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The rule is part of the `my-scale-settings` profile (`autoscale-name` matches the `name` of the profile). The value of its `condition` argument says the rule should trigger when "The average CPU consumption among the VM instances exceeds 70% for five minutes." When that condition is satisfied, two more VM instances are allocated.
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> This uses Bash syntax for setting variables -- adjust as needed for your shell. You can also replace the values in commands below inline rather than using variables.
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If it doesn't already exist, you can create the Azure resource group:
Copy file name to clipboardExpand all lines: articles/machine-learning/how-to-deploy-batch-with-rest.md
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@@ -47,7 +47,7 @@ In this article, you learn how to use the new REST APIs to:
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> [!NOTE]
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> Batch endpoint names need to be unique at the Azure region level. For example, there can be only one batch endpoint with the name mybatchendpoint in westus2.
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Administrative REST requests a [service principal authentication token](how-to-manage-rest.md#retrieve-a-service-principal-authentication-token). Replace `TOKEN` with your own value. You can retrieve this token with the following command:
The service provider uses the `api-version` argument to ensure compatibility. The `api-version` argument varies from service to service. Set the API version as a variable to accommodate future versions:
Batch scoring runs only on cloud computing resources, not locally. The cloud computing resource is a reusable virtual computer cluster where you can run batch scoring workflows.
> If you want to use an existing compute instead, you must specify the full Azure Resource Manager ID when [creating the batch deployment](#create-batch-deployment). The full ID uses the format `/subscriptions/$SUBSCRIPTION_ID/resourceGroups/$RESOURCE_GROUP/providers/Microsoft.MachineLearningServices/workspaces/$WORKSPACE/computes/<your-compute-name>`.
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You can use the tool [jq](https://stedolan.github.io/jq/) to parse the JSON result and get the required values. You can also use the Azure portal to find the same information:
> You can also use other methods to upload, such as the Azure portal or [Azure Storage Explorer](https://azure.microsoft.com/features/storage-explorer/).
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Once you upload your code, you can specify your code with a PUT request:
The deployment needs to run in an environment that has the required dependencies. Create the environment with a PUT request. Use a docker image from Microsoft Container Registry. You can configure the docker image with `image` and add conda dependencies with `condaFile`.
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Run the following code to read the `condaFile` defined in json. The source file is at `/cli/endpoints/batch/mnist/environment/conda.json` in the example repository:
If your data is stored in an Azure Machine Learning registered datastore, you can invoke the batch endpoint with a dataset. The following code creates a new dataset:
In the previous code snippet, a custom output location is provided by using `datastoreId`, `path`, and `outputFileName`. These settings allow you to configure where to store the batch scoring results.
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For this example, the output is stored in the default blob storage for the workspace. The folder name is the same as the endpoint name, and the file name is randomly generated by the following code:
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> [!TIP]
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> The example invokes the default deployment of the batch endpoint. To invoke a non-default deployment, use the `azureml-model-deployment` HTTP header and set the value to the deployment name. For example, using a parameter of `--header "azureml-model-deployment: $DEPLOYMENT_NAME"` with curl.
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If you aren't going use the batch endpoint, you should delete it with the below command (it deletes the batch endpoint and all the underlying deployments):
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