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README.md
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@ -23,16 +23,20 @@ your experimentation logs, outputs, and data to one centralized server.
## TRAINS Automatically Logs Everything ## TRAINS Automatically Logs Everything
**With only two lines of code, this is what you are getting:** **With only two lines of code, this is what you are getting:**
* Git repository, branch, commit id and entry point (git diff coming soon) * Git repository, branch, commit id, entry point and local git diff
* Hyper-parameters, including * Python packages (including specific version)
* StdOut and StdErr
* 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))
* Hyper-parameters
* ArgParser for command line parameters with currently used values * ArgParser for command line parameters with currently used values
* Tensorflow Defines (absl-py) * Explicit parameters dictionary
* Explicit parameters dictionary * Tensorflow Defines (absl-py)
* Initial model weights file * Initial model weights file
* Model snapshots * Model snapshots
* stdout and stderr
* Tensorboard/TensorboardX scalars, metrics, histograms, images (with audio coming soon) * Tensorboard/TensorboardX scalars, metrics, histograms, images (with audio coming soon)
* Matplotlib & Seaborn * Matplotlib & Seaborn
* Tensorflow, PyTorch, Keras, XGBoost and Scikit-Learn are supported (MxNet is coming soon)
**Detailed overview of TRAINS offering and system design can be found [Here](https://github.com/allegroai/trains/blob/master/docs/brief.md).** **Detailed overview of TRAINS offering and system design can be found [Here](https://github.com/allegroai/trains/blob/master/docs/brief.md).**
@ -42,6 +46,7 @@ your experimentation logs, outputs, and data to one centralized server.
TRAINS is a two part solution: 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)) 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.** **TRAINS requires only two lines of code for full integration.**
To connect your code with TRAINS: To connect your code with TRAINS:
@ -64,6 +69,8 @@ TRAINS is a two part solution:
https://demoapp.trainsai.io/projects/76e5e2d45e914f52880621fe64601e85/experiments/241f06ae0f5c4b27b8ce8b64890ce152/output/log https://demoapp.trainsai.io/projects/76e5e2d45e914f52880621fe64601e85/experiments/241f06ae0f5c4b27b8ce8b64890ce152/output/log
- Open the link and view your experiment parameters, model and tensorboard metrics - Open the link and view your experiment parameters, model and tensorboard metrics
See full examples [here](https://github.com/allegroai/trains/tree/master/examples)
2. [TRAINS-server](https://github.com/allegroai/trains-server) for logging, querying, control and UI ([Web-App](https://github.com/allegroai/trains-web)) 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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docs/brief.md
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## Main Features ## Main Features
* Integrate with your current work flow with minimal effort * Integrate with your current work flow with minimal effort
* Seamless integration with leading frameworks, including: *PyTorch*, *TensorFlow*, *Keras*, and others coming soon * 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)) * 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)) and *PyCharm* remote debugging (see [trains-pycharm-plugin](https://github.com/allegroai/trains-pycharm-plugin))
* Log everything. Experiments become truly repeatable * Log everything. Experiments become truly repeatable
@ -38,16 +38,17 @@ your experimentation logs, outputs, and data to one centralized server.
## TRAINS Automatically Logs ## TRAINS Automatically Logs
* Git repository, branch, commit id and entry point (git diff coming soon) * Git repository, branch, commit id, entry point and local git diff
* Hyper-parameters, including * Python packages (including specific version)
* Hyper-parameters
* ArgParser for command line parameters with currently used values * ArgParser for command line parameters with currently used values
* Tensorflow Defines (absl-py) * Explicit parameters dictionary
* Explicit parameters dictionary * Tensorflow Defines (absl-py)
* Initial model weights file * Initial model weights file
* Model snapshots * Model snapshots
* stdout and stderr * StdOut and StdErr
* Tensorboard/TensorboardX scalars, metrics, histograms, images (with audio coming soon) * Tensorboard/TensorboardX scalars, metrics, histograms, images (with audio coming soon)
* Matplotlib * Matplotlib & Seaborn
## How TRAINS Works ## How TRAINS Works