### Description
<!-- Describe your changes. -->
* Remove deprecated gpu arch to control nuget/python package size
(latest TRT supports sm75 Turing and newer arch)
* Add 90 to support blackwell series in next release (86;89 not
considered as adding them will rapidly increase package size)
| arch_range | Python-cuda12 | Nuget-cuda12 |
| -------------- |
------------------------------------------------------------ |
---------------------------------- |
| 60;61;70;75;80 | Linux: 279MB Win: 267MB | Linux: 247MB Win: 235MB |
| 75;80 | Linux: 174MB Win: 162MB | Linux: 168MB Win: 156MB |
| **75;80;90** | **Linux: 299MB Win: 277MB** | **Linux: 294MB Win:
271MB** |
| 75;80;86;89 | [Linux: MB Win:
390MB](https://aiinfra.visualstudio.com/Lotus/_build/results?buildId=647457&view=results)
| Linux: 416MB Win: 383MB |
| 75;80;86;89;90 | [Linux: MB Win:
505MB](https://aiinfra.visualstudio.com/Lotus/_build/results?buildId=646536&view=results)
| Linux: 541MB Win: 498MB |
### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
Callout: While adding sm90 support, the build of cuda11.8+cudnn8 will be
dropped in the coming ORT release,
as the build has issue with blackwell (mentioned in comments) and demand
on cuda 11 is minor, according to internal ort-cuda11 repo.
### Description
#22380 removes the file
`tools/ci_build/github/linux/docker/inference/x86_64/python/cpu/scripts/requirements.txt`
but it is still used in `dockerfiles/Dockerfile.cuda`.
This change updates the file path of the requirements.txt
fixes#22945.
### Description
* Build cuda nhwc ops by default.
* Deprecate `--enable_cuda_nhwc_ops` in build.py and add
`--disable_cuda_nhwc_ops` option
Note that it requires cuDNN 9.x. If you build with cuDNN 8, NHWC ops
will be disabled automatically.
### Motivation and Context
In general, NHWC is faster than NCHW for convolution in Nvidia GPUs with
Tensor Cores, and this could improve performance for vision models.
This is the first step to prefer NHWC for CUDA in 1.21 release. Next
step is to do some tests on popular vision models. If it help in most
models and devices, set `prefer_nhwc=1` as default cuda provider option.
### Description
* Add a few arguments CUDA_VERSION, CUDNN_VERSION, OS, GIT_COMMIT,
GIT_BRANCH and ONNXRUNTIME_VERSION to the Dockerfile.cuda to allow for
more flexibility in the build process.
* Update README.md to include the new arguments and their usage.
* Output labels to image so that it is easy to inspect the image.
Available CUDA versions for ubuntu 24.04 can be found
[here](https://hub.docker.com/r/nvidia/cuda/tags), and available CUDNN
versions can be found
[here](https://pypi.org/project/nvidia-cudnn-cu12/#history). Example
command line to build docker image:
```
docker build -t onnxruntime-cuda --build-arg CUDA_VERSION=12.6.1 \
--build-arg CUDNN_VERSION=9.5.0.50 \
--build-arg GIT_BRANCH=$(git rev-parse --abbrev-ref HEAD) \
--build-arg GIT_COMMIT=$(git rev-parse HEAD) \
--build-arg ONNXRUNTIME_VERSION=$(cat ../VERSION_NUMBER) \
-f Dockerfile.cuda ..
```
Example labels from `docker inspect onnxruntime-cuda`:
```
"Labels": {
"CUDA_VERSION": "12.6.1",
"CUDNN_VERSION": "9.5.0.50",
"maintainer": "Changming Sun <chasun@microsoft.com>",
"onnxruntime_git_branch": "main",
"onnxruntime_git_commit": "bc84958dcef5c6017ae58085f55b669efd74f4a5",
"onnxruntime_version": "1.20.0",
"org.opencontainers.image.ref.name": "ubuntu",
"org.opencontainers.image.version": "24.04"
}
```
### Motivation and Context
https://github.com/microsoft/onnxruntime/pull/22339 has hard-coded the
cuda and cudnn versions. User might want to choose specified cuda and
cudnn version during building docker image.
### Description
Serve as example to build and run onnxruntime-gpu with latest software
stack.
To build docker image:
```
git clone https://github.com/microsoft/onnxruntime
cd onnxruntime/dockerfiles
docker build -t onnxruntime-cuda -f Dockerfile.cuda ..
