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Edit hyperparameter tuning section of readme (aws#219)
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README.rst

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@@ -268,7 +268,10 @@ The example notebook is is located here:
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SageMaker Automatic Model Tuning
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All of the estimators can be used with SageMaker Automatic Model Tuning, which performs hyperparameter tuning jobs. A hyperparameter tuning job runs multiple training jobs that differ by their hyperparameters to find the best one. The SageMaker Python SDK contains a ``HyperparameterTuner`` class for creating and interacting with hyperparameter training jobs. You can read more about SageMaker Automatic Model Tuning in the `AWS documentation <https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning.html>`__.
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All of the estimators can be used with SageMaker Automatic Model Tuning, which performs hyperparameter tuning jobs.
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A hyperparameter tuning job runs multiple training jobs that differ by the values of their hyperparameters to find the best training job.
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The SageMaker Python SDK contains a ``HyperparameterTuner`` class for creating and interacting with hyperparameter training jobs.
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You can read more about SageMaker Automatic Model Tuning in the `AWS documentation <https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning.html>`__.
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Here is a basic example of how to use ``HyperparameterTuner`` to start tuning jobs instead of using an estimator to start training jobs:
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# Tear down the SageMaker endpoint
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my_tuner.delete_endpoint()
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There is also an analytics object with each ``HyperparameterTuner`` instance, which presents useful information about the hyperparameter tuning job, like a pandas dataframe summarizing the associated training jobs:
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There is also an analytics object associated with each ``HyperparameterTuner`` instance that presents useful information about the hyperparameter tuning job.
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For example, the ``dataframe`` method gets a pandas dataframe summarizing the associated training jobs:
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.. code:: python
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For more detailed examples of running hyperparameter tuning jobs, see: https://github.com/awslabs/amazon-sagemaker-examples.
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For more detailed explanations of the classes mentioned, see:
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For more detailed explanations of the classes that this library provides for automatic model tuning, see:
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- `API docs for HyperparameterTuner and parameter range classes <https://sagemaker.readthedocs.io/en/latest/tuner.html>`__
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- `API docs for analytics classes <https://sagemaker.readthedocs.io/en/latest/analytics.html>`__

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