mirror of
https://github.com/saymrwulf/onnxruntime.git
synced 2026-07-07 04:39:07 +00:00
a6a23db130
* build for .net5 * only reference cswinrt for .net5 * remove netstandard2.0 references * upgrade language version * net5 * remove extra comment closure * add targetframework * set target framework * remove net* * pep8 errors * make test project build with .net windows SDK projection * disable c# builds for non-x64 builds * fix pep8 errors * disable for store build * fix tests * remove cswinrt and sdk references from package * bump cswinrt down to 1.0.1 * fix bin path Co-authored-by: Sheil Kumar <sheilk@microsoft.com>
493 lines
25 KiB
Python
493 lines
25 KiB
Python
# Copyright (c) Microsoft Corporation. All rights reserved.
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# Licensed under the MIT License.
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import argparse
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import sys
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import os
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def parse_arguments():
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parser = argparse.ArgumentParser(description="ONNX Runtime create nuget spec script "
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"(for hosting native shared library artifacts)",
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usage='')
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# Main arguments
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parser.add_argument("--package_name", required=True, help="ORT package name. Eg: Microsoft.ML.OnnxRuntime.Gpu")
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parser.add_argument("--package_version", required=True, help="ORT package version. Eg: 1.0.0")
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parser.add_argument("--target_architecture", required=True, help="Eg: x64")
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parser.add_argument("--build_config", required=True, help="Eg: RelWithDebInfo")
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parser.add_argument("--ort_build_path", required=True, help="ORT build directory.")
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parser.add_argument("--native_build_path", required=True, help="Native build output directory.")
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parser.add_argument("--packages_path", required=True, help="Nuget packages output directory.")
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parser.add_argument("--sources_path", required=True, help="OnnxRuntime source code root.")
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parser.add_argument("--commit_id", required=True, help="The last commit id included in this package.")
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parser.add_argument("--is_store_build", default=False, type=lambda x: x.lower() == 'true',
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help="Build for the Microsoft Store")
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parser.add_argument("--is_release_build", required=False, default=None, type=str,
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help="Flag indicating if the build is a release build. Accepted values: true/false.")
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parser.add_argument("--execution_provider", required=False, default='None', type=str,
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choices=['dnnl', 'openvino', 'tensorrt', 'None'],
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help="The selected execution provider for this build.")
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return parser.parse_args()
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def generate_id(list, package_name):
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list.append('<id>' + package_name + '</id>')
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def generate_version(list, package_version):
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list.append('<version>' + package_version + '</version>')
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def generate_authors(list, authors):
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list.append('<authors>' + authors + '</authors>')
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def generate_owners(list, owners):
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list.append('<owners>' + owners + '</owners>')
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def generate_description(list, description):
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list.append('<description>' + description + '</description>')
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def generate_copyright(list, copyright):
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list.append('<copyright>' + copyright + '</copyright>')
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def generate_tags(list, tags):
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list.append('<tags>' + tags + '</tags>')
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def generate_icon_url(list, icon_url):
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list.append('<iconUrl>' + icon_url + '</iconUrl>')
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def generate_license(list):
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list.append('<license type="file">LICENSE.txt</license>')
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def generate_project_url(list, project_url):
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list.append('<projectUrl>' + project_url + '</projectUrl>')
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def generate_repo_url(list, repo_url, commit_id):
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list.append('<repository type="git" url="' + repo_url + '"' + ' commit="' + commit_id + '" />')
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def generate_dependencies(list, package_name, version):
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dml_dependency = '<dependency id="Microsoft.AI.DirectML" version="1.4.0"/>'
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if (package_name == 'Microsoft.AI.MachineLearning'):
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list.append('<dependencies>')
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# Support .Net Core
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list.append('<group targetFramework="net5.0">')
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list.append(dml_dependency)
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list.append('</group>')
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# UAP10.0.16299, This is the earliest release of the OS that supports .NET Standard apps
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list.append('<group targetFramework="UAP10.0.16299">')
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list.append(dml_dependency)
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list.append('</group>')
