LabML
LabML: A focused open-source workspace for recording, comparing, and monitoring deep-learning runs. Ranked #18 of 38 in ML Experiment Tracking Software by our editors (7.1/10); pricing: Open source; best for open-source researchers tracking training runs.
At a glance
- Editor score7.1 / 10
- PricingOpen source
- Best forOpen-source researchers tracking training runs
- Paid fromFree
- Run comparisonYes
- Founded2020
- Facts checked21 Sep 2026
Where it wins
- Tracks configurations, hyperparameters, metrics, Git details, and hardware usage
- Compares multiple runs with custom charts and visualizations
- Supports distributed training, Python APIs, CLI capture, and self-hosting
Where it doesn't
- Does not cover artifact management
- Does not cover dataset management
- Does not cover model registry features
Our verdict on LabML
LabML is an open-source machine-learning experiment tracking and training-monitoring platform for researchers and engineers developing and evaluating deep-learning models. It records experiment names, comments, tags, configurations, hyperparameters, Git information, metrics, training steps, and hardware usage. The platform is available through web access, APIs, and self-hosted deployment, with command-line support for experiment capture and dashboard access.
Its strongest fit is run-level visibility during model development. LabML supports comparison across multiple experiments, custom charts and visualizations, distributed training, and hardware and GPU usage monitoring. Python APIs provide programmatic access, while the command-line interface supports capturing experiments and opening dashboard views. Together, these capabilities give research teams a focused way to connect training configuration, code state, performance metrics, and compute usage in one tracking workflow.
LabML is most suitable for open-source researchers who need experiment recording, metric tracking, run comparison, and training monitoring without a broader artifact or data-management layer. Its scope is narrower for teams that need artifact management, dataset management, or model registry features as part of the same system. Those requirements may call for a more expansive machine-learning platform, while teams centered on reproducible run tracking and GPU-aware training visibility should find LabML’s open-source, API-enabled, self-hosted approach well aligned with their workflow.
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LabML pricing
LabML fact sheet
| Free plan | Not verified |
|---|---|
| Paid from | Free |
| Run comparison | Yes |
| Artifact tracking | Not verified |
| Dataset versioning | Not verified |
| Model registry | Not verified |
| Deployment options | Self_hosted |
| API and SDK access | Yes |
| Deployment | Self-hosted, Browser extension |
| Platforms | Web |
| Support | Email, Docs |
| Built for | Solo, Small business, Mid-market (editorial estimate) |
| Pricing | Open source |
| Website | labml.ai |
| Facts checked | 21 Sep 2026 |
Alternatives to LabML
- Weights & BiasesA broad ML platform that extends experiment tracking into production workflows.8.9
- ClearMLA broad MLOps platform with experiment tracking, data versioning, orchestration, and serving.8.8
- CometA polished experiment tracking platform with strong comparison, lineage, and registry tools.8.7
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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
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