switch back to azureml-examples main branch

Author: sdgilley

articles/machine-learning/concept-automated-ml.md

@@ -191,7 +191,7 @@ See the [AutoML package](/python/api/azure-ai-ml/azure.ai.ml.automl) for changin
 
 With Azure Machine Learning, you can use automated ML to build a Python model and have it converted to the ONNX format. Once the models are in the ONNX format, they can be run on a variety of platforms and devices. Learn more about [accelerating ML models with ONNX](concept-onnx.md).
 
-See how to convert to ONNX format [in this Jupyter notebook example](https://github.com/Azure/azureml-examples/tree/v2samplesreorg/v1/python-sdk/tutorials/automl-with-azureml/classification-bank-marketing-all-features). Learn which [algorithms are supported in ONNX](how-to-configure-auto-train.md#supported-algorithms).
+See how to convert to ONNX format [in this Jupyter notebook example](https://github.com/Azure/azureml-examples/tree/main/v1/python-sdk/tutorials/automl-with-azureml/classification-bank-marketing-all-features). Learn which [algorithms are supported in ONNX](how-to-configure-auto-train.md#supported-algorithms).
 
 The ONNX runtime also supports C#, so you can use the model built automatically in your C# apps without any need for recoding or any of the network latencies that REST endpoints introduce. Learn more about [using an AutoML ONNX model in a .NET application with ML.NET](./how-to-use-automl-onnx-model-dotnet.md) and [inferencing ONNX models with the ONNX runtime C# API](https://onnxruntime.ai/docs/api/csharp-api.html). 
 

articles/machine-learning/concept-component.md

@@ -63,4 +63,4 @@ To learn more about how to build a component, see:
 - [Component CLI v2 YAML reference](./reference-yaml-component-command.md).
 - [What is Azure Machine Learning Pipeline?](concept-ml-pipelines.md).
 - Try out [CLI v2 component example](https://github.com/Azure/azureml-examples/tree/sdk-preview/cli/jobs/pipelines-with-components).
-- Try out [Python SDK v2 component example](https://github.com/Azure/azureml-examples/tree/v2samplesreorg/sdk/python/jobs/pipelines).
+- Try out [Python SDK v2 component example](https://github.com/Azure/azureml-examples/tree/main/sdk/python/jobs/pipelines).

articles/machine-learning/concept-ml-pipelines.md

@@ -67,4 +67,4 @@ Azure Machine Learning pipelines are a powerful facility that begins delivering
 + [Define pipelines with the Azure ML SDK v2](./how-to-create-component-pipeline-python.md)
 + [Define pipelines with Designer](./how-to-create-component-pipelines-ui.md)
 + Try out [CLI v2 pipeline example](https://github.com/Azure/azureml-examples/tree/sdk-preview/cli/jobs/pipelines-with-components)
-+ Try out [Python SDK v2 pipeline example](https://github.com/Azure/azureml-examples/tree/v2samplesreorg/sdk/python/jobs/pipelines)
++ Try out [Python SDK v2 pipeline example](https://github.com/Azure/azureml-examples/tree/main/sdk/python/jobs/pipelines)

