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Jun 7, 2018
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edited hyperparameter tuning section of readme #219

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edited hyperparameter tuning section of readme
eslesar-aws Jun 7, 2018
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made link intro text more generic
eslesar-aws Jun 7, 2018
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Merge branch 'master' of https://github.com/aws/sagemaker-python-sdk …
eslesar-aws Jun 7, 2018
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10 changes: 7 additions & 3 deletions README.rst
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Expand Up @@ -268,7 +268,10 @@ The example notebook is is located here:
SageMaker Automatic Model Tuning
--------------------------------

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>`__.
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 the values of their hyperparameters to find the best training job.
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>`__.

Here is a basic example of how to use ``HyperparameterTuner`` to start tuning jobs instead of using an estimator to start training jobs:

Expand Down Expand Up @@ -296,7 +299,8 @@ Here is a basic example of how to use ``HyperparameterTuner`` to start tuning jo
# Tear down the SageMaker endpoint
my_tuner.delete_endpoint()

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:
There is also an analytics object associated with each ``HyperparameterTuner`` instance that presents useful information about the hyperparameter tuning job.
For example, the ``dataframe`` method gets a pandas dataframe summarizing the associated training jobs:

.. code:: python

Expand All @@ -308,7 +312,7 @@ There is also an analytics object with each ``HyperparameterTuner`` instance, wh

For more detailed examples of running hyperparameter tuning jobs, see: https://github.com/awslabs/amazon-sagemaker-examples.

For more detailed explanations of the classes mentioned, see:
For more detailed explanations of the HyperparameterTuner and analytics classes, see:
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one thing that I probably didn't call out explicitly enough is that we have classes for parameter ranges, which are also included in the first link. not sure if it's worth also mentioning here since it's in addition to the HyperparameterTuner and analytics classes.

also, might want to put backticks around 'HyperparameterTuner'


- `API docs for HyperparameterTuner and parameter range classes <https://sagemaker.readthedocs.io/en/latest/tuner.html>`__.
- `API docs for analytics classes <https://sagemaker.readthedocs.io/en/latest/analytics.html>`__.
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