labmlai/labml

Health v2 · maintenance only

Adoption not part of the score

2325 stars · 152 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A Python library for tracking and monitoring deep learning experiments, with a self-hostable server app for viewing metrics and hardware usage from a mobile phone or laptop. It integrates with PyTorch, TensorFlow, Keras, PyTorch Lightning, and FastAI with minimal code.

Use cases

• monitor deep learning training runs from my phone
• track loss and accuracy curves during pytorch training
• log gpu and hardware usage while training models
• self-hosted tensorboard alternative for experiment tracking
• record experiment configs and git commits automatically
• visualize custom metrics during machine learning experiments

When to choose

• you want lightweight, two-line integration for experiment tracking in PyTorch or TensorFlow
• you prefer a self-hosted monitoring server with mobile access
• you need hardware usage monitoring alongside training metrics

When to avoid

• you need a managed cloud experiment platform with team collaboration features
• you require full MLOps pipelines beyond tracking and visualization
• you don't want to run your own MongoDB-backed server

Facets

library · maturity active

monitoring data-visualization machine-learning deep-learning analytics machine-learning deep-learning data-visualization monitoring python cross-platform self-hosted experiment-tracking pytorch tensorflow keras tensorboard-alternative hardware-monitoring mobile-dashboard mobile web-server

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markdown · JSON · MCP: product_card(name="labmlai/labml")