LabML
LabML is an open-source platform for monitoring deep learning experiments and hardware usage.
What LabML is
LabML is an open-source platform for monitoring deep learning experiments and hardware usage. It can show experiment results, hardware usage, and performance metrics in real time on mobile devices and laptops. Users can record metrics such as training loss, track hyperparameters and Git commits, build custom charts, and compare runs. The platform also supports distributed training across multiple machines. Setup uses pip to install the app and `labml app-server` to start a local server; hosting the experiments server requires MongoDB. Documentation lists sample integrations for FastAI, Keras, and PyTorch Lightning, along with Python API and CLI references. A related collection provides documented PyTorch implementations covering topics such as transformers, diffusion models, reinforcement learning, optimizers, and normalization layers; it installs with `pip install labml-nn`. LabML software is provided under the MIT License, which states it comes without warranty. The site lists [email protected] for inquiries.
Good
- Compare experiment results across runs
- Tracks hyperparameters and Git commits
- Supports distributed training across multiple machines
- Accessible from mobile devices and laptops
Watch out
- Hosting the experiments server requires MongoDB
- The MIT License states the software comes without warranty
- Self-hosted deployment
Who it suits
LabML suits people tracking deep learning experiments who want to monitor metrics and hardware use across multiple runs. It also supports distributed training and offers documented PyTorch implementations of research topics.
Verdict
LabML brings experiment metrics, run comparisons, and hardware monitoring together, with distributed-training support. Hosting requires MongoDB, so include that prerequisite in setup planning.
The facts
Each one read from LabML's own pages, with the date it was checked.
What it does
- Access
- The app page says the platform is accessible from any device, including mobile phones and laptops.checked 11 Oct 2026
- Additional project
- LabML's main site links to annotated PyTorch implementations of research papers, with examples covering transformers, diffusion models, reinforcement learning, and optimizers.checked 11 Oct 2026
- API and CLI
- The documentation describes itself as the Python API and CLI reference and lists experiment, tracker, monitor, configuration, logger, and other modules.checked 11 Oct 2026
- Contact
- The LabML site lists [email protected] as its contact address.checked 11 Oct 2026
- Distributed training
- The app page says it has built-in support for distributed training across multiple machines.checked 11 Oct 2026
- Examples
- The implementations site lists topics including transformers, diffusion models, reinforcement learning, optimizers and normalization layers.checked 11 Oct 2026
- Experiment details
- The platform can track hyperparameters and Git commits to support reproducibility.checked 11 Oct 2026
- Experiment metrics
- The example uses Python’s `tracker.save` to record a training loss metric.checked 11 Oct 2026
- Experiment tracking
- The platform tracks metrics and experiment details including hyperparameters and Git commits.checked 11 Oct 2026
- Framework integrations
- LabML documentation lists sample integrations for FastAI, Keras, and PyTorch Lightning.checked 11 Oct 2026
- Installation
- The annotated implementations collection can be installed with `pip install labml-nn`.checked 11 Oct 2026
- License
- The LabML repository provides its software under the MIT License, which states that it is provided without warranty.checked 11 Oct 2026
- Live monitoring
- The app can show experiment results, hardware usage, and performance metrics in real time from mobile devices and laptops.checked 11 Oct 2026
- Paper implementations
- LabML’s annotated implementations site provides documented PyTorch implementations of neural networks and related algorithms.checked 11 Oct 2026
- Purpose
- LabML is an open-source platform for monitoring deep learning experiments and hardware usage.checked 11 Oct 2026
- Self-hosting
- The quick start installs the app with pip and starts a local server with `labml app-server`.checked 11 Oct 2026
- Server requirement
- The repository instructions list MongoDB as a prerequisite for hosting the experiments server.checked 11 Oct 2026
- Setup
- The quick start installs the app with pip install labml-app and starts it with labml app-server.checked 11 Oct 2026
- Support
- The LabML app page lists contact email [email protected].checked 11 Oct 2026
- Visualizations
- Users can create custom charts and compare results across multiple runs.checked 11 Oct 2026
The company
- Company name
- A LabML privacy policy identifies the operator as LabML, Inc.checked 11 Oct 2026
- Founded
- 2020checked 28 Sep 2026
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