tuner = HyperparameterTuner(estimator, objective_metric_name, hyperparameter_ranges, metric_definitions, max_jobs=9, max_parallel_jobs=3, objective_type=objective_type) #Launching the tuning job tuner.fit({'training': inputs}) #Creating endpoint predictor = tuner.deploy(initial_instance_count=1, instance_type='ml.m4.xlarge') #Evaluate from IPython.display import HTML HTML(open("input.html").read()) import numpy as np image = np.array([data], dtype=np.float32) response = predictor.predict(image) prediction = response.argmax(axis=1)[0] print(prediction) #Cleanup tuner.delete_endpoint()