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# Support Native C++
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list.append('<group targetFramework="native">')
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list.append(dml_dependency)
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list.append('</group>')
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list.append('</dependencies>')
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else:
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include_dml = package_name == 'Microsoft.ML.OnnxRuntime.DirectML'
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list.append('<dependencies>')
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# Support .Net Core
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list.append('<group targetFramework="NETCOREAPP">')
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list.append('<dependency id="Microsoft.ML.OnnxRuntime.Managed"' + ' version="' + version + '"/>')
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if include_dml:
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list.append(dml_dependency)
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list.append('</group>')
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# Support .Net Standard
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list.append('<group targetFramework="NETSTANDARD">')
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list.append('<dependency id="Microsoft.ML.OnnxRuntime.Managed"' + ' version="' + version + '"/>')
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if include_dml:
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list.append(dml_dependency)
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list.append('</group>')
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# Support .Net Framework
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list.append('<group targetFramework="NETFRAMEWORK">')
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list.append('<dependency id="Microsoft.ML.OnnxRuntime.Managed"' + ' version="' + version + '"/>')
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if include_dml:
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list.append(dml_dependency)
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list.append('</group>')
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# Support Native C++
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if include_dml:
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list.append('<group targetFramework="native">')
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list.append(dml_dependency)
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list.append('</group>')
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list.append('</dependencies>')
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def get_env_var(key):
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return os.environ.get(key)
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def generate_release_notes(list):
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list.append('<releaseNotes>')
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list.append('Release Def:')
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branch = get_env_var('BUILD_SOURCEBRANCH')
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list.append('\t' + 'Branch: ' + (branch if branch is not None else ''))
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version = get_env_var('BUILD_SOURCEVERSION')
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list.append('\t' + 'Commit: ' + (version if version is not None else ''))
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build_id = get_env_var('BUILD_BUILDID')
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list.append('\t' + 'Build: https://aiinfra.visualstudio.com/Lotus/_build/results?buildId=' +
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(build_id if build_id is not None else ''))
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list.append('</releaseNotes>')
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def generate_metadata(list, args):
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metadata_list = ['<metadata>']
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generate_id(metadata_list, args.package_name)
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generate_version(metadata_list, args.package_version)
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generate_authors(metadata_list, 'Microsoft')
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generate_owners(metadata_list, 'Microsoft')
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generate_description(metadata_list, 'This package contains native shared library artifacts '
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'for all supported platforms of ONNX Runtime.')
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generate_copyright(metadata_list, '\xc2\xa9 ' + 'Microsoft Corporation. All rights reserved.')
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generate_tags(metadata_list, 'ONNX ONNX Runtime Machine Learning')
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generate_icon_url(metadata_list, 'https://go.microsoft.com/fwlink/?linkid=2049168')
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generate_license(metadata_list)
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generate_project_url(metadata_list, 'https://github.com/Microsoft/onnxruntime')
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generate_repo_url(metadata_list, 'https://github.com/Microsoft/onnxruntime.git', args.commit_id)
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generate_dependencies(metadata_list, args.package_name, args.package_version)
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generate_release_notes(metadata_list)
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metadata_list.append('</metadata>')
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list += metadata_list
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def generate_files(list, args):
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files_list = ['<files>']
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is_cpu_package = args.package_name in ['Microsoft.ML.OnnxRuntime', 'Microsoft.ML.OnnxRuntime.NoOpenMP']
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is_mklml_package = args.package_name == 'Microsoft.ML.OnnxRuntime.MKLML'
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is_cuda_gpu_package = args.package_name == 'Microsoft.ML.OnnxRuntime.Gpu'
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is_dml_package = args.package_name == 'Microsoft.ML.OnnxRuntime.DirectML'
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is_windowsai_package = args.package_name == 'Microsoft.AI.MachineLearning'
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includes_cuda = is_cuda_gpu_package or is_cpu_package # Why does the CPU package ship the cuda provider headers?
