traceml
Engine for ML/Data tracking, visualization, dashboards, and model UI for Polyaxon.
Decision gist · record as of 2026-08-14
Yes, if you need lightweight experiment tracking for ML workflows. Low install friction, no dependencies, active maintenance, and integrations with major frameworks make it practical. Best suited for teams already using or planning to use Polyaxon; standalone offline use is possible but the full value emerges with the Polyaxon platform.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low install friction: pure Python wheel with no runtime dependencies.
- Active maintenance with a recent release (2026-04-13) and ongoing repository activity.
License · maintenance · safety
Apache 2.0 (permissive) — Apache 2.0 is permissive; you can use, modify, and distribute this package freely in commercial and private projects with minimal restrictions.
last release 2026-04-13 (123 days) · last repo commit 2026-08-07 · 534 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 132,753 downloads/mo, #11,537 on PyPI
Alternatives
Verify before relying
pip install traceml
from traceml import tracking
tracking.init(
is_offline=True,
project='my-project',
name='my-run',
artifacts_path='./artifacts'
)
tracking.log_inputs(learning_rate=0.001, batch_size=64)
tracking.log_metrics(loss=0.5)
tracking.log_outputs(validation_score=0.66)
tracking.stop()- Whether offline mode fully supports all tracking features or if some require API connectivity
- Performance characteristics when logging large numbers of metrics or artifacts
- Whether the package can be used standalone or requires Polyaxon for full functionality
What it is and what it does
TraceML is an experiment tracking engine designed for machine learning workflows. It logs hyperparameters, metrics, data references, artifacts, and model outputs during training runs, with built-in callbacks for popular frameworks like Keras, PyTorch, TensorFlow, Fastai, PyTorch Lightning, and HuggingFace. It supports both online tracking via API and offline mode for local-only tracking.
The package provides a unified logging interface across different ML frameworks, allowing you to record training progress, save visualizations (matplotlib, Plotly, Altair, Bokeh), and organize experiments by project and run name. It has no runtime dependencies and works with Python 3.8 and later, making it lightweight to integrate into existing ML pipelines.
Use it for
- Track metrics and hyperparameters during Keras model training using the built-in callback
- Log PyTorch training metrics and save model checkpoints with artifact references
- Record TensorFlow experiment metrics and histograms via the integration callback
- Store visualization artifacts (charts, plots, heatmaps) alongside experiment metadata
- Run offline experiment tracking without connecting to an API server
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need lightweight experiment tracking for ML workflows.
Low install friction, no dependencies, active maintenance, and integrations with major frameworks make it practical. Best suited for teams already using or planning to use Polyaxon; standalone offline use is possible but the full value emerges with the Polyaxon platform.
Install
traceml on PyPI
Before you install
Low install friction: pure Python wheel with no runtime dependencies. Active maintenance with a recent release (2026-04-13) and ongoing repository activity.
License in practice
Apache 2.0 is permissive; you can use, modify, and distribute this package freely in commercial and private projects with minimal restrictions.
Quickstart
pip install traceml
from traceml import tracking
tracking.init(
is_offline=True,
project='my-project',
name='my-run',
artifacts_path='./artifacts'
)
tracking.log_inputs(learning_rate=0.001, batch_size=64)
tracking.log_metrics(loss=0.5)
tracking.log_outputs(validation_score=0.66)
tracking.stop()Verify before relying
- Whether offline mode fully supports all tracking features or if some require API connectivity
- Performance characteristics when logging large numbers of metrics or artifacts
- Whether the package can be used standalone or requires Polyaxon for full functionality
Package facts
| License | Apache 2.0 permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | None |
| Maintenance | Actively maintained 123 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 132,753 / month, #11,537 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersIntended Audience :: Information TechnologyIntended Audience :: Science/ResearchIntended Audience :: System AdministratorsOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.6Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Software DevelopmentTopic :: Software Development :: LibrariesTopic :: Software Development :: Libraries :: Application FrameworksTopic :: Software Development :: Libraries :: Python ModulesTyping :: Typed |
Evidence: traceml-1.3.0-py3-none-any.whl
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