clearml/docs/brief.md

# What is TRAINS?
Behind every great scientist are great repeatable methods. Sadly, this is easier said than done.

When talented scientists, engineers, or developers work on their own, a mess may be unavoidable.
Yet, it may still be manageable. However, with time and more people joining your project, managing the clutter takes
its toll on productivity. As your project moves toward production, visibility and provenance for scaling your
deep-learning efforts are a must.

For teams or entire companies, TRAINS logs everything in one central server and takes on the responsibilities for
visibility and provenance so productivity does not suffer. TRAINS records and manages various deep learning
research workloads and does so with practically zero integration costs.

We designed TRAINS specifically to require effortless integration so that teams can preserve their existing methods
and practices. Use it on a daily basis to boost collaboration and visibility, or use it to automatically collect
your experimentation logs, outputs, and data to one centralized server.

## Main Features

* Integrate with your current work flow with minimal effort
    * Seamless integration with leading frameworks, including: *PyTorch*, *TensorFlow*, *Keras*, *XGBoost*, *SciKit-Learn* and others coming soon
    * Support for *Jupyter Notebook* (see [trains-jupyter-plugin](https://github.com/allegroai/trains-jupyter-plugin))
    and *PyCharm* remote debugging (see [trains-pycharm-plugin](https://github.com/allegroai/trains-pycharm-plugin))
* Log everything. Experiments become truly repeatable
    * Model logging with **automatic association** of **model + code + parameters + initial weights**
    * Automatically create a copy of models on centralized storage 
    ([supports shared folders, S3, GS,](https://github.com/allegroai/trains/blob/master/docs/faq.md#i-read-there-is-a-feature-for-centralized-model-storage-how-do-i-use-it-) and Azure is coming soon!)
* Share and collaborate
    * Multi-user process tracking and collaboration
    * Centralized server for aggregating logs, records, and general bookkeeping
* Increase productivity
    * Comprehensive **experiment comparison**: code commits, initial weights, hyper-parameters and metric results
* Order & Organization
    * Manage and organize your experiments in projects
    * Query capabilities; sort and filter experiments by results metrics
* And more
    * Stop an experiment on a remote machine using the web-app
    * A field-tested, feature-rich SDK for your on-the-fly customization needs

## TRAINS Automatically Logs

* Git repository, branch, commit id, entry point and local git diff
* Python packages (including specific version) 
* Hyper-parameters 
    * ArgParser for command line parameters with currently used values
    * Explicit parameters dictionary
* Tensorflow Defines (absl-py)
* Initial model weights file
* Model snapshots
* StdOut and StdErr
* Tensorboard/TensorboardX scalars, metrics, histograms, images (with audio coming soon)
* Matplotlib & Seaborn

## How TRAINS Works

TRAINS is a two part solution:

1. TRAINS [python package](https://pypi.org/project/trains/) (auto-magically connects your code, see [Using TRAINS](https://github.com/allegroai/trains#using-trains))
2. [TRAINS-server](https://github.com/allegroai/trains-server) for logging, querying, control and UI ([Web-App](https://github.com/allegroai/trains-web))

The following diagram illustrates the interaction of the [TRAINS-server](https://github.com/allegroai/trains-server)
and a GPU training machine using the TRAINS python package

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