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README.md
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@ -10,17 +10,17 @@
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
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
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
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.
(See TRAINS live at [https://demoapp.trainsai.io](https://demoapp.trainsai.io))
@ -29,17 +29,17 @@ your experimentation logs, outputs, and data to one centralized server.
## Main Features
TRAINS is our solution to a problem we shared 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.
TRAINS tracks and controls the process by associating code version control, research projects,
performance metrics, and model provenance.
TRAINS is our solution to a problem we shared 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.
TRAINS tracks and controls the process by associating code version control, research projects,
performance metrics, and model provenance.
* Start today!
* TRAINS is free and open-source
* TRAINS requires only two lines of code for full integration
* Use it with your favorite tools
* Seamless integration with leading frameworks, including: *PyTorch*, *TensorFlow*, *Keras*, and others coming soon
* Support for *Jupyter Notebook* (see [trains-jupyter-plugin](https://github.com/allegroai/trains-jupyter-plugin))
* 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**
@ -73,7 +73,7 @@ performance metrics, and model provenance.
## See for Yourself
We have a demo server up and running at https://demoapp.trainsai.io. You can try out TRAINS and test your code with it.
We have a demo server up and running at https://demoapp.trainsai.io. You can try out TRAINS and test your code with it.
Note that it resets every 24 hours and all of the data is deleted.
Connect your code with TRAINS:
@ -103,7 +103,7 @@ TRAINS is composed of the following:
* [Web-App](https://github.com/allegroai/trains-web) (web user interface)
* Python SDK (auto-magically connects your code, see [Using TRAINS](#using-trains-example))
The following diagram illustrates the interaction of the [trains-server](https://github.com/allegroai/trains-server)
The following diagram illustrates the interaction of the [trains-server](https://github.com/allegroai/trains-server)
and a GPU training machine using TRAINS
![Alt Text](https://github.com/allegroai/trains/blob/master/docs/system_diagram.png?raw=true)
@ -117,12 +117,12 @@ and a GPU training machine using TRAINS
pip install trains
3. Run the initial configuration wizard and follow the instructions to setup TRAINS package
3. Run the initial configuration wizard and follow the instructions to setup TRAINS package
(http://**_trains-server ip_**:__port__ and user credentials)
trains-init
After installing and configuring, you can access your configuration file at `~/trains.conf`.
After installing and configuring, you can access your configuration file at `~/trains.conf`.
View a sample configuration file [here](https://github.com/allegroai/trains/blob/master/docs/trains.conf).
## Using TRAINS
@ -149,18 +149,18 @@ For more examples and use cases, see [examples](https://github.com/allegroai/tra
## Who Supports TRAINS?
TRAINS is supported by the same team behind *allegro.ai*,
TRAINS is supported by the same team behind *allegro.ai*,
where we build deep learning pipelines and infrastructure for enterprise companies.
We built TRAINS to track and control the glorious but messy process of training production-grade deep learning models.
We are committed to vigorously supporting and expanding the capabilities of TRAINS.
We built TRAINS to track and control the glorious but messy process of training production-grade deep learning models.
We are committed to vigorously supporting and expanding the capabilities of TRAINS.
## Why Are We Releasing TRAINS?
We believe TRAINS is ground-breaking. We wish to establish new standards of experiment management in
We believe TRAINS is ground-breaking. We wish to establish new standards of experiment management in
deep-learning and ML. Only the greater community can help us do that.
We promise to always be backwardly compatible. If you start working with TRAINS today,
We promise to always be backwardly compatible. If you start working with TRAINS today,
even though this project is currently in the beta stage, your logs and data will always upgrade with you.
## License