ECP-CANDLE 1.0.0

pts/ecp-candle-1.0.0 - 12 August 2020 - Initial commit of CANDLE cancer deep learning benchmark.

downloads.xml

<?xml version="1.0"?>
<!--Phoronix Test Suite v9.8.0-->
<PhoronixTestSuite>
  <Downloads>
    <Package>
      <URL>https://github.com/ECP-CANDLE/Benchmarks/archive/v0.3.tar.gz</URL>
      <MD5>f10773333ae054ed169f0df88bd51553</MD5>
      <SHA256>6309173e56ae4d2dfba8ff879fd19fba70bbba77be2291892f8191a94f4e4470</SHA256>
      <FileName>ECP-CANDLE-Benchmarks-0.3.tar.gz</FileName>
      <FileSize>1323611</FileSize>
    </Package>
    <Package>
      <URL>https://ftp.mcs.anl.gov/pub/candle/public/benchmarks/P1B2/P1B2.test.csv</URL>
      <MD5>91a2737ec53ba84c812048ddf92c188c</MD5>
      <SHA256>0abbc1130637d9706189ba54bc59120d9d275f6f8771f1bb36c254777e42a36c</SHA256>
      <FileName>P1B2.test.csv</FileName>
      <FileSize>56690533</FileSize>
    </Package>
    <Package>
      <URL>https://ftp.mcs.anl.gov/pub/candle/public/benchmarks/P1B2/P1B2.train.csv</URL>
      <MD5>f81edf56d1efce246d4a48002ff30f3d</MD5>
      <SHA256>2bc072811fb022aa014d93f95c9e9aaa03568e97366a81eeb59a8c656ce2ec56</SHA256>
      <FileName>P1B2.train.csv</FileName>
      <FileSize>169631113</FileSize>
    </Package>
    <Package>
      <URL>ftp://ftp.mcs.anl.gov/pub/candle/public/benchmarks/Pilot2/3k_run10_10us.35fs-DPPC.10-DOPC.70-CHOL.20.dir.tar.gz</URL>
      <MD5>ece75b704ec63ac9c39afd74b63497dc</MD5>
      <SHA256>1321b53b8944d95eeee91b52438c6f4423667dcb5b2715b517fc6a9ac2834362</SHA256>
      <FileName>3k_run10_10us.35fs-DPPC.10-DOPC.70-CHOL.20.dir.tar.gz</FileName>
      <FileSize>3088534718</FileSize>
    </Package>
    <Package>
      <URL>ftp://ftp.mcs.anl.gov/pub/candle/public/benchmarks/P3B1/P3B1_data.tar.gz</URL>
      <MD5>d0ba862c83f07d45a099292536b53cbb</MD5>
      <SHA256>c89c9d3aa0f74d19c099f94c16729693a207480de71e10c90a7341523127dbcd</SHA256>
      <FileName>P3B1_data.tar.gz</FileName>
      <FileSize>108587183</FileSize>
    </Package>
    <Package>
      <URL>http://ftp.mcs.anl.gov/pub/candle/public/benchmarks/P3B2/P3B2_data.tgz</URL>
      <MD5>1d4a3d6177529ac853194f9c0d3f655e</MD5>
      <SHA256>e0106cc9eca9d55f4e17a302f4e7d1f6b6845b5113d0ee38f6419d4f94ab64cd</SHA256>
      <FileName>P3B2_data.tgz</FileName>
      <FileSize>7786187</FileSize>
    </Package>
  </Downloads>
</PhoronixTestSuite>

install.sh

#!/bin/sh

tar -xf ECP-CANDLE-Benchmarks-0.3.tar.gz
mkdir -p Benchmarks-0.3/Data/Pilot1
mkdir -p Benchmarks-0.3/Data/Pilot2
mkdir -p Benchmarks-0.3/Data/Pilot3
cp *.csv Benchmarks-0.3/Data/Pilot1
cp 3k_run10_10us.35fs-DPPC.10-DOPC.70-CHOL.20.dir.tar.gz Benchmarks-0.3/Data/Pilot2
cp P3B1_data.tar.gz Benchmarks-0.3/Data/Pilot3
cp P3B2_data.tgz Benchmarks-0.3/Data/Pilot3
cd ~/Benchmarks-0.3/Data/Pilot2
tar -xf 3k_run10_10us.35fs-DPPC.10-DOPC.70-CHOL.20.dir.tar.gz
cd ~/Benchmarks-0.3/Data/Pilot3
tar -xf P3B1_data.tar.gz
tar -xf P3B2_data.tgz
pip3 install --user torch numpy keras tensorflow sklearn pandas matplotlib numba astropy patsy statsmodels
echo $? > ~/install-exit-status

cd ~
echo "#!/bin/sh
cd Benchmarks-0.3

case \$@ in
	\"P1B2\")
		cd Pilot1/P1B2/
		python3 ./p1b2_baseline_keras2.py
		echo \$? > ~/test-exit-status
	;;
	\"P2B1\")
		cd Pilot2/P2B1/
		python3 ./p2b1_baseline_keras2.py
		echo \$? > ~/test-exit-status
	;;
	\"P3B1\")
		cd Pilot3/P3B1/
		python3 ./p3b1_baseline_keras2.py
		echo \$? > ~/test-exit-status
	;;
	\"P3B2\")
		cd Pilot3/P3B2/
		python3 ./p3b2_baseline_keras2.py
		echo \$? > ~/test-exit-status
	;;
esac
" > ecp-candle

chmod +x ecp-candle

results-definition.xml

<?xml version="1.0"?>
<!--Phoronix Test Suite v9.8.0-->
<PhoronixTestSuite>
  <SystemMonitor>
    <Sensor>sys.time</Sensor>
  </SystemMonitor>
</PhoronixTestSuite>

test-definition.xml

<?xml version="1.0"?>
<!--Phoronix Test Suite v9.8.0-->
<PhoronixTestSuite>
  <TestInformation>
    <Title>ECP-CANDLE</Title>
    <AppVersion>0.3</AppVersion>
    <Description>The CANDLE benchmark codes implement deep learning architectures relevant to problems in cancer. These architectures address problems at different biological scales, specifically problems at the molecular, cellular and population scales.</Description>
    <ResultScale>Seconds</ResultScale>
    <Proportion>LIB</Proportion>
    <SubTitle>Time To Completion</SubTitle>
    <TimesToRun>1</TimesToRun>
  </TestInformation>
  <TestProfile>
    <Version>1.0.0</Version>
    <SupportedPlatforms>Linux, BSD</SupportedPlatforms>
    <SoftwareType>Utility</SoftwareType>
    <TestType>System</TestType>
    <License>Free</License>
    <Status>Verified</Status>
    <ExternalDependencies>python</ExternalDependencies>
    <EnvironmentSize>14800</EnvironmentSize>
    <ProjectURL>https://github.com/ECP-CANDLE/Benchmarks</ProjectURL>
    <InternalTags>Python, CUDA</InternalTags>
    <Maintainer>Michael Larabel</Maintainer>
  </TestProfile>
  <TestSettings>
    <Option>
      <DisplayName>Benchmark</DisplayName>
      <Identifier>benchmark</Identifier>
      <Menu>
        <Entry>
          <Name>P1B2</Name>
          <Value>P1B2</Value>
        </Entry>
        <Entry>
          <Name>P2B1</Name>
          <Value>P2B1</Value>
        </Entry>
        <Entry>
          <Name>P3B1</Name>
          <Value>P3B1</Value>
        </Entry>
        <Entry>
          <Name>P3B2</Name>
          <Value>P3B2</Value>
        </Entry>
      </Menu>
    </Option>
  </TestSettings>
</PhoronixTestSuite>