max_concurrent_iterations should default to all nodes of the target cluster

A user submitting a parallelizable job like an AutoML run to a compute cluster wants to make use of all nodes in the cluster.
But by default AutoMLConfig defaults to max_concurrent_iterations=1. This is the opposite of the behavior expected by users, and contradicts the default AutoML behavior when configuring a run via the ml.azure.com UI, which defaults to using all nodes of the target compute.

More expected behavior would be default to using all nodes in target compute cluster, along the lines of

max_concurrent_iterations=-1

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#### Document Details

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* ID: 0bc2b21e-6b1a-cb94-2857-147177a29d7c
* Version Independent ID: d14620a6-a2f6-49f1-632e-73903d41de8c
* Content: [azureml.train.automl.automlconfig.AutoMLConfig class - Azure Machine Learning Python](https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig?view=azure-ml-py)
* Content Source: [AzureML-Docset/stable/docs-ref-autogen/azureml-train-automl-client/azureml.train.automl.automlconfig.AutoMLConfig.yml](https://github.com/MicrosoftDocs/MachineLearning-Python-pr/blob/live/AzureML-Docset/stable/docs-ref-autogen/azureml-train-automl-client/azureml.train.automl.automlconfig.AutoMLConfig.yml)
* Service: **machine-learning**
* Sub-service: **core**
* GitHub Login: @DebFro
* Microsoft Alias: **debfro**