use global variables in AWS Sagemaker script - amazon-web-services

Question

After having correctly deployed our model, I need to invoke it via lambda function. The script features two cleaning function, the first one (cleaning()) gives us 5 variables: the cleaned dataset and 4 other variables (scaler, monthdummies, compadummies, parceldummies) that we need to use in the second cleaning function (cleaning_test()).
The reason behind this is that in the use case I'll have only one instance at a time to perform predictions on, not an entire dataset. This means that I pass the row to the first cleaning() function since some commands won't work. I can't also use a scaler and neither create dummy variables, so the aim is to import the scaler and some dummies used in the cleaning() function, since they come from the whole dataset, that I used to train the model.
Hence, in the input_fn() function, the input needs to be cleaned using the cleaning_test() function, that requires the scaler and the three lists of dummies from the cleaning() one.
When I train the model, the cleaning() function works fine, but after the deployment, if we invoke the endpoint, it raises the error that variable "scaler" is not defined.
Below is the script.py:
Note that the test is # since I've already tested it, so now I'm training on the whole dataset and I want to predict completely new instances
def cleaning(data):
some cleaning on data stored