%global _empty_manifest_terminate_build 0 Name: python-monai-weekly Version: 1.2.dev2318 Release: 1 Summary: AI Toolkit for Healthcare Imaging License: Apache License 2.0 URL: https://monai.io/ Source0: https://mirrors.nju.edu.cn/pypi/web/packages/a0/80/76a653b7776408041f2c15d7607c8ac8c93781e6806a3a0625c8b388664e/monai-weekly-1.2.dev2318.tar.gz BuildArch: noarch Requires: python3-torch Requires: python3-numpy Requires: python3-nibabel Requires: python3-ninja Requires: python3-scikit-image Requires: python3-pillow Requires: python3-tensorboard Requires: python3-gdown Requires: python3-pytorch-ignite Requires: python3-torchvision Requires: python3-itk Requires: python3-tqdm Requires: python3-lmdb Requires: python3-psutil Requires: python3-cucim Requires: python3-openslide-python Requires: python3-tifffile Requires: python3-imagecodecs Requires: python3-pandas Requires: python3-einops Requires: python3-transformers Requires: python3-mlflow Requires: python3-clearml Requires: python3-matplotlib Requires: python3-tensorboardX Requires: python3-pyyaml Requires: python3-fire Requires: python3-jsonschema Requires: python3-pynrrd Requires: python3-pydicom Requires: python3-h5py Requires: python3-nni Requires: python3-optuna Requires: python3-onnx Requires: python3-onnxruntime Requires: python3-cucim Requires: python3-einops Requires: python3-fire Requires: python3-gdown Requires: python3-h5py Requires: python3-pytorch-ignite Requires: python3-imagecodecs Requires: python3-itk Requires: python3-jsonschema Requires: python3-lmdb Requires: python3-matplotlib Requires: python3-mlflow Requires: python3-nibabel Requires: python3-ninja Requires: python3-nni Requires: python3-onnx Requires: python3-onnxruntime Requires: python3-openslide-python Requires: python3-optuna Requires: python3-pandas Requires: python3-pillow Requires: python3-psutil Requires: python3-pydicom Requires: python3-pynrrd Requires: python3-pyyaml Requires: python3-scikit-image Requires: python3-tensorboard Requires: python3-tensorboardX Requires: python3-tifffile Requires: python3-torchvision Requires: python3-tqdm Requires: python3-transformers %description

project-monai

**M**edical **O**pen **N**etwork for **AI** ![Supported Python versions](https://raw.githubusercontent.com/Project-MONAI/MONAI/dev/docs/images/python.svg) [![License](https://img.shields.io/badge/license-Apache%202.0-green.svg)](https://opensource.org/licenses/Apache-2.0) [![PyPI version](https://badge.fury.io/py/monai.svg)](https://badge.fury.io/py/monai) [![docker](https://img.shields.io/badge/docker-pull-green.svg?logo=docker&logoColor=white)](https://hub.docker.com/r/projectmonai/monai) [![conda](https://img.shields.io/conda/vn/conda-forge/monai?color=green)](https://anaconda.org/conda-forge/monai) [![premerge](https://github.com/Project-MONAI/MONAI/actions/workflows/pythonapp.yml/badge.svg?branch=dev)](https://github.com/Project-MONAI/MONAI/actions/workflows/pythonapp.yml) [![postmerge](https://img.shields.io/github/checks-status/project-monai/monai/dev?label=postmerge)](https://github.com/Project-MONAI/MONAI/actions?query=branch%3Adev) [![docker](https://github.com/Project-MONAI/MONAI/actions/workflows/docker.yml/badge.svg?branch=dev)](https://github.com/Project-MONAI/MONAI/actions/workflows/docker.yml) [![Documentation Status](https://readthedocs.org/projects/monai/badge/?version=latest)](https://docs.monai.io/en/latest/) [![codecov](https://codecov.io/gh/Project-MONAI/MONAI/branch/dev/graph/badge.svg?token=6FTC7U1JJ4)](https://codecov.io/gh/Project-MONAI/MONAI) MONAI is a [PyTorch](https://pytorch.org/)-based, [open-source](https://github.com/Project-MONAI/MONAI/blob/dev/LICENSE) framework for deep learning in healthcare imaging, part of [PyTorch Ecosystem](https://pytorch.org/ecosystem/). Its ambitions are: - developing a community of academic, industrial and clinical researchers collaborating on a common foundation; - creating state-of-the-art, end-to-end training workflows for healthcare imaging; - providing researchers with the optimized and standardized way to create and evaluate deep learning models. ## Features > _Please see [the technical highlights](https://docs.monai.io/en/latest/highlights.html) and [What's New](https://docs.monai.io/en/latest/whatsnew.html) of the