# 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()