LabML

Spec sheet

Purpose

LabML is an open-source platform for monitoring deep learning experiments and hardware usage.Read 11 Oct 2026 · labml.ai

Support

The LabML app page lists contact email [email protected].Read 11 Oct 2026 · labml.ai

License

The LabML repository provides its software under the MIT License, which states that it is provided without warranty.Read 11 Oct 2026 · github.com

Founded

2020Read 28 Sep 2026 · labml.ai

Access

The app page says the platform is accessible from any device, including mobile phones and laptops.Read 11 Oct 2026 · labml.ai

Additional project

LabML's main site links to annotated PyTorch implementations of research papers, with examples covering transformers, diffusion models, reinforcement learning, and optimizers.Read 11 Oct 2026 · nn.labml.ai

API and CLI

The documentation describes itself as the Python API and CLI reference and lists experiment, tracker, monitor, configuration, logger, and other modules.Read 11 Oct 2026 · docs.labml.ai

Company name

A LabML privacy policy identifies the operator as LabML, Inc.Read 11 Oct 2026 · labml.ai

Contact

The LabML site lists [email protected] as its contact address.Read 11 Oct 2026 · labml.ai

Distributed training

The app page says it has built-in support for distributed training across multiple machines.Read 11 Oct 2026 · labml.ai

Examples

The implementations site lists topics including transformers, diffusion models, reinforcement learning, optimizers and normalization layers.Read 11 Oct 2026 · nn.labml.ai

Experiment details

The platform can track hyperparameters and Git commits to support reproducibility.Read 11 Oct 2026 · labml.ai

Experiment metrics

The example uses Python’s `tracker.save` to record a training loss metric.Read 11 Oct 2026 · labml.ai

Experiment tracking

The platform tracks metrics and experiment details including hyperparameters and Git commits.Read 11 Oct 2026 · labml.ai

Framework integrations

LabML documentation lists sample integrations for FastAI, Keras, and PyTorch Lightning.Read 11 Oct 2026 · docs.labml.ai

Installation

The annotated implementations collection can be installed with `pip install labml-nn`.Read 11 Oct 2026 · nn.labml.ai

Live monitoring

The app can show experiment results, hardware usage, and performance metrics in real time from mobile devices and laptops.Read 11 Oct 2026 · labml.ai

Paper implementations

LabML’s annotated implementations site provides documented PyTorch implementations of neural networks and related algorithms.Read 11 Oct 2026 · nn.labml.ai

Self-hosting

The quick start installs the app with pip and starts a local server with `labml app-server`.Read 11 Oct 2026 · labml.ai

Server requirement

The repository instructions list MongoDB as a prerequisite for hosting the experiments server.Read 11 Oct 2026 · github.com

Setup

The quick start installs the app with pip install labml-app and starts it with labml app-server.Read 11 Oct 2026 · labml.ai

Visualizations

Users can create custom charts and compare results across multiple runs.Read 11 Oct 2026 · labml.ai

Where Specifiction places it

• ML Experiment Tracking Software#12 of 397.7