Update README for stabilization

Author: dzier

README.rst

@@ -18,159 +18,21 @@
 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            3.3.0.dev0
-
 .. 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