labml.ai

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 information 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})

TensorFlow 2.0 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)

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)

Monitoring hardware usage

pip install labml psutil py3nvml
labml monitor