import json
from datetime import datetime
from clipper_admin import ClipperConnection, DockerContainerManager
clipper_conn = ClipperConnection(DockerContainerManager())
from thermal_model import get_model_per_zone, normal_schedule, dr_schedule, execute_schedule
try:
clipper_conn.start_clipper()
default_output = json.dumps([-1] * 24)
clipper_conn.register_application(name="ciee_thermal",
input_type="string",
default_output=default_output,
slo_micros=1000000000)
print 'apps', clipper_conn.get_all_apps()
models = get_model_per_zone("2018-01-30 00:00:00 PST")
# model parameters:
# zone: string
# date: string
# schedule: [(hsp, csp), ... x 24 ...]
def execute_thermal_model(params):
"""
Accepts list of JSON string as argument
"""
ret = []
for param in params:
args = json.loads(param)
zone = args['zone']
date = str(args['date'])
schedule = args['schedule']
temps, actions = execute_schedule(date, schedule, models[zone], 65)
# clipper_start
from clipper_admin import ClipperConnection, DockerContainerManager
clipper_conn = ClipperConnection(DockerContainerManager())
clipper_conn.start_clipper()
clipper_conn.connect()
clipper_conn.register_application(
name="digit",
input_type="doubles",
default_output="-1.0",
slo_micros=10000000) # 10,000,000 micros == 10 sec
clipper_conn.get_all_apps()
#################################################
######### Define Own Prediction Function ########
#################################################
import sklearn
import numpy as np
from sklearn.neural_network import MLPClassifier
from sklearn.externals import joblib
from clipper_admin.deployers import python as python_deployer
for version_postfix in ["10x1k", "10x2k", "20x1k", "15x2k"]:
model_path = "../../models/sklearn/"
model_name = "dig_nn_model_" + version_postfix + ".sav"
clf = joblib.load(model_path + model_name)
try:
clipper_conn.start_clipper()
clipper_conn.register_application(name=APP_NAME,
input_type="doubles",
default_output="-1.0",
slo_micros=1000000)
deploy_pytorch_model(clipper_conn,
name=MODEL_NAME,
version="1",
input_type="doubles",
func=predict,
pytorch_model=model,
pkgs_to_install=pip_deps)
clipper_conn.link_model_to_app(app_name=APP_NAME, model_name=MODEL_NAME)
except:
clipper_conn.connect()
# Check all apps
print(clipper_conn.get_all_apps())
# Test inference
# inputs = np.array([[1., 2., 3.], [2., 3., 4.], [3., 4., 5.]])
# print(predict(model, inputs))
inputs = np.array([1., 2., 3.]).tolist(
) # Inputs can only be one-dimensional or there will be json serialization error
headers = {"Content-type": "aplication/json"}
result = requests.post("http://localhost:1337/" + APP_NAME + "/predict",
headers=headers,
data=json.dumps({"input": inputs})).json()
print(result)
