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

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### Get the best run
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# [CLI v2](#tab/CLI-v2)
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# [Azure CLI](#tab/cli)
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[!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
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```yaml
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Register the model either using the azureml path or your locally downloaded path.
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# [CLI v2](#tab/CLI-v2)
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# [Azure CLI](#tab/cli)
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[!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
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### Configure online endpoint
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# [CLI v2](#tab/CLI-v2)
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# [Azure CLI](#tab/cli)
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[!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
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Using the `MLClient` created earlier, we'll now create the Endpoint in the workspace. This command will start the endpoint creation and return a confirmation response while the endpoint creation continues.
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# [CLI v2](#tab/CLI-v2)
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# [Azure CLI](#tab/cli)
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[!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
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```azurecli
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A deployment is a set of resources required for hosting the model that does the actual inferencing. We'll create a deployment for our endpoint using the `ManagedOnlineDeployment` class. You can use either GPU or CPU VM SKUs for your deployment cluster.
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# [CLI v2](#tab/CLI-v2)
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# [Azure CLI](#tab/cli)
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[!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
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Using the `MLClient` created earlier, we'll now create the deployment in the workspace. This command will start the deployment creation and return a confirmation response while the deployment creation continues.
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# [CLI v2](#tab/CLI-v2)
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# [Azure CLI](#tab/cli)
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[!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
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### update traffic:
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By default the current deployment is set to receive 0% traffic. you can set the traffic percentage current deployment should receive. Sum of traffic percentages of all the deployments with one end point shouldn't exceed 100%.
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# [CLI v2](#tab/CLI-v2)
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# [Azure CLI](#tab/cli)
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[!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
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‎articles/machine-learning/tutorial-auto-train-image-models.md

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### Get the best run
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# [CLI v2](#tab/CLI-v2)
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# [Azure CLI](#tab/cli)
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[!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
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```yaml
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to be supported
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```
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# [Python SDK v2 (preview)](#tab/SDK-v2)
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# [Python SDK](#tab/python)
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[!INCLUDE [sdk v2](../../includes/machine-learning-sdk-v2.md)]
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[!Notebook-python[] (~/azureml-examples-main/sdk/jobs/automl-standalone-jobs/automl-image-object-detection-task-fridge-items/automl-image-object-detection-task-fridge-items.ipynb?name=best_run)]
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Register the model either using the azureml path or your locally downloaded path.
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# [CLI v2](#tab/CLI-v2)
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# [Azure CLI](#tab/cli)
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[!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
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```azurecli
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az ml model create --name od-fridge-items-mlflow-model --version 1 --path azureml://jobs/$best_run/outputs/artifacts/outputs/mlflow-model/ --type mlflow_model --workspace-name [YOUR_AZURE_WORKSPACE] --resource-group [YOUR_AZURE_RESOURCE_GROUP] --subscription [YOUR_AZURE_SUBSCRIPTION]
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```
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# [Python SDK v2 (preview)](#tab/SDK-v2)
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# [Python SDK](#tab/python)
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[!INCLUDE [sdk v2](../../includes/machine-learning-sdk-v2.md)]
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[!Notebook-python[] (~/azureml-examples-main/sdk/jobs/automl-standalone-jobs/automl-image-object-detection-task-fridge-items/automl-image-object-detection-task-fridge-items.ipynb?name=register_model)]
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---
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### Configure online endpoint
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# [CLI v2](#tab/CLI-v2)
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# [Azure CLI](#tab/cli)
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[!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
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```
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# [Python SDK v2 (preview)](#tab/SDK-v2)
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# [Python SDK](#tab/python)
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[!INCLUDE [sdk v2](../../includes/machine-learning-sdk-v2.md)]
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[!Notebook-python[] (~/azureml-examples-main/sdk/jobs/automl-standalone-jobs/automl-image-object-detection-task-fridge-items/automl-image-object-detection-task-fridge-items.ipynb?name=endpoint)]
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---
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Using the `MLClient` created earlier, we'll now create the Endpoint in the workspace. This command will start the endpoint creation and return a confirmation response while the endpoint creation continues.
