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diff --git a/python-ai-benchmark.spec b/python-ai-benchmark.spec new file mode 100644 index 0000000..b401149 --- /dev/null +++ b/python-ai-benchmark.spec @@ -0,0 +1,354 @@ +%global _empty_manifest_terminate_build 0 +Name: python-ai-benchmark +Version: 0.1.2 +Release: 1 +Summary: AI Benchmark is an open source python library for evaluating AI performance of various hardware platforms, including CPUs, GPUs and TPUs. +License: Apache License Version 2.0 +URL: http://ai-benchmark.com +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/99/9e/6685285db14f407d5061e6022f96400f6fe958a70ba320472178151ded4b/ai-benchmark-0.1.2.tar.gz +BuildArch: noarch + +Requires: python3-numpy +Requires: python3-psutil +Requires: python3-py-cpuinfo +Requires: python3-pillow +Requires: python3-setuptools +Requires: python3-requests + +%description +[AI Benchmark Alpha](http://ai-benchmark.com/alpha) is an open source python library for evaluating AI performance of various hardware platforms, including CPUs, GPUs and TPUs. The benchmark is relying on [TensorFlow](https://www.tensorflow.org) machine learning library, and is providing a lightweight and accurate solution for assessing inference and training speed for key Deep Learning models.</br></br> + +In total, AI Benchmark consists of <b>42 tests</b> and <b>19 sections</b> provided below:</br> + +1. MobileNet-V2 `[classification]` +2. Inception-V3 `[classification]` +3. Inception-V4 `[classification]` +4. Inception-ResNet-V2 `[classification]` +5. ResNet-V2-50 `[classification]` +6. ResNet-V2-152 `[classification]` +7. VGG-16 `[classification]` +8. SRCNN 9-5-5 `[image-to-image mapping]` +9. VGG-19 `[image-to-image mapping]` +10. ResNet-SRGAN `[image-to-image mapping]` +11. ResNet-DPED `[image-to-image mapping]` +12. U-Net `[image-to-image mapping]` +13. Nvidia-SPADE `[image-to-image mapping]` +14. ICNet `[image segmentation]` +15. PSPNet `[image segmentation]` +16. DeepLab `[image segmentation]` +17. Pixel-RNN `[inpainting]` +18. LSTM `[sentence sentiment analysis]` +19. GNMT `[text translation]` + +For more information and results, please visit the project website: [http://ai-benchmark.com/alpha](http://ai-benchmark.com/alpha)</br></br> + +#### Installation Instructions </br> + +The benchmark requires TensorFlow machine learning library to be present in your system. + +On systems that <b>do not have Nvidia GPUs</b>, run the following commands to install AI Benchmark: + +```bash +pip install tensorflow +pip install ai-benchmark +``` +</br> + +If you want to check the <b>performance of Nvidia graphic cards</b>, run the following commands: + +```bash +pip install tensorflow-gpu +pip install ai-benchmark +``` + +<b>`Note 1:`</b> If Tensorflow is already installed in your system, you can skip the first command. + +<b>`Note 2:`</b> For running the benchmark on Nvidia GPUs, <b>`NVIDIA CUDA`</b> and <b>`cuDNN`</b> libraries should be installed first. Please find detailed instructions [here](https://www.tensorflow.org/install/gpu). </br></br> + +#### Getting Started </br> + +To run AI Benchmark, use the following code: + +```bash +from ai_benchmark import AIBenchmark +benchmark = AIBenchmark() +results = benchmark.run() +``` + +Alternatively, on Linux systems you can type `ai-benchmark` in the command line to start the tests. + +To run inference or training only, use `benchmark.run_inference()` or `benchmark.run_training()`. </br></br> + +#### Advanced settings </br> + +```bash +AIBenchmark(use_CPU=None, verbose_level=1): +``` +> use_CPU=`{True, False, None}`: whether to run the tests on CPUs (if tensorflow-gpu is installed) + +> verbose_level=`{0, 1, 2, 3}`: run tests silently | with short summary | with information about each run | with TF logs + +```bash +benchmark.run(precision="normal"): +``` + +> precision=`{"normal", "high"}`: if `high` is selected, the benchmark will execute 10 times more runs for each