labml 0.4.34

Organize Machine Learning Experiments

LabML

LabML lets you monitor AI model training on mobile phones.

You just need to create an experiment, and save stats with tracker. You can obtain a token from LabML App (Githup repo).

from labml import tracker, experiment

with experiment.record(name='sample', exp_conf=conf, web_api: 'TOKEN from web.lab-ml.com'):
    for i in range(50):
        loss, accuracy = train()
        tracker.save(i, {'loss': loss, 'accuracy': accuracy})

It automatically pushes data to Tensorboard, and you can keep your old experiments organized with the LabML Dashboard

All these software is open source, and your logs will be stored locally for Tensorboard and LabML Dashboard. You will only be sending data away for LabML App if you include a token url. This can also be locally installed.

LabML can also do a bunch of other things like keeping track of git commits, handling configurations, hyper-parameters, saving and loading checkpoints, and providing pretty logs.

Installation

pip install labml

Links

💬 Slack workspace for discussions

📗 Documentation

📑 Articles & Tutorials

👨‍🏫 Samples

Citing LabML

If you use LabML for academic research, please cite the library using the following BibTeX entry.

@misc{labml,
 author = {Varuna Jayasiri, Nipun Wijerathne},
 title = {LabML: A library to organize machine learning experiments},
 year = {2020},
 url = {https://lab-ml.com/},
}