def autotune(benchmark,
returnBest=None,
tester_lambda=None,
pop_lambda=None,
hlconfig_lambda=None,
config_lambda=None):
return storagedirs.callWithLogDir(
lambda: autotuneInner(benchmark, returnBest, tester_lambda, pop_lambda,
hlconfig_lambda, config_lambda),
config.output_dir, config.delete_output_dir)
def regression_check(benchmark):
tunerconfig.applypatch(tunerconfig.patch_regression)
storagedirs.callWithLogDir(lambda: autotuneInner(benchmark),
config.output_dir,
config.delete_output_dir)
def autotune(benchmark, returnBest=None, tester_lambda=None, pop_lambda=None, hlconfig_lambda=None, config_lambda=None):
return storagedirs.callWithLogDir(lambda: autotuneInner(benchmark, returnBest, tester_lambda, pop_lambda, hlconfig_lambda, config_lambda),
config.output_dir,
config.delete_output_dir)
def onlinelearn(benchmark):
storagedirs.callWithLogDir(lambda: onlinelearnInner(benchmark),
config.output_dir,
config.delete_output_dir)
]
print '#', ','.join(headers)
t = csv.DictWriter(sys.stdout, headers, extrasaction='ignore')
t.writerows(rows)
if __name__ == "__main__":
from optparse import OptionParser
parser = OptionParser(
usage="usage: graphgen.py [options] benchmark candidatelog.csv")
parser.add_option('--trials', type='int', default=10)
parser.add_option('--confidence', type='float', default=.95)
parser.add_option('--timeout', type='float', default=5.0)
parser.add_option('--onlyrounds', type='int', default=True)
parser.add_option('-n', type='int', default=1024)
warnings.simplefilter('ignore', tunerwarnings.TooManyTrials)
(options, args) = parser.parse_args()
if len(args) != 2:
parser.print_usage()
sys.exit(1)
benchmark = args[0]
config = os.path.abspath(args[1])
pbutil.chdirToPetabricksRoot()
#pbutil.compilePetabricks();
benchmark = pbutil.normalizeBenchmarkName(benchmark)
#pbutil.compileBenchmarks([benchmark])
storagedirs.callWithLogDir(lambda: main(benchmark, options.n, config),
'/tmp', True)
def onlinelearn(benchmark):
storagedirs.callWithLogDir(lambda: onlinelearnInner(benchmark),
config.output_dir, config.delete_output_dir)
def autotune(benchmark):
return storagedirs.callWithLogDir(lambda: autotuneInner(benchmark),
config.output_dir,
config.delete_output_dir)
def regression_check(benchmark):
tunerconfig.applypatch(tunerconfig.patch_regression)
storagedirs.callWithLogDir(lambda: autotuneInner(benchmark),
config.output_dir, config.delete_output_dir)
headers = ['time','minperf', 'perf_on_%d'%n, 'perf_on_%d_ci'%n, 'tests', 'candidates', 'input_size', 'invperf', 'tests_timeout']
print '#',','.join(headers)
t=csv.DictWriter(sys.stdout, headers, extrasaction='ignore')
t.writerows(rows)
if __name__ == "__main__":
from optparse import OptionParser
parser = OptionParser(usage="usage: graphgen.py [options] benchmark candidatelog.csv")
parser.add_option('--trials', type='int', default=10)
parser.add_option('--confidence', type='float', default=.95)
parser.add_option('--timeout', type='float', default=5.0)
parser.add_option('--onlyrounds', type='int', default=True)
parser.add_option('-n', type='int', default=1024)
warnings.simplefilter('ignore', tunerwarnings.TooManyTrials)
(options, args) = parser.parse_args()
if len(args)!=2:
parser.print_usage()
sys.exit(1)
benchmark=args[0]
config=os.path.abspath(args[1])
pbutil.chdirToPetabricksRoot();
#pbutil.compilePetabricks();
benchmark=pbutil.normalizeBenchmarkName(benchmark)
#pbutil.compileBenchmarks([benchmark])
storagedirs.callWithLogDir(lambda: main(benchmark, options.n, config), '/tmp', True)