From a47a7e681075d2862ec7e9eabbca9c862839bbd6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Lejoly?= <57676655+LLejoly@users.noreply.github.com> Date: Fri, 17 Feb 2023 15:08:55 +0100 Subject: [PATCH] update container version - updating the `framework_version` with a version using a maintained version of python (i.e., > 3.6) - typo correction --- doc/frameworks/sklearn/using_sklearn.rst | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/doc/frameworks/sklearn/using_sklearn.rst b/doc/frameworks/sklearn/using_sklearn.rst index c636bf8aaf..954ab36fb8 100644 --- a/doc/frameworks/sklearn/using_sklearn.rst +++ b/doc/frameworks/sklearn/using_sklearn.rst @@ -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 `__. 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 `_ repository. +For more information about the framework, see the `Scikit-Learn `_ repository. For general information about using the SageMaker Python SDK, see :ref:`overview:Using the SageMaker Python SDK`. .. contents:: @@ -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. @@ -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'}) @@ -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 @@ -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)