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# [CLI v2](#tab/CLI-v2)
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# [Azure CLI](#tab/cli)
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[!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
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```azurecli
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az ml online-endpoint create --file .\create_endpoint.yml --workspace-name [YOUR_AZURE_WORKSPACE] --resource-group [YOUR_AZURE_RESOURCE_GROUP] --subscription [YOUR_AZURE_SUBSCRIPTION]
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```
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# [Python SDK v2 (preview)](#tab/SDK-v2)
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# [Python SDK](#tab/python)
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[!INCLUDE [sdk v2](../../includes/machine-learning-sdk-v2.md)]
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[!Notebook-python[] (~/azureml-examples-main/sdk/jobs/automl-standalone-jobs/automl-image-object-detection-task-fridge-items/automl-image-object-detection-task-fridge-items.ipynb?name=create_endpoint)]
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---
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A deployment is a set of resources required for hosting the model that does the actual inferencing. We'll create a deployment for our endpoint using the `ManagedOnlineDeployment` class. You can use either GPU or CPU VM SKUs for your deployment cluster.
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# [CLI v2](#tab/CLI-v2)
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# [Azure CLI](#tab/cli)
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[!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
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initial_delay: 2000
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```
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# [Python SDK v2 (preview)](#tab/SDK-v2)
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# [Python SDK](#tab/python)
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[!INCLUDE [sdk v2](../../includes/machine-learning-sdk-v2.md)]
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[!Notebook-python[] (~/azureml-examples-main/sdk/jobs/automl-standalone-jobs/automl-image-object-detection-task-fridge-items/automl-image-object-detection-task-fridge-items.ipynb?name=deploy)]
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Using the `MLClient` created earlier, we'll now create the deployment in the workspace. This command will start the deployment creation and return a confirmation response while the deployment creation continues.
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# [CLI v2](#tab/CLI-v2)
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# [Azure CLI](#tab/cli)
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[!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
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```azurecli
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az ml online-deployment create --file .\create_deployment.yml --workspace-name [YOUR_AZURE_WORKSPACE] --resource-group [YOUR_AZURE_RESOURCE_GROUP] --subscription [YOUR_AZURE_SUBSCRIPTION]
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```
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# [Python SDK v2 (preview)](#tab/SDK-v2)
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# [Python SDK](#tab/python)
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[!INCLUDE [sdk v2](../../includes/machine-learning-sdk-v2.md)]
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[!Notebook-python[] (~/azureml-examples-main/sdk/jobs/automl-standalone-jobs/automl-image-object-detection-task-fridge-items/automl-image-object-detection-task-fridge-items.ipynb?name=create_deploy)]
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### Update traffic:
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# [CLI v2](#tab/CLI-v2)
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# [Azure CLI](#tab/cli)
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[!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
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```azurecli
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# [Python SDK v2 (preview)](#tab/SDK-v2)
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# [Python SDK](#tab/python)
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[!Notebook-python[] (~/azureml-examples-main/sdk/jobs/automl-standalone-jobs/automl-image-object-detection-task-fridge-items/automl-image-object-detection-task-fridge-items.ipynb?name=update_traffic)]
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## Test the deployment
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# [Python SDK v2 (preview)](#tab/SDK-v2)
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# [Python SDK](#tab/python)
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[!INCLUDE [sdk v2](../../includes/machine-learning-sdk-v2.md)]
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[!Notebook-python[] (~/azureml-examples-main/sdk/jobs/automl-standalone-jobs/automl-image-object-detection-task-fridge-items/automl-image-object-detection-task-fridge-items.ipynb?name=create_inference_request)]
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## Visualize detections
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Now that you have scored a test image, you can visualize the bounding boxes for this image. To do so, be sure you have matplotlib installed.
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# [Azure CLI](#tab/cli)
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# [Python SDK v2 (preview)](#tab/SDK-v2)
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# [Python SDK](#tab/python)
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[!INCLUDE [sdk v2](../../includes/machine-learning-sdk-v2.md)]
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[!Notebook-python[] (~/azureml-examples-main/sdk/jobs/automl-standalone-jobs/automl-image-object-detection-task-fridge-items/automl-image-object-detection-task-fridge-items.ipynb?name=visualize_detections)]
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* [Learn how to configure incremental training on computer vision models](how-to-auto-train-image-models.md#incremental-training-optional).
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* See [what hyperparameters are available for computer vision tasks](reference-automl-images-hyperparameters.md).
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* Code examples:
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[!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
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* Review detailed code examples and use cases in the [azureml-examples repository for automated machine learning samples](https://github.com/Azure/azureml-examples/tree/sdk-preview/cli/jobs/automl-standalone-jobs). Please check the folders with 'cli-automl-image-' prefix for samples specific to building computer vision models.
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# [Python SDK](#tab/python)
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[!INCLUDE [sdk v2](../../includes/machine-learning-sdk-v2.md)]
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