articles/machine-learning/how-to-attach-kubernetes-anywhere.md

@@ -106,5 +106,5 @@ For any AzureML example, you only need to update the compute target name to your
 * Explore training job samples with CLI v2 - [https://github.com/Azure/azureml-examples/tree/main/cli/jobs](https://github.com/Azure/azureml-examples/tree/main/cli/jobs)
 * Explore model deployment with online endpoint samples with CLI v2 - [https://github.com/Azure/azureml-examples/tree/main/cli/endpoints/online/kubernetes](https://github.com/Azure/azureml-examples/tree/main/cli/endpoints/online/kubernetes)
 * Explore batch endpoint samples with CLI v2 - [https://github.com/Azure/azureml-examples/tree/main/cli/endpoints/batch](https://github.com/Azure/azureml-examples/tree/main/cli/endpoints/batch)
-* Explore training job samples with SDK v2 -[https://github.com/Azure/azureml-examples/tree/v2samplesreorg/sdk/python/jobs](https://github.com/Azure/azureml-examples/tree/v2samplesreorg/sdk/python/jobs)
-* Explore model deployment with online endpoint samples with SDK v2 -[https://github.com/Azure/azureml-examples/tree/v2samplesreorg/sdk/python/endpoints/online/kubernetes](https://github.com/Azure/azureml-examples/tree/v2samplesreorg/sdk/python/endpoints/online/kubernetes)
+* Explore training job samples with SDK v2 -[https://github.com/Azure/azureml-examples/tree/main/sdk/python/jobs](https://github.com/Azure/azureml-examples/tree/main/sdk/python/jobs)
+* Explore model deployment with online endpoint samples with SDK v2 -[https://github.com/Azure/azureml-examples/tree/main/sdk/python/endpoints/online/kubernetes](https://github.com/Azure/azureml-examples/tree/main/sdk/python/endpoints/online/kubernetes)

articles/machine-learning/how-to-auto-train-forecast.md

@@ -428,7 +428,7 @@ hts_parameters = HTSTrainParameters(
 
 ## Example notebooks
 
-See the [forecasting sample notebooks](https://github.com/Azure/azureml-examples/tree/v2samplesreorg/v1/python-sdk/tutorials/automl-with-azureml) for detailed code examples of advanced forecasting configuration including:
+See the [forecasting sample notebooks](https://github.com/Azure/azureml-examples/tree/main/v1/python-sdk/tutorials/automl-with-azureml) for detailed code examples of advanced forecasting configuration including:
 
 * [holiday detection and featurization](https://github.com/Azure/azureml-examples/blob/v2samplesreorg/v1/python-sdk/tutorials/automl-with-azureml/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb)
 * [rolling-origin cross validation](https://github.com/Azure/azureml-examples/blob/v2samplesreorg/v1/python-sdk/tutorials/automl-with-azureml/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb)

articles/machine-learning/how-to-auto-train-image-models.md

@@ -848,7 +848,7 @@ image_object_detection_job.set_training_parameters(
 
 
 ## Example notebooks
-Review detailed code examples and use cases in the [GitHub notebook repository for automated machine learning samples](https://github.com/Azure/azureml-examples/tree/v2samplesreorg/sdk/python/jobs/automl-standalone-jobs). Please check the folders with 'automl-image-' prefix for samples specific to building computer vision models.
+Review detailed code examples and use cases in the [GitHub notebook repository for automated machine learning samples](https://github.com/Azure/azureml-examples/tree/main/sdk/python/jobs/automl-standalone-jobs). Please check the folders with 'automl-image-' prefix for samples specific to building computer vision models.
 
 
 
@@ -863,7 +863,7 @@ Review detailed code examples and use cases in the [azureml-examples repository
 
  [!INCLUDE [sdk v2](../../includes/machine-learning-sdk-v2.md)]
 
-Review detailed code examples and use cases in the [GitHub notebook repository for automated machine learning samples](https://github.com/Azure/azureml-examples/tree/v2samplesreorg/sdk/python/jobs/automl-standalone-jobs). 
+Review detailed code examples and use cases in the [GitHub notebook repository for automated machine learning samples](https://github.com/Azure/azureml-examples/tree/main/sdk/python/jobs/automl-standalone-jobs). 
 
 ---
 ## Next steps

articles/machine-learning/how-to-configure-auto-train.md

@@ -213,7 +213,7 @@ With additional algorithms below.
 * [NLP Text Classification Multi-label Algorithms](how-to-auto-train-nlp-models.md#language-settings)
 * [NLP Text Named Entity Recognition (NER) Algorithms](how-to-auto-train-nlp-models.md#language-settings)
 
-Follow [this link](https://github.com/Azure/azureml-examples/tree/v2samplesreorg/sdk/python/jobs/automl-standalone-jobs) for example notebooks of each task type.
+Follow [this link](https://github.com/Azure/azureml-examples/tree/main/sdk/python/jobs/automl-standalone-jobs) for example notebooks of each task type.
 
