Merge pull request #272510 from fbsolo-ms1/update-data-science-virtual-machine-files

Freshness update for dsvm-tools-deep-learning-frameworks.md . . .

Author: American-Dipper

articles/machine-learning/data-science-virtual-machine/dsvm-tools-deep-learning-frameworks.md

@@ -10,55 +10,54 @@ ms.custom:
 author: michalmar
 ms.author: mimarusa
 ms.topic: conceptual
-ms.date: 07/27/2021
+ms.date: 04/17/2024
 ---
 
-# Deep learning and AI frameworks for the Azure Data Science VM
-Deep learning frameworks on the DSVM are listed below.
-
+# Deep learning and AI frameworks for the Azure Data Science Virtual Machine
+Deep learning frameworks on the DSVM are listed here:
 
 ## [CUDA, cuDNN, NVIDIA Driver](https://developer.nvidia.com/cuda-toolkit)
 
 | Category | Value |
 |--|--|
-| Version(s) supported | 11 |
+| Supported versions | 11 |
 | Supported DSVM editions | Windows Server 2019<br>Linux |
-| How is it configured / installed on the DSVM? | _nvidia-smi_ is available on the system path. |
+| How is it configured and installed on the DSVM? | _nvidia-smi_ is available on the system path. |
 | How to run it | Open a command prompt (on Windows) or a terminal (on Linux), and then run _nvidia-smi_. |
+
 ## [Horovod](https://github.com/uber/horovod)
 
 | Category | Value |
 | ------------- | ------------- |
-| Version(s) supported | 0.21.3|
+| Supported versions | 0.21.3|
 | Supported DSVM editions      | Linux |
-| How is it configured / installed on the DSVM?  | Horovod is installed in Python 3.5 |
+| How is it configured and installed on the DSVM?  | Horovod is installed in Python 3.5 |
 | How to run it      | Activate the correct environment at the terminal, and then run Python. |
 
-
 ## [NVidia System Management Interface (nvidia-smi)](https://developer.nvidia.com/nvidia-system-management-interface)
 
 | Category | Value |
 |--|--|
-| Version(s) supported |  |
+| Supported versions |  |
 | Supported DSVM editions | Windows Server 2019<br>Linux |
-| What is it for? | NVIDIA tool for querying GPU activity |
-| How is it configured / installed on the DSVM? | `nvidia-smi` is on the system path. |
-| How to run it | On a virtual machine **with GPU's**, open a command prompt (on Windows) or a terminal (on Linux), and then run `nvidia-smi`. |
+| What is it used for? | As an NVIDIA tool to query GPU activity |
+| How is it configured and installed on the DSVM? | `nvidia-smi` is on the system path. |
+| How to run it | On a virtual machine **with GPU's**, open a command prompt (on Windows), or a terminal (on Linux), and then run `nvidia-smi`. |
 
 ## [PyTorch](https://pytorch.org/)
 
 | Category | Value |
 |--|--|
-| Version(s) supported | 1.9.0 (Linux, Windows 2019) |
+| Supported versions | 1.9.0 (Linux, Windows 2019) |
 | Supported DSVM editions | Windows Server 2019<br>Linux |
-| How is it configured / installed on the DSVM? | Installed in Python, conda environments 'py38_default', 'py38_pytorch' |
-| How to run it | Terminal: Activate the correct environment, and then run Python.<br/>* [JupyterHub](dsvm-ubuntu-intro.md#how-to-access-the-ubuntu-data-science-virtual-machine): Connect, and then open the PyTorch directory for samples. |
+| How is it configured and installed on the DSVM? | Installed in Python, conda environments 'py38_default', 'py38_pytorch' |
+| How to run it | At the terminal, activate the appropriate environment, and then run Python.<br/>* [JupyterHub](dsvm-ubuntu-intro.md#how-to-access-the-ubuntu-data-science-virtual-machine): Connect, and then open the PyTorch directory for samples. |
 
 ## [TensorFlow](https://www.tensorflow.org/)
 
 | Category | Value |
 |--|--|
-| Version(s) supported | 2.5 |
+| Supported versions | 2.5 |
 | Supported DSVM editions | Windows Server 2019<br>Linux |
-| How is it configured / installed on the DSVM? | Installed in Python, conda environments 'py38_default', 'py38_tensorflow' |
-| How to run it | Terminal: Activate the correct environment, and then run Python. <br/> * Jupyter: Connect to [Jupyter](provision-vm.md) or [JupyterHub](dsvm-ubuntu-intro.md#how-to-access-the-ubuntu-data-science-virtual-machine), and then open the TensorFlow directory for samples. |
+| How is it configured and installed on the DSVM? | Installed in Python, conda environments 'py38_default', 'py38_tensorflow' |
+| How to run it | At the terminal, activate the correct environment, and then run Python. <br/> * Jupyter: Connect to [Jupyter](provision-vm.md) or [JupyterHub](dsvm-ubuntu-intro.md#how-to-access-the-ubuntu-data-science-virtual-machine), and then open the TensorFlow directory for samples. |