Metadata-Version: 2.1
Name: ai-benchmark
Version: 0.1.0
Summary: AI Benchmark is an open source python library for evaluating AI performance of various hardware platforms, including CPUs, GPUs and TPUs.
Home-page: http://ai-benchmark.com
Author: Andrey Ignatov
Author-email: andrey@vision.ee.ethz.ch
License: Apache License Version 2.0
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.
        
        
Keywords: AI Benchmark Tensorflow Machine Learning Inference Training
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Information Technology
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Testing
Classifier: Topic :: System :: Benchmark
Description-Content-Type: text/markdown
