Organize Machine Learning Experiments
Project description
LabML
LabML is a library to track PyTorch experiments.
LabML keeps track of every detail of the experiments: source code, configurations, hyper-parameters, checkpoints, Tensorboard logs and other statistics. LabML saves all these automatically in a clean folder structure.
This is an example usage of Tracker
from labml import monit, tracker
for epoch in monit.loop(50):
for i in monit.iterate("Train", 10):
time.sleep(1e-2)
loss = 50 - epoch + np.random.randint(100) / 100
tracker.save('loss.train', loss)
if (epoch + 1) % 5 == 0:
logger.log()
Here’s the output,
Create an experiment and save the configurations with a couple of lines of codes,
from labml import experiment
experiment.create(name='sin_wave')
experiment.configs(configs)
experiment.start()
View all your experiments locally with Dashboard:
You can also monitor your experiments on Slack. When configured you will be receiving updates like following on a Slack thread. Join our Slack workspace to see samples.
Installation
pip install labml
Links
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/},
}
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