:
 - [API calls to save and load checkpoints](#call-apis-to-save-and-load-checkpoints)
 
 ### Using ACPT environment
-[Azure Container for PyTorch (ACPT)](how-to-manage-environments-v2.md?tabs=cli#curated-environments), a curated environment for PyTorch model training, includes Nebula as a preinstalled, dependent Python package. See [Azure Container for PyTorch (ACPT)](resource-curated-environments.md#azure-container-for-pytorch-acpt-preview) to view the curated environment, and [Enabling Deep Learning with Azure Container for PyTorch in Azure Machine Learning](https://techcommunity.microsoft.com/t5/ai-machine-learning-blog/enabling-deep-learning-with-azure-container-for-pytorch-in-azure/ba-p/3650489) to learn more about the ACPT image.
+[Azure Container for PyTorch (ACPT)](how-to-manage-environments-v2.md?tabs=cli#curated-environments), a curated environment for PyTorch model training, includes Nebula as a preinstalled, dependent Python package. See [Azure Container for PyTorch (ACPT)](resource-curated-environments.md#azure-container-for-pytorch-acpt) to view the curated environment, and [Enabling Deep Learning with Azure Container for PyTorch in Azure Machine Learning](https://techcommunity.microsoft.com/t5/ai-machine-learning-blog/enabling-deep-learning-with-azure-container-for-pytorch-in-azure/ba-p/3650489) to learn more about the ACPT image.
             
 ### Initializing Nebula
 
diff --git a/articles/machine-learning/resource-azure-container-for-pytorch.md b/articles/machine-learning/resource-azure-container-for-pytorch.md
@@ -45,15 +45,16 @@ The following configurations are supported:
 | Environment Name | OS | GPU Version| Python Version | PyTorch Version | ORT-training Version | DeepSpeed Version | torch-ort Version |
 | --- | --- | --- | --- | --- | --- | --- | --- |
 |acpt-pytorch-2.0-cuda11.7|Ubuntu 20.04|cu117|3.8|2.0|1.14.1|0.8.2 |0.14.0|
-|acpt-pytorch-1.13-cuda11.7|Ubuntu 20.04|cu117|3.8|1.13.1|1.14.1|0.8.2| 1.14.0|
-|acpt-pytorch-1.12-py39-cuda11.6|Ubuntu 20.04|cu116|3.9 |1.12.1|1.14.1| 0.8.2|1.14.0|
-|acpt-pytorch-1.12-cuda11.6|Ubuntu 20.04|cu116|3.8|1.12.1|1.14.1|0.8.2| 1.14.0|
-|acpt-pytorch-1.11-cuda11.5|Ubuntu 20.04|cu115|3.8|1.11.0|1.11.1|0.7.3| 1.11.0| 
-|acpt-pytorch-1.11-cuda11.3|Ubuntu 20.04|cu113|3.8|1.11.0|1.14.1|0.8.2| 1.14.0| 
-
+|acpt-pytorch-1.13-cuda11.7|Ubuntu 20.04|cu117|3.8|1.13.1|1.14.1|0.8.2|1.14.0|
+|acpt-pytorch-1.12-py39-cuda11.6|Ubuntu 20.04|cu116|3.9|1.12.1|1.14.1|0.8.2|1.14.0|
+|acpt-pytorch-1.12-cuda11.6|Ubuntu 20.04|cu116|3.8|1.12.1|1.14.1|0.8.2|1.14.0|
+|acpt-pytorch-1.11-cuda11.5|Ubuntu 20.04|cu115|3.8|1.11.0|1.11.1|0.7.3|1.11.0|
+|acpt-pytorch-1.11-cuda11.3|Ubuntu 20.04|cu113|3.8|1.11.0|1.14.1|0.8.2|1.14.0|
 
 Other packages like fairscale, horovod, msccl, protobuf, pyspark, pytest, pytorch-lightning, tensorboard, NebulaML, torchvision, torchmetrics to support all training needs
 
+To learn more, see [Create custom ACPT curated environments](how-to-azure-container-for-pytorch-environment.md).
+
 [!NOTE]
 > Currently, due to underlying cuda and cluster incompatibilities, on [NC series](../virtual-machines/nc-series.md) only acpt-pytorch-1.11-cuda11.3 with cuda 11.3 and torch 1.11 can be used.
 
diff --git a/articles/machine-learning/resource-curated-environments.md b/articles/machine-learning/resource-curated-environments.md
@@ -37,7 +37,7 @@ This article lists the curated environments with latest framework versions in Az
 **Name**: AzureML-ACPT-pytorch-1.12-py39-cuda11.6-gpu  
 **Description**: The Azure Curated Environment for PyTorch is our latest PyTorch curated environment. It is optimized for large, distributed deep learning workloads and comes pre-packaged with the best of Microsoft technologies for accelerated training, e.g., OnnxRuntime Training (ORT), DeepSpeed, MSCCL, etc.
 
-The learn more, see [Azure Container for PyTorch (ACPT)](reference-azure-container-for-pytorch.md).
+The learn more, see [Azure Container for PyTorch (ACPT)](resource-azure-container-for-pytorch.md).
 
 [!NOTE]
 > Currently, due to underlying cuda and cluster incompatibilities, on [NC series](../virtual-machines/nc-series.md) only acpt-pytorch-1.11-cuda11.3 with cuda 11.3 and torch 1.11 can be used.
