sagemaker
Open source library for training and deploying models on Amazon SageMaker.
Decision gist · record as of 2026-08-14
Yes, with conditions. Install if you are starting a new SageMaker project or actively migrating from v2—the modular architecture and unified APIs reduce complexity. Do not install if you have existing v2 code relying on Estimator, Model, or Predictor classes; those are unsupported in v3 and require rewriting. The package is actively maintained, permissively licensed, and has no known vulnerabilities, but it is still in Alpha (Development Status 3), so expect potential API changes in minor releases.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; AWS credentials and IAM role configured for SageMaker access.
- Low install friction; the package is actively maintained with a release 3 days ago and has no known vulnerabilities.
- It depends on four modular SageMaker subpackages (sagemaker-core, sagemaker-train, sagemaker-serve, sagemaker-mlops) that are installed as runtime dependencies.
License · maintenance · safety
permissive license (permissive) — Licensed under Apache Software License (permissive), which allows commercial and private use with minimal restrictions—suitable for most production deployments.
last release 2026-08-11 (3 days) · last repo commit 2026-08-14 · 2,259 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 20,923,205 downloads/mo, #1,022 on PyPI
Alternatives
Verify before relying
pip install sagemaker
from sagemaker.train import ModelTrainer
from sagemaker.train.configs import InputData
trainer = ModelTrainer(
training_image="my-training-image",
role="arn:aws:iam::123456789012:role/SageMakerRole"
)
train_data = InputData(
channel_name="training",
data_source="s3://my-bucket/train"
)
trainer.train(input_data_config=[train_data])- Whether the modular architecture (separate PyPI packages) requires explicit installation of sagemaker-core, sagemaker-train, sagemaker-serve, sagemaker-mlops or if they are pulled automatically as dependencies.
- Compatibility and migration path for code written against SageMaker 2.x (Estimator, Model, Predictor classes are no longer supported in V3).
- Performance characteristics and resource overhead of the new unified ModelTrainer and ModelBuilder classes compared to V2 framework-specific classes.
What it is and what it does
SageMaker Python SDK v3 is a complete rewrite of AWS's machine learning library, introducing a modular architecture split across four PyPI packages (core, train, serve, mlops) and unified APIs for training and inference. Version 3 replaces the older Estimator and Model classes with ModelTrainer and ModelBuilder, reducing boilerplate and aligning the SDK with AWS's object-oriented API design. The library supports training with PyTorch, MXNet, and Amazon's built-in algorithms, as well as custom Docker containers, and handles both distributed training and real-time inference endpoints.
The SDK is actively maintained (released 3 days ago, last commit 2026-08-14) with no known vulnerabilities and supports Python 3.10, 3.11, and 3.12. It has low install friction and is classified as Alpha (Development Status 3), indicating the v3 architecture is still stabilizing. The modular design allows you to install only the components you need (training, serving, or MLOps), though the main sagemaker package depends on all four subpackages.
Use it for
- Train custom ML models on SageMaker using your own Docker containers or built-in frameworks like PyTorch, with data sourced from S3.
- Deploy trained models as real-time inference endpoints using ModelBuilder, replacing the V2 Model and Predictor workflow.
- Run hyperparameter tuning jobs to optimize model performance across distributed SageMaker infrastructure.
- Build end-to-end ML pipelines combining training, processing, and model registry operations via the MLOps module.
- Perform local training and inference testing before deploying to SageMaker, reducing iteration time.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, with conditions.
Install if you are starting a new SageMaker project or actively migrating from v2—the modular architecture and unified APIs reduce complexity. Do not install if you have existing v2 code relying on Estimator, Model, or Predictor classes; those are unsupported in v3 and require rewriting. The package is actively maintained, permissively licensed, and has no known vulnerabilities, but it is still in Alpha (Development Status 3), so expect potential API changes in minor releases.
Install
sagemaker on PyPI
Before you install
Low install friction; the package is actively maintained with a release 3 days ago and has no known vulnerabilities. It depends on four modular SageMaker subpackages (sagemaker-core, sagemaker-train, sagemaker-serve, sagemaker-mlops) that are installed as runtime dependencies.
Requires Python 3.10 or later; AWS credentials and IAM role configured for SageMaker access.
License in practice
Licensed under Apache Software License (permissive), which allows commercial and private use with minimal restrictions—suitable for most production deployments.
Quickstart
pip install sagemaker
from sagemaker.train import ModelTrainer
from sagemaker.train.configs import InputData
trainer = ModelTrainer(
training_image="my-training-image",
role="arn:aws:iam::123456789012:role/SageMakerRole"
)
train_data = InputData(
channel_name="training",
data_source="s3://my-bucket/train"
)
trainer.train(input_data_config=[train_data])Verify before relying
- Whether the modular architecture (separate PyPI packages) requires explicit installation of sagemaker-core, sagemaker-train, sagemaker-serve, sagemaker-mlops or if they are pulled automatically as dependencies.
- Compatibility and migration path for code written against SageMaker 2.x (Estimator, Model, Predictor classes are no longer supported in V3).
- Performance characteristics and resource overhead of the new unified ModelTrainer and ModelBuilder classes compared to V2 framework-specific classes.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagessagemaker-coresagemaker-trainsagemaker-servesagemaker-mlops |
| Maintenance | Actively maintained 3 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 20,923,205 / month, #1,022 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaIntended Audience :: DevelopersLicense :: OSI Approved :: Apache Software LicenseNatural Language :: EnglishProgramming Language :: PythonProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12 |
Evidence: sagemaker-3.19.0-py3-none-any.whl
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See also sagemaker-core · sagemaker-datawrangler · sagemaker-mlops · sagemaker-serve · sagemaker-studio · sagemaker-train · smdebug-rulesconfig · sagemaker-data-insights · sagemaker-training · sagemaker-containers