ecs-cli configure --cluster ec2-tutorial --default-launch-type EC2 --config-name ec2-tutorial --region ap-northeast

ecs-cli configure profile --profile-name ec2-tutorial-profile --access-key save_the_world --secret-key love_your_self

ecs-cli up --keypair tree_rsa --capability-iam --size 1 --instance-type t2.micro --cluster-config ec2-tutorial --ecs-profile ec2-tutorial-profile

ecs-cli compose up --create-log-groups --cluster-config ec2-tutorial --ecs-profile ec2-tutorial-profile …

aws sagemaker list-models
aws sagemaker list-endpoint-configs
aws sagemaker list-endpoints

for x in $(aws sagemaker list-models | jq ".[][].ModelName" | xargs -isrc echo src); do; aws sagemaker delete-model --model-name  $x ; echo $x; done;
for x in $(aws sagemaker list-endpoint-configs | jq ".[][].EndpointConfigName" | xargs -isrc echo src); do; aws sagemaker delete-endpoint-config --endpoint-config-name  $x …

    # Feature columns describe how to use the input.
    my_feature_columns = []
    for key in train_x.keys():
        my_feature_columns.append(tf.feature_column.numeric_column(key=key))

    feature_spec = tf.feature_column.make_parse_example_spec(my_feature_columns)
    serving_input_receiver_fn = tf.estimator.export.build_parsing_serving_input_receiver_fn(feature_spec)
    export_dir = classifier.export_savedmodel('export', serving_input_receiver_fn)

def serving_input_receiver_fn():
  max_seq_length = FLAGS.max_seq_length
  batch_size = 1
  feature_spec = {
  "unique_ids …

git ls-tree -r master --name-only

youtube-dl --add-metadata -i -x --audio-format flac -- [youtube id]

$ python manage.py createsuperuser …