{"library":"dvclive","type":"library","category":null,"description":"DVCLive is a Python library for logging machine learning metrics and other metadata. It is designed to be fully compatible with DVC (Data Version Control) and stores logged information in simple, human-readable file formats (like .tsv, .json, .yaml) that can be versioned by Git. It provides real-time experiment tracking and integrates with various ML frameworks, helping users maintain reproducible ML workflows. The current version is 3.49.0, and the library is actively developed with frequent releases.","language":"python","status":"active","version":"3.49.0","tags":["MLOps","experiment tracking","metrics","logging","DVC","machine learning","reproducibility"],"last_verified":"Fri May 22","install":[{"cmd":"pip install dvclive","imports":["from dvclive import Live"]},{"cmd":"pip install dvclive[sklearn,image,huggingface,lightning,tf,fastai,optuna,xgb,lgbm,mmcv]","imports":[]}],"homepage":"https://dvc.org/doc/dvclive","github":"https://github.com/iterative/dvclive","docs":"https://dvc.org/doc/dvclive","changelog":"https://github.com/iterative/dvclive/releases","pypi":"https://pypi.org/project/dvclive/","npm":null,"openapi_spec":null,"status_page":null,"smithery":null,"compatibility":{"summary":{"python_range":"3.10–3.9","success_rate":50,"avg_install_s":19.4,"avg_import_s":1,"wheel_type":"sdist"},"url":"https://checklist.day/v1/registry/dvclive/compatibility"}}