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        "author": "Andrey Ignatov",
        "author_email": "andrey@vision.ee.ethz.ch",
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            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: Apache Software License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Software Development :: Testing",
            "Topic :: System :: Benchmark"
        ],
        "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>\n\nIn total, AI Benchmark consists of <b>42 tests</b> and <b>19 sections</b> provided below:</br>\n\n1. MobileNet-V2&nbsp; `[classification]`\n2. Inception-V3&nbsp; `[classification]`\n3. Inception-V4&nbsp; `[classification]`\n4. Inception-ResNet-V2&nbsp; `[classification]`\n5. ResNet-V2-50&nbsp; `[classification]`\n6. ResNet-V2-152&nbsp; `[classification]`\n7. VGG-16&nbsp; `[classification]`\n8. SRCNN 9-5-5&nbsp; `[image-to-image mapping]`\n9. VGG-19&nbsp; `[image-to-image mapping]`\n10. ResNet-SRGAN&nbsp; `[image-to-image mapping]`\n11. ResNet-DPED&nbsp; `[image-to-image mapping]`\n12. U-Net&nbsp; `[image-to-image mapping]`\n13. Nvidia-SPADE&nbsp; `[image-to-image mapping]`\n14. ICNet&nbsp; `[image segmentation]`\n15. PSPNet&nbsp; `[image segmentation]`\n16. DeepLab&nbsp; `[image segmentation]`\n17. Pixel-RNN&nbsp; `[inpainting]`\n18. LSTM&nbsp; `[sentence sentiment analysis]`\n19. GNMT&nbsp; `[text translation]`\n\nFor more information and results, please visit the project website: [http://ai-benchmark.com/alpha](http://ai-benchmark.com/alpha)</br></br>\n\n#### Installation Instructions </br>\n\nThe benchmark requires TensorFlow machine learning library to be present in your system.\n\nOn systems that <b>do not have Nvidia GPUs</b>, run the following commands to install AI Benchmark:\n\n```bash\npip install tensorflow\npip install ai-benchmark\n```\n</br>\n\nIf you want to check the <b>performance of Nvidia graphic cards</b>, run the following commands:\n\n```bash\npip install tensorflow-gpu\npip install ai-benchmark\n```\n\n<b>`Note 1:`</b> If Tensorflow is already installed in your system, you can skip the first command.\n\n<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>\n\n#### Getting Started </br>\n\nTo run AI Benchmark, use the following code:\n\n```bash\nfrom ai_benchmark import AIBenchmark\nbenchmark = AIBenchmark()\nresults = benchmark.run()\n```\n\nAlternatively, on Linux systems you can type `ai-benchmark` in the command line to start the tests.\n\nTo run inference or training only, use `benchmark.run_inference()` or `benchmark.run_training()`. </br></br>\n\n#### Advanced settings </br>\n\n```bash\nAIBenchmark(use_CPU=None, verbose_level=1):\n```\n> use_CPU=`{True, False, None}`:&nbsp;&nbsp; whether to run the tests on CPUs&nbsp; (if tensorflow-gpu is installed)\n\n> verbose_level=`{0, 1, 2, 3}`:&nbsp;&nbsp; run tests silently | with short summary | with information about each run | with TF logs\n\n```bash\nbenchmark.run(precision=\"normal\"):\n```\n\n> precision=`{\"normal\", \"high\"}`:&nbsp;&nbsp; if `high` is selected, the benchmark will execute 10 times more runs for each test.\n\n</br>\n\n### Additional Notes and Requirements </br>\n\nGPU with at least 2GB of RAM is required for running inference tests / 4GB of RAM for training tests.\n\nThe benchmark is compatible with both `TensorFlow 1.x` and `2.x` versions. </br></br>\n\n### Contacts </br>\n\nPlease contact `andrey@vision.ee.ethz.ch` for any feedback or information.\n\n\n\n",
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