tfserve_vision15.py

# save sample data
np.save('serve_warmup_data.npy', sample_test_data)

# model warmup functions
def warmup_model1_serve(warmup_data):
    warmup_data_processed = np.expand_dims(warmup_data / 255., axis=3)
    data = json.dumps({"signature_name": "serving_default", 
                       "instances": warmup_data_processed.tolist()})

    HEADERS = {'content-type': 'application/json'}
    MODEL1_API_URL = 'http://localhost:8501/v1/models/fashion_model_serving/versions/1:predict'

    json_response = requests.post(MODEL1_API_URL, data=data, headers=HEADERS)
    predictions = json.loads(json_response.text)['predictions']
    print('Model 1 warmup complete')
    
def warmup_model2_serve(warmup_data, img_dims=(32, 32)):
    warmup_data_processed = (np.array([resize_image_array(img, 
                                                          img_size_dims=img_dims) 
                                            for img in np.stack([warmup_data]*3, 
                                                                axis=-1)])) / 255.
    data = json.dumps({"signature_name": "serving_default", 
                       "instances": warmup_data_processed.tolist()})

    HEADERS = {'content-type': 'application/json'}
    MODEL2_API_URL = 'http://localhost:8501/v1/models/fashion_model_serving/versions/2:predict'

    json_response = requests.post(MODEL2_API_URL, data=data, headers=HEADERS)
    predictions = json.loads(json_response.text)['predictions']
    print('Model 2 warmup complete')

# warmup models    
warmup_data = np.load('serve_warmup_data.npy')
warmup_model1_serve(warmup_data)
warmup_model2_serve(warmup_data)