# turn into predictions
with open('submission.csv') as fh:
lines = fh.readlines()[1:] # ignore first line
preds = [line.strip().split(',')[1] for line in lines]
return preds
# pickle function and write to appropriate location
s = six.StringIO()
c = CloudPickler(s, 2)
c.dump(libffm)
serialized_prediction_function = s.getvalue()
filepath = 'docker/lib/func.pkl'
with open(filepath, 'w') as fh:
fh.write(serialized_prediction_function)
# refresh creds
os.system('gcloud container clusters get-credentials redis-cluster')
os.system('kubectl cluster-info')
clipper_conn = ClipperConnection(KubernetesContainerManager(clipper_ip, useInternalIP=True))
clipper_conn.connect()
# Build model and deploy to clipper
version = int(time.time())
clipper_conn.build_and_deploy_model('ffm', version, 'strings', 'docker/lib', 'clipper/python-closure-container:develop', container_registry='ryanhoque')
# Uncomment the following if first time
#clipper_conn.link_model_to_app(app_name="testbed", model_name="ffm")
# finally deploy new version of model to clipper (set version as timestamp)
print('Successfully deployed model ffm version ' + str(version) + ' to Clipper.')
import sys
sys.path.insert(0,
'/Users/xunzhang/Desktop/2018/github/clipper/clipper_admin/')
import tensorflow as tf
from clipper_admin import ClipperConnection, DockerContainerManager
from clipper_admin.deployers.tensorflow import deploy_tensorflow_model
cur_dir = os.path.dirname(os.path.abspath(__file__))
app_name = "tf-lr-app"
model_name = "tf-lr-model"
if __name__ == "__main__":
clipper_conn = ClipperConnection(DockerContainerManager())
clipper_conn.stop_all()
clipper_conn.start_clipper()
clipper_conn.register_application(name=app_name,
input_type="integers",
default_output="rabbit",
slo_micros=100000)
print(os.path.abspath("data"))
clipper_conn.build_and_deploy_model(
name=model_name,
version=1,
input_type="integers",
model_data_path=os.path.abspath("data"),
base_image="xunzhang/tf_lr_container:latest",
num_replicas=1)
clipper_conn.link_model_to_app(app_name, model_name)
print(clipper_conn.get_clipper_logs())
