GitLab

Fix cuda 11.6 and 11.7

Check nvjpeg from CONDA Path

More cuda11.6

Trying to fix the build

Trying to fix the build

Removing nvjpeg logic

Add NvToolsExt dll path

Fix lint

* [BE] Unify version computation

Instead of hardcoding dev version in various script, use one from
`version.txt` if `setup_build_version` is called without arguments

Also, pass `--pre` option to M1 build/test pip install commands to build
TorchVision against nightly pytorch

* Pin torchvision dependency to a specific pytorch version

* remove explicit install of Pillow throughout CI

* explicitly install zlib in windows conda env

* check pillow on Windows

* move check into conda env

* fix

* more debug output

* more debug

* add debug also to linux

* retry w/ and w/o packaging third party library

* tag debug

* more debug

* pin setuptools < 61

* install setuptools from pip

* cleanup

* try remove one more
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

To mitigate missing `libcupti.so` dependency

* bump version

Otherwise, it `setuptools.py`  will try to install latest, which is not compatible with Python runtime older than 3.8

Fixes https://github.com/pytorch/vision/issues/4985

Co-authored-by: Philip Meier <github.pmeier@posteo.de>

* Move TorchVision conda package to use pytorch-mutex

This is follow up after https://github.com/pytorch/builder/pull/823

 that gets rids of `feature` and migrate it to `run_constrained`

* Update packaging/torchvision/meta.yaml
Co-authored-by: Eli Uriegas <1700823+seemethere@users.noreply.github.com>

* Update packaging/torchvision/meta.yaml
Co-authored-by: Eli Uriegas <1700823+seemethere@users.noreply.github.com>
Co-authored-by: Eli Uriegas <1700823+seemethere@users.noreply.github.com>

* add cuda envs

* packaging doesn't need cuda env now

* fix cmake_windows_gpu

* minor fix

* rename suffix

* minor change

* update vs integration

* Add deprecation warnings on vision::models

* Change the C++ example.

* Chage readme.

* Update deprecation warning.

* fix MacOS cmake workflow

* try only mkl

* only pin mkl on MacOs

* fix
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* Fix inconsistent version pinning across yaml files.

* Pin pillow to >=5.3.0

* Pin pillow>=5.3.0 everywhere

* Speedup test_ucf101 (#2623 

* Speedup Cmake builds (#3186)

* Speedup test_autoaugment (#3190)

* Speedup DeformConvTester (#3191)

* Speedup InceptionV3 and GoogleNet on Windows (#3196)

Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* Run C++ torchvision example as part of cmake tests

* Enable C++14 support

* Minor error correction

* Add GPU forward on example

* Use arrow operator

* Add CMake build pipeline

* Add CMake build workflow

* Add executable permissions to script

* Install cmake on Windows/MacOS

* Install conda-build before setting up MSVC

* Install PyTorch from nightly

* Do not use conda-build variables

* Add path to CMake

* Install libpng and libjpeg

* Perform make

* Call msbuild on Windows

* Add missing yq

* Use vc_env_helper

* Use string instruction

* Escape configuration option

* Remove configuration flag

* Try to pass -p

* Use caret to escape equal sign

* Escape string option in Windows

* Try to call other bat

* Remove Windows/GPU CMake

* Add tracing cpp test

* Script model instead of tracing it

* Try to register operators manually

* Use manylinux-cuda102

* Activate conda env on Linux

* Build and run sample tracing test

* Add empty echo

* Remove unnecessary register

* Copy headers on Mac

* Revert to 2xlarge

* Include /usr/local/include on Mac

* Install pillow on Windows

* Install future

* Install torchvision on Windows

* Set include flag

* Add torchlib to PATH

* Normalize path via cygpath

* Register ops on Windows

* Minor error correction

* Register CPU/GPU ops on DLL library and register ops via reference

* Install dataclasses

* Install dataclasses using pip

* Address clang formatting issue

* Try to use an actual GPU instance on Linux

* Remove extra environment section

* Declare environment explicitly

* Regenerate

* Pass env variables to Dokcer

* Regenerate circleci

* Test tracing on GPU

* Use GPU medium

* Regenerate

* Use cuda101

* Regenerate

* Do not use pre-trained weights

Avoids having to download pretrained files, which could cause flaky tests
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>