Merge pull request #248718 from sdgilley/sdg-update-v1-links

Move v1 article references to v1-archive branch on azureml-examples

Author: v-dirichards

.openpublishing.publish.config.json

@@ -416,6 +416,12 @@
       "branch": "main",
       "branch_mapping": {}
     },
+    {
+      "path_to_root": "azureml-examples-archive",
+      "url": "https://github.com/azure/azureml-examples",
+      "branch": "v1-archive",
+      "branch_mapping": {}
+    },
     {
       "path_to_root": "azureml-examples-batch-pup",
       "url": "https://github.com/azure/azureml-examples",

articles/machine-learning/includes/machine-learning-service-local-deploy-config.md

@@ -16,6 +16,6 @@ The entries in the `deploymentconfig.json` document map to the parameters for [L
 This JSON is an example deployment configuration for use with the CLI:
 
 
-:::code language="json" source="~/azureml-examples-main/v1/python-sdk/tutorials/deploy-local/deploymentconfig.json":::
+:::code language="json" source="~/azureml-examples-archive/v1/python-sdk/tutorials/deploy-local/deploymentconfig.json":::
 
 Save this JSON as a file called `deploymentconfig.json`.

articles/machine-learning/v1/how-to-deploy-and-where.md

@@ -138,7 +138,7 @@ For more information on `az ml model register`, see the [reference documentation
 
 You can register a model by providing the local path of the model. You can provide the path of either a folder or a single file on your local machine.
 <!-- pyhton nb call -->
-[!Notebook-python[] (~/azureml-examples-main/v1/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=register-model-from-local-file-code)]
+[!Notebook-python[] (~/azureml-examples-archive/v1/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=register-model-from-local-file-code)]
 
 
 To include multiple files in the model registration, set `model_path` to the path of a folder that contains the files.
@@ -204,7 +204,7 @@ The two things you need to accomplish in your entry script are:
 
 For your initial deployment, use a dummy entry script that prints the data it receives.
 
-:::code language="python" source="~/azureml-examples-main/v1/python-sdk/tutorials/deploy-local/source_dir/echo_score.py":::
+:::code language="python" source="~/azureml-examples-archive/v1/python-sdk/tutorials/deploy-local/source_dir/echo_score.py":::
 
 Save this file as `echo_score.py` inside of a directory called `source_dir`. This dummy script returns the data you send to it, so it doesn't use the model. But it is useful for testing that the scoring script is running.
 
@@ -225,7 +225,7 @@ You can use any [Azure Machine Learning inference curated environments](../conce
 
 A minimal inference configuration can be written as:
 
-:::code language="json" source="~/azureml-examples-main/v1/python-sdk/tutorials/deploy-local/dummyinferenceconfig.json":::
+:::code language="json" source="~/azureml-examples-archive/v1/python-sdk/tutorials/deploy-local/dummyinferenceconfig.json":::
 
 Save this file with the name `dummyinferenceconfig.json`.
 
@@ -236,7 +236,7 @@ Save this file with the name `dummyinferenceconfig.json`.
 
 The following example demonstrates how to create a minimal environment with no pip dependencies, using the dummy scoring script you defined above.
 
-[!Notebook-python[] (~/azureml-examples-main/v1/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=inference-configuration-code)]
+[!Notebook-python[] (~/azureml-examples-archive/v1/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=inference-configuration-code)]
 
 For more information on environments, see [Create and manage environments for training and deployment](../how-to-use-environments.md).
 
