Documentation

README.md

@@ -23,16 +23,20 @@ your experimentation logs, outputs, and data to one centralized server.
 ## TRAINS Automatically Logs Everything
 **With only two lines of code, this is what you are getting:**
 
-* Git repository, branch, commit id and entry point (git diff coming soon)
-    * Hyper-parameters, including
+* Git repository, branch, commit id, entry point and local git diff
+* 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
-    * Tensorflow Defines (absl-py)
-* Explicit parameters dictionary
+    * 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
+* 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).**
 
@@ -42,6 +46,7 @@ your experimentation logs, outputs, and data to one centralized server.
 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:
@@ -64,6 +69,8 @@ TRAINS is a two part solution:
             https://demoapp.trainsai.io/projects/76e5e2d45e914f52880621fe64601e85/experiments/241f06ae0f5c4b27b8ce8b64890ce152/output/log
 
     - 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))
 

docs/brief.md

@@ -17,7 +17,7 @@ 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*, 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))
     and *PyCharm* remote debugging (see [trains-pycharm-plugin](https://github.com/allegroai/trains-pycharm-plugin))
 * Log everything. Experiments become truly repeatable
@@ -38,16 +38,17 @@ your experimentation logs, outputs, and data to one centralized server.
 
 ## TRAINS Automatically Logs
 
-* Git repository, branch, commit id and entry point (git diff coming soon)
-    * Hyper-parameters, including
+* 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
-    * Tensorflow Defines (absl-py)
-* Explicit parameters dictionary
+    * Explicit parameters dictionary
+* Tensorflow Defines (absl-py)
 * Initial model weights file
 * Model snapshots
-* stdout and stderr
+* StdOut and StdErr
 * Tensorboard/TensorboardX scalars, metrics, histograms, images (with audio coming soon)
-* Matplotlib
+* Matplotlib & Seaborn
 
 ## How TRAINS Works