Update README and versions for 21.04 branch

Dockerfile

@@ -12,15 +12,15 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-ARG BASE_IMAGE=nvcr.io/nvidia/pytorch:21.02-py3
+ARG BASE_IMAGE=nvcr.io/nvidia/pytorch:21.04-py3
 
 ############################################################################
 ## Install PyProf
 ############################################################################
 FROM $BASE_IMAGE
 
-ARG PYPROF_VERSION=3.10.0dev
-ARG PYPROF_CONTAINER_VERSION=21.04dev
+ARG PYPROF_VERSION=3.10.0
+ARG PYPROF_CONTAINER_VERSION=21.04
 
 # Copy entire repo into container even though some is not needed for the 
 # build itself... because we want to be able to copyright check on 
@@ -37,4 +37,4 @@ RUN pip install --no-cache-dir .
 RUN cp VERSION qa/L0_docs/. && \
     cp README.rst qa/L0_docs/. && \
     cp -r docs qa/L0_docs/.
-
+

README.rst

@@ -18,49 +18,13 @@
 PyProf - PyTorch Profiling tool
 ===============================
 
-    **ANNOUNCEMENT: The default branch for PyProf has changed to 'main'. Please
-    update all pulls and PRs accordingly.**
-
-    **LATEST RELEASE: You are currently working on the main branch which
-    tracks under-development progress towards the next release. The
-    latest release of the PyProf is 3.8.0 and is available on branch** `r21.02
-    <https://github.com/NVIDIA/PyProf/blob/r21.02>`_.
+    **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:
-
-* 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 3.8.0 and is available in the 21.02 release of
-the PyTorch container on `NVIDIA GPU Cloud (NGC) <https://ngc.nvidia.com>`_. The 
-branch for this release is `r21.02
-<https://github.com/NVIDIA/PyProf/tree/r21.02>`_.
-
 Quick Installation Instructions
 -------------------------------
 
@@ -82,7 +46,7 @@ Quick Installation Instructions
 
 * Should display ::
 
-   pyprof            3.10.0.dev0
+   pyprof            3.10.0
 
 .. quick-install-end-marker-do-not-remove
 
@@ -111,57 +75,5 @@ 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

VERSION

@@ -1 +1 @@
-3.10.0dev
+3.10.0