automatic import of python-ai-benchmark

3 files changed, 356 insertions, 0 deletions

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.</br></br>
+
+In total, AI Benchmark consists of <b>42 tests</b> and <b>19 sections</b> provided below:</br>
+
+1. MobileNet-V2&nbsp; `[classification]`
+2. Inception-V3&nbsp; `[classification]`
+3. Inception-V4&nbsp; `[classification]`
+4. Inception-ResNet-V2&nbsp; `[classification]`
+5. ResNet-V2-50&nbsp; `[classification]`
+6. ResNet-V2-152&nbsp; `[classification]`
+7. VGG-16&nbsp; `[classification]`
+8. SRCNN 9-5-5&nbsp; `[image-to-image mapping]`
+9. VGG-19&nbsp; `[image-to-image mapping]`
+10. ResNet-SRGAN&nbsp; `[image-to-image mapping]`
+11. ResNet-DPED&nbsp; `[image-to-image mapping]`
+12. U-Net&nbsp; `[image-to-image mapping]`
+13. Nvidia-SPADE&nbsp; `[image-to-image mapping]`
+14. ICNet&nbsp; `[image segmentation]`
+15. PSPNet&nbsp; `[image segmentation]`
+16. DeepLab&nbsp; `[image segmentation]`
+17. Pixel-RNN&nbsp; `[inpainting]`
+18. LSTM&nbsp; `[sentence sentiment analysis]`
+19. GNMT&nbsp; `[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}`:&nbsp;&nbsp; whether to run the tests on CPUs&nbsp; (if tensorflow-gpu is installed)
+
+> verbose_level=`{0, 1, 2, 3}`:&nbsp;&nbsp; run tests silently | with short summary | with information about each run | with TF logs
+
+```bash
+benchmark.run(precision="normal"):
+```
+
+> precision=`{"normal", "high"}`:&nbsp;&nbsp; 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&nbsp; `[classification]`
+2. Inception-V3&nbsp; `[classification]`
+3. Inception-V4&nbsp; `[classification]`
+4. Inception-ResNet-V2&nbsp; `[classification]`
+5. ResNet-V2-50&nbsp; `[classification]`
+6. ResNet-V2-152&nbsp; `[classification]`
+7. VGG-16&nbsp; `[classification]`
+8. SRCNN 9-5-5&nbsp; `[image-to-image mapping]`
+9. VGG-19&nbsp; `[image-to-image mapping]`
+10. ResNet-SRGAN&nbsp; `[image-to-image mapping]`
+11. ResNet-DPED&nbsp; `[image-to-image mapping]`
+12. U-Net&nbsp; `[image-to-image mapping]`
+13. Nvidia-SPADE&nbsp; `[image-to-image mapping]`
+14. ICNet&nbsp; `[image segmentation]`
+15. PSPNet&nbsp; `[image segmentation]`
+16. DeepLab&nbsp; `[image segmentation]`
+17. Pixel-RNN&nbsp; `[inpainting]`
+18. LSTM&nbsp; `[sentence sentiment analysis]`
+19. GNMT&nbsp; `[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}`:&nbsp;&nbsp; whether to run the tests on CPUs&nbsp; (if tensorflow-gpu is installed)
+
+> verbose_level=`{0, 1, 2, 3}`:&nbsp;&nbsp; run tests silently | with short summary | with information about each run | with TF logs
+
+```bash
+benchmark.run(precision="normal"):
+```
+
+> precision=`{"normal", "high"}`:&nbsp;&nbsp; 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&nbsp; `[classification]`
+2. Inception-V3&nbsp; `[classification]`
+3. Inception-V4&nbsp; `[classification]`
+4. Inception-ResNet-V2&nbsp; `[classification]`
+5. ResNet-V2-50&nbsp; `[classification]`
+6. ResNet-V2-152&nbsp; `[classification]`
+7. VGG-16&nbsp; `[classification]`
+8. SRCNN 9-5-5&nbsp; `[image-to-image mapping]`
+9. VGG-19&nbsp; `[image-to-image mapping]`
+10. ResNet-SRGAN&nbsp; `[image-to-image mapping]`
+11. ResNet-DPED&nbsp; `[image-to-image mapping]`
+12. U-Net&nbsp; `[image-to-image mapping]`
+13. Nvidia-SPADE&nbsp; `[image-to-image mapping]`
+14. ICNet&nbsp; `[image segmentation]`
+15. PSPNet&nbsp; `[image segmentation]`
+16. DeepLab&nbsp; `[image segmentation]`
+17. Pixel-RNN&nbsp; `[inpainting]`
+18. LSTM&nbsp; `[sentence sentiment analysis]`
+19. GNMT&nbsp; `[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}`:&nbsp;&nbsp; whether to run the tests on CPUs&nbsp; (if tensorflow-gpu is installed)
+
+> verbose_level=`{0, 1, 2, 3}`:&nbsp;&nbsp; run tests silently | with short summary | with information about each run | with TF logs
+
+```bash
+benchmark.run(precision="normal"):
+```
+
+> precision=`{"normal", "high"}`:&nbsp;&nbsp; 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
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