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.

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 ...

JumpStart takes models from popular open source model hubs, such as TensorFlow and HuggingFace, and pre-trains them on an open source dataset. Using the SageMaker Python SDK, you can select a prebuilt model from the model zoo to train on custom data or deploy to a SageMaker endpoint for inference without signing up for SageMaker Studio.

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

Provides APIs for creating and managing SageMaker resources.

The SageMaker Scikit-learn model server provides a default implementation of input_fn. This function deserializes JSON, CSV, or NPY encoded data into a NumPy array. Default NPY deserialization requires request_body to follow the NPY format. For Scikit-learn, the Python SDK defaults to sending ...

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.

August 14, 2020 - ➜ ~> pip3 install sagemaker ...

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....

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

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

"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.

June 19, 2024 - sagemaker is the official Python SDK that trains and deploys machine learning models on Amazon SageMaker.

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....

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

The SageMaker PyTorch model server provides a default implementation of input_fn. This function deserializes JSON, CSV, or NPY encoded data into a torch.Tensor. Default NPY deserialization requires request_body to follow the NPY format. For PyTorch, the Python SDK defaults to sending prediction requests with this format.