From 5e3756a779aee4b673adbc614b3a0ac96dc94485 Mon Sep 17 00:00:00 2001 From: dzier Date: Thu, 18 Feb 2021 15:47:30 -0800 Subject: [PATCH] Update README and versions for 21.03 branch --- Dockerfile | 6 ++-- README.rst | 94 ++---------------------------------------------------- VERSION | 2 +- 3 files changed, 7 insertions(+), 95 deletions(-) diff --git a/Dockerfile b/Dockerfile index 4688b2b..2220033 100644 --- a/Dockerfile +++ b/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:20.12-py3 +ARG BASE_IMAGE=nvcr.io/nvidia/pytorch:21.03-py3 ############################################################################ ## Install PyProf ############################################################################ FROM $BASE_IMAGE -ARG PYPROF_VERSION=3.9.0dev -ARG PYPROF_CONTAINER_VERSION=21.03dev +ARG PYPROF_VERSION=3.9.0 +ARG PYPROF_CONTAINER_VERSION=21.03 # 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 diff --git a/README.rst b/README.rst index be19fc3..cc3c6d1 100644 --- a/README.rst +++ b/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 3.7.0 and is available on branch** `r20.12 - `_. + **NOTE: You are currently on the r21.03 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 -`_ or `NvProf -`_ 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 `_. - -* 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 3.7.0 and is available in the 20.12 release of -the PyTorch container on `NVIDIA GPU Cloud (NGC) `_. The -branch for this release is `r20.12 -`_. - Quick Installation Instructions ------------------------------- @@ -82,7 +46,7 @@ Quick Installation Instructions * Should display :: - pyprof 3.9.0.dev0 + pyprof 3.9.0 .. 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 -`_, and -provides instructions on how to install and profile with PyProf. - -A complete `Quick Start Guide `_ -provides step-by-step instructions to get you quickly started using PyProf. - -An `FAQ `_ provides -answers for frequently asked questions. - -The `Release Notes -`_ -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 `_. - * `Presentation slides `_. - -Contributing ------------- - -Contributions to PyProf are more than welcome. To -contribute make a pull request and follow the guidelines outlined in -the `Contributing `_ 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 diff --git a/VERSION b/VERSION index 787c78a..a5c4c76 100644 --- a/VERSION +++ b/VERSION @@ -1 +1 @@ -3.9.0dev +3.9.0