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includes_winml = is_windowsai_package
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includes_directml = (is_dml_package or is_windowsai_package) and not args.is_store_build and (
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args.target_architecture == 'x64' or args.target_architecture == 'x86')
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is_windows_build = is_windows()
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nuget_dependencies = {}
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if is_windows_build:
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nuget_dependencies = {'mklml': 'mklml.dll',
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'openmp': 'libiomp5md.dll',
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'dnnl': 'dnnl.dll',
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'tvm': 'tvm.dll',
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'providers_shared_lib': 'onnxruntime_providers_shared.dll',
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'dnnl_ep_shared_lib': 'onnxruntime_providers_dnnl.dll',
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'tensorrt_ep_shared_lib': 'onnxruntime_providers_tensorrt.dll',
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'openvino_ep_shared_lib': 'onnxruntime_providers_openvino.dll',
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'onnxruntime_perf_test': 'onnxruntime_perf_test.exe',
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'onnx_test_runner': 'onnx_test_runner.exe'}
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copy_command = "copy"
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runtimes_target = '" target="runtimes\\win-'
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else:
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nuget_dependencies = {'mklml': 'libmklml_intel.so',
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'mklml_1': 'libmklml_gnu.so',
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'openmp': 'libiomp5.so',
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'dnnl': 'libdnnl.so.1',
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'tvm': 'libtvm.so.0.5.1',
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'providers_shared_lib': 'libonnxruntime_providers_shared.so',
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'dnnl_ep_shared_lib': 'libonnxruntime_providers_dnnl.so',
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'tensorrt_ep_shared_lib': 'libonnxruntime_providers_tensorrt.so',
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'openvino_ep_shared_lib': 'libonnxruntime_providers_openvino.so',
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'onnxruntime_perf_test': 'onnxruntime_perf_test',
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'onnx_test_runner': 'onnx_test_runner'}
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copy_command = "cp"
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runtimes_target = '" target="runtimes\\linux-'
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runtimes = '{}{}\\{}"'.format(runtimes_target,
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args.target_architecture,
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'uap' if args.is_store_build else 'native')
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# Process headers
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files_list.append('<file src=' + '"' + os.path.join(args.sources_path,
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'include\\onnxruntime\\core\\session\\onnxruntime_*.h') +
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'" target="build\\native\\include" />')
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files_list.append('<file src=' + '"' +
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os.path.join(args.sources_path,
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'include\\onnxruntime\\core\\providers\\cpu\\cpu_provider_factory.h') +
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'" target="build\\native\\include" />')
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if includes_cuda:
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files_list.append('<file src=' + '"' +
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os.path.join(args.sources_path,
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'include\\onnxruntime\\core\\providers\\cuda\\cuda_provider_factory.h') +
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'" target="build\\native\\include" />')
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if args.execution_provider == 'openvino':
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files_list.append('<file src=' + '"' +
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os.path.join(args.sources_path,
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'include\\onnxruntime\\core\\providers\\openvino\\openvino_provider_factory.h') +
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'" target="build\\native\\include" />')
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if args.execution_provider == 'tensorrt':
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files_list.append('<file src=' + '"' +
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os.path.join(args.sources_path,
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'include\\onnxruntime\\core\\providers\\tensorrt\\tensorrt_provider_factory.h') +
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'" target="build\\native\\include" />')
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if args.execution_provider == 'dnnl':
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files_list.append('<file src=' + '"' +
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os.path.join(args.sources_path,
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'include\\onnxruntime\\core\\providers\\dnnl\\dnnl_provider_factory.h') +
