update docs

docs/index.rst

-Welcome to tensorpack!
-======================================
+Tensorpack Documentation
+==============================
+
+.. image:: ../.github/tensorpack.png
+
+Tensorpack is a **training interface** based on TensorFlow.
+
+It's Yet Another TF wrapper, but different in:
+
+- Focus on **training speed**.
+
+  - Speed comes for free with tensorpack -- it uses TensorFlow in the
+    **correct way** with no extra overhead. On various CNNs, it runs 1.5~1.7x faster than the equivalent Keras code.
+
+  - Data-parallel multi-GPU training is off-the-shelf to use. It is as fast as Google's
+    `official benchmark <https://www.tensorflow.org/performance/benchmarks>`_.
+    You cannot beat its speed unless you're a TensorFlow expert.
+
+  - See `tensorpack/benchmarks <https://github.com/tensorpack/benchmarks>`_ for some benchmark scripts.
+
+- Focus on large datasets.
+
+  - It's painful to read/preprocess data through TF. Tensorpack helps you load large datasets (e.g. ImageNet) in
+    **pure Python** with autoparallelization.
+
+- It's not a model wrapper.
+
+  - There are already too many symbolic function wrappers.
+    Tensorpack includes only a few common models, but you can use any other wrappers within tensorpack, including sonnet/Keras/slim/tflearn/tensorlayer/....
+
+See :doc:`tutorial/index` to know more about these features:
+
 
 .. toctree::
   :maxdepth: 3

docs/tutorial/intro.rst

@@ -38,7 +38,7 @@ A High Level Glance
   a small set of functions within tensorpack. ``ModelDesc`` is an interface to connect the model with the
   ``InputSource`` interface.
 
-* tensorpack trainers manage the training loops for you.
+* Tensorpack trainers manage the training loops for you.
   They also include data parallel logic for multi-GPU or distributed training.
   At the same time, you have the power of customization through callbacks.
 

docs/tutorial/performance-tuning.md

@@ -6,7 +6,7 @@ Performance is different across machines and tasks.
 So you need to figure out most parts by your own.
 Here's a list of things you can do when your training is slow.
 
-If you're going to open an issue about slow training,
+If you need help improving the speed,
 PLEASE do them and include your findings.
 
 ## Figure out the bottleneck
@@ -33,10 +33,10 @@ A benchmark will give you more precise information about which part you should i
 
 Understand the [Efficient DataFlow](efficient-dataflow.html) tutorial, so you know what your DataFlow is doing.
 
-Benchmark your DataFlow with modifications and you'll understand which part is the bottleneck. Some examples
+Benchmark your DataFlow with modifications to understand which part is the bottleneck. Some examples
 include:
 
-1. Benchmark only raw reader (and perhaps add some prefetching).
+1. Benchmark only raw reader (and perhaps add some parallel prefetching).
 2. Gradually add some pre-processing and see how the performance changes.
 3. Change the number of parallel processes or threads.
 

docs/tutorial/summary.md

@@ -39,7 +39,8 @@ are likely to have too much variance. To address this issue, you can:
 ### Other Logging Data
 
 Besides TensorFlow summaries,
-a callback can also write other data to the monitor backend anytime once the training has started.
+a callback can also write other data to the monitor backend anytime once the training has started,
+by `trainer.monitors.put_xxx`.
 As long as the type of data is supported, the data will be dispatched to and logged to the same place.
 
 As a result, tensorboard will show not only summaries in the graph, but also your custom data.

examples/README.md

 
-# tensorpack examples
+# Tensorpack Examples
 
 Training examples with __reproducible performance__.