Merge pull request #2390 from MicrosoftDocs/repo_sync_working_branch

Confirm merge from repo_sync_working_branch to main to sync with https://github.com/MicrosoftDocs/azure-ai-docs (branch main)

Author: Taojunshen

articles/ai-services/immersive-reader/reference.md

@@ -1,5 +1,5 @@
 ---
-title: Immersive Reader SDK Javascript reference
+title: Immersive Reader SDK JavaScript reference
 titleSuffix: Azure AI services
 description: Learn about the Immersive Reader JavaScript library that allows you to integrate Immersive Reader into your application.
 #services: cognitive-services

articles/ai-services/personalizer/whats-new.md

@@ -19,7 +19,7 @@ Learn what's new in Azure AI Personalizer. These items may include release notes
 
 ### September 2022
 * Personalizer Inference Explainabiltiy is now available as a Public Preview. Enabling inference explainability returns feature scores on every Rank API call, providing insight into how influential each feature is to the actions chosen by your Personalizer model. [Learn more about Inference Explainability](how-to-inference-explainability.md).
-* Personalizer SDK now available in [Java](https://search.maven.org/artifact/com.azure/azure-ai-personalizer/1.0.0-beta.1/jar) and [Javascript](https://www.npmjs.com/package/@azure-rest/ai-personalizer).
+* Personalizer SDK now available in [Java](https://search.maven.org/artifact/com.azure/azure-ai-personalizer/1.0.0-beta.1/jar) and [JavaScript](https://www.npmjs.com/package/@azure-rest/ai-personalizer).
 
 ### April 2022
 * Local inference SDK (Preview): Personalizer now supports near-realtime (sub-10ms) inference without the need to wait for network API calls. Your Personalizer models can be used locally for lightning fast Rank calls using the [C# SDK (Preview)](https://www.nuget.org/packages/Azure.AI.Personalizer/2.0.0-beta.2), empowering your applications to personalize quickly and efficiently. Your model continues to train in Azure while your local model is seamlessly updated. 

articles/machine-learning/component-reference/component-reference.md

@@ -63,7 +63,7 @@ For help with choosing algorithms, see
 
 | Functionality | Description | component |
 | --- |--- | --- |
-| Model Training | Run data through the algorithm. |  [Train Clustering Model](train-clustering-model.md) <br/> [Train Model](train-model.md) <br/> [Train Pytorch Model](train-pytorch-model.md) <br/> [Tune Model Hyperparameters](tune-model-hyperparameters.md) |
+| Model Training | Run data through the algorithm. |  [Train Clustering Model](train-clustering-model.md) <br/> [Train Model](train-model.md) <br/> [Train PyTorch Model](train-pytorch-model.md) <br/> [Tune Model Hyperparameters](tune-model-hyperparameters.md) |
 | Model Scoring and Evaluation | Measure the accuracy of the trained model. | [Apply Transformation](apply-transformation.md) <br/> [Assign Data to Clusters](assign-data-to-clusters.md) <br/> [Cross Validate Model](cross-validate-model.md) <br/> [Evaluate Model](evaluate-model.md) <br/> [Score Image Model](score-image-model.md) <br/> [Score Model](score-model.md) |
 | Python Language | Write code and embed it in a component to integrate Python with your pipeline. | [Create Python Model](create-python-model.md) <br/> [Execute Python Script](execute-python-script.md) |
 | R Language | Write code and embed it in a component to integrate R with your pipeline. | [Execute R Script](execute-r-script.md) |

articles/machine-learning/component-reference/densenet.md

@@ -21,7 +21,7 @@ This classification algorithm is a supervised learning method, and requires a la
 > [!NOTE]
 > This component does not support labeled dataset generated from *Data Labeling* in the studio, but only support labeled image directory generated from [Convert to Image Directory](convert-to-image-directory.md) component. 
 
