{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/7"}],"enrichment":{"capability":"Adds training telemetry collection to Lightning applications via callback hooks, capturing training loop events, checkpointing, and lifecycle metrics automatically or on-demand.","skillfed_tags":["training-telemetry","performance-monitoring"],"use_cases":["Automatically instrument training jobs to collect performance metrics and training timelines without modifying training code.","Track model initialization, optimizer setup, and dataloader creation times alongside training loop metrics.","Export training telemetry to external systems via configurable exporters for centralized monitoring.","Measure checkpoint save/load performance and async checkpoint operations during distributed training.","Correlate training events with system performance data for end-to-end training job analysis."],"what_it_does":"This package wraps the Trainer class to inject telemetry collection hooks that automatically capture training lifecycle events\u2014training loops, validation iterations, checkpointing, and model/optimizer initialization. It supplements the Lightning callback mechanism with async checkpoint tracking and app lifecycle events that Lightning's native callbacks don't expose.\n\nYou integrate it by calling `hook_trainer_cls()` on your Trainer class, then instantiate the hooked version as you normally would. Many training events (training loop, validation, checkpoint save) are captured implicitly when you call `trainer.fit()`, while others (model init, dataloader init, testing, checkpoint load) require explicit callback method calls. The package depends on nv-one-logger-core and nv-one-logger-training-telemetry to handle configuration and export.","worth_installing":"Yes, if you use lightning and need built-in training telemetry without manual instrumentation. The package is permissively licensed, has low install friction, and carries no known vulnerabilities. The aging status (289 days since release) is a minor signal to verify that the telemetry output format and exporter ecosystem meet your needs before committing to it in production."},"id":"nv-one-logger-pytorch-lightning-integration","links":{"html":"https://skillfed.io/packages/nv-one-logger-pytorch-lightning-integration","md":"https://skillfed.io/packages/nv-one-logger-pytorch-lightning-integration.md","pypi":"https://pypi.org/project/nv-one-logger-pytorch-lightning-integration/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-10-29","license_spdx":null,"license_treatment":"permissive","name":"nv-one-logger-pytorch-lightning-integration","python_support":"supports_current","summary":"Wrappers that facilitate enabling training job telemetry for a set of supported training frameworks."},"popularity":{"monthly_downloads":227783,"position":9168,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.3.1"}