opentelemetry-instrumentation-sklearn
OpenTelemetry sklearn instrumentation
Decision gist · record as of 2026-08-14
Yes, if you are already using OpenTelemetry in your application and need visibility into scikit-learn operations. The low install friction, active maintenance, permissive license, and zero known vulnerabilities make it a safe addition. Beta status means the API may change. Install only if you have an OpenTelemetry tracer/exporter already configured to receive spans.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.8 or later; requires an active OpenTelemetry tracer/exporter configured separately to capture and export spans.
- Low install friction with only two runtime dependencies.
- Actively maintained with recent commits; still in beta status.
License · maintenance · safety
permissive license (permissive) — Licensed under Apache Software License (permissive), imposing no significant restrictions on use or redistribution.
last release 2024-05-31 (805 days) · last repo commit 2026-08-14 · 1,086 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 171,235 downloads/mo, #10,376 on PyPI
Alternatives
Verify before relying
pip install opentelemetry-instrumentation-sklearn
from opentelemetry.instrumentation.sklearn import SklearnInstrumentor
SklearnInstrumentor().instrument()- Which specific scikit-learn operations are instrumented (fit, predict, transform, etc.)
- Whether instrumentation works with scikit-learn pipelines and composite estimators
- Performance overhead of tracing during model training or inference
What it is and what it does
This package integrates scikit-learn with OpenTelemetry's distributed tracing system. When installed and activated, it automatically wraps scikit-learn operations to emit trace spans, allowing you to observe model training, inference, and other ML workflows as part of a broader observability stack. It depends on opentelemetry-api and opentelemetry-instrumentation to function.
The instrumentation is designed for developers building ML systems who need visibility into scikit-learn's execution within distributed or complex applications. It is currently in beta status and supports Python 3.8 through 3.11. The package has been actively maintained and carries no known security vulnerabilities.
Use it for
- Trace scikit-learn model training and prediction calls in microservice architectures to correlate ML operations with other service spans.
- Monitor performance and latency of scikit-learn operations in production ML pipelines using a centralized observability backend.
- Debug and profile scikit-learn workflows by examining detailed trace data alongside application logs and metrics.
- Integrate scikit-learn observability into existing OpenTelemetry-instrumented applications without modifying model code.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are already using OpenTelemetry in your application and need visibility into scikit-learn operations.
The low install friction, active maintenance, permissive license, and zero known vulnerabilities make it a safe addition. Beta status means the API may change. Install only if you have an OpenTelemetry tracer/exporter already configured to receive spans.
Install
opentelemetry-instrumentation-sklearn on PyPI
Before you install
Low install friction with only two runtime dependencies. Actively maintained with recent commits; still in beta status.
Requires Python 3.8 or later; requires an active OpenTelemetry tracer/exporter configured separately to capture and export spans.
License in practice
Licensed under Apache Software License (permissive), imposing no significant restrictions on use or redistribution.
Quickstart
pip install opentelemetry-instrumentation-sklearn
from opentelemetry.instrumentation.sklearn import SklearnInstrumentor
SklearnInstrumentor().instrument()Verify before relying
- Which specific scikit-learn operations are instrumented (fit, predict, transform, etc.)
- Whether instrumentation works with scikit-learn pipelines and composite estimators
- Performance overhead of tracing during model training or inference
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesopentelemetry-apiopentelemetry-instrumentation |
| Maintenance | Actively maintained 805 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 171,235 / month, #10,376 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersLicense :: OSI Approved :: Apache Software LicenseProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9 |
Evidence: opentelemetry_instrumentation_sklearn-0.46b0-py3-none-any.whl
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