@@ -263,7 +263,7 @@ For more information, see the [deployment schema](reference-azure-machine-learni
 
 The following Python demonstrates how to create a local deployment configuration: 
 
-[!Notebook-python[] (~/azureml-examples-main/v1/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=deployment-configuration-code)]
+[!Notebook-python[] (~/azureml-examples-archive/v1/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=deployment-configuration-code)]
 
 ---
 
@@ -290,9 +290,9 @@ az ml model deploy -n myservice \
 # [Python SDK](#tab/python)
 
 
-[!Notebook-python[] (~/azureml-examples-main/v1/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=deploy-model-code)]
+[!Notebook-python[] (~/azureml-examples-archive/v1/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=deploy-model-code)]
 
-[!Notebook-python[] (~/azureml-examples-main/v1/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=deploy-model-print-logs)]
+[!Notebook-python[] (~/azureml-examples-archive/v1/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=deploy-model-print-logs)]
 
 For more information, see the documentation for [Model.deploy()](/python/api/azureml-core/azureml.core.model.model#deploy-workspace--name--models--inference-config-none--deployment-config-none--deployment-target-none--overwrite-false-) and [Webservice](/python/api/azureml-core/azureml.core.webservice.webservice).
 
@@ -315,7 +315,7 @@ curl -v -X POST -H "content-type:application/json" \
 
 # [Python SDK](#tab/python)
 <!-- python nb call -->
-[!Notebook-python[] (~/azureml-examples-main/v1/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=call-into-model-code)]
+[!Notebook-python[] (~/azureml-examples-archive/v1/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=call-into-model-code)]
 
 ---
 
@@ -324,7 +324,7 @@ curl -v -X POST -H "content-type:application/json" \
 Now it's time to actually load your model. First, modify your entry script:
 
 
-:::code language="python" source="~/azureml-examples-main/v1/python-sdk/tutorials/deploy-local/source_dir/score.py":::
+:::code language="python" source="~/azureml-examples-archive/v1/python-sdk/tutorials/deploy-local/source_dir/score.py":::
 
 Save this file as `score.py` inside of `source_dir`.
 
@@ -334,7 +334,7 @@ Notice the use of the `AZUREML_MODEL_DIR` environment variable to locate your re
 
 [!INCLUDE [cli v1](../includes/machine-learning-cli-v1.md)]
 
-:::code language="json" source="~/azureml-examples-main/v1/python-sdk/tutorials/deploy-local/inferenceconfig.json":::
+:::code language="json" source="~/azureml-examples-archive/v1/python-sdk/tutorials/deploy-local/inferenceconfig.json":::
 
 Save this file as `inferenceconfig.json` 
 
@@ -377,9 +377,9 @@ az ml model deploy -n myservice \
 
 # [Python SDK](#tab/python)
 
-[!Notebook-python[] (~/azureml-examples-main/v1/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=re-deploy-model-code)]
+[!Notebook-python[] (~/azureml-examples-archive/v1/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=re-deploy-model-code)]
 
-[!Notebook-python[] (~/azureml-examples-main/v1/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=re-deploy-model-print-logs)]
+[!Notebook-python[] (~/azureml-examples-archive/v1/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=re-deploy-model-print-logs)]
 
 For more information, see the documentation for [Model.deploy()](/python/api/azureml-core/azureml.core.model.model#deploy-workspace--name--models--inference-config-none--deployment-config-none--deployment-target-none--overwrite-false-) and [Webservice](/python/api/azureml-core/azureml.core.webservice.webservice).
 
@@ -398,7 +398,7 @@ curl -v -X POST -H "content-type:application/json" \
 
 # [Python SDK](#tab/python)
 
-[!Notebook-python[] (~/azureml-examples-main/v1/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=send-post-request-code)]
+[!Notebook-python[] (~/azureml-examples-archive/v1/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=send-post-request-code)]
 
 ---
 
@@ -418,15 +418,15 @@ Change your deploy configuration to correspond to the compute target you've chos
 
 The options available for a deployment configuration differ depending on the compute target you choose.
 
-:::code language="json" source="~/azureml-examples-main/v1/python-sdk/tutorials/deploy-local/re-deploymentconfig.json":::
+:::code language="json" source="~/azureml-examples-archive/v1/python-sdk/tutorials/deploy-local/re-deploymentconfig.json":::
 
 Save this file as `re-deploymentconfig.json`.
 
