---
id: nv-one-logger-pytorch-lightning-integration
version: "2.3.1"
license: Apache-2.0
license_treatment: permissive
maintenance: aging
---

# nv-one-logger-pytorch-lightning-integration — Wrappers that facilitate enabling training job telemetry for a set of supported training frameworks.

License: permissive · Maintenance: aging · Downloads: 227.8K/mo

## What it is and what it does

This package wraps the Trainer class to inject telemetry collection hooks that automatically capture training lifecycle events—training 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.

You 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.

Use it for:

- 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.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Adds training telemetry collection to Lightning applications via callback hooks, capturing training loop events, checkpointing, and lifecycle metrics automatically or on-demand.

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.

## Install

pip install nv-one-logger-pytorch-lightning-integration
uv add nv-one-logger-pytorch-lightning-integration
poetry add nv-one-logger-pytorch-lightning-integration

## Installing nv-one-logger-pytorch-lightning-integration

Before you install: Low install friction with a pure-Python wheel. The package is aging (289 days since release) but carries no known vulnerabilities and depends on stable libraries (lightning, nv-one-logger-core, nv-one-logger-training-telemetry, setuptools).

License in practice: Licensed under Apache-2.0 (permissive), allowing commercial and private use with minimal restrictions—suitable for most production and research contexts.

Quickstart:

from nv_one_logger.training_telemetry.api.training_telemetry_provider import TrainingTelemetryProvider
from nv_one_logger.training_telemetry.integration.pytorch_lightning import hook_trainer_cls

TrainingTelemetryProvider.instance().with_base_config(config).with_exporter(exporter).configure_provider()
HookedTrainer, callback = hook_trainer_cls(Trainer, TrainingTelemetryProvider.instance())
trainer = HookedTrainer(max_epochs=10, devices=1)
trainer.fit(model, train_loader)
callback.on_app_end()

Requires torch >= 2.8.0 and lightning >= 2.5.3; Python >= 3.9, < 3.14.

Verify before relying:

- Whether the telemetry output format and exporter ecosystem are documented or stable across versions.
- Performance overhead of telemetry collection during training runs.
- Compatibility with custom callbacks and trainer subclasses.
- Whether async checkpoint operations work with all distributed training backends.

## Package facts

- License: Apache-2.0 (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: aging
- Downloads: 227.8K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

lightning telemetry integration, training metrics collection, lightning callback hooks, training job monitoring, training instrumentation, trainer event tracking, training performance telemetry, training-telemetry, performance-monitoring

[View on SkillFed](https://skillfed.io/packages/nv-one-logger-pytorch-lightning-integration) · [View on PyPI](https://pypi.org/project/nv-one-logger-pytorch-lightning-integration/)