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May 16, 2023
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update container version #3665

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10 changes: 5 additions & 5 deletions doc/frameworks/sklearn/using_sklearn.rst
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Expand Up @@ -7,7 +7,7 @@ With Scikit-learn Estimators, you can train and host Scikit-learn models on Amaz
For information about supported versions of Scikit-learn, see the `AWS documentation <https://docs.aws.amazon.com/sagemaker/latest/dg/sklearn.html>`__.
We recommend that you use the latest supported version because that's where we focus most of our development efforts.

For more information about the framework, see the `Sciket-Learn <https://github.com/scikit-learn/scikit-learn>`_ repository.
For more information about the framework, see the `Scikit-Learn <https://github.com/scikit-learn/scikit-learn>`_ repository.
For general information about using the SageMaker Python SDK, see :ref:`overview:Using the SageMaker Python SDK`.

.. contents::
Expand All @@ -31,7 +31,7 @@ To train a Scikit-learn model by using the SageMaker Python SDK:
Prepare a Scikit-learn Training Script
======================================

Your Scikit-learn training script must be a Python 3.6 compatible source file.
Your Scikit-learn training script must be a Python 3.7 compatible source file.

The training script is similar to a training script you might run outside of SageMaker, but you
can access useful properties about the training environment through various environment variables.
Expand Down Expand Up @@ -140,7 +140,7 @@ directories ('train' and 'test').

sklearn_estimator = SKLearn('sklearn-train.py',
instance_type='ml.m4.xlarge',
framework_version='0.20.0',
framework_version='1.0-1',
hyperparameters = {'epochs': 20, 'batch-size': 64, 'learning-rate': 0.1})
sklearn_estimator.fit({'train': 's3://my-data-bucket/path/to/my/training/data',
'test': 's3://my-data-bucket/path/to/my/test/data'})
Expand Down Expand Up @@ -204,7 +204,7 @@ operation.
# Train my estimator
sklearn_estimator = SKLearn(entry_point='train_and_deploy.py',
instance_type='ml.m4.xlarge',
framework_version='0.20.0')
framework_version='1.0-1')
sklearn_estimator.fit('s3://my_bucket/my_training_data/')

# Deploy my estimator to a SageMaker Endpoint and get a Predictor
Expand Down Expand Up @@ -478,7 +478,7 @@ The following code sample shows how to do this, using the ``SKLearnModel`` class
sklearn_model = SKLearnModel(model_data="s3://bucket/model.tar.gz",
role="SageMakerRole",
entry_point="transform_script.py",
framework_version="0.20.0")
framework_version="1.0-1")

predictor = sklearn_model.deploy(instance_type="ml.c4.xlarge", initial_instance_count=1)

Expand Down