{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/15"},{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/9"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/18"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/9"},{"label":"Application Frameworks","url":"https://skillfed.io/packages/category/software-development-libraries-application-frameworks/5"}],"enrichment":{"capability":"TraceML tracks metrics, parameters, artifacts, and data references for machine learning experiments, with integrations for Keras, PyTorch, TensorFlow, Fastai, PyTorch Lightning, and HuggingFace, plus offline mode for tracking without an API.","skillfed_tags":["ml-experiment-tracking","framework-integrations"],"use_cases":["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"],"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.\n\nThe 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.","worth_installing":"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."},"id":"traceml","links":{"html":"https://skillfed.io/packages/traceml","md":"https://skillfed.io/packages/traceml.md","pypi":"https://pypi.org/project/traceml/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-04-13","license_spdx":null,"license_treatment":"permissive","name":"traceml","python_support":"supports_current","summary":"Engine for ML/Data tracking, visualization, dashboards, and model UI for Polyaxon."},"popularity":{"monthly_downloads":132753,"position":11537,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.3.0"}