# Tell Clipper to route requests for the "iris" application to the "iris-model"
clipper_conn.link_model_to_app(app_name="iris", model_name="iris-model")
# Your iris application is now ready to serve predictions
# In[ ]:
clipper_conn.get_clipper_logs()
# In[ ]:
clipper_conn.cm.get_num_replicas(name="iris-model", version='1')
# In[ ]:
clipper_conn.get_linked_models(app_name="iris")
# In[ ]:
clipper_conn.cm.get_num_replicas(name="iris-model", version="8")
# In[ ]:
q1 = [4.3, 2.0, 1.0, 0.1]
q2 = [5.84, 3.05, 3.76, 1.2]
q3 = [7.9, 4.4, 6.9, 2.5]
# In[ ]:
import requests, json, numpy as np
headers = {"Content-type": "application/json"}
data_transforms = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(train_mean, train_std)])
image = get_image(data_transforms, image_url)
pred = np.argmax(model(image).detach().numpy())
return CLASSES[pred]
model = get_model()
clipper_conn.stop_models(model_names='logo-detector')
deploy_pytorch_model(
clipper_conn,
name="logo-detector",
version=1,
input_type="strings",
func=predict,
pytorch_model=model,
pkgs_to_install=['Pillow', 'torchvision', 'torch', 'numpy', 'requests']
)
clipper_conn.get_clipper_logs()
clipper_conn.link_model_to_app(app_name="logo-detector", model_name="logo-detector")
clipper_conn.get_linked_models(app_name="logo-detector")
clipper_conn.cm.get_num_replicas(name="logo-detector", version="1")
clipper_conn.get_clipper_logs()