-You can train the model by providing the model and the labeled image directory as inputs to [Train Pytorch Model](train-pytorch-model.md). The trained model can then be used to predict values for the new input examples using [Score Image Model](score-image-model.md).
+You can train the model by providing the model and the labeled image directory as inputs to [Train PyTorch Model](train-pytorch-model.md). The trained model can then be used to predict values for the new input examples using [Score Image Model](score-image-model.md).
 
 ### More about DenseNet
 
@@ -37,14 +37,14 @@ For more information on DenseNet, see the research paper, [Densely Connected Con
 
 4.  For **Memory efficient**, specify whether to use checkpointing, which is much more memory-efficient but slower. For more information, see the research paper, [Memory-Efficient Implementation of DenseNets](https://arxiv.org/pdf/1707.06990.pdf).
 
-5.  Connect the output of **DenseNet** component, training, and validation image dataset component to the [Train Pytorch Model](train-pytorch-model.md). 
+5.  Connect the output of **DenseNet** component, training, and validation image dataset component to the [Train PyTorch Model](train-pytorch-model.md). 
 
 6. Submit the pipeline.
 
 
 ## Results
 
-After pipeline run is completed, to use the model for scoring, connect the [Train Pytorch Model](train-pytorch-model.md) to [Score Image Model](score-image-model.md), to predict values for new input examples.
+After pipeline run is completed, to use the model for scoring, connect the [Train PyTorch Model](train-pytorch-model.md) to [Score Image Model](score-image-model.md), to predict values for new input examples.
 
 ## Technical notes  
 
@@ -60,7 +60,7 @@ After pipeline run is completed, to use the model for scoring, connect the [Trai
 
 | Name            | Type                    | Description                              |
 | --------------- | ----------------------- | ---------------------------------------- |
-| Untrained model | UntrainedModelDirectory | An untrained DenseNet model that can be connected to Train Pytorch Model. |
+| Untrained model | UntrainedModelDirectory | An untrained DenseNet model that can be connected to Train PyTorch Model. |
 
 ## Next steps
 

articles/machine-learning/component-reference/toc.yml

@@ -112,7 +112,7 @@
     href: train-clustering-model.md
   - name: Train Model
     href: train-model.md
-  - name: Train Pytorch Model
+  - name: Train PyTorch Model
     href: train-pytorch-model.md
   - name: Tune Model Hyperparameters
     href: tune-model-hyperparameters.md

articles/machine-learning/concept-compute-target.md

@@ -202,7 +202,7 @@ If you use the GPU-enabled compute targets, it's important to ensure that the co
 
 In addition to ensuring the CUDA version and hardware are compatible, also ensure that the CUDA version is compatible with the version of the machine learning framework you're using:
 
-- For PyTorch, you can check the compatibility by visiting [Pytorch's previous versions page](https://pytorch.org/get-started/previous-versions/).
+- For PyTorch, you can check the compatibility by visiting [PyTorch's previous versions page](https://pytorch.org/get-started/previous-versions/).
 - For Tensorflow, you can check the compatibility by visiting [Tensorflow's build from source page](https://www.tensorflow.org/install/source#gpu).
 
 ### Compute isolation

articles/machine-learning/how-to-convert-custom-model-to-mlflow.md

@@ -24,7 +24,7 @@ With Azure Machine Learning, MLflow models get the added benefits of:
 - Portability as an open source standard format
 - Ability to deploy both locally and on cloud
 
-MLflow provides support for various [machine learning frameworks](https://mlflow.org/docs/latest/models.html#built-in-model-flavors), such as scikit-learn, Keras, and Pytorch. MLflow might not cover every use case. For example, you might want to create an MLflow model with a framework that MLflow doesn't natively support. You might want to change the way your model does preprocessing or post-processing when running jobs. To learn more about MLflow models, see [From artifacts to models in MLflow](concept-mlflow-models.md).
+MLflow provides support for various [machine learning frameworks](https://mlflow.org/docs/latest/models.html#built-in-model-flavors), such as scikit-learn, Keras, and PyTorch. MLflow might not cover every use case. For example, you might want to create an MLflow model with a framework that MLflow doesn't natively support. You might want to change the way your model does preprocessing or post-processing when running jobs. To learn more about MLflow models, see [From artifacts to models in MLflow](concept-mlflow-models.md).
 
