Merge pull request #268686 from sophies927/patch-1

Update resource-azure-container-for-pytorch.md with new configurations

Author: v-shils

articles/machine-learning/resource-azure-container-for-pytorch.md

@@ -22,33 +22,31 @@ Azure Container for PyTorch is a lightweight, standalone environment that includ
 
 ## Why should I use ACPT?
 
-* Use as is with preinstalled packages or build on top of the curated environment.
-* Optimized training framework to set up, develop, accelerate PyTorch model on large workloads.
-* Up-to-date stack with the latest compatible versions of Ubuntu, Python, PyTorch, CUDA\RocM, etc.
-* Ease of use: All components installed and validated against dozens of Microsoft workloads to reduce setup costs and accelerate time to value.
-* Latest Training Optimization Technologies: [ONNX RunTime](https://onnxruntime.ai/) , [DeepSpeed](https://www.deepspeed.ai/),  [MSCCL](https://github.com/microsoft/msccl),and others.
-* Integration with Azure Machine Learning: Track your PyTorch experiments on Azure Machine Learning studio or using the SDK.
-* The image is also available as a [Data Science Virtual Machine (DSVM)](https://azure.microsoft.com/products/virtual-machines/data-science-virtual-machines/). To learn more about Data Science Virtual Machines, see [the DSVM overview documentation](data-science-virtual-machine/overview.md).
-* Azure customer support reduces training and deployment latency.
-* Improves training and deployment success rate.
-* Avoid unnecessary image builds.
-* Only have required dependencies and access right in the image/container.
+* **Flexibility**: Use as-is with preinstalled packages or build on top of the curated environment.
+* **Ease of use**: All components are installed and validated against dozens of Microsoft workloads to reduce setup costs and accelerate time to value.
+* **Efficiency**: Avoid unnecessary image builds and only have required dependencies that are accessible right in the image/container.
+* **Optimized training framework**: Set up, develop, and accelerate PyTorch models on large workloads, and improve training and deployment success rate.
+* **Up-to-date stack**: Access the latest compatible versions of Ubuntu, Python, PyTorch, CUDA/RocM, etc.
+* **Latest training optimization technologies**: Make use of [ONNX Runtime](https://onnxruntime.ai/) , [DeepSpeed](https://www.deepspeed.ai/),  [MSCCL](https://github.com/microsoft/msccl), and more.
+* **Integration with Azure Machine Learning**: Track your PyTorch experiments on Azure Machine Learning studio or using the SDK. Azure customer support also reduces training and deployment latency.
+* **Availability as DSVM**: The image is also available as a [Data Science Virtual Machine (DSVM)](https://azure.microsoft.com/products/virtual-machines/data-science-virtual-machines/). To learn more about Data Science Virtual Machines, see [the DSVM overview documentation](data-science-virtual-machine/overview.md).
 
 >[!IMPORTANT]
 > To view more information about curated environment packages and versions, visit the Environments tab in the Azure Machine Learning [studio](./how-to-manage-environments-in-studio.md).
 
 ## Supported configurations for Azure Container for PyTorch (ACPT)
 
-**Description**: The Azure Curated Environment for PyTorch is our latest PyTorch curated environment. It's optimized for large, distributed deep learning workloads and comes prepackaged with the best of Microsoft technologies for accelerated training, for example, OnnxRuntime Training (ORT), DeepSpeed, MSCCL, etc.
+**Description**: The Azure Curated Environment for PyTorch is our latest PyTorch curated environment. It's optimized for large, distributed deep learning workloads and comes prepackaged with the best of Microsoft technologies for accelerated training (e.g., Onnx Runtime Training (ORT), DeepSpeed, MSCCL, etc.).
 
 The following configurations are supported:
 
 | Environment Name | OS | GPU Version| Python Version | PyTorch Version | ORT-training Version | DeepSpeed Version | torch-ort Version | Nebula Version | 
 | --- | --- | --- | --- | --- | --- | --- | --- | --- |
-|acpt-pytorch-2.0-cuda11.7|Ubuntu 20.04|cu117|3.8|2.0.1|1.15.1|0.9.5 |1.15.0|0.16.5|
-|acpt-pytorch-1.13-cuda11.7|Ubuntu 20.04|cu117|3.8|1.13.1|1.15.1|0.9.5|1.15.0|0.16.5|
+|acpt-pytorch-2.1-cuda12.1|Ubuntu 20.04|cu121|3.8|2.1.2|1.17.1|0.13.1|1.17.0|0.16.10|
+|acpt-pytorch-2.0-cuda11.7|Ubuntu 20.04|cu117|3.8|2.0.1|1.17.1|0.13.1|1.17.0|0.16.10|
+|acpt-pytorch-1.13-cuda11.7|Ubuntu 20.04|cu117|3.8|1.13.1|1.17.1|0.13.1|1.17.0|0.16.10|
 
-Other packages like fairscale, horovod, msccl, protobuf, pyspark, pytest, pytorch-lightning, tensorboard, NebulaML, torchvision, torchmetrics to support all training needs
+Other packages like fairscale, horovod, msccl, protobuf, pyspark, pytest, pytorch-lightning, tensorboard, NebulaML, torchvision, and torchmetrics are provided to support all training needs.
 
 To learn more, see [Create custom ACPT curated environments](how-to-azure-container-for-pytorch-environment.md).