milestone releases._ - flexible pre-processing for multi-dimensional medical imaging data; - compositional & portable APIs for ease of integration in existing workflows; - domain-specific implementations for networks, losses, evaluation metrics and more; - customizable design for varying user expertise; - multi-GPU data parallelism support. ## Installation To install [the current release](https://pypi.org/project/monai/), you can simply run: ```bash pip install monai ``` Please refer to [the installation guide](https://docs.monai.io/en/latest/installation.html) for other installation options. ## Getting Started [MedNIST demo](https://colab.research.google.com/drive/1wy8XUSnNWlhDNazFdvGBHLfdkGvOHBKe) and [MONAI for PyTorch Users](https://colab.research.google.com/drive/1boqy7ENpKrqaJoxFlbHIBnIODAs1Ih1T) are available on Colab. Examples and notebook tutorials are located at [Project-MONAI/tutorials](https://github.com/Project-MONAI/tutorials). Technical documentation is available at [docs.monai.io](https://docs.monai.io). ## Citation If you have used MONAI in your research, please cite us! The citation can be exported from: https://arxiv.org/abs/2211.02701. ## Model Zoo [The MONAI Model Zoo](https://github.com/Project-MONAI/model-zoo) is a place for researchers and data scientists to share the latest and great models from the community. Utilizing [the MONAI Bundle format](https://docs.monai.io/en/latest/bundle_intro.html) makes it easy to [get started](https://github.com/Project-MONAI/tutorials/tree/main/model_zoo) building workflows with MONAI. ## Contributing For guidance on making a contribution to MONAI, see the [contributing guidelines](https://github.com/Project-MONAI/MONAI/blob/dev/CONTRIBUTING.md). ## Community Join the conversation on Twitter [@ProjectMONAI](https://twitter.com/ProjectMONAI) or join our [Slack channel](https://forms.gle/QTxJq3hFictp31UM9). Ask and answer questions over on [MONAI's GitHub Discussions tab](https://github.com/Project-MONAI/MONAI/discussions). ## Links - Website: https://monai.io/ - API documentation (milestone): https://docs.monai.io/ - API documentation (latest dev): https://docs.monai.io/en/latest/ - Code: https://github.com/Project-MONAI/MONAI - Project tracker: https://github.com/Project-MONAI/MONAI/projects - Issue tracker: https://github.com/Project-MONAI/MONAI/issues - Wiki: https://github.com/Project-MONAI/MONAI/wiki - Test status: https://github.com/Project-MONAI/MONAI/actions - PyPI package: https://pypi.org/project/monai/ - conda-forge: https://anaconda.org/conda-forge/monai - Weekly previews: https://pypi.org/project/monai-weekly/ - Docker Hub: https://hub.docker.com/r/projectmonai/monai %package -n python3-monai-weekly Summary: AI Toolkit for Healthcare Imaging Provides: python-monai-weekly BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-monai-weekly

project-monai

**M**edical **O**pen **N**etwork for **AI** ![Supported Python versions](https://raw.githubusercontent.com/Project-MONAI/MONAI/dev/docs/images/python.svg) [![License](https://img.shields.io/badge/license-Apache%202.0-green.svg)](https://opensource.org/licenses/Apache-2.0) [![PyPI version](https://badge.fury.io/py/monai.svg)](https://badge.fury.io/py/monai) [![docker](https://img.shields.io/badge/docker-pull-green.svg?logo=docker&logoColor=white)](https://hub.docker.com/r/projectmonai/monai) [![conda](https://img.shields.io/conda/vn/conda-forge/monai?color=green)](https://anaconda.org/conda-forge/monai) [![premerge](https://github.com/Project-MONAI/MONAI/actions/workflows/pythonapp.yml/badge.svg?branch=dev)](https://github.com/Project-MONAI/MONAI/actions/workflows/pythonapp.yml) [![postmerge](https://img.shields.io/github/checks-status/project-monai/monai/dev?label=postmerge)](https://github.com/Project-MONAI/MONAI/actions?query=branch%3Adev) [![docker](https://github.com/Project-MONAI/MONAI/actions/workflows/docker.yml/badge.svg?branch=dev)](https://github.com/Project-MONAI/MONAI/actions/workflows/docker.yml) [![Documentation Status](https://readthedocs.org/projects/monai/badge/?version=latest)](https://docs.monai.io/en/latest/) [![codecov](https://codecov.io/gh/Project-MONAI/MONAI/branch/dev/graph/badge.svg?token=6FTC7U1JJ4)](https://codecov.io/gh/Project-MONAI/MONAI) MONAI is a [PyTorch](https://pytorch.org/)-based, [open-source](https://github.com/Project-MONAI/MONAI/blob/dev/LICENSE) framework for deep learning in healthcare imaging, part of [PyTorch Ecosystem](https://pytorch.org/ecosystem/). Its ambitions are: - developing a community of academic, industrial and clinical researchers collaborating on a common foundation; - creating state-of-the-art, end-to-end training workflows for healthcare imaging; - providing researchers with the optimized and standardized way to create and evaluate deep learning models. ## Features > _Please see [the technical highlights](https://docs.monai.io/en/latest/highlights.html) and [What's New](https://docs.monai.io/en/latest/whatsnew.html) of the milestone releases._ - flexible pre-processing for multi-dimensional medical imaging data; - compositional & portable APIs for ease of integration in existing workflows; - domain-specific implementations for networks, losses, evaluation metrics and more; - customizable design for varying user expertise; - multi-GPU data parallelism support. ## Installation To install [the current release](https://pypi.org/project/monai/), you can simply run: ```bash pip install monai ``` Please refer to [the installation guide](https://docs.monai.io/en/latest/installation.html) for other installation options. ## Getting Started [MedNIST demo](https://colab.research.google.com/drive/1wy8XUSnNWlhDNazFdvGBHLfdkGvOHBKe) and [MONAI for PyTorch Users](https://colab.research.google.com/drive/1boqy7ENpKrqaJoxFlbHIBnIODAs1Ih1T) are available on Colab. Examples and notebook tutorials are located at [Project-MONAI/tutorials](https://github.com/Project-MONAI/tutorials). Technical documentation is available at [docs.monai.io](https://docs.monai.io). ## Citation If you have used MONAI in your research, please cite us! The citation can be exported from: https://arxiv.org/abs/2211.02701. ## Model Zoo [The MONAI Model Zoo](https://github.com/Project-MONAI/model-zoo) is a place for researchers and data scientists to share the latest and great models from the community. Utilizing [the MONAI Bundle format](https://docs.monai.io/en/latest/bundle_intro.html) makes it easy to [get started](https://github.com/Project-MONAI/tutorials/tree/main/model_zoo) building workflows with MONAI. ## Contributing For guidance on making a contribution to MONAI, see the [contributing guidelines](https://github.com/Project-MONAI/MONAI/blob/dev/CONTRIBUTING.md). ## Community Join the conversation on Twitter [@ProjectMONAI](https://twitter.com/ProjectMONAI) or join our [Slack channel](https://forms.gle/QTxJq3hFictp31UM9). Ask and answer questions over on [MONAI's GitHub Discussions tab](https://github.com/Project-MONAI/MONAI/discussions). ## Links - Website: https://monai.io/ - API documentation (milestone): https://docs.monai.io/ - API documentation (latest dev): https://docs.monai.io/en/latest/ - Code: https://github.com/Project-MONAI/MONAI - Project tracker: https://github.com/Project-MONAI/MONAI/projects - Issue tracker: https://github.com/Project-MONAI/MONAI/issues - Wiki: https://github.com/Project-MONAI/MONAI/wiki - Test status: https://github.com/Project-MONAI/MONAI/actions - PyPI package: https://pypi.org/project/monai/ - conda-forge: https://anaconda.org/conda-forge/monai - Weekly previews: https://pypi.org/project/monai-weekly/ - Docker Hub: https://hub.docker.com/r/projectmonai/monai %package help Summary: Development documents and examples for monai-weekly Provides: python3-monai-weekly-doc %description help

project-monai

**M**edical **O**pen **N**etwork for **AI** ![Supported Python versions](https://raw.githubusercontent.com/Project-MONAI/MONAI/dev/docs/images/python.svg) [![License](https://img.shields.io/badge/license-Apache%202.0-green.svg)](https://opensource.org/licenses/Apache-2.0) [![PyPI version](https://badge.fury.io/py/monai.svg)](https://badge.fury.io/py/monai) [![docker](https://img.shields.io/badge/docker-pull-green.svg?logo=docker&logoColor=white)](https://hub.docker.com/r/projectmonai/monai) [![conda](https://img.shields.io/conda/vn/conda-forge/monai?color=green)](https://anaconda.org/conda-forge/monai) [![premerge](https://github.com/Project-MONAI/MONAI/actions/workflows/pythonapp.yml/badge.svg?branch=dev)](https://github.com/Project-MONAI/MONAI/actions/workflows/pythonapp.yml) [![postmerge](https://img.shields.io/github/checks-status/project-monai/monai/dev?label=postmerge)](https://github.com/Project-MONAI/MONAI/actions?query=branch%3Adev) [![docker](https://github.com/Project-MONAI/MONAI/actions/workflows/docker.yml/badge.svg?branch=dev)](https://github.com/Project-MONAI/MONAI/actions/workflows/docker.yml) [![Documentation