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[![PyPI version shields.io](https://img.shields.io/pypi/v/trains.svg)](https://img.shields.io/pypi/v/trains.svg)
[![PyPI status](https://img.shields.io/pypi/status/trains.svg)](https://pypi.python.org/pypi/trains/)
### Help improve Trains by filling our 2-min [user survey](https://allegro.ai/lp/trains-user-survey/)
### :point_right: Help improve Trains by filling our 2-min [user survey](https://allegro.ai/lp/trains-user-survey/)
TRAINS is our solution to a problem we share with countless other researchers and developers in the machine
learning/deep learning universe: Training production-grade deep learning models is a glorious but messy process.
@ -25,6 +25,8 @@ your experimentation logs, outputs, and data to one centralized server.
**We have a demo server up and running at [https://demoapp.trains.allegro.ai](https://demoapp.trains.allegro.ai).**
### :steam_locomotive: [Getting Started Tutorial](https://allegro.ai/blog/setting-up-allegro-ai-platform/) :rocket:
**You can try out TRAINS and [test your code](#integrate-trains), with no additional setup.**
<a href="https://demoapp.trains.allegro.ai"><img src="https://github.com/allegroai/trains/blob/master/docs/webapp_screenshots.gif?raw=true" width="100%"></a>
@ -42,7 +44,7 @@ your experimentation logs, outputs, and data to one centralized server.
* Initial model weights file
* Model snapshots (With optional automatic upload to central storage: Shared folder, S3, GS, Azure, Http)
* Artifacts log & store (Shared folder, S3, GS, Azure, Http)
* Tensorboard/TensorboardX scalars, metrics, histograms, images (with audio coming soon)
* Tensorboard/TensorboardX scalars, metrics, histograms, **images, audio and video**
* Matplotlib & Seaborn
* Supported frameworks: Tensorflow, PyTorch, Keras, XGBoost and Scikit-Learn (MxNet is coming soon)
* Seamless integration (including version control) with **Jupyter Notebook**