{"categories":[{"label":"Monitoring","url":"https://skillfed.io/packages/category/system-monitoring"}],"enrichment":{"capability":"Provides logging integrations for spaCy model training, supporting Weights & Biases, MLflow, ClearML, PyTorch, and CuPy as alternate loggers decoupled from the core library.","skillfed_tags":["experiment-tracking","model-monitoring","spacy-integration"],"use_cases":["Track spaCy NLP model training metrics and model artifacts in Weights & Biases dashboards.","Log training runs and model performance to MLflow for experiment comparison and reproducibility.","Monitor training progress across multiple spaCy jobs using ClearML's centralized task management.","Chain multiple loggers together to send training data to multiple platforms simultaneously.","Exclude sensitive paths or configuration values from logged configs before uploading to external services."],"what_it_does":"spacy-loggers is a companion package to spaCy that decouples experiment tracking and monitoring from the core library. Starting with spaCy v3.2, loggers were moved into this separate package so they can be updated independently. It provides integration layers for five external logging platforms\u2014Weights & Biases, MLflow, ClearML, PyTorch, and CuPy\u2014plus utility loggers for interoperating between them.\n\nYou use it by installing it alongside spaCy (it often installs automatically) and then configuring which logger to use in your spaCy training config file. Each logger sends training metrics, model checkpoints, and system information to its respective dashboard or tracking service. The package is designed for machine learning practitioners who want to monitor spaCy model training without adding bloat to the core library.","worth_installing":"Yes, if you use spaCy for model training and want to integrate with external experiment tracking platforms. It installs automatically with spaCy v3.2+ and has no runtime dependencies, so there is minimal friction. The dormant maintenance status is not a concern for a stable integration layer, but verify that your chosen logger (WandbLogger, MLflowLogger, etc.) is compatible with your current spaCy and external service versions before relying on it for production workflows."},"id":"spacy-loggers","links":{"html":"https://skillfed.io/packages/spacy-loggers","md":"https://skillfed.io/packages/spacy-loggers.md","pypi":"https://pypi.org/project/spacy-loggers/"},"maintenance":{"status":"dormant"},"meta":{"latest_release":"2023-09-11","license_spdx":null,"license_treatment":"permissive","name":"spacy-loggers","python_support":"supports_current","summary":"Logging utilities for SpaCy"},"popularity":{"monthly_downloads":20619567,"position":1031,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"1.0.5"}