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Documentation
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README.md
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README.md
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# TRAINS
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## Auto-Magical Experiment Manager & Version Control for AI
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# Allegro Trains
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## Auto-Magical Experiment Manager, Version Control and ML-Ops for AI
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## :confetti_ball: Now with Full ML/DL DevOps - See [TRAINS AGENT](https://github.com/allegroai/trains-agent) and [Services](https://github.com/allegroai/trains-server#trains-agent-services--)
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## :station: [Documentation is here!](https://allegro.ai/docs) `wubba lubba dub dub` and a [Slack Channel](https://join.slack.com/t/allegroai-trains/shared_invite/enQtOTQyMTI1MzQxMzE4LTY5NTUxOTY1NmQ1MzQ5MjRhMGRhZmM4ODE5NTNjMTg2NTBlZGQzZGVkMWU3ZDg1MGE1MjQxNDEzMWU2NmVjZmY) :train2:
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### :point_right: Help improve Trains by filling our 2-min [user survey](https://allegro.ai/lp/trains-user-survey/)
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TRAINS is our solution to a problem we share with countless other researchers and developers in the machine
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Trains is our solution to a problem we share with countless other researchers and developers in the machine
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learning/deep learning universe: Training production-grade deep learning models is a glorious but messy process.
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TRAINS tracks and controls the process by associating code version control, research projects,
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Trains tracks and controls the process by associating code version control, research projects,
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performance metrics, and model provenance.
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We designed TRAINS specifically to require effortless integration so that teams can preserve their existing methods
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We designed Trains specifically to require effortless integration so that teams can preserve their existing methods
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and practices. Use it on a daily basis to boost collaboration and visibility, or use it to automatically collect
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your experimentation logs, outputs, and data to one centralized server.
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### :steam_locomotive: [Getting Started Tutorial](https://allegro.ai/blog/setting-up-allegro-ai-platform/) :rocket:
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**You can try out TRAINS and [test your code](#integrate-trains), with no additional setup.**
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**You can try out Trains and [test your code](#integrate-trains), with no additional setup.**
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<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>
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## TRAINS Automatically Logs Everything
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## Trains Automatically Logs Everything
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**With only two lines of code, this is what you are getting:**
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* Git repository, branch, commit id, entry point and local git diff
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* Seamless integration (including version control) with **Jupyter Notebook**
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and [*PyCharm* remote debugging](https://github.com/allegroai/trains-pycharm-plugin)
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**Additionally, log data explicitly using [TRAINS Explicit Logging](https://github.com/allegroai/trains/blob/master/docs/logger.md).**
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**Additionally, log data explicitly using [Trains Explicit Logging](https://github.com/allegroai/trains/blob/master/docs/logger.md).**
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## Using TRAINS <a name="using-trains"></a>
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## Using Trains <a name="using-trains"></a>
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TRAINS is a two part solution:
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Trains is a two part solution:
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1. TRAINS [python package](https://pypi.org/project/trains/) auto-magically connects with your code
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1. Trains [python package](https://pypi.org/project/trains/) auto-magically connects with your code
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**TRAINS requires only two lines of code for full integration.**
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**Trains requires only two lines of code for full integration.**
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To connect your code with TRAINS:
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To connect your code with Trains:
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- Install TRAINS <a name="integrate-trains"></a>
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- Install Trains <a name="integrate-trains"></a>
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pip install trains
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<details>
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* If project_name is not provided, the repository name will be used instead
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* If task_name (experiment) is not provided, the current filename will be used instead
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- Run your code. When TRAINS connects to the server, a link is printed. For example
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- Run your code. When Trains connects to the server, a link is printed. For example
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TRAINS Results page:
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Trains Results page:
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https://demoapp.trains.allegro.ai/projects/76e5e2d45e914f52880621fe64601e85/experiments/241f06ae0f5c4b27b8ce8b64890ce152/output/log
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- Open the link and view your experiment parameters, model and tensorboard metrics
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**See examples [here](https://github.com/allegroai/trains/tree/master/examples)**
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2. [TRAINS-server](https://github.com/allegroai/trains-server) for logging, querying, control and UI ([Web-App](https://github.com/allegroai/trains-web))
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2. [Trains Server](https://github.com/allegroai/trains-server) for logging, querying, control and UI ([Web-App](https://github.com/allegroai/trains-web))
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**We already have a demo server up and running for you at [https://demoapp.trains.allegro.ai](https://demoapp.trains.allegro.ai).**
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**You can try out TRAINS without the need to install your own *trains-server*, just add the two lines of code, and it will automatically connect to the TRAINS demo-server.**
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**You can try out Trains without the need to install your own *trains-server*, just add the two lines of code, and it will automatically connect to the Trains demo-server.**
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*Note that the demo server resets every 24 hours and all of the logged data is deleted.*
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When you are ready to use your own TRAINS server, go ahead and [install *TRAINS-server*](https://github.com/allegroai/trains-server).
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When you are ready to use your own Trains server, go ahead and [install *trains-server*](https://github.com/allegroai/trains-server).
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<img src="https://github.com/allegroai/trains/blob/master/docs/system_diagram.png?raw=true" width="50%">
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## Configuring Your Own TRAINS server <a name="configuration"></a>
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## Configuring Your Own Trains server <a name="configuration"></a>
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1. Install and run *TRAINS-server* (see [Installing the TRAINS Server](https://github.com/allegroai/trains-server))
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1. Install and run *trains-server* (see [Installing the Trains Server](https://github.com/allegroai/trains-server))
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2. Run the initial configuration wizard for your TRAINS installation and follow the instructions to setup TRAINS package
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2. Run the initial configuration wizard for your Trains installation and follow the instructions to setup Trains package
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(http://**_trains-server-ip_**:__port__ and user credentials)
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trains-init
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## Who We Are
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TRAINS is supported by the same team behind *allegro.ai*,
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Trains is supported by the same team behind *allegro.ai*,
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where we build deep learning pipelines and infrastructure for enterprise companies.
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We built TRAINS to track and control the glorious but messy process of training production-grade deep learning models.
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We are committed to vigorously supporting and expanding the capabilities of TRAINS.
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We built Trains to track and control the glorious but messy process of training production-grade deep learning models.
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We are committed to vigorously supporting and expanding the capabilities of Trains.
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## Why Are We Releasing TRAINS?
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## Why Are We Releasing Trains?
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We believe TRAINS is ground-breaking. We wish to establish new standards of experiment management in
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We believe Trains is ground-breaking. We wish to establish new standards of experiment management in
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deep-learning and ML. Only the greater community can help us do that.
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We promise to always be backwardly compatible. If you start working with TRAINS today,
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We promise to always be backwardly compatible. If you start working with Trains today,
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even though this project is currently in the beta stage, your logs and data will always upgrade with you.
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## License
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@ -154,7 +154,7 @@ Additionally, you can always find us at *trains@allegro.ai*
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## Contributing
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See the TRAINS [Guidelines for Contributing](https://github.com/allegroai/trains/blob/master/docs/contributing.md).
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See the Trains [Guidelines for Contributing](https://github.com/allegroai/trains/blob/master/docs/contributing.md).
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_May the force (and the goddess of learning rates) be with you!_
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