test. + +</br> + +### Additional Notes and Requirements </br> + +GPU with at least 2GB of RAM is required for running inference tests / 4GB of RAM for training tests. + +The benchmark is compatible with both `TensorFlow 1.x` and `2.x` versions. </br></br> + +### Contacts </br> + +Please contact `andrey@vision.ee.ethz.ch` for any feedback or information. + + + + + +%package -n python3-ai-benchmark +Summary: AI Benchmark is an open source python library for evaluating AI performance of various hardware platforms, including CPUs, GPUs and TPUs. +Provides: python-ai-benchmark +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-ai-benchmark +[AI Benchmark Alpha](http://ai-benchmark.com/alpha) is an open source python library for evaluating AI performance of various hardware platforms, including CPUs, GPUs and TPUs. The benchmark is relying on [TensorFlow](https://www.tensorflow.org) machine learning library, and is providing a lightweight and accurate solution for assessing inference and training speed for key Deep Learning models.</br></br> + +In total, AI Benchmark consists of <b>42 tests</b> and <b>19 sections</b> provided below:</br> + +1. MobileNet-V2 `[classification]` +2. Inception-V3 `[classification]` +3. Inception-V4 `[classification]` +4. Inception-ResNet-V2 `[classification]` +5. ResNet-V2-50 `[classification]` +6. ResNet-V2-152 `[classification]` +7. VGG-16 `[classification]` +8. SRCNN 9-5-5 `[image-to-image mapping]` +9. VGG-19 `[image-to-image mapping]` +10. ResNet-SRGAN `[image-to-image mapping]` +11. ResNet-DPED `[image-to-image mapping]` +12. U-Net `[image-to-image mapping]` +13. Nvidia-SPADE `[image-to-image mapping]` +14. ICNet `[image segmentation]` +15. PSPNet `[image segmentation]` +16. DeepLab `[image segmentation]` +17. Pixel-RNN `[inpainting]` +18. LSTM `[sentence sentiment analysis]` +19. GNMT `[text translation]` + +For more information and results, please visit the project website: [http://ai-benchmark.com/alpha](http://ai-benchmark.com/alpha)</br></br> + +#### Installation Instructions </br> + +The benchmark requires TensorFlow machine learning library to be present in your system. + +On systems that <b>do not have Nvidia GPUs</b>, run the following commands to install AI Benchmark: + +```bash +pip install tensorflow +pip install ai-benchmark +``` +</br> + +If you want to check the <b>performance of Nvidia graphic cards</b>, run the following commands: + +```bash +pip install tensorflow-gpu +pip install ai-benchmark +``` + +<b>`Note 1:`</b> If Tensorflow is already installed in your system, you can skip the first command. + +<b>`Note 2:`</b> For running the benchmark on Nvidia GPUs, <b>`NVIDIA CUDA`</b> and <b>`cuDNN`</b> libraries should be installed first. Please find detailed instructions [here](https://www.tensorflow.org/install/gpu). </br></br> + +#### Getting Started </br> + +To run AI Benchmark, use the following code: + +```bash +from ai_benchmark import AIBenchmark +benchmark = AIBenchmark() +results = benchmark.run() +``` + +Alternatively, on Linux systems you can type `ai-benchmark` in the command line to start the tests. + +To run inference or training only, use `benchmark.run_inference()` or `benchmark.run_training()`. </br></br> + +#### Advanced settings </br> + +```bash +AIBenchmark(use_CPU=None, verbose_level=1): +``` +> use_CPU=`{True, False, None}`: whether to run the tests on CPUs (if tensorflow-gpu is installed) + +> verbose_level=`{0, 1, 2, 3}`: run tests silently | with short summary | with information about each run | with TF logs + +```bash +benchmark.run(precision="normal"): +``` + +> precision=`{"normal", "high"}`: if `high` is selected, the benchmark will execute 10 times more runs for each test. + +</br> + +### Additional Notes and Requirements </br> + +GPU with at least 2GB of RAM is required for running inference tests / 4GB of RAM for training tests. + +The benchmark is compatible with both `TensorFlow 1.x` and `2.x` versions. </br></br> + +### Contacts </br> + +Please contact `andrey@vision.ee.ethz.ch` for any feedback or information. + + + + + +%package help +Summary: Development documents and examples for ai-benchmark +Provides: python3-ai-benchmark-doc +%description help +[AI Benchmark Alpha](http://ai-benchmark.com/alpha) is an open source python library for evaluating AI performance of various hardware platforms, including CPUs, GPUs and TPUs. The benchmark is relying on [TensorFlow](https://www.tensorflow.org) machine learning library, and is providing a lightweight and accurate solution for assessing inference and training speed for key Deep Learning models.</br></br> + +In total, AI Benchmark consists of <b>42 tests</b> and <b>19 sections</b> provided below:</br> + +1. MobileNet-V2 `[classification]` +2. Inception-V3 `[classification]` +3. Inception-V4 `[classification]` +4. Inception-ResNet-V2 `[classification]` +5. ResNet-V2-50 `[classification]` +6. ResNet-V2-152 `[classification]` +7. VGG-16 `[classification]` +8. SRCNN 9-5-5 `[image-to-image mapping]` +9. VGG-19 `[image-to-image mapping]` +10. ResNet-SRGAN `[image-to-image mapping]` +11. ResNet-DPED `[image-to-image mapping]` +12. U-Net `[image-to-image mapping]` +13. Nvidia-SPADE `[image-to-image mapping]` +14. ICNet `[image segmentation]` +15. PSPNet `[image segmentation]` +16. DeepLab `[image segmentation]` +17. Pixel-RNN `[inpainting]` +18. LSTM `[sentence sentiment analysis]` +19. GNMT `[text translation]` + +For more information and results, please visit the project website: [http://ai-benchmark.com/alpha](http://ai-benchmark.com/alpha)</br></br> + +#### Installation Instructions </br> + +The benchmark requires TensorFlow machine learning library to be present in your system. + +On systems that <b>do not have Nvidia GPUs</b>, run the following commands to install AI Benchmark: + +```bash +pip install tensorflow +pip install ai-benchmark +``` +</br> + +If you want to check the <b>performance of Nvidia graphic cards</b>, run the following commands: + +```bash +pip install tensorflow-gpu +pip install ai-benchmark +``` + +<b>`Note 1:`</b> If Tensorflow is already installed in your system, you can skip the first command. + +<b>`Note 2:`</b> For running the benchmark on Nvidia GPUs, <b>`NVIDIA CUDA`</b> and <b>`cuDNN`</b> libraries should be installed first. Please find detailed instructions [here](https://www.tensorflow.org/install/gpu). </br></br> + +#### Getting Started </br> + +To run AI Benchmark, use the following code: + +```bash +from ai_benchmark import AIBenchmark +benchmark = AIBenchmark() +results = benchmark.run() +``` + +Alternatively, on Linux systems you can type `ai-benchmark` in the command line to start the tests. + +To run inference or training only, use `benchmark.run_inference()` or `benchmark.run_training()`. </br></br> + +#### Advanced settings </br> + +```bash +AIBenchmark(use_CPU=None, verbose_level=1): +``` +> use_CPU=`{True, False, None}`: whether to run the tests on CPUs (if tensorflow-gpu is installed) + +> verbose_level=`{0, 1, 2, 3}`: run tests silently | with short summary | with information about each run | with TF logs + +```bash +benchmark.run(precision="normal"): +``` + +> precision=`{"normal", "high"}`: if `high` is selected, the benchmark will execute 10 times more runs for each test. + +</br> + +### Additional Notes and Requirements </br> + +GPU with at least 2GB of RAM is required for running inference tests / 4GB of RAM for training tests. + +The benchmark is compatible with both `TensorFlow 1.x` and `2.x` versions. </br></br> + +### Contacts </br> + +Please contact `andrey@vision.ee.ethz.ch` for any feedback or information. + + + + + +%prep +%autosetup -n ai-benchmark-0.1.2 + +%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-ai-benchmark -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Wed May 31 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.2-1 +- Package Spec generated |