<?xml version="1.0"?>
<!--Phoronix Test Suite v8.2.0-->
<PhoronixTestSuite>
  <TestInformation>
    <Title>NVIDIA GPU Cloud TensorFlow</Title>
    <AppVersion>18.09</AppVersion>
    <Description>This test profile uses the NVIDIA GPU Cloud (NGC/nvcr.io) for running the TensorFlow image inside Docker for benchmarking. You must have already signed into NGC for this test profile to work.</Description>
    <ResultScale>Images Per Second</ResultScale>
    <Proportion>HIB</Proportion>
    <TimesToRun>3</TimesToRun>
    <PreInstallMessage>This test profile requires you have already signed into the NVIDIA GPU Cloud (https://ngc.nvidia.com/) via Docker on this system/user (via docker login nvcr.io) in order for this test profile to work.</PreInstallMessage>
  </TestInformation>
  <TestProfile>
    <Version>1.0.0</Version>
    <SupportedPlatforms>Linux</SupportedPlatforms>
    <SoftwareType>Utility</SoftwareType>
    <TestType>Graphics</TestType>
    <License>Restricted</License>
    <Status>Experimental</Status>
    <RequiresDisplay>TRUE</RequiresDisplay>
    <EnvironmentSize>700</EnvironmentSize>
    <ProjectURL>https://ngc.nvidia.com/</ProjectURL>
    <InternalTags>CUDA</InternalTags>
    <Maintainer>Michael Larabel</Maintainer>
    <SystemDependencies>docker</SystemDependencies>
  </TestProfile>
  <TestSettings>
    <Option>
      <DisplayName>Test</DisplayName>
      <Identifier>test</Identifier>
      <Menu>
        <Entry>
          <Name>ResNet-50, FP16</Name>
          <Value>/workspace/nvidia-examples/cnn/resnet.py --layers 50 -b 64 --precision fp16</Value>
        </Entry>
        <Entry>
          <Name>ResNet-50, FP32</Name>
          <Value>/workspace/nvidia-examples/cnn/resnet.py --layers 50 -b 64 --precision fp32</Value>
        </Entry>
        <Entry>
          <Name>AlexNet, FP16</Name>
          <Value>/workspace/nvidia-examples/cnn/alexnet.py -b 256 --precision fp16</Value>
        </Entry>
        <Entry>
          <Name>AlexNet, FP32</Name>
          <Value>/workspace/nvidia-examples/cnn/alexnet.py -b 256 --precision fp32</Value>
        </Entry>
        <Entry>
          <Name>Googlenet, FP16</Name>
          <Value>/workspace/nvidia-examples/cnn/googlenet.py --precision fp16</Value>
        </Entry>
        <Entry>
          <Name>Googlenet, FP32</Name>
          <Value>/workspace/nvidia-examples/cnn/googlenet.py --precision fp32</Value>
        </Entry>
        <Entry>
          <Name>Inception v4, FP16</Name>
          <Value>/workspace/nvidia-examples/cnn/inception_v4.py -b 32 --precision fp16</Value>
        </Entry>
        <Entry>
          <Name>Inception v4, FP32</Name>
          <Value>/workspace/nvidia-examples/cnn/inception_v4.py -b 32 --precision fp32</Value>
        </Entry>
        <Entry>
          <Name>VGG-16, FP16</Name>
          <Value>/workspace/nvidia-examples/cnn/vgg.py --layers 16 -b 32 --precision fp16</Value>
        </Entry>
        <Entry>
          <Name>VGG-16, FP32</Name>
          <Value>/workspace/nvidia-examples/cnn/vgg.py --layers 16 -b 32 --precision fp32</Value>
        </Entry>
      </Menu>
    </Option>
  </TestSettings>
</PhoronixTestSuite>
