adrien1018 / labml

🧪 Monitor PyTorch & TensorFlow model training from your mobile phone 📱

Organize machine learning experiments and monitor training progress and hardware usage from mobile.

🔥 Features

• Monitor running experiments from mobile phone
• Monitor hardware usage on any computer with a single command
• Integrate with just 2 lines of code (see examples below)
• Keeps track of experiments including infomation like git commit, configurations and hyper-parameters
• Keep Tensorboard logs organized
• Dashboard to locally browse and manage experiment runs
• Save and load checkpoints
• API for custom visualizations
• Pretty logs of training progress
• Open source! we also have a small hosted server for the mobile web app

Installation

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)

Monitoring hardware usage

pip install labml psutil py3nvml
labml monitor

📚 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
• Monitoring hardware usage

🖥 Screenshots

Dashboard

Formatted training loop output

Custom visualizations based on Tensorboard logs

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.ai: A library to organize machine learning experiments},
 year = {2020},
 url = {https://labml.ai/},
}