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()