Status](https://readthedocs.org/projects/monai/badge/?version=latest)](https://docs.monai.io/en/latest/) [![codecov](https://codecov.io/gh/Project-MONAI/MONAI/branch/dev/graph/badge.svg?token=6FTC7U1JJ4)](https://codecov.io/gh/Project-MONAI/MONAI) MONAI is a [PyTorch](https://pytorch.org/)-based, [open-source](https://github.com/Project-MONAI/MONAI/blob/dev/LICENSE) framework for deep learning in healthcare imaging, part of [PyTorch Ecosystem](https://pytorch.org/ecosystem/). Its ambitions are: - developing a community of academic, industrial and clinical researchers collaborating on a common foundation; - creating state-of-the-art, end-to-end training workflows for healthcare imaging; - providing researchers with the optimized and standardized way to create and evaluate deep learning models. ## Features > _Please see [the technical highlights](https://docs.monai.io/en/latest/highlights.html) and [What's New](https://docs.monai.io/en/latest/whatsnew.html) of the milestone releases._ - flexible pre-processing for multi-dimensional medical imaging data; - compositional & portable APIs for ease of integration in existing workflows; - domain-specific implementations for networks, losses, evaluation metrics and more; - customizable design for varying user expertise; - multi-GPU data parallelism support. ## Installation To install [the current release](https://pypi.org/project/monai/), you can simply run: ```bash pip install monai ``` Please refer to [the installation guide](https://docs.monai.io/en/latest/installation.html) for other installation options. ## Getting Started [MedNIST demo](https://colab.research.google.com/drive/1wy8XUSnNWlhDNazFdvGBHLfdkGvOHBKe) and [MONAI for PyTorch Users](https://colab.research.google.com/drive/1boqy7ENpKrqaJoxFlbHIBnIODAs1Ih1T) are available on Colab. Examples and notebook tutorials are located at [Project-MONAI/tutorials](https://github.com/Project-MONAI/tutorials). Technical documentation is available at [docs.monai.io](https://docs.monai.io). ## Citation If you have used MONAI in your research, please cite us! The citation can be exported from: https://arxiv.org/abs/2211.02701. ## Model Zoo [The MONAI Model Zoo](https://github.com/Project-MONAI/model-zoo) is a place for researchers and data scientists to share the latest and great models from the community. Utilizing [the MONAI Bundle format](https://docs.monai.io/en/latest/bundle_intro.html) makes it easy to [get started](https://github.com/Project-MONAI/tutorials/tree/main/model_zoo) building workflows with MONAI. ## Contributing For guidance on making a contribution to MONAI, see the [contributing guidelines](https://github.com/Project-MONAI/MONAI/blob/dev/CONTRIBUTING.md). ## Community Join the conversation on Twitter [@ProjectMONAI](https://twitter.com/ProjectMONAI) or join our [Slack channel](https://forms.gle/QTxJq3hFictp31UM9). Ask and answer questions over on [MONAI's GitHub Discussions tab](https://github.com/Project-MONAI/MONAI/discussions). ## Links - Website: https://monai.io/ - API documentation (milestone): https://docs.monai.io/ - API documentation (latest dev): https://docs.monai.io/en/latest/ - Code: https://github.com/Project-MONAI/MONAI - Project tracker: https://github.com/Project-MONAI/MONAI/projects - Issue tracker: https://github.com/Project-MONAI/MONAI/issues - Wiki: https://github.com/Project-MONAI/MONAI/wiki - Test status: https://github.com/Project-MONAI/MONAI/actions - PyPI package: https://pypi.org/project/monai/ - conda-forge: https://anaconda.org/conda-forge/monai - Weekly previews: https://pypi.org/project/monai-weekly/ - Docker Hub: https://hub.docker.com/r/projectmonai/monai %prep %autosetup -n monai-weekly-1.2.dev2318 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-monai-weekly -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Fri May 05 2023 Python_Bot - 1.2.dev2318-1 - Package Spec generated