Update README and NGC versions post-20.09 release

Author: dzier

README.rst

@@ -17,22 +17,160 @@
 
 PyProf - PyTorch Profiling tool
 ===============================
-
-    **NOTE: You are currently on the r20.09 branch which tracks
-    stabilization towards the release. This branch is not usable
-    during stabilization.**
 
 .. overview-begin-marker-do-not-remove
 
+PyProf is a tool that profiles and analyzes the GPU performance of PyTorch
+models. PyProf aggregates kernel performance from `Nsight Systems
+<https://developer.nvidia.com/nsight-systems>`_ or `NvProf
+<https://developer.nvidia.com/nvidia-visual-profiler>`_.
+
+What's New in 3.4.0
+-------------------
+
+* README and User Guide documentation has been updated with more installation 
+  options and pointers
+
+Known Issues
+------------
+
+* Forward-Backward kernel correlation heuristics do not work correctly with 
+  PyTorch 1.6. Recommended work arounds include:
+
+  * Use with PyTorch 1.5
+  * Use DLProf in the `20.09 NGC Pytorch container <https://ngc.nvidia.com/catalog/containers/nvidia:pytorch>`_
+
+Features
+--------
+
+* Identifies the layer that launched a kernel: e.g. the association of 
+  `ComputeOffsetsKernel` with a concrete PyTorch layer or API is not obvious.
+
+* Identifies the tensor dimensions and precision: without knowing the tensor 
+  dimensions and precision, it's impossible to reason about whether the actual 
+  (silicon) kernel time is close to maximum performance of such a kernel on 
+  the GPU. Knowing the tensor dimensions and precision, we can figure out the 
+  FLOPs and bandwidth required by a layer, and then determine how close to 
+  maximum performance the kernel is for that operation.
+
+* Forward-backward correlation: PyProf determines what the forward pass step 
+  is that resulted in the particular weight and data gradients (wgrad, dgrad), 
+  which makes it possible to determine the tensor dimensions required by these
+  backprop steps to assess their performance.
+ 
+* Determines Tensor Core usage: PyProf can highlight the kernels that use 
+  `Tensor Cores <https://developer.nvidia.com/tensor-cores>`_.
+ 
+* Correlate the line in the user's code that launched a particular kernel (program trace).
+
 .. overview-end-marker-do-not-remove
 
+The current release of PyProf is 3.4.0 and is available in the 20.09 release of
+the PyTorch container on `NVIDIA GPU Cloud (NGC) <https://ngc.nvidia.com>`_. The 
+branch for this release is `r20.09
+<https://github.com/NVIDIA/PyProf/tree/r20.09>`_.
+
+Quick Installation Instructions
+-------------------------------
+
 .. quick-install-start-marker-do-not-remove
 
+* Clone the git repository ::
+    
+    $ git clone https://github.com/NVIDIA/PyProf.git
+
+* Navigate to the top level PyProf directory
+
+* Install PyProf ::
+
+   $ pip install .
+
+* Verify installation is complete with pip list ::
+
+   $ pip list | grep pyprof 
+
+* Should display ::
+
+   pyprof            3.4.0
+
 .. quick-install-end-marker-do-not-remove
 
+Quick Start Instructions
+------------------------
+
 .. quick-start-start-marker-do-not-remove
 
+* Add the following lines to the PyTorch network you want to profile: ::
+
+    import torch.cuda.profiler as profiler
+    import pyprof
+    pyprof.init()
+
+* Profile with NVProf or Nsight Systems to generate a SQL file. ::
+
+    $ nsys profile -f true -o net --export sqlite python net.py
+
+* Run the parse.py script to generate the dictionary. ::
+  
+    $ python -m pyprof.parse net.sqlite > net.dict
+
+* Run the prof.py script to generate the reports. ::
+
+    $ python -m pyprof.prof --csv net.dict
+
 .. quick-start-end-marker-do-not-remove
 
+Documentation
+-------------
+
+The User Guide can be found in the 
+`documentation for current release 
+<https://docs.nvidia.com/deeplearning/frameworks/pyprof-user-guide/index.html>`_, and 
+provides instructions on how to install and profile with PyProf.
+
+A complete `Quick Start Guide <https://docs.nvidia.com/deeplearning/frameworks/pyprof-user-guide/quickstart.html>`_ 
+provides step-by-step instructions to get you quickly started using PyProf.
+
+An `FAQ <https://docs.nvidia.com/deeplearning/frameworks/pyprof-user-guide/faqs.html>`_ provides
+answers for frequently asked questions.
+
+The `Release Notes 
+<https://docs.nvidia.com/deeplearning/frameworks/pyprof-release-notes/index.html>`_
+indicate the required versions of the NVIDIA Driver and CUDA, and also describe 
+which GPUs are supported by PyProf
+
+Presentation and Papers
+^^^^^^^^^^^^^^^^^^^^^^^
+
+* `Automating End-toEnd PyTorch Profiling <https://developer.nvidia.com/gtc/2020/video/s21143>`_.
+   * `Presentation slides <https://developer.download.nvidia.com/video/gputechconf/gtc/2020/presentations/s21143-automating-end-to-end-pytorch-profiling.pdf>`_.
+
+Contributing
+------------
+
+Contributions to PyProf are more than welcome. To
+contribute make a pull request and follow the guidelines outlined in
+the `Contributing <CONTRIBUTING.md>`_ document.
+
+Reporting problems, asking questions
+------------------------------------
+
+We appreciate any feedback, questions or bug reporting regarding this
+project. When help with code is needed, follow the process outlined in
+the Stack Overflow (https://stackoverflow.com/help/mcve)
+document. Ensure posted examples are:
+
+* minimal – use as little code as possible that still produces the
+  same problem
+
+* complete – provide all parts needed to reproduce the problem. Check
+  if you can strip external dependency and still show the problem. The
+  less time we spend on reproducing problems the more time we have to
+  fix it
+
+* verifiable – test the code you're about to provide to make sure it
+  reproduces the problem. Remove all other problems that are not
+  related to your request/question.
+
 .. |License| image:: https://img.shields.io/badge/License-Apache2-green.svg
    :target: http://www.apache.org/licenses/LICENSE-2.0