sagemaker-core
An python package for sagemaker core functionalities
Decision gist · record as of 2026-08-14
Yes, with conditions. Install if you are building on AWS SageMaker and want a higher-level, type-safe interface over raw boto3 calls. The active maintenance, permissive license, and zero known vulnerabilities are strong signals. However, the Alpha status means the API may change; pin a version and monitor releases. The 24 dependencies are all reputable, but verify they fit your environment constraints before committing to production.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; requires AWS credentials configured in your environment.
- Low friction install with a pure-wheel distribution.
- Active maintenance as of 4 days ago with recent commits and 2259 repository stars.
License · maintenance · safety
permissive license (permissive) — Licensed under Apache 2.0 (permissive). No restrictions on commercial use, modification, or distribution; suitable for proprietary projects provided you include the license notice.
last release 2026-08-10 (4 days) · last repo commit 2026-08-14 · 2,259 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 8,323,113 downloads/mo, #1,633 on PyPI
Alternatives
Verify before relying
pip install sagemaker-core
import sagemaker_core
from sagemaker_core import SageMakerSession
session = SageMakerSession()- Whether resource chaining works seamlessly across all SageMaker resource types or has documented limitations.
- Performance characteristics when managing large numbers of resources or long-running training jobs.
- Stability guarantees given the 'Alpha' development status and recent first release (2024-07-18).
What it is and what it does
sagemaker-core is a Python SDK that wraps Amazon SageMaker's APIs in an object-oriented interface, released in 2024 and actively maintained. It replaces low-level API calls with dedicated resource classes, type hints, and intelligent defaults, allowing developers to chain SageMaker resources together and focus on model building rather than infrastructure plumbing. The package depends on boto3 for AWS communication, pydantic for validation, and a suite of data science libraries (pandas, numpy, scipy) for model work.
The SDK abstracts state management and polling logic, handles resource transitions automatically, and provides IDE auto-completion. It targets Python 3.10 or later and carries 24 runtime dependencies. The package is in Alpha status, meaning its API may still evolve, but it has no known security vulnerabilities and is actively developed.
Use it for
- Build SageMaker training pipelines using object-oriented resource chaining instead of manual API calls.
- Deploy ML models to SageMaker endpoints with type-safe, IDE-assisted code and intelligent defaults.
- Manage SageMaker notebooks, processing jobs, and feature stores programmatically with less boilerplate.
- Migrate from lower-level boto3 calls to a higher-level, more maintainable SDK interface.
- Prototype ML workflows quickly using auto-completion and comprehensive type hints in your IDE.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, with conditions.
Install if you are building on AWS SageMaker and want a higher-level, type-safe interface over raw boto3 calls. The active maintenance, permissive license, and zero known vulnerabilities are strong signals. However, the Alpha status means the API may change; pin a version and monitor releases. The 24 dependencies are all reputable, but verify they fit your environment constraints before committing to production.
Install
sagemaker-core on PyPI
Before you install
Low friction install with a pure-wheel distribution. Active maintenance as of 4 days ago with recent commits and 2259 repository stars. Requires Python 3.10 or later. Pulls in 24 runtime dependencies including boto3, pydantic, and scientific libraries; all are widely-used packages with established track records.
Requires Python 3.10 or later; requires AWS credentials configured in your environment.
License in practice
Licensed under Apache 2.0 (permissive). No restrictions on commercial use, modification, or distribution; suitable for proprietary projects provided you include the license notice.
Quickstart
pip install sagemaker-core
import sagemaker_core
from sagemaker_core import SageMakerSession
session = SageMakerSession()Verify before relying
- Whether resource chaining works seamlessly across all SageMaker resource types or has documented limitations.
- Performance characteristics when managing large numbers of resources or long-running training jobs.
- Stability guarantees given the 'Alpha' development status and recent first release (2024-07-18).
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 24 packagesboto3pydanticPyYAMLjsonschemaplatformdirsrichmockimportlib-metadatatyping_extensionspytzrequestsattrspackagingprotobufpandasnumpysmdebug_rulesconfigschemaomegaconfscipycloudpickleparamikotblibcryptography |
| Maintenance | Actively maintained 4 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 8,323,113 / month, #1,633 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 LicenseProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12 |
Evidence: sagemaker_core-2.19.0-py3-none-any.whl
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See also sagemaker · sagemaker-serve · sagemaker-train · sagemaker-mlops · sagemaker-training · sagemaker-schema-inference-artifacts · sagemaker-data-insights · sagemaker-datawrangler · sagemaker-feature-store-pyspark-3.1 · sagemaker-studio