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'" target="build\\native\\include" />')
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if includes_directml:
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files_list.append('<file src=' + '"' +
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os.path.join(args.sources_path,
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'include\\onnxruntime\\core\\providers\\dml\\dml_provider_factory.h') +
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'" target="build\\native\\include" />')
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if includes_winml:
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# Add microsoft.ai.machinelearning headers
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files_list.append('<file src=' + '"' + os.path.join(args.ort_build_path, args.build_config,
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'microsoft.ai.machinelearning.h') +
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'" target="build\\native\\include\\abi\\Microsoft.AI.MachineLearning.h" />')
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files_list.append('<file src=' + '"' + os.path.join(args.sources_path,
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'winml\\api\\dualapipartitionattribute.h') +
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'" target="build\\native\\include\\abi\\dualapipartitionattribute.h" />')
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files_list.append('<file src=' + '"' + os.path.join(args.ort_build_path, args.build_config,
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'microsoft.ai.machinelearning.native.h') +
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'" target="build\\native\\include\\Microsoft.AI.MachineLearning.Native.h" />')
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# Add custom operator headers
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mlop_path = 'onnxruntime\\core\\providers\\dml\\dmlexecutionprovider\\inc\\mloperatorauthor.h'
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files_list.append('<file src=' + '"' + os.path.join(args.sources_path, mlop_path) +
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'" target="build\\native\\include" />')
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# Process microsoft.ai.machinelearning.winmd
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files_list.append('<file src=' + '"' + os.path.join(args.ort_build_path, args.build_config,
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'microsoft.ai.machinelearning.winmd') +
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'" target="lib\\uap\\Microsoft.AI.MachineLearning.winmd" />')
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if args.target_architecture == 'x64' and not args.is_store_build:
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interop_dll_path = 'Microsoft.AI.MachineLearning.Interop\\net5.0-windows10.0.19041.0'
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interop_dll = interop_dll_path + '\\Microsoft.AI.MachineLearning.Interop.dll'
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files_list.append('<file src=' + '"' + os.path.join(args.native_build_path, interop_dll) +
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'" target="lib\\net5.0\\Microsoft.AI.MachineLearning.Interop.dll" />')
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interop_pdb_path = 'Microsoft.AI.MachineLearning.Interop\\net5.0-windows10.0.19041.0'
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interop_pdb = interop_pdb_path + '\\Microsoft.AI.MachineLearning.Interop.pdb'
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files_list.append('<file src=' + '"' + os.path.join(args.native_build_path, interop_pdb) +
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'" target="lib\\net5.0\\Microsoft.AI.MachineLearning.Interop.pdb" />')
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# Process runtimes
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# Process onnxruntime import lib, dll, and pdb
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if is_windows_build:
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files_list.append('<file src=' + '"' + os.path.join(args.native_build_path, 'onnxruntime.lib') +
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runtimes + ' />')
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files_list.append('<file src=' + '"' + os.path.join(args.native_build_path, 'onnxruntime.dll') +
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runtimes + ' />')
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if os.path.exists(os.path.join(args.native_build_path, 'onnxruntime.pdb')):
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files_list.append('<file src=' + '"' + os.path.join(args.native_build_path, 'onnxruntime.pdb') +
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runtimes + ' />')
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else:
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files_list.append('<file src=' + '"' + os.path.join(args.native_build_path, 'nuget-staging/usr/local/lib',
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'libonnxruntime.so') + '" target="runtimes\\linux-' + args.target_architecture +
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'\\native" />')
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if includes_winml:
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# Process microsoft.ai.machinelearning import lib, dll, and pdb
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files_list.append('<file src=' + '"' +
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os.path.join(args.native_build_path, 'microsoft.ai.machinelearning.lib') +
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runtimes_target + args.target_architecture + '\\' +
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('uap' if args.is_store_build else 'native') +
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'\\Microsoft.AI.MachineLearning.lib" />')
