def _make_cloud_mlengine_request(examples):
"""Builds and sends requests to Cloud ML Engine."""
api = discovery.build("ml", "v1", credentials=credentials)
parent = "projects/%s/models/%s/versions/%s" % (cloud.default_project(),
model_name, version)
input_data = {
"instances": [{
"input": {
"b64": base64.b64encode(ex.SerializeToString())
}
} for ex in examples]
}
prediction = api.projects().predict(body=input_data, name=parent).execute()
return prediction["predictions"]
def _make_cloud_mlengine_request(examples):
"""Builds and sends requests to Cloud ML Engine."""
api = discovery.build("ml", "v1", credentials=credentials)
parent = "projects/%s/models/%s/versions/%s" % (cloud.default_project(),
model_name, version)
input_data = {
"instances": [{
"input": {
"b64": base64.b64encode(ex.SerializeToString())
}
} for ex in examples]
}
prediction = api.projects().predict(body=input_data, name=parent).execute()
return prediction["predictions"]
def _make_cloud_mlengine_request(examples):
"""Builds and sends requests to Cloud ML Engine."""
api = discovery.build("ml", "v1", credentials=credentials)
parent = "projects/%s/models/%s/versions/%s" % (cloud.default_project(),
model_name, version)
input_data = {
"instances": [{ # pylint: disable=g-complex-comprehension
"input": {
"b64": base64.b64encode(ex.SerializeToString())
}
} for ex in examples]
}
response = api.projects().predict(body=input_data, name=parent).execute()
predictions = response["predictions"]
for prediction in predictions:
prediction["outputs"] = np.array([prediction["outputs"]])
prediction["scores"] = np.array(prediction["scores"])
return predictions