nhduong/labml

🧪 Monitor PyTorch & TensorFlow model training on mobile phones

LabML

Organize machine learning experiments and monitor training progress from mobile.

You can install this package using PIP.

pip install labml

PyTorch example

from labml import tracker, experiment

with experiment.record(name='sample', exp_conf=conf):
    for i in range(50):
        loss, accuracy = train()
        tracker.save(i, {'loss': loss, 'accuracy': accuracy})

PyTorch Lightning example

from labml import experiment
from labml.utils.lightning import LabMLLightningLogger

trainer = pl.Trainer(gpus=1, max_epochs=5, progress_bar_refresh_rate=20, logger=LabMLLightningLogger())

with experiment.record(name='sample', exp_conf=conf, disable_screen=True):
    trainer.fit(model, data_loader)

TensorFlow 2.X Keras example

from labml import experiment
from labml.utils.keras import LabMLKerasCallback

with experiment.record(name='sample', exp_conf=conf):
    for i in range(50):
        model.fit(x_train, y_train, epochs=conf['epochs'], validation_data=(x_test, y_test),
                  callbacks=[LabMLKerasCallback()], verbose=None)

🔥 Features

• Monitor running experiments from mobile phone
• Keeps track of experiments including infomation like git commit, configurations and hyper-parameters
• Keep Tensorboard logs organized
• Dashboard) to browse and manage experiment runs
• Save and load checkpoints
• API for custom visualizations
• Pretty training loop logs
• Open source! we also have a small hosted server for the mobile web app

📚 Documentation

• API to create experiments
• Track training metrics
• Monitored training loop and other iterators
• API for custom visualizations
• Configurations management API
• Logger for stylized logging

🖥 Screenshots

Dashboard

Formatted training loop output

Custom visualizations

Links

💬 Slack workspace for discussions

📗 Documentation

👨‍🏫 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/},
}