def Run(self, args):
"""This is what gets called when the user runs this command.
Args:
args: an argparse namespace. All the arguments that were provided to this
command invocation.
Returns:
Some value that we want to have printed later.
"""
instances = predict_utilities.ReadInstancesFromArgs(
args.json_instances,
args.text_instances,
limit=INPUT_INSTANCES_LIMIT)
model_or_version_ref = predict_utilities.ParseModelOrVersionRef(
args.model, args.version)
results = predict.Predict(model_or_version_ref, instances)
if not args.IsSpecified('format'):
# default format is based on the response.
args.format = self._DefaultFormat(results.get('predictions'))
return results
def Run(self, args):
"""This is what gets called when the user runs this command."""
instances = predict_utilities.ReadInstancesFromArgs(
args.json_instances, args.text_instances)
# Start local prediction in a subprocess.
proc = subprocess.Popen(
['python', local_predict.__file__, '--model-dir', args.model_dir],
stdin=subprocess.PIPE,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
# Pass the instances to the process that actually runs local prediction.
for instance in instances:
proc.stdin.write(json.dumps(instance) + '\n')
proc.stdin.flush()
# Get the results for the local prediction.
output, err = proc.communicate()
if proc.returncode != 0:
raise LocalPredictRuntimeError(err)
if err:
log.warn(err)
try:
return json.loads(output)
except ValueError:
raise InvalidReturnValueError('The output for prediction is not '
'in JSON format: ' + output)
def _RunPredict(version, args):
"""Run ML Engine local prediction."""
instances = predict_utilities.ReadInstancesFromArgs(
args.json_instances, args.text_instances)
sdk_root = config.Paths().sdk_root
if not sdk_root:
raise LocalPredictEnvironmentError(
'You must be running an installed Cloud SDK to perform local '
'prediction.')
env = {'CLOUDSDK_ROOT': sdk_root}
# We want to use whatever the user's Python was, before the Cloud SDK started
# changing the PATH. That's where Tensorflow is installed.
python_executables = files.SearchForExecutableOnPath('python')
if not python_executables:
# This doesn't have to be actionable because things are probably beyond help
# at this point.
raise LocalPredictEnvironmentError(
'Something has gone really wrong; we can\'t find a valid Python '
'executable on your PATH.')
python_executable = python_executables[0]
# Start local prediction in a subprocess.
proc = subprocess.Popen([
python_executable, local_predict.__file__, '--model-dir',
args.model_dir, '--version', version
],
stdin=subprocess.PIPE,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
env=env)
# Pass the instances to the process that actually runs local prediction.
for instance in instances:
proc.stdin.write(json.dumps(instance) + '\n')
proc.stdin.flush()
# Get the results for the local prediction.
output, err = proc.communicate()
if proc.returncode != 0:
raise LocalPredictRuntimeError(err)
if err:
log.warn(err)
try:
return json.loads(output)
except ValueError:
raise InvalidReturnValueError('The output for prediction is not '
'in JSON format: ' + output)
def Run(self, args):
"""This is what gets called when the user runs this command.
Args:
args: an argparse namespace. All the arguments that were provided to this
command invocation.
Returns:
Some value that we want to have printed later.
"""
instances = predict_utilities.ReadInstancesFromArgs(
args.json_instances, args.text_instances, limit=INPUT_INSTANCES_LIMIT)
model_or_version_ref = predict_utilities.ParseModelOrVersionRef(
args.model, args.version)
results = predict.Predict(model_or_version_ref, instances)
# Hack to make the results available to Format() method
self.predictions = results.get('predictions')
return results