Amazon SageMaker Python SDK is an open source library for training and deploying machine-learned models on Amazon SageMaker.

SageMaker Python SDK is an open source library for training and deploying machine learning models on Amazon SageMaker.

Amazon SageMaker AI provides APIs, SDKs, and a command line interface that you can use to create and manage notebook instances and train and deploy models.

SageMaker maintains a model zoo of over 600 models from popular open source model hubs, such as TensorFlow Hub, Pytorch Hub, and HuggingFace. You can use the SageMaker Python SDK to fine-tune a model on your own dataset or deploy it directly to a SageMaker endpoint for inference.

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With the SDK, you can train and deploy models using popular deep learning frameworks: **Apache MXNet** and **TensorFlow**. You can also train and deploy models with **Amazon algorithms**, these are scalable implementations of core machine learning ...

"processes_per_host" – Required. Specify the number of processes MPI should launch on each host. In SageMaker AI, a host is a single Amazon EC2 ML instance. The SageMaker Python SDK maintains a one-to-one mapping between processes and GPUs across model and data parallelism.

A library for training and deploying machine learning models on Amazon SageMaker - aws/sagemaker-python-sdk

February 28, 2025 - We’re excited to announce the ... lifecycle. This new SDK streamlines data processing, training, and inference and features resource chaining, intelligent defaults, and enhanced logging capabilities....

To use those versions of TensorFlow, ... than using SDK parameters. For more information, see Upgrade from Legacy TensorFlow Support. The SageMaker Python SDK CLI has been removed....

Creates a configuration for running a SageMaker AI image as a KernelGateway app. The configuration specifies the Amazon Elastic File System storage volume on the image, and a list of the kernels in the image. ... Result of the CreateAppImageConfig operation returned by the service. ... (Consumer<CreateAppImageConfigRequest.Builder> createAppImageConfigRequest) throws ResourceInUseException, AwsServiceException, SdkClientException, SageMakerException

Making API calls directly from code is cumbersome, and requires you to write code to authenticate your requests. Amazon SageMaker AI provides the following alternatives:

Provides APIs for creating and managing SageMaker resources.

This SDK introduces the resource chaining feature, allowing developers to pass resource objects as parameters, eliminating manual parameter specification and simplifying code management.

We recommend that you migrate to AWS SDK for JavaScript v3. For dates, additional details, and information on how to migrate, please refer to the linked announcement. ... Constructs a service interface object. Each API operation is exposed as a function on service. Provides APIs for creating and managing SageMaker ...

February 27, 2023 - S3 is the primary location for storing training data and the destination for exporting training artifacts like models. The SDK provides Preprocessors and Estimators as the fundamental interfaces for data preprocessing and model training. These two APIs are simply wrappers for Sagemaker Docker containers.

Welcome to the sagemaker-core Python SDK, an SDK designed to provide an object-oriented interface for interacting with Amazon SageMaker resources. It offers full parity with SageMaker APIs, allowing developers to leverage all SageMaker capabilities ...

Package sagemaker provides the client and types for making API requests to Amazon SageMaker Service.

AWS SDK for JavaScript SageMaker Client for Node.js, Browser and React Native.

Open source library for training and deploying models on Amazon SageMaker. copied from cf-post-staging / sagemaker-python-sdk