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

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πŸ”₯ Features

  • Monitor running experiments from mobile phone View Run
  • 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 Open In Colab Open In Colab
  • 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

πŸ“š Documentation

πŸ–₯ Screenshots

Dashboard

Dashboard Screenshot

Formatted training loop output

Sample Logs

Custom visualizations based on Tensorboard logs

Analytics

Links

πŸ’¬ Slack workspace for discussions

πŸ“— Documentation

πŸ‘¨β€πŸ« 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/},
}