Sagemaker-Python-Sdk

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SageMaker Python SDK is an open-source library for training and deploying machine learning models on Amazon SageMaker. It simplifies ML workflows with a modular architecture and unified APIs for training and inference.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is sagemaker-python-sdk?

The SageMaker Python SDK is an open-source library designed to streamline the process of training and deploying machine learning models on Amazon SageMaker. It supports popular deep learning frameworks like Apache MXNet and PyTorch, as well as Amazon's optimized algorithms. The SDK also allows users to leverage custom algorithms built into SageMaker-compatible Docker containers. Version 3.0.0 introduces a modernized, modular architecture with separate PyPI packages for core, training, and serving capabilities. Key benefits include unified ModelTrainer and ModelBuilder classes, replacing multiple framework-specific classes, and an object-oriented API aligned with AWS APIs, reducing boilerplate and simplifying workflows for developers.

Best used for

Ideal for developers who need to efficiently train and deploy machine learning models on Amazon SageMaker, leveraging popular frameworks or custom algorithms. Especially valuable for simplifying complex ML workflows and integrating seamlessly with AWS infrastructure.

Common actions

train machine learning models

deploy machine learning models

manage machine learning workflows

integrate with AWS SageMaker

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Capabilities

Key features

Train ML models
Deploy ML models
Modular architecture
Unified training API
Unified inference API
Support popular ML frameworks
Custom Docker container support

Target Audience

developer

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What are the key differences between SageMaker Python SDK V2 and V3?

Version 3 introduces a modular architecture with separate packages for core, training, and serving. It unifies training and inference with `ModelTrainer` and `ModelBuilder` classes, replacing multiple framework-specific classes from V2. V3 also features an object-oriented API aligned with AWS APIs, simplifying workflows and reducing boilerplate code.

Which Python versions are supported by the SageMaker Python SDK?

The SageMaker Python SDK is tested and supports Python versions 3.9, 3.10, 3.11, and 3.12. Users should ensure their development environment uses one of these supported versions for optimal compatibility and performance when working with the SDK.

Does the SageMaker Python SDK collect telemetry data?

Yes, the `sagemaker` library has telemetry enabled by default to help understand user needs and improve features. Users can opt out of telemetry by setting the `TelemetryOptOut` parameter to `true` in the SDK defaults configuration, providing control over data sharing.

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