From c6ec2298b6080bca918604cb3e7f1adb2de16616 Mon Sep 17 00:00:00 2001 From: CoprDistGit Date: Wed, 31 May 2023 06:39:51 +0000 Subject: automatic import of python-ai-benchmark --- .gitignore | 1 + python-ai-benchmark.spec | 354 +++++++++++++++++++++++++++++++++++++++++++++++ sources | 1 + 3 files changed, 356 insertions(+) create mode 100644 python-ai-benchmark.spec create mode 100644 sources diff --git a/.gitignore b/.gitignore index e69de29..50cf709 100644 --- a/.gitignore +++ b/.gitignore @@ -0,0 +1 @@ +/ai-benchmark-0.1.2.tar.gz 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.

+ +In total, AI Benchmark consists of 42 tests and 19 sections provided below:
+ +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)

+ +#### Installation Instructions
+ +The benchmark requires TensorFlow machine learning library to be present in your system. + +On systems that do not have Nvidia GPUs, run the following commands to install AI Benchmark: + +```bash +pip install tensorflow +pip install ai-benchmark +``` +
+ +If you want to check the performance of Nvidia graphic cards, run the following commands: + +```bash +pip install tensorflow-gpu +pip install ai-benchmark +``` + +`Note 1:` If Tensorflow is already installed in your system, you can skip the first command. + +`Note 2:` For running the benchmark on Nvidia GPUs, `NVIDIA CUDA` and `cuDNN` libraries should be installed first. Please find detailed instructions [here](https://www.tensorflow.org/install/gpu).

+ +#### Getting Started
+ +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()`.

+ +#### Advanced settings
+ +```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. + +
+ +### Additional Notes and Requirements
+ +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.

+ +### Contacts
+ +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.

+ +In total, AI Benchmark consists of 42 tests and 19 sections provided below:
+ +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)

+ +#### Installation Instructions
+ +The benchmark requires TensorFlow machine learning library to be present in your system. + +On systems that do not have Nvidia GPUs, run the following commands to install AI Benchmark: + +```bash +pip install tensorflow +pip install ai-benchmark +``` +
+ +If you want to check the performance of Nvidia graphic cards, run the following commands: + +```bash +pip install tensorflow-gpu +pip install ai-benchmark +``` + +`Note 1:` If Tensorflow is already installed in your system, you can skip the first command. + +`Note 2:` For running the benchmark on Nvidia GPUs, `NVIDIA CUDA` and `cuDNN` libraries should be installed first. Please find detailed instructions [here](https://www.tensorflow.org/install/gpu).

+ +#### Getting Started
+ +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()`.

+ +#### Advanced settings
+ +```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. + +
+ +### Additional Notes and Requirements
+ +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.

+ +### Contacts
+ +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.

+ +In total, AI Benchmark consists of 42 tests and 19 sections provided below:
+ +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)

+ +#### Installation Instructions
+ +The benchmark requires TensorFlow machine learning library to be present in your system. + +On systems that do not have Nvidia GPUs, run the following commands to install AI Benchmark: + +```bash +pip install tensorflow +pip install ai-benchmark +``` +
+ +If you want to check the performance of Nvidia graphic cards, run the following commands: + +```bash +pip install tensorflow-gpu +pip install ai-benchmark +``` + +`Note 1:` If Tensorflow is already installed in your system, you can skip the first command. + +`Note 2:` For running the benchmark on Nvidia GPUs, `NVIDIA CUDA` and `cuDNN` libraries should be installed first. Please find detailed instructions [here](https://www.tensorflow.org/install/gpu).

+ +#### Getting Started
+ +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()`.

+ +#### Advanced settings
+ +```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. + +
+ +### Additional Notes and Requirements
+ +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.

+ +### Contacts
+ +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 - 0.1.2-1 +- Package Spec generated diff --git a/sources b/sources new file mode 100644 index 0000000..24fd651 --- /dev/null +++ b/sources @@ -0,0 +1 @@ +c9ae2ad78d1c71b40e14df6e56d9c00a ai-benchmark-0.1.2.tar.gz -- cgit v1.2.3