def launch_job(job_spec): """Launch job on ML Engine.""" project_id = 'projects/{}'.format(cloud.default_project()) credentials = GoogleCredentials.get_application_default() cloudml = discovery.build('ml', 'v1', credentials=credentials) request = cloudml.projects().jobs().create(body=job_spec, parent=project_id) request.execute()
def launch_job(job_spec): """Launch job on ML Engine.""" project_id = "projects/{}".format( text_encoder.native_to_unicode(cloud.default_project())) credentials = GoogleCredentials.get_application_default() cloudml = discovery.build("ml", "v1", credentials=credentials, cache_discovery=False) request = cloudml.projects().jobs().create(body=job_spec, parent=project_id) request.execute()
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"]