PyTorch 1.0.1

pts/pytorch-1.0.1 - 06 January 2024 - Add NVIDIA CUDA GPU option.

install.sh

#!/bin/sh
pip install --user torch==2.1.1 torchvision==0.16.1 torchaudio==2.1.1 pytorch-benchmark==0.3.6
echo $? > ~/install-exit-status
echo "#!/bin/sh
echo \"import torch
import yaml
from torchvision.models import \$3
from pytorch_benchmark import benchmark
#print(torchvision.models.list_models())
num_threads = torch.get_num_threads()
print(f'Benchmarking on {num_threads} threads')
model = \$3().to(\\\"\$1\\\")
sample = torch.randn(2, 3, 224, 224)  # (B, C, H, W)
results = benchmark(model, sample, num_runs=1000, print_details=True, batch_size=\$2)
print(yaml.dump(results))
\" > pytorch-benchmark.py
python3 pytorch-benchmark.py > \$LOG_FILE 2>&1
echo \$? > ~/test-exit-status" > pytorch
chmod +x pytorch

results-definition.xml

<?xml version="1.0"?>
<!--Phoronix Test Suite v10.8.4-->
<PhoronixTestSuite>
  <ResultsParser>
    <OutputTemplate>        batches_per_second_max: #_MAX_RESULT_#
        batches_per_second_mean: #_RESULT_#
        batches_per_second_min: #_MIN_RESULT_#
        batches_per_second_std: 0.2647488016338466</OutputTemplate>
  </ResultsParser>
</PhoronixTestSuite>

test-definition.xml

<?xml version="1.0"?>
<!--Phoronix Test Suite v10.8.4-->
<PhoronixTestSuite>
  <TestInformation>
    <Title>PyTorch</Title>
    <AppVersion>2.1</AppVersion>
    <Description>This is a benchmark of PyTorch making use of pytorch-benchmark [https://github.com/LukasHedegaard/pytorch-benchmark]. Currently this test profile is catered to CPU-based testing.</Description>
    <ResultScale>batches/sec</ResultScale>
    <Proportion>HIB</Proportion>
    <TimesToRun>3</TimesToRun>
  </TestInformation>
  <TestProfile>
    <Version>1.0.1</Version>
    <SupportedPlatforms>Linux</SupportedPlatforms>
    <SoftwareType>Benchmark</SoftwareType>
    <TestType>System</TestType>
    <License>Free</License>
    <Status>Verified</Status>
    <ExternalDependencies>python</ExternalDependencies>
    <EnvironmentSize>4800</EnvironmentSize>
    <ProjectURL>https://pytorch.org/</ProjectURL>
    <RepositoryURL>https://github.com/pytorch/pytorch</RepositoryURL>
    <InternalTags>SMP</InternalTags>
    <Maintainer>Michael Larabel</Maintainer>
    <SystemDependencies>pip3</SystemDependencies>
  </TestProfile>
  <TestSettings>
    <Option>
      <DisplayName>Device</DisplayName>
      <Identifier>device</Identifier>
      <Menu>
        <Entry>
          <Name>CPU</Name>
          <Value>cpu</Value>
        </Entry>
        <Entry>
          <Name>NVIDIA CUDA GPU</Name>
          <Value>cuda</Value>
        </Entry>
      </Menu>
    </Option>
    <Option>
      <DisplayName>Batch Size</DisplayName>
      <Identifier>batch-size</Identifier>
      <Menu>
        <Entry>
          <Name>1</Name>
          <Value>1</Value>
        </Entry>
        <Entry>
          <Name>16</Name>
          <Value>16</Value>
        </Entry>
        <Entry>
          <Name>32</Name>
          <Value>32</Value>
        </Entry>
        <Entry>
          <Name>64</Name>
          <Value>64</Value>
        </Entry>
        <Entry>
          <Name>256</Name>
          <Value>256</Value>
        </Entry>
        <Entry>
          <Name>512</Name>
          <Value>512</Value>
        </Entry>
      </Menu>
    </Option>
    <Option>
      <DisplayName>Model</DisplayName>
      <Identifier>Model</Identifier>
      <Menu>
        <Entry>
          <Name>ResNet-50</Name>
          <Value>resnet50</Value>
        </Entry>
        <Entry>
          <Name>ResNet-152</Name>
          <Value>resnet152</Value>
        </Entry>
        <Entry>
          <Name>Efficientnet_v2_l</Name>
          <Value>efficientnet_v2_l</Value>
        </Entry>
      </Menu>
    </Option>
  </TestSettings>
</PhoronixTestSuite>