 For more information, see [this reference](reference-azure-machine-learning-cli.md#deployment-configuration-schema).
 
 # [Python SDK](#tab/python)
 
-[!Notebook-python[] (~/azureml-examples-main/v1/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=deploy-model-on-cloud-code)]
+[!Notebook-python[] (~/azureml-examples-archive/v1/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=deploy-model-on-cloud-code)]
 
 ---
 
@@ -460,9 +460,9 @@ az ml service get-logs -n myservice \
 # [Python SDK](#tab/python)
 
 
-[!Notebook-python[] (~/azureml-examples-main/v1/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=re-deploy-service-code)]
+[!Notebook-python[] (~/azureml-examples-archive/v1/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=re-deploy-service-code)]
 
-[!Notebook-python[] (~/azureml-examples-main/v1/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=re-deploy-service-print-logs)]
+[!Notebook-python[] (~/azureml-examples-archive/v1/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=re-deploy-service-print-logs)]
 
 For more information, see the documentation for [Model.deploy()](/python/api/azureml-core/azureml.core.model.model#deploy-workspace--name--models--inference-config-none--deployment-config-none--deployment-target-none--overwrite-false-) and [Webservice](/python/api/azureml-core/azureml.core.webservice.webservice).
 
@@ -473,9 +473,9 @@ For more information, see the documentation for [Model.deploy()](/python/api/azu
 
 When you deploy remotely, you may have key authentication enabled. The example below shows how to get your service key with Python in order to make an inference request.
 
-[!Notebook-python[] (~/azureml-examples-main/v1/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=call-remote-web-service-code)]
+[!Notebook-python[] (~/azureml-examples-archive/v1/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=call-remote-web-service-code)]
 
-[!Notebook-python[] (~/azureml-examples-main/v1/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=call-remote-webservice-print-logs)]
+[!Notebook-python[] (~/azureml-examples-archive/v1/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=call-remote-webservice-print-logs)]
 
 
 
@@ -515,7 +515,7 @@ The following table describes the different service states:
 [!INCLUDE [cli v1](../includes/machine-learning-cli-v1.md)]
 
 
-[!Notebook-python[] (~/azureml-examples-main/v1/python-sdk/tutorials/deploy-local/2.deploy-local-cli.ipynb?name=delete-resource-code)]
+[!Notebook-python[] (~/azureml-examples-archive/v1/python-sdk/tutorials/deploy-local/2.deploy-local-cli.ipynb?name=delete-resource-code)]
 
 ```azurecli-interactive
 az ml service delete -n myservice
@@ -531,7 +531,7 @@ Read more about [deleting a webservice](/cli/azure/ml(v1)/computetarget/create#a
 
 # [Python SDK](#tab/python)
 
-[!Notebook-python[] (~/azureml-examples-main/v1/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=delete-resource-code)]
+[!Notebook-python[] (~/azureml-examples-archive/v1/python-sdk/tutorials/deploy-local/1.deploy-local.ipynb?name=delete-resource-code)]
 
 To delete a deployed web service, use `service.delete()`.
 To delete a registered model, use `model.delete()`.

articles/machine-learning/v1/tutorial-pipeline-python-sdk.md

@@ -269,7 +269,7 @@ Once the data has been converted from the compressed format to CSV files, it can
 
 With larger pipelines, it's a good practice to put each step's source code in a separate directory (`src/prepare/`, `src/train/`, and so on) but for this tutorial, just use or create the file `train.py` in the same `keras-mnist-fashion/` source directory.
 
-:::code language="python" source="~/azureml-examples-main/v1/python-sdk/tutorials/using-pipelines/keras-mnist-fashion/train.py" highlight="16-19,84-89,104-119":::
+:::code language="python" source="~/azureml-examples-archive/v1/python-sdk/tutorials/using-pipelines/keras-mnist-fashion/train.py" highlight="16-19,84-89,104-119":::
 
 Most of this code should be familiar to ML developers: