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|
%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.aliyun.com/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
* Fri Jun 09 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.2-1
- Package Spec generated
|