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files_list.append('<file src=' + '"' + os.path.join(args.native_build_path,
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'microsoft.ai.machinelearning.dll') +
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runtimes_target + args.target_architecture + '\\' +
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('uap' if args.is_store_build else 'native') +
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'\\Microsoft.AI.MachineLearning.dll" />')
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files_list.append('<file src=' + '"' + os.path.join(args.native_build_path,
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'microsoft.ai.machinelearning.pdb') +
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runtimes_target + args.target_architecture + '\\' +
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('uap' if args.is_store_build else 'native') +
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'\\Microsoft.AI.MachineLearning.pdb" />')
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# Process execution providers which are built as shared libs
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if args.execution_provider == "tensorrt":
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files_list.append('<file src=' + '"' + os.path.join(args.native_build_path,
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nuget_dependencies['providers_shared_lib']) +
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runtimes_target + args.target_architecture + '\\native" />')
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files_list.append('<file src=' + '"' + os.path.join(args.native_build_path,
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nuget_dependencies['tensorrt_ep_shared_lib']) +
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runtimes_target + args.target_architecture + '\\native" />')
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if args.execution_provider == "dnnl":
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files_list.append('<file src=' + '"' + os.path.join(args.native_build_path,
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nuget_dependencies['providers_shared_lib']) +
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runtimes_target + args.target_architecture + '\\native" />')
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files_list.append('<file src=' + '"' + os.path.join(args.native_build_path,
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nuget_dependencies['dnnl_ep_shared_lib']) +
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runtimes_target + args.target_architecture + '\\native" />')
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if args.execution_provider == "openvino":
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files_list.append('<file src=' + '"' + os.path.join(args.native_build_path,
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nuget_dependencies['providers_shared_lib']) +
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runtimes_target + args.target_architecture + '\\native" />')
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files_list.append('<file src=' + '"' + os.path.join(args.native_build_path,
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nuget_dependencies['openvino_ep_shared_lib']) +
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runtimes_target + args.target_architecture + '\\native" />')
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# process all other library dependencies
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if is_cpu_package or is_cuda_gpu_package or is_dml_package or is_mklml_package:
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# Process dnnl dependency
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if os.path.exists(os.path.join(args.native_build_path, nuget_dependencies['dnnl'])):
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files_list.append('<file src=' + '"' + os.path.join(args.native_build_path, nuget_dependencies['dnnl']) +
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runtimes + ' />')
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# Process mklml dependency
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if os.path.exists(os.path.join(args.native_build_path, nuget_dependencies['mklml'])):
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files_list.append('<file src=' + '"' + os.path.join(args.native_build_path, nuget_dependencies['mklml']) +
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runtimes + ' />')
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if is_linux() and os.path.exists(os.path.join(args.native_build_path, nuget_dependencies['mklml_1'])):
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files_list.append('<file src=' + '"' + os.path.join(args.native_build_path, nuget_dependencies['mklml_1']) +
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runtimes + ' />')
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# Process libiomp5md dependency
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if os.path.exists(os.path.join(args.native_build_path, nuget_dependencies['openmp'])):
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files_list.append('<file src=' + '"' + os.path.join(args.native_build_path, nuget_dependencies['openmp']) +
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runtimes + ' />')
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# Process tvm dependency
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if os.path.exists(os.path.join(args.native_build_path, nuget_dependencies['tvm'])):
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files_list.append('<file src=' + '"' + os.path.join(args.native_build_path, nuget_dependencies['tvm']) +
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runtimes + ' />')
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# Some tools to be packaged in nightly build only, should not be released