 If you didn't train your model with MLFlow and want to use Azure Machine Learning's MLflow no-code deployment offering, you need to convert your custom model to MLFLow. For more information, see [Custom Python Models](https://mlflow.org/docs/latest/models.html#custom-python-models).
 

articles/machine-learning/how-to-interactive-jobs.md

@@ -26,7 +26,7 @@ Interactive training is supported on **Azure Machine Learning Compute Clusters**
 - Review [getting started with training on Azure Machine Learning](./how-to-train-model.md).
 - For more information, see this link for [VS Code](how-to-setup-vs-code.md) to set up the Azure Machine Learning extension.
 - Make sure your job environment has the `openssh-server` and `ipykernel ~=6.0` packages installed (all Azure Machine Learning curated training environments have these packages installed by default).
-- Interactive applications can't be enabled on distributed training runs where the distribution type is anything other than Pytorch, TensorFlow, or MPI. Custom distributed training setup (configuring multi-node training without using the above distribution frameworks) isn't currently supported.
+- Interactive applications can't be enabled on distributed training runs where the distribution type is anything other than PyTorch, TensorFlow, or MPI. Custom distributed training setup (configuring multi-node training without using the above distribution frameworks) isn't currently supported.
 - To use SSH, you need an SSH key pair. You can use the `ssh-keygen -f "<filepath>"` command to generate a public and private key pair.
 
 ## Interact with your job container

articles/machine-learning/how-to-train-distributed-gpu.md

@@ -68,9 +68,9 @@ Azure Machine Learning sets the `MASTER_ADDR`, `MASTER_PORT`, `WORLD_SIZE`, and
 
 [!notebook-python[](~/azureml-examples-main/sdk/python/jobs/single-step/pytorch/distributed-training/distributed-cifar10.ipynb?name=job)]
 
-### Pytorch example
+### PyTorch example
 
-* For the full notebook to run the Pytorch example, see [azureml-examples: Distributed training with PyTorch on CIFAR-10](https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/single-step/pytorch/distributed-training/distributed-cifar10.ipynb).
+* For the full notebook to run the PyTorch example, see [azureml-examples: Distributed training with PyTorch on CIFAR-10](https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/single-step/pytorch/distributed-training/distributed-cifar10.ipynb).
 
 ## DeepSpeed
 
@@ -79,9 +79,9 @@ Azure Machine Learning supports [DeepSpeed](https://www.deepspeed.ai/tutorials/a
 * Increase in model size
 * Increase in number of GPUs
 
-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.
+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) 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) 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
 

articles/machine-learning/v1/how-to-deploy-model-designer.md

@@ -38,7 +38,7 @@ Models trained in the designer can also be deployed through the SDK or command-l
     - [Train Model Component](../algorithm-module-reference/train-model.md)
     - [Train Anomaly Detection Model component](../algorithm-module-reference/train-anomaly-detection-model.md)
     - [Train Clustering Model component](../algorithm-module-reference/train-clustering-model.md)
-    - [Train Pytorch Model component](../algorithm-module-reference/train-pytorch-model.md)
+    - [Train PyTorch Model component](../algorithm-module-reference/train-pytorch-model.md)
     - [Train SVD Recommender component](../algorithm-module-reference/train-svd-recommender.md)
     - [Train Vowpal Wabbit Model component](../algorithm-module-reference/train-vowpal-wabbit-model.md)
     - [Train Wide & Deep Model component](../algorithm-module-reference/train-wide-and-deep-recommender.md)
@@ -176,7 +176,7 @@ image_transform = [
 ]
 transform = ImageTransformationDirectory.create(transforms=image_transform).torch_transform
 
-# download _samples.json file under Outputs+logs tab in the right pane of Train Pytorch Model component
+# download _samples.json file under Outputs+logs tab in the right pane of Train PyTorch Model component
 sample_file_path = '_samples.json'
 with open(sample_file_path, 'r') as f:
     sample_data = json.load(f)