tensorflow_script_mode_debug_local_training/tensorflow_script_mode_debug_local_training.py [22:37]:
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DUMMY_IAM_ROLE = 'arn:aws:iam::111111111111:role/service-role/AmazonSageMaker-ExecutionRole-20200101T000001'
data_files_list = ('train_data.npy', 'train_labels.npy', 'eval_data.npy', 'eval_labels.npy')


def download_training_and_eval_data():
    if os.path.isfile('./data/train_data.npy') and \
            os.path.isfile('./data/train_labels.npy') and \
            os.path.isfile('./data/eval_data.npy') and \
            os.path.isfile('./data/eval_labels.npy'):
        print('Training and evaluation datasets exist. Skipping Download')
    else:
        print('Downloading training and evaluation dataset')
        s3 = boto3.resource('s3')
        for filename in data_files_list:
            s3.meta.client.download_file('sagemaker-sample-data-us-east-1', 'tensorflow/mnist/' + filename,
                                         './data/' + filename)
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tensorflow_script_mode_local_training_and_serving/tensorflow_script_mode_local_training_and_serving.py [26:41]:
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DUMMY_IAM_ROLE = 'arn:aws:iam::111111111111:role/service-role/AmazonSageMaker-ExecutionRole-20200101T000001'
data_files_list = ('train_data.npy', 'train_labels.npy', 'eval_data.npy', 'eval_labels.npy')


def download_training_and_eval_data():
    if os.path.isfile('./data/train_data.npy') and \
            os.path.isfile('./data/train_labels.npy') and \
            os.path.isfile('./data/eval_data.npy') and \
            os.path.isfile('./data/eval_labels.npy'):
        print('Training and evaluation datasets exist. Skipping Download')
    else:
        print('Downloading training and evaluation dataset')
        s3 = boto3.resource('s3')
        for filename in data_files_list:
            s3.meta.client.download_file('sagemaker-sample-data-us-east-1', 'tensorflow/mnist/' + filename,
                                         './data/' + filename)
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