Merge branch 'main' of https://github.com/MicrosoftDocs/azure-docs-pr into CH-multitenant

Author: cilwerner

articles/hpc-cache/hpc-cache-add-storage.md

@@ -4,7 +4,7 @@ description: How to define storage targets so that your Azure HPC Cache can use
 author: ekpgh
 ms.service: hpc-cache
 ms.topic: how-to
-ms.date: 09/29/2022
+ms.date: 10/05/2022
 ms.custom: subject-rbac-steps
 ms.author: v-erinkelly
 ---
@@ -236,7 +236,7 @@ These three options cover most situations:
 
 * **Greater than 15% writes** - This option speeds up both read and write performance.
 
-  Client reads and client writes are both cached. Files in the cache are assumed to be newer than files on the back-end storage system. Cached files are only automatically checked against the files on back-end storage every eight hours. Modified files in the cache are written to the back-end storage system after they have been in the cache for 20 minutes with no other changes.
+  Client reads and client writes are both cached. Files in the cache are assumed to be newer than files on the back-end storage system. Cached files are only automatically checked against the files on back-end storage every eight hours. Modified files in the cache are written to the back-end storage system after they have been in the cache for an hour with no other changes.
 
   Do not use this option if any clients mount the back-end storage volume directly, because there is a risk it will have outdated files.
 

articles/machine-learning/how-to-deploy-with-triton.md

@@ -27,7 +27,7 @@ ms.devlang: azurecli
 
 Learn how to use [NVIDIA Triton Inference Server](https://aka.ms/nvidia-triton-docs) in Azure Machine Learning with [online endpoints](concept-endpoints.md#what-are-online-endpoints).
 
-Triton is multi-framework, open-source software that is optimized for inference. It supports popular machine learning frameworks like TensorFlow, ONNX Runtime, PyTorch, NVIDIA TensorRT, and more. It can be used for your CPU or GPU workloads. No-code deployment for Triton models are supported in both [managed online endpoints and Kubernetes online endpoints](concept-endpoints.md#managed-online-endpoints-vs-kubernetes-online-endpoints).
+Triton is multi-framework, open-source software that is optimized for inference. It supports popular machine learning frameworks like TensorFlow, ONNX Runtime, PyTorch, NVIDIA TensorRT, and more. It can be used for your CPU or GPU workloads. No-code deployment for Triton models is supported in both [managed online endpoints and Kubernetes online endpoints](concept-endpoints.md#managed-online-endpoints-vs-kubernetes-online-endpoints).
 
 In this article, you will learn how to deploy Triton and a model to a [managed online endpoint](concept-endpoints.md#managed-online-endpoints). Information is provided on using the CLI (command line), Python SDK v2, and Azure Machine Learning studio. 
 
@@ -93,19 +93,18 @@ The information in this document is based on using a model stored in ONNX format
     > [!IMPORTANT]
     > You may need to request a quota increase for your subscription before you can use this series of VMs. For more information, see [NCv3-series](../virtual-machines/ncv3-series.md).
 
-The information in this article is based on the [Deploy a model to online endpoints using Triton](https://github.com/Azure/azureml-examples/blob/main/sdk/endpoints/online/triton/single-model/online-endpoints-triton.ipynb) notebook contained in the [azureml-examples](https://github.com/azure/azureml-examples) repository. To run the commands locally without having to copy/paste files, clone the repo and then change directories to the `sdk/endpoints/online/triton/single-model/online-endpoints-triton.ipynb` directory in the repo:
+The information in this article is based on the [online-endpoints-triton.ipynb](https://github.com/Azure/azureml-examples/blob/main/sdk/python/endpoints/online/triton/single-model/online-endpoints-triton.ipynb) notebook contained in the [azureml-examples](https://github.com/azure/azureml-examples) repository. To run the commands locally without having to copy/paste files, clone the repo, and then change directories to the `sdk/endpoints/online/triton/single-model/` directory in the repo:
 
 ```azurecli
 git clone https://github.com/Azure/azureml-examples --depth 1
-cd azureml-examples
-cd sdk/endpoints/online/triton/single-model/online-endpoints-triton.ipynb
+cd azureml-examples/sdk/python/endpoints/online/triton/single-model/
 ```
 
 # [Studio](#tab/azure-studio)
 
 * 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 [Manage Azure Machine Learning workspaces in the portal or with the Python SDK](how-to-manage-workspace.md) to create one.
+* An Azure Machine Learning workspace. If you don't have one, use the steps in [Manage Azure Machine Learning workspaces in the portal, or with the Python SDK](how-to-manage-workspace.md) to create one.
 
 --- 
 
@@ -428,9 +427,9 @@ Once your deployment completes, use the following command to make a scoring requ
 
 # [Studio](#tab/azure-studio)
 
-Azure Machine Learning Studio provides the ability to test endpoints with JSON. However, serialized JSON is not currently included for this example. 
+Azure Machine Learning studio provides the ability to test endpoints with JSON. However, serialized JSON is not currently included for this example. 
 
-To test an endpoint using Azure Machine Learning Studio, click `Test` from the Endpoint page. 
+To test an endpoint using Azure Machine Learning studio, click `Test` from the Endpoint page. 
 
 ---