nv-one-logger-pytorch-lightning-integration
Wrappers that facilitate enabling training job telemetry for a set of supported training frameworks.
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires torch >= 2.8.0 and lightning >= 2.5.3; Python >= 3.9, < 3.14.
- 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 · maintenance · safety
Apache-2.0 (permissive) — Licensed under Apache-2.0 (permissive), allowing commercial and private use with minimal restrictions—suitable for most production and research contexts.
last release 2025-10-29 (289 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 227,783 downloads/mo, #9,168 on PyPI
Alternatives
Verify before relying
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()- 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.
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 on this page. Verify before relying on it.
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
nv-one-logger-pytorch-lightning-integration on PyPI
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).
Requires torch >= 2.8.0 and lightning >= 2.5.3; Python >= 3.9, < 3.14.
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()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 the current Python release <3.14,>=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagesStrEnumlightningnv-one-logger-corenv-one-logger-training-telemetrysetuptools |
| Maintenance | Aging 289 days since the last release |
| First released | |
| Downloads | 227,783 / month, #9,168 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: Apache Software LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.9 |
Evidence: nv_one_logger_pytorch_lightning_integration-2.3.1-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “lightning telemetry integration”
- nv-one-logger-pytorch-lightning-integrationAdds training telemetry collection to Lightning applications via…
- lightning-cloudProvides a Python client for interacting with Lightning AI Cloud…
- breez-sdk-sparkPython bindings for the Breez Spark SDK, enabling non-custodial…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also lightning · pytorch-lightning · trainer · nv-one-logger-core · pytorch-forecasting · torchmetrics · traceml · coqui-tts-trainer · liger-kernel