Update README and docs for 21.04 release

README.rst

@@ -18,11 +18,42 @@
 PyProf - PyTorch Profiling tool
 ===============================
 
- **NOTE: You are currently on the 21.04 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>`_ and provides the 
+following additional features:
+
+What's New in 3.10.0
+-------------------
+
+* Compatibility with NGC 21.04 release
+
+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
 
 Quick Installation Instructions
@@ -75,5 +106,57 @@ Quick Start Instructions
 
 .. 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

docs/install.rst

@@ -48,6 +48,6 @@ the most recent version of CUDA, Docker, and nvidia-docker.
 After performing the above setup, you can pull the PyProf container
 using the following command::
 
- docker pull nvcr.io/nvidia/pytorch:20.12-py3
+ docker pull nvcr.io/nvidia/pytorch:21.04-py3
 
-Replace *20.12* with the version of PyTorch container that you want to pull.
+Replace *21.04* with the version of PyTorch container that you want to pull.

docs/quickstart.rst

@@ -39,7 +39,7 @@ Prerequisites
  drop down button. After cloning the repo be sure to select the r<xx.yy> 
  release branch that corresponds to the version of PyProf want to use::
 
- $ git checkout r20.12
+ $ git checkout r21.04
 
 * If you are starting with a pre-built NGC container, you will need to install 
  Docker and nvidia-docker. For DGX users, see `Preparing to use NVIDIA Containers
@@ -75,7 +75,7 @@ the GitHub repo and checkout the release version of the branch that
 you want to build (or the `main` branch if you want to build the
 under-development version)::
 
- $ git checkout r20.12
+ $ git checkout r21.04
 
 Then use docker to build::