configure_estimator

sagemaker_functions.py

sess = sagemaker.Session()

# define the specifications of the sagemaker training instance
bt_model = sagemaker.estimator.Estimator(container,
                                         role, 
                                         train_instance_count=2, 
                                         train_instance_type='ml.c4.2xlarge',
                                         train_volume_size = 5,
                                         train_max_run = 360000,
                                         input_mode= 'File',
                                         output_path=s3_output_location,
                                         sagemaker_session=sess)

# set the hyperparameters of the BlazingText model
bt_model.set_hyperparameters(mode="batch_skipgram",
                             epochs=15,
                             min_count=5,
                             sampling_threshold=0.0001,
                             learning_rate=0.05,
                             window_size=5,
                             vector_dim=300,
                             negative_samples=5,
                             batch_size=11,
                             evaluation=True,
                             subwords=False)