labml 0.4.85

Organize Machine Learning Experiments

LabML

LabML lets you monitor AI model training on mobile phones.

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)

You can read the guides about creating an experiment, and saving statistics with tracker for details.

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

All these software is 100% open source. By default, the library will send experiment data to our hosted server web.lab-ml.com you can run this on your own too.

LabML also keeps track of git commits, handle configurations, hyper-parameters, save and load checkpoints, and providing pretty logs.

We also have an API to create custom visualizations from artifacts and logs on Jupyter notebooks.

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/},
}