def deploy_(clipper_conn):
python_deployer.create_endpoint(
clipper_conn,
"testapp0-model",
"doubles",
feature_sum,
registry=CLIPPER_CONTAINER_REGISTRY)
def deploy_(clipper_conn, use_kubernetes=False):
if use_kubernetes:
python_deployer.create_endpoint(clipper_conn,
"testapp0-model",
"doubles",
feature_sum,
registry=CLIPPER_CONTAINER_REGISTRY)
else:
python_deployer.create_endpoint(clipper_conn, "testapp0-model",
"doubles", feature_sum)
def test_deployed_models_are_logged(self):
# Deploy models
python_deployer.create_endpoint(self.clipper_conn,
"simple-example",
"doubles",
feature_sum,
num_replicas=2)
time.sleep(2)
self.logger.info(
"Making 100 predictions using two model container; Should takes 25 seconds."
)
for _ in range(100):
predict(self.clipper_conn.get_query_addr(), np.random.random(200))
time.sleep(0.2)
self.check_fluentd_has_correct_model_logs(self.clipper_conn,
'simple-example')
from clipper_admin import ClipperConnection, DockerContainerManager
from clipper_admin.deployers import python as py_deployer
import random
cl = ClipperConnection(DockerContainerManager())
cl.stop_all()
cl.start_clipper()
# cl.register_application(name="pong", input_type="doubles", slo_micros=1000000, default_output="1")
def random_predict(xs):
action = random.randint(0, 2)
return [str(action) for _ in xs]
# cl.register_application(name="pong", input_type="doubles", default_output="-1.0", slo_micros=100000)
# py_deployer.deploy_python_closure(cl, name="rand-model", version=1, input_type="doubles", func=random_predict, registry="hsubbaraj")
# cl.link_model_to_app(app_name="pong", model_name="rand-model")
py_deployer.create_endpoint(cl,
name="pong",
input_type="doubles",
func=random_predict,
default_output="0",
slo_micros=100000)
print("CLIPPER ADDRESS: " + cl.get_query_addr())
return [size]
# Stop Clipper on Ctrl-C
def signal_handler(signal, frame):
print("Stopping Clipper...")
clipper_conn = ClipperConnection(DockerContainerManager())
clipper_conn.stop_all()
sys.exit(0)
if __name__ == '__main__':
signal.signal(signal.SIGINT, signal_handler)
parser = argparse.ArgumentParser(
description='Use Clipper to Query Images.')
parser.add_argument('image', nargs='+', help='Path to an image')
imgs = parser.parse_args().image
clipper_conn = ClipperConnection(DockerContainerManager())
clipper_conn.start_clipper()
python_deployer.create_endpoint(clipper_conn, "image-example", "bytes",
image_size)
time.sleep(2)
try:
for f in imgs:
if f.endswith('.jpg') or f.endswith('.png'):
predict(clipper_conn.get_query_addr(), f)
except Exception as e:
clipper_conn.stop_all()
elif i % 5 == 0:
return produce_query_arr_for_ms(100)
elif i % 3 == 0:
return produce_query_arr_for_ms(50)
else:
return produce_query_arr_for_ms(10)
if __name__ == '__main__':
signal.signal(signal.SIGINT, signal_handler)
clipper_conn = ClipperConnection(DockerContainerManager())
clipper_conn.start_clipper()
print("Starting Clipper")
python_deployer.create_endpoint(clipper_conn,
"simple-example",
"doubles",
feature_sum,
num_replicas=2)
time.sleep(2)
print("Starting Prediction")
try:
counter = 0
while True:
print(counter)
predict(clipper_conn.get_query_addr(), fizz_buzz(counter))
counter += 1
time.sleep(0.2)
except Exception as e:
clipper_conn.stop_all()
print("'%s', %f ms" % (r.text, latency))
# Stop Clipper on Ctrl-C
def signal_handler(signal, frame):
print("Stopping Clipper...")
clipper_conn = ClipperConnection(DockerContainerManager())
clipper_conn.stop_all()
sys.exit(0)
if __name__ == '__main__':
signal.signal(signal.SIGINT, signal_handler)
clipper_conn = ClipperConnection(DockerContainerManager())
clipper_conn.start_clipper()
python_deployer.create_endpoint(clipper_conn, "ham-spam-classifier",
"strings", predict_spam)
sleep(2)
try:
queue = deque(queries)
while True:
query = queue.pop()
print("Length {} query sent!".format(len(query)))
predict(clipper_conn.get_query_addr(), query)
queue.append(query)
shuffle(queue)
sleep(0.2)
except Exception as e:
print(e)
clipper_conn.stop_all()
tmp = tempfile.NamedTemporaryFile('wb', delete=False, suffix='.png')
tmp.write(io.BytesIO(imgs[i]).getvalue())
tmp.close()
# Use PIL to read in the file and compute size
size = PIL.Image.open(tmp.name, 'r').size
# Remove the temp file
os.unlink(tmp.name)
sizes.append(size)
return sizes
from clipper_admin import ClipperConnection, DockerContainerManager
from clipper_admin.deployers import python as python_deployer
clipper_conn = ClipperConnection(DockerContainerManager())
clipper_conn.start_clipper()
python_deployer.create_endpoint(clipper_conn=clipper_conn,
name="image-size",
input_type="bytes",
func=image_size,
pkgs_to_install=['pillow'])
print('clipper_conn.get_query_addr()' + clipper_conn.get_query_addr())
# To test
# query(clipper_conn.get_query_addr(), 'imgs/clipper-logo.png')
