sagemaker-experiments
Open source library for Experiment Tracking in SageMaker Jobs and Notebooks
Decision gist · record as of 2026-08-14
Yes, if you are already using SageMaker and need lightweight experiment tracking in existing jobs or notebooks. No, if you are starting a new SageMaker project—use the official SageMaker SDK instead, as this package is dormant (last update 2023-05-17) and the description recommends the SDK for up-to-date features. Install only for legacy codebases or when you specifically need the standalone Tracker interface.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires AWS account credentials configured for boto3 and IAM permissions for SageMaker.
- Low install friction with a single runtime dependency (boto3).
- Maintenance is dormant—last commit was 2023-11-14, over 1185 days ago—but the package is marked Production/Stable and the description explicitly recommends using the SageMaker SDK instead for up-to-date features.
License · maintenance · safety
Apache License 2.0 (permissive) — Licensed under Apache License 2.0 (permissive), so you can use, modify, and distribute it freely in commercial and private projects without restriction.
last release 2023-05-17 (1185 days) · last repo commit 2023-11-14 · 129 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 130,247 downloads/mo, #11,648 on PyPI
Alternatives
Verify before relying
pip install sagemaker-experiments
import boto3
# Create and manage experiments via the package's API
# See documentation for Experiment.create() and trial management- Whether this package receives security patches or bug fixes despite dormant status since 2023-11-14.
- Current compatibility with recent SageMaker SDK versions and whether the official SDK is now the recommended path for all use cases.
- Specific API methods and usage patterns beyond what the description excerpt provides.
What it is and what it does
sagemaker-experiments is a Python SDK for tracking machine learning experiments within AWS SageMaker. It provides a high-level interface to organize and log experiment metadata—experiments (collections of trials), trials (multi-step workflows), and trial components (individual steps like data cleaning or model training)—directly from Python scripts, notebooks, and SageMaker jobs. The package depends on boto3 and offers a Tracker context manager for logging trial component information.
The package is designed to integrate with SageMaker Studio and enable comparison of multiple training runs. However, the description explicitly notes that the official SageMaker SDK is now the recommended path and this repository may not reflect the latest product improvements. It remains useful for legacy workflows or direct experiment tracking in existing SageMaker setups.
Use it for
- Log and organize multiple training runs in SageMaker notebooks to compare hyperparameters and model performance.
- Track multi-step ML workflows where each step (preprocessing, training, evaluation) is a separate trial component.
- Record experiment metadata from inside running SageMaker training and processing jobs for later analysis.
- Build experiment lineage and audit trails for reproducibility in production ML pipelines.
- Query and analyze experiment results using the ExperimentAnalytics interface to extract metrics across trials.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are already using SageMaker and need lightweight experiment tracking in existing jobs or notebooks.
No, if you are starting a new SageMaker project—use the official SageMaker SDK instead, as this package is dormant (last update 2023-05-17) and the description recommends the SDK for up-to-date features. Install only for legacy codebases or when you specifically need the standalone Tracker interface.
Install
sagemaker-experiments on PyPI
Before you install
Low install friction with a single runtime dependency (boto3). Maintenance is dormant—last commit was 2023-11-14, over 1185 days ago—but the package is marked Production/Stable and the description explicitly recommends using the SageMaker SDK instead for up-to-date features.
Requires AWS account credentials configured for boto3 and IAM permissions for SageMaker.
License in practice
Licensed under Apache License 2.0 (permissive), so you can use, modify, and distribute it freely in commercial and private projects without restriction.
Quickstart
pip install sagemaker-experiments
import boto3
# Create and manage experiments via the package's API
# See documentation for Experiment.create() and trial managementVerify before relying
- Whether this package receives security patches or bug fixes despite dormant status since 2023-11-14.
- Current compatibility with recent SageMaker SDK versions and whether the official SDK is now the recommended path for all use cases.
- Specific API methods and usage patterns beyond what the description excerpt provides.
Package facts
| License | Apache License 2.0 permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packageboto3 |
| Maintenance | Dormant 1,185 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 130,247 / month, #11,648 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersLicense :: OSI Approved :: Apache Software LicenseNatural Language :: EnglishProgramming Language :: PythonProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9 |
Evidence: sagemaker_experiments-0.1.45-py3-none-any.whl
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