From ab4dc5a28ac4daeefc4f9d490433a9007d897828 Mon Sep 17 00:00:00 2001 From: dzier Date: Fri, 14 Aug 2020 13:50:35 -0700 Subject: [PATCH] Update README for stabilization --- README.rst | 146 ++--------------------------------------------------- 1 file changed, 4 insertions(+), 142 deletions(-) diff --git a/README.rst b/README.rst index 540234b..34a4bd5 100644 --- a/README.rst +++ b/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 -`_ or `NvProf -`_. - -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 `_ - -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 3.4.0 and is available in the 20.09 release of -the PyTorch container on `NVIDIA GPU Cloud (NGC) `_. The -branch for this release is `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 -`_, 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