AWS sagemaker documentation change
Summary
Updated documentation links and replaced references to 'Estimator' with 'ModelTrainer' in container usage instructions.
Security assessment
Changes involve updated hyperlinks to SDK documentation and terminology updates from 'Estimator' to 'ModelTrainer'. No security vulnerabilities, fixes, or security features are mentioned. These are routine documentation updates reflecting SDK changes.
Diff
diff --git a/sagemaker/latest/dg/docker-containers.md b/sagemaker/latest/dg/docker-containers.md index dd58a2c0b..be41fc612 100644 --- a//sagemaker/latest/dg/docker-containers.md +++ b//sagemaker/latest/dg/docker-containers.md @@ -78 +78 @@ The following are use cases for extending a pre-built Docker container: - * [Sci-kit learn](https://sagemaker.readthedocs.io/en/stable/frameworks/sklearn/using_sklearn.html?highlight=requirements.txt) + * [Sci-kit learn](https://sagemaker.readthedocs.io/en/stable/api/sagemaker_train.html) @@ -99 +99 @@ Assume that you have the following requirements for training and deploying a Ten - * If you require custom packages in either your [ entrypoint](https://sagemaker.readthedocs.io/en/stable/frameworks/tensorflow/using_tf.html#train-a-model-with-tensorflow) script or [inference script, either extend the pre-built container or use a requirements.txt file to install dependencies at runtime.](https://sagemaker.readthedocs.io/en/stable/frameworks/tensorflow/deploying_tensorflow_serving.html#how-to-implement-the-pre-and-or-post-processing-handler-s) + * If you require custom packages in either your [ entrypoint](https://sagemaker.readthedocs.io/en/stable/api/sagemaker_train.html) script or [inference script, either extend the pre-built container or use a requirements.txt file to install dependencies at runtime.](https://sagemaker.readthedocs.io/en/stable/api/sagemaker_train.html) @@ -106 +106 @@ After you determine the type of container that you need, the following list prov - * **Use a built-in SageMaker AI algorithm or framework**. For most use cases, you can use the built-in algorithms and frameworks without worrying about containers. You can train and deploy these algorithms from the SageMaker AI console, the AWS Command Line Interface (AWS CLI), a Python notebook, or the [Amazon SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable). You can do that by specifying the algorithm or framework version when creating your Estimator. The available built-in algorithms are itemized and described in the [Built-in algorithms and pretrained models in Amazon SageMaker](./algos.html) topic. For more information about the available frameworks, see [ML Frameworks and Languages](./frameworks.html). For an example of how to train and deploy a built-in algorithm using a Jupyter notebook running in a SageMaker notebook instance, see the [Guide to getting set up with Amazon SageMaker AI](./gs.html) topic. + * **Use a built-in SageMaker AI algorithm or framework**. For most use cases, you can use the built-in algorithms and frameworks without worrying about containers. You can train and deploy these algorithms from the SageMaker AI console, the AWS Command Line Interface (AWS CLI), a Python notebook, or the [Amazon SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable). You can do that by specifying the algorithm or framework version when creating your ModelTrainer. The available built-in algorithms are itemized and described in the [Built-in algorithms and pretrained models in Amazon SageMaker](./algos.html) topic. For more information about the available frameworks, see [ML Frameworks and Languages](./frameworks.html). For an example of how to train and deploy a built-in algorithm using a Jupyter notebook running in a SageMaker notebook instance, see the [Guide to getting set up with Amazon SageMaker AI](./gs.html) topic. @@ -108 +108 @@ After you determine the type of container that you need, the following list prov - * **Use pre-built SageMaker AI container images**. Alternatively, you can use the built-in algorithms and frameworks using Docker containers. SageMaker AI provides containers for its built-in algorithms and pre-built Docker images for some of the most common machine learning frameworks, such as Apache MXNet, TensorFlow, PyTorch, and Chainer. For a full list of the available SageMaker Images, see [Available Deep Learning Containers Images](https://github.com/aws/deep-learning-containers/blob/master/available_images.md). It also supports machine learning libraries such as scikit-learn and SparkML. If you use the [Amazon SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable), you can deploy the containers by passing the full container URI to their respective SageMaker SDK `Estimator` class. For the full list of deep learning frameworks that are currently supported by SageMaker AI, see [Prebuilt SageMaker AI Docker images for deep learning](./pre-built-containers-frameworks-deep-learning.html). For information about the scikit-learn and SparkML pre-built container images, see [Accessing Docker Images for Scikit-learn and Spark ML](./pre-built-docker-containers-scikit-learn-spark.html). For more information about using frameworks with the [Amazon SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable), see their respective topics in [Machine Learning Frameworks and Languages](./frameworks.html). + * **Use pre-built SageMaker AI container images**. Alternatively, you can use the built-in algorithms and frameworks using Docker containers. SageMaker AI provides containers for its built-in algorithms and pre-built Docker images for some of the most common machine learning frameworks, such as Apache MXNet, TensorFlow, PyTorch, and Chainer. For a full list of the available SageMaker Images, see [Available Deep Learning Containers Images](https://github.com/aws/deep-learning-containers/blob/master/available_images.md). It also supports machine learning libraries such as scikit-learn and SparkML. If you use the [Amazon SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable), you can deploy the containers by passing the full container URI to their respective SageMaker SDK `ModelTrainer` class. For the full list of deep learning frameworks that are currently supported by SageMaker AI, see [Prebuilt SageMaker AI Docker images for deep learning](./pre-built-containers-frameworks-deep-learning.html). For information about the scikit-learn and SparkML pre-built container images, see [Accessing Docker Images for Scikit-learn and Spark ML](./pre-built-docker-containers-scikit-learn-spark.html). For more information about using frameworks with the [Amazon SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable), see their respective topics in [Machine Learning Frameworks and Languages](./frameworks.html).