The machine learning library enabling "small train", which requires lower machine power and fewer training data.
Table of Contents
- Docs & Community
SmallTrain is a machine learning library running on TensorFlow (and Keras and PyTorch in the future version). It enables small train, which requires lower machine power and fewer training data because you can develop your machine learning(ML) models from our pre-trained models.
- Easy to develop for POC to production
- Almost no programming for building your pre-trained model.
- Available as both TensorFlow and Pytorch wrapper.
- Always adapting to algorithms which evolve.
- Build using state-of-the-art algorithms from Scientific and Mathematical papers
- Accuracy is always going to be better even with minimal data and training time
- Licensed under MIT Open Source
Docs & Community
Standalone Docker Application
this guide for detailed instruction to build SmallTrain as a standalone docker container from source.
Installation is done using the
pip install command:
$ pip install smalltrain
Follow our installing guide for more information.
You can run tutorial codes on Jupyter Lab Notebook.
Launch Jupyter Lab Notebook
# Enable password jupyter notebook password # Run Jupyter Lab Notebook cd /var/smalltrain/tutorials nohup jupyter lab &
For example, a tutorial notebook for image detection is available on http://YOURHOST:JUPYTER_NOTEBOOK_PORT/lab/tree/image_recognition/notebooks/cifar10.ipynb (Default JUPYTER_NOTEBOOK_PORT is 8888).
See Tutorials for more tutorials.