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# These are copied to the runtimes folder for convenience of loading with the dlls
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if args.is_release_build.lower() != 'true' and args.target_architecture == 'x64' and \
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os.path.exists(os.path.join(args.native_build_path, nuget_dependencies['onnxruntime_perf_test'])):
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files_list.append('<file src=' + '"' + os.path.join(args.native_build_path,
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nuget_dependencies['onnxruntime_perf_test']) +
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runtimes + ' />')
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if args.is_release_build.lower() != 'true' and args.target_architecture == 'x64' and \
|
|
os.path.exists(os.path.join(args.native_build_path, nuget_dependencies['onnx_test_runner'])):
|
|
files_list.append('<file src=' + '"' + os.path.join(args.native_build_path,
|
|
nuget_dependencies['onnx_test_runner']) +
|
|
runtimes + ' />')
|
|
|
|
# Process props and targets files
|
|
if is_windowsai_package:
|
|
windowsai_src = 'Microsoft.AI.MachineLearning'
|
|
windowsai_props = 'Microsoft.AI.MachineLearning.props'
|
|
windowsai_targets = 'Microsoft.AI.MachineLearning.targets'
|
|
windowsai_native_props = os.path.join(args.sources_path, 'csharp', 'src', windowsai_src, windowsai_props)
|
|
windowsai_rules = 'Microsoft.AI.MachineLearning.Rules.Project.xml'
|
|
windowsai_native_rules = os.path.join(args.sources_path, 'csharp', 'src', windowsai_src, windowsai_rules)
|
|
windowsai_native_targets = os.path.join(args.sources_path, 'csharp', 'src', windowsai_src, windowsai_targets)
|
|
build = 'build\\{}'.format('uap' if args.is_store_build else 'native')
|
|
files_list.append('<file src=' + '"' + windowsai_native_props + '" target="' + build + '" />')
|
|
# Process native targets
|
|
files_list.append('<file src=' + '"' + windowsai_native_targets + '" target="' + build + '" />')
|
|
# Process rules
|
|
files_list.append('<file src=' + '"' + windowsai_native_rules + '" target="' + build + '" />')
|
|
# Process .net5.0 targets
|
|
if args.target_architecture == 'x64' and not args.is_store_build:
|
|
interop_src = 'Microsoft.AI.MachineLearning.Interop'
|
|
interop_targets = 'Microsoft.AI.MachineLearning.targets'
|
|
windowsai_net50_targets = os.path.join(args.sources_path, 'csharp', 'src', interop_src, interop_targets)
|
|
files_list.append('<file src=' + '"' + windowsai_net50_targets + '" target="build\\net5.0" />')
|
|
|
|
if is_cpu_package or is_cuda_gpu_package or is_dml_package or is_mklml_package:
|
|
# Process props file
|
|
source_props = os.path.join(args.sources_path, 'csharp', 'src', 'Microsoft.ML.OnnxRuntime', 'props.xml')
|
|
target_props = os.path.join(args.sources_path, 'csharp', 'src', 'Microsoft.ML.OnnxRuntime',
|
|
args.package_name + '.props')
|
|
os.system(copy_command + ' ' + source_props + ' ' + target_props)
|
|
files_list.append('<file src=' + '"' + target_props + '" target="build\\native" />')
|
|
files_list.append('<file src=' + '"' + target_props + '" target="build\\netstandard1.1" />')
|
|
|
|
# Process targets file
|
|
source_targets = os.path.join(args.sources_path, 'csharp', 'src', 'Microsoft.ML.OnnxRuntime', 'targets.xml')
|
|
target_targets = os.path.join(args.sources_path, 'csharp', 'src', 'Microsoft.ML.OnnxRuntime',
|
|
args.package_name + '.targets')
|
|
os.system(copy_command + ' ' + source_targets + ' ' + target_targets)
|
|
files_list.append('<file src=' + '"' + target_targets + '" target="build\\native" />')
|
|
files_list.append('<file src=' + '"' + target_targets + '" target="build\\netstandard1.1" />')
|
|
|
|
# Process License, ThirdPartyNotices, Privacy, README
|
|
files_list.append('<file src=' + '"' + os.path.join(args.sources_path, 'LICENSE.txt') + '" target="LICENSE.txt" />')
|
|
files_list.append('<file src=' + '"' + os.path.join(args.sources_path, 'ThirdPartyNotices.txt') +
|
|
'" target="ThirdPartyNotices.txt" />')
|
|
files_list.append('<file src=' + '"' + os.path.join(args.sources_path, 'docs', 'Privacy.md') +
|
|
'" target="Privacy.md" />')
|
|
files_list.append('<file src=' + '"' + os.path.join(args.sources_path, 'docs', 'C_API.md') +
|
|
'" target="README.md" />')
|
|
|
|
files_list.append('</files>')
|
|
|
|
list += files_list
|
|
|
|
|
|
def generate_nuspec(args):
|
|
lines = ['<?xml version="1.0"?>']
|
|
lines.append('<package>')
|
|
generate_metadata(lines, args)
|
|
generate_files(lines, args)
|
|
lines.append('</package>')
|
|
return lines
|
|
|
|
|
|
def is_windows():
|
|
return sys.platform.startswith("win")
|
|
|
|
|
|
def is_linux():
|
|
return sys.platform.startswith("linux")
|
|
|
|
|
|
def validate_platform():
|
|
if not(is_windows() or is_linux()):
|
|
raise Exception('Native Nuget generation is currently supported only on Windows and Linux')
|
|
|
|
|
|
def validate_execution_provider(execution_provider):
|
|
if is_linux():
|
|
if not (execution_provider == 'None' or execution_provider == 'dnnl'
|
|
or execution_provider == 'tensorrt' or execution_provider == 'openvino'):
|
|
raise Exception('On Linux platform nuget generation is supported only '
|
|
'for cpu|cuda|dnnl|tensorrt|openvino execution providers.')
|
|
|
|
|
|
def main():
|
|
# Parse arguments
|
|
args = parse_arguments()
|
|
|
|
validate_platform()
|
|
|
|
validate_execution_provider(args.execution_provider)
|
|
|
|
if (args.is_release_build.lower() != 'true' and args.is_release_build.lower() != 'false'):
|
|
raise Exception('Only valid options for IsReleaseBuild are: true and false')
|
|
|
|
# Generate nuspec
|
|
lines = generate_nuspec(args)
|
|
|
|
# Create the nuspec needed to generate the Nuget
|
|
with open(os.path.join(args.native_build_path, 'NativeNuget.nuspec'), 'w') as f:
|
|
for line in lines:
|
|
f.write(line)
|
|
f.write('\n')
|
|
|
|
|
|
if __name__ == "__main__":
|
|
sys.exit(main())
|