# labmlai/labml

🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱

Repository: https://github.com/labmlai/labml
Canonical: https://ross.abutalabs.com/products/labml
Homepage: https://labml.ai
Language: Python
License: MIT
License Family: permissive
Topics: machine-learning, deep-learning, pytorch, experiment, analytics, visualization, tensorboard, mobile, keras, tensorflow, tensorflow2, keras-tensorflow, pytorch-lightning, fastai
Last push: 2025-04-10T09:30:35+00:00

## Health v2 (maintenance only)
Score: 30/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 15, release rhythm 8, longevity 100
- inputs: {"age_days": 2847, "days_push": 510, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2325, forks 152 (observed 2026-08-28T04:06:37.390657+00:00)

## What it is
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
- artifact type: library
- maturity: active
- function: monitoring, data-visualization, machine-learning, deep-learning, analytics
- domain: machine-learning, deep-learning, data-visualization, monitoring
- platform: python, cross-platform, self-hosted
- tags: experiment-tracking, pytorch, tensorflow, keras, tensorboard-alternative, hardware-monitoring, mobile-dashboard, mobile, web-server

## Member repositories
- labmlai/labml (main) score 30

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:37.390657+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:38:24.284408+00:00, confidence not recorded.
  - readme: https://github.com/labmlai/labml (fetched 2026-08-28T04:06:37.390657+00:00, sha eca3de4214a3)
  - homepage: https://labml.ai (fetched 2026-08-29T10:18:46.882346+00:00, sha bf2ff9da0563)
  - registry_pypi: https://pypi.org/pypi/labml/json (fetched 2026-08-29T10:18:46.891750+00:00, sha 485d1078b2e0)
- Data as of 2026-08-30T08:39:29.467469+00:00.