```
To launch the docker image built from previous step (and mount the code
directory to run a unit test below):
```
cd ..
docker run --rm -it --gpus all -v $PWD:/code onnxruntime-cuda /bin/bash
```
Then run the following in docker image to verify that the cuda provider
is good:
```
python /code/onnxruntime/test/python/onnxruntime_test_python_cudagraph.py
```
### Motivation and Context
https://github.com/microsoft/onnxruntime/issues/22335
### Description
Previous image is based on cuda 12.1 and cudnn 8, which is out of date
since we have moved to cudnn 9 since 1.19 release.
(1) Upgrade base image to cuda 12.5.1 and cudnn 9.
(2) Update CMAKE_CUDA_ARCHITECTURES from 52;60;61;70;75;86 to
61;70;75;80;86;90 to support A100 and H100
(3) Make the build faster: exclude unit test; use ninja etc.
(4) upgrade some packages (like packaging etc) before building to avoid
build error.
### Motivation and Context
https://github.com/microsoft/onnxruntime/issues/21792https://github.com/microsoft/onnxruntime/issues/21532
### Description
1. Update docker files and their build instructions.
ARM64 and x86_64 can use the same docker file.
2. Upgrade Linux CUDA pipeline's base docker image from CentOS7 to UBI8
AB#18990
### Description
All our Windows build pipelines already uses cmake 3.26 except one
pipeline: QNN ARM64.
This PR does the same for Linux build pipelines.
### Motivation and Context
This change is related to #15704 .
### Description
Upgrade cmake version to 3.24 because I need to use a new feature that
is only provided in that version and later. Starting from cmake 3.24,
the
[FetchContent](https://cmake.org/cmake/help/latest/module/FetchContent.html#module:FetchContent)
module and the
[find_package()](https://cmake.org/cmake/help/latest/command/find_package.html#command:find_package)
command now support integration capabilities, which means calls to
"FetchContent" can be implicitly redirected to "find_package", and vice
versa. Users can use a cmake variable to control the behavior. So, we
don't need to provide such a build option. We can delete our
"onnxruntime_PREFER_SYSTEM_LIB" build option and let cmake handle it.
And it would be easier for who wants to use vcpkg.
### Motivation and Context
Provide a unified package management method, and get aligned with the
community. This change is split from #13523 for easier review.
* initial update from 11.1 to 11.4
* change 11.4.1 to 11.4.0
* adjusting to match nvidia/cuda image tags
* adjusting to match nvidia/cuda image tags centos7
* correction to 11.4.0
* correction to 11.4.0
* update to cuda 11.4
* change training back to 11.1
* change training back to 11.1
* point to correct nvcr.io/nvidia/cuda 11.4.1 image
* change centos8 to centos7
* correct cudnn path
* Update linux-gpu-ci-pipeline.yml for Azure Pipelines
* Update c-api-noopenmp-packaging-pipelines.yml
* need to resolve centos images but remove space and change to 11.4
* Update linux-gpu-ci-pipeline.yml
* add cudnn to docker image
* bump devtoolset to 10
* revert cuda 11.4 change to setup_env_trt
* orttraining back to 11.1
* use nvcr.io
* Fix previous change back to cuda 11.1
* update cudnn path
* use cudnn image (revert if failure)
1. Update SDLNativeRules from v2 to v3. The new one allows us setting excluded paths.
2. Update TSAUpload from v1 to v2. And add a config file ".gdn/.gdntsa" for it.
3. Fix some parentheses warnings
4. Update cmake to the latest.
5. Remove "--x86" build option from pipeline yaml files. Now we can auto-detect cpu architecture from python. So we don't need to ask user to specify it.
1. Remove conda from the images. Because conda contains a file named /opt/miniconda/lib/libcrypto.so.1.0.0 which can't pass our security scan. Also, it will be easier for us to manage the third party usage registrations.
2. Remove openssh from the images. Because the official openssh package provided by Ubuntu can't pass our security scan.
3. Reduce the image size to 1/3 by using stages. Also, because it contains less packages, it will be less often needed to update.
4. Put the LICENSE-IMAGE.txt file in right place. It is missed in current images. You can see it was added to a temp folder "/code" but it got deleted afterwards.
5. Update the CPU docker image's base image to Ubuntu 18.04. The GPU one is already 18.04. It's better to keep them the same.
6. Remove the build arg ONNXRUNTIME_REPO/ONNXRUNTIME_BRANCH. Instead, the new one always uses the local source. I feel it can reduce confusion.