 ### Primary metric
 
@@ -392,7 +392,7 @@ After you test a model and confirm you want to use it in production, you can reg
 
 To leverage AutoML in your MLOps workflows, you can add AutoML Job steps to your [AzureML Pipelines](./how-to-create-component-pipeline-python.md). This allows you to automate your entire workflow by hooking up your data prep scripts to AutoML and then registering and validating the resulting best model.
 
-Below is a [sample pipeline](https://github.com/Azure/azureml-examples/tree/v2samplesreorg/sdk/python/jobs/pipelines/1h_automl_in_pipeline/automl-classification-bankmarketing-in-pipeline) with an AutoML classification component and a command component that shows the resulting AutoML output. Note how the inputs (training & validation data) and the outputs (best model) are referenced in different steps.
+Below is a [sample pipeline](https://github.com/Azure/azureml-examples/tree/main/sdk/python/jobs/pipelines/1h_automl_in_pipeline/automl-classification-bankmarketing-in-pipeline) with an AutoML classification component and a command component that shows the resulting AutoML output. Note how the inputs (training & validation data) and the outputs (best model) are referenced in different steps.
 
 ``` python
 # Define pipeline
@@ -432,11 +432,11 @@ pipeline_classification = automl_classification(
 )
 
 # ...
-# Note that the above is only a snippet from the bankmarketing example you can find in our examples repo -> https://github.com/Azure/azureml-examples/tree/v2samplesreorg/sdk/python/jobs/pipelines/1h_automl_in_pipeline/automl-classification-bankmarketing-in-pipeline
+# Note that the above is only a snippet from the bankmarketing example you can find in our examples repo -> https://github.com/Azure/azureml-examples/tree/main/sdk/python/jobs/pipelines/1h_automl_in_pipeline/automl-classification-bankmarketing-in-pipeline
 
 ```
 
-For more examples on how to do include AutoML in your pipelines, please check out our [examples repo](https://github.com/Azure/azureml-examples/tree/v2samplesreorg/sdk/python/jobs/pipelines/1h_automl_in_pipeline/).
+For more examples on how to do include AutoML in your pipelines, please check out our [examples repo](https://github.com/Azure/azureml-examples/tree/main/sdk/python/jobs/pipelines/1h_automl_in_pipeline/).
 
 ## Next steps
 

articles/machine-learning/how-to-configure-databricks-automl-environment.md

@@ -108,7 +108,7 @@ In AutoML config, when using Azure Databricks add the following parameters:
 ## ML notebooks that work with Azure Databricks
 
 Try it out:
-+ While many sample notebooks are available, **only [these sample notebooks](https://github.com/Azure/azureml-examples/tree/v2samplesreorg/v1/python-sdk/tutorials/automl-with-databricks) work with Azure Databricks.**
++ While many sample notebooks are available, **only [these sample notebooks](https://github.com/Azure/azureml-examples/tree/main/v1/python-sdk/tutorials/automl-with-databricks) work with Azure Databricks.**
 
 + Import these samples directly from your workspace. See below:
 ![Select Import](./media/how-to-configure-environment/azure-db-screenshot.png)

articles/machine-learning/how-to-create-component-pipeline-python.md

@@ -97,7 +97,7 @@ The next section will show create components in two different ways: the first tw
 
 The first component in this pipeline will convert the compressed data files of `fashion_ds` into two csv files, one for training and the other for scoring. You'll use python function to define this component.
 
-If you're following along with the example in the [AzureML Examples repo](https://github.com/Azure/azureml-examples/tree/v2samplesreorg/sdk/python/jobs/pipelines/2e_image_classification_keras_minist_convnet), the source files are already available in `prep/` folder. This folder contains two files to construct the component: `prep_component.py`, which defines the component and `conda.yaml`, which defines the run-time environment of the component.
+If you're following along with the example in the [AzureML Examples repo](https://github.com/Azure/azureml-examples/tree/main/sdk/python/jobs/pipelines/2e_image_classification_keras_minist_convnet), the source files are already available in `prep/` folder. This folder contains two files to construct the component: `prep_component.py`, which defines the component and `conda.yaml`, which defines the run-time environment of the component.
 
 #### Define component using python function
 
@@ -145,15 +145,15 @@ In this section, you'll create a component for training the image classification
 
 The difference is that since the training logic is more complicated, you can put the original training code in a separate Python file.
 
-The source files of this component are under `train/` folder in the [AzureML Examples repo](https://github.com/Azure/azureml-examples/tree/v2samplesreorg/sdk/python/jobs/pipelines/2e_image_classification_keras_minist_convnet). This folder contains three files to construct the component:
+The source files of this component are under `train/` folder in the [AzureML Examples repo](https://github.com/Azure/azureml-examples/tree/main/sdk/python/jobs/pipelines/2e_image_classification_keras_minist_convnet). This folder contains three files to construct the component:
 
 - `train.py`: contains the actual logic to train model.
 - `train_component.py`: defines the interface of the component and imports the function in `train.py`.
 -  `conda.yaml`: defines the run-time environment of the component.
 
 #### Get a script containing execution logic
 
-The `train.py` file contains a normal python function, which performs the training model logic to train a Keras neural network for image classification. You can find the code [here](https://github.com/Azure/azureml-examples/tree/v2samplesreorg/sdk/python/jobs/pipelines/2e_image_classification_keras_minist_convnet/train/train.py).
+The `train.py` file contains a normal python function, which performs the training model logic to train a Keras neural network for image classification. You can find the code [here](https://github.com/Azure/azureml-examples/tree/main/sdk/python/jobs/pipelines/2e_image_classification_keras_minist_convnet/train/train.py).
 
 #### Define component using python function
 
@@ -177,7 +177,7 @@ Now, you've prepared all source files for the `Train Image Classification Keras`
 
 In this section, other than the previous components, you'll create a component to score the trained model via Yaml specification and script.
 
-If you're following along with the example in the [AzureML Examples repo](https://github.com/Azure/azureml-examples/tree/v2samplesreorg/sdk/python/jobs/pipelines/2e_image_classification_keras_minist_convnet), the source files are already available in `score/` folder. This folder contains three files to construct the component:
+If you're following along with the example in the [AzureML Examples repo](https://github.com/Azure/azureml-examples/tree/main/sdk/python/jobs/pipelines/2e_image_classification_keras_minist_convnet), the source files are already available in `score/` folder. This folder contains three files to construct the component:
 
 - `score.py`: contains the source code of the component.
 - `score.yaml`: defines the interface and other details of the component.
@@ -314,6 +314,6 @@ Using `ml_client.components.get()`, you can get a registered component by name a
 
 ## Next steps
 
-* For more examples of how to build pipelines by using the machine learning SDK, see the [example repository](https://github.com/Azure/azureml-examples/tree/v2samplesreorg/sdk/python/jobs/pipelines).
+* For more examples of how to build pipelines by using the machine learning SDK, see the [example repository](https://github.com/Azure/azureml-examples/tree/main/sdk/python/jobs/pipelines).
 * For how to use studio UI to submit and debug your pipeline, refer to [how to create pipelines using component in the UI](how-to-create-component-pipelines-ui.md).
 * For how to use Azure Machine Learning CLI to create components and pipelines, refer to [how to create pipelines using component with CLI](how-to-create-component-pipelines-cli.md).

articles/machine-learning/how-to-customize-compute-instance.md

@@ -56,7 +56,7 @@ If your script was doing something specific to azureuser such as installing cond
 
 The command `sudo -u azureuser` changes the current working directory to `/home/azureuser`. You also can't access the script arguments in this block.
 
-For other example scripts, see [azureml-examples](https://github.com/Azure/azureml-examples/tree/v2samplesreorg/setup/setup-ci).
+For other example scripts, see [azureml-examples](https://github.com/Azure/azureml-examples/tree/main/setup/setup-ci).
 
 You can also use the following environment variables in your script: