def _startModelRunnerSubprocess(inputOpt, modelOpt, aggOpt):
"""Start the unicorn model_runner subprocess
:param dict inputOpt: input options per input_opt_schema.json
:param dict modelOpt: input options per model_opt_schema.json
:param dict aggOpt: aggregation options per agg_opt_schema.json; None if no
aggregation
:returns: the started subprocess.Popen object wrapped in
ManagedSubprocessTerminator
:rtype: nta.utils.test_utils.ManagedSubprocessTerminator
"""
args = [
sys.executable,
"-m", "unicorn_backend.model_runner_2",
"--input={}".format(json.dumps(inputOpt)),
"--model={}".format(json.dumps(modelOpt)),
]
if aggOpt is not None:
args.append("--agg={}".format(json.dumps(aggOpt)))
process = subprocess.Popen(
args=args,
stdin=subprocess.PIPE,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
close_fds=True)
_LOGGER.info("Started unicorn model_runner subprocess=%s", process)
return test_utils.ManagedSubprocessTerminator(process)
def _startModelRunnerSubprocess(inputSpec, aggSpec, modelSpec):
"""Start the unicorn model_runner subprocess
:param str inputSpec: JSON object describing the input petric data
per input_opt_schema.json
:param str aggSpec: JSON object describing agregation of the input
metric per agg_opt_schema.json
:param str modelSpec: JSON object describing the model per
model_opt_schema.json
:returns: the started subprocess.Popen object wrapped in
ManagedSubprocessTerminator
:rtype: nta.utils.test_utils.ManagedSubprocessTerminator
"""
argumentPattern = [
sys.executable, "-m", "unicorn_backend.model_runner_2",
"--input=%s" % inputSpec,
"--model=%s" % modelSpec
]
if aggSpec is not None:
argumentPattern += ["--agg=%s" % aggSpec]
process = subprocess.Popen(args=argumentPattern,
stdin=subprocess.PIPE,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
close_fds=True)
_LOGGER.info("Started unicorn model_runner subprocess=%s", process)
return test_utils.ManagedSubprocessTerminator(process)
def _startParamFinderRunnerSubprocess(inputSpec):
"""Start the unicorn param_finder subprocess
:param str inputSpec: JSON object describing the input petric data
per input_opt_schema.json
:returns: the started subprocess.Popen object wrapped in
ManagedSubprocessTerminator
:rtype: nta.utils.test_utils.ManagedSubprocessTerminator
"""
process = subprocess.Popen(args=[
sys.executable, "-m", "unicorn_backend.param_finder_runner",
"--input=%s" % inputSpec
],
stdin=subprocess.PIPE,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
close_fds=True)
_LOGGER.info("Started unicorn model_runner subprocess=%s", process)
return test_utils.ManagedSubprocessTerminator(process)
def _startModelRunnerSubprocess(modelId, stats):
"""Start the unicorn model_runner subprocess
:param str modelId: model identifier
:param dict stats: Metric data stats per stats_schema.json in the
unicorn_backend package
:returns: the started subprocess.Popen object wrapped in
ManagedSubprocessTerminator
:rtype: nta.utils.test_utils.ManagedSubprocessTerminator
"""
process = subprocess.Popen(args=[
sys.executable, "-m", "unicorn_backend.model_runner",
"--model=%s" % modelId,
"--stats=%s" % (json.dumps(stats), )
],
stdin=subprocess.PIPE,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
close_fds=True)
_LOGGER.info("Started unicorn model_runner subprocess=%s", process)
return test_utils.ManagedSubprocessTerminator(process)