LabML
6.2#15 in ml experiment tracking softwareAndroidiOSWebAPI
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| Fact | LabML | Weights & Biases |
|---|---|---|
| Experiment tracking | The platform tracks metrics and experiment details including hyperparameters and Git commits. | The platform automatically records machine-learning pipeline details and provides contextual visual analysis. |
| Founded | 2020 | LinkedIn lists the company as founded in 2017. |
| Self-hosting | The quick start installs the app with pip and starts a local server with `labml app-server`. | Self-hosting requires Docker and Python and can run on Linux, macOS or Windows; customers manage their own storage and backups. |
| Support | The LabML app page lists contact email [email protected]. | Support tiers are Standard, Standard Plus and Premium; Premium adds 24/7 coverage, faster response targets, roadmap sessions and engineering access. |
No prices published.
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.
It suits AI developers working on model training, fine-tuning, AI applications, or production monitoring. The free tier may fit smaller projects, while larger teams should note the stated Pro-to-Enterprise transition guideline.