Awesome Open Source
Awesome Open Source

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

PyPI - Python Version PyPI Status Join Slack Docs Twitter

🔥 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

📚 Documentation

🖥 Screenshots

Dashboard

Dashboard Screenshot

Formatted training loop output

Sample Logs

Custom visualizations based on Tensorboard logs

Analytics

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/},
}

Get A Weekly Email With Trending Projects For These Topics
No Spam. Unsubscribe easily at any time.
jupyter-notebook (6,020) 
deep-learning (3,847) 
machine-learning (3,526) 
pytorch (2,274) 
tensorflow (2,131) 
visualization (794) 
keras (755) 
mobile (478) 
analytics (314) 
tensorflow2 (91) 
keras-tensorflow (68) 
tensorboard (43) 
experiment (32) 

Find Open Source By Browsing 7,000 Topics Across 59 Categories

Advertising 📦 10
All Projects
Application Programming Interfaces 📦 124
Applications 📦 192
Artificial Intelligence 📦 78
Blockchain 📦 73
Build Tools 📦 113
Cloud Computing 📦 80
Code Quality 📦 28
Collaboration 📦 32
Command Line Interface 📦 49
Community 📦 83
Companies 📦 60
Compilers 📦 63
Computer Science 📦 80
Configuration Management 📦 42
Content Management 📦 175
Control Flow 📦 213
Data Formats 📦 78
Data Processing 📦 276
Data Storage 📦 135
Economics 📦 64
Frameworks 📦 215
Games 📦 129
Graphics 📦 110
Hardware 📦 152
Integrated Development Environments 📦 49
Learning Resources 📦 166
Legal 📦 29
Libraries 📦 129
Lists Of Projects 📦 22
Machine Learning 📦 347
Mapping 📦 64
Marketing 📦 15
Mathematics 📦 55
Media 📦 239
Messaging 📦 98
Networking 📦 315
Operating Systems 📦 89
Operations 📦 121
Package Managers 📦 55
Programming Languages 📦 245
Runtime Environments 📦 100
Science 📦 42
Security 📦 396
Social Media 📦 27
Software Architecture 📦 72
Software Development 📦 72
Software Performance 📦 58
Software Quality 📦 133
Text Editors 📦 49
Text Processing 📦 136
User Interface 📦 330
User Interface Components 📦 514
Version Control 📦 30
Virtualization 📦 71
Web Browsers 📦 42
Web Servers 📦 26
Web User Interface 📦 210