Skip to content

Commit

Permalink
Update README for stabilization
Browse files Browse the repository at this point in the history
  • Loading branch information
@dzier
dzier committed Aug 14, 2020
1 parent 662ddc0 commit ab4dc5a
Showing 1 changed file with 4 additions and 142 deletions.
146 changes: 4 additions & 142 deletions README.rst
View file
Original file line number Diff line number Diff line change
Expand Up @@ -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

0 comments on commit ab4dc5a

Please sign in to comment.