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@ -20,48 +20,64 @@ your experimentation logs, outputs, and data to one centralized server.
(Experience TRAINS live at [https://demoapp.trainsai.io](https://demoapp.trainsai.io))
![Alt Text](https://github.com/allegroai/trains/blob/master/docs/webapp_screenshots.gif?raw=true)
## Main Features
## TRAINS Automatically Logs Everything
**With only two lines of code, this is what you are getting:**
* Seamless integration with leading frameworks, including: *PyTorch*, *TensorFlow*, *Keras*, and others coming soon
* Support for *Jupyter Notebook* and *PyCharm* remote debugging
* Automatic log collection.
* Query, Filter, and Compare your experiment data and results
* Share and collaborate
* Git repository, branch, commit id and entry point (git diff coming soon)
* Hyper-parameters, including
* ArgParser for command line parameters with currently used values
* Tensorflow Defines (absl-py)
* Explicit parameters dictionary
* Initial model weights file
* Model snapshots
* stdout and stderr
* Tensorboard/TensorboardX scalars, metrics, histograms, images (with audio coming soon)
* Matplotlib & Seaborn
**Detailed overview of TRAINS offering and system design can be found [Here](https://github.com/allegroai/trains/blob/master/docs/brief.md).**
## Using TRAINS
We have a demo server up and running at https://demoapp.trainsai.io. You can try out TRAINS and test your code with it.
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))
**TRAINS requires only two lines of code for full integration.**
To connect your code with TRAINS:
- Install TRAINS
pip install trains
- Add the following lines to your code
from trains import Task
task = Task.init(project_name="my project", task_name="my task")
* If project_name is not provided, the repository name will be used instead
* If task_name (experiment) is not provided, the current filename will be used instead
- Run your code. When TRAINS connects to the server, a link is printed. For example
TRAINS Results page:
https://demoapp.trainsai.io/projects/76e5e2d45e914f52880621fe64601e85/experiments/241f06ae0f5c4b27b8ce8b64890ce152/output/log
- Open the link and view your experiment parameters, model and tensorboard metrics
2. [TRAINS-server](https://github.com/allegroai/trains-server) for logging, querying, control and UI ([Web-App](https://github.com/allegroai/trains-web))
We have a demo server up and running at [https://demoapp.trainsai.io](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.
When you are ready to use your own TRAINS server, go ahead and [install *TRAINS-server*](#configuring-your-own-trains).
TRAINS requires only two lines of code for full integration.
System diagram of [TRAINS](https://github.com/allegroai/trains-server) in action
To connect your code with TRAINS:
<img src="https://github.com/allegroai/trains/blob/master/docs/system_diagram.png?raw=true" width="30%">
1. Install TRAINS
pip install trains
2. Add the following lines to your code
from trains import Task
task = Task.init(project_name="my project", task_name="my task")
* If project_name is not provided, the repository name will be used instead
* If task_name (experiment) is not provided, the current filename will be used instead
3. Run your code. When TRAINS connects to the server, a link is printed. For example
TRAINS Results page:
https://demoapp.trainsai.io/projects/76e5e2d45e914f52880621fe64601e85/experiments/241f06ae0f5c4b27b8ce8b64890ce152/output/log
4. Open the link and view your experiment parameters, model and tensorboard metrics
## Configuring Your Own TRAINS
## Configuring Your Own TRAINS server
1. Install and run *TRAINS-server* (see [Installing the TRAINS Server](https://github.com/allegroai/trains-server))
@ -112,4 +128,3 @@ See the TRAINS [Guidelines for Contributing](https://github.com/allegroai/trains
_May the force (and the goddess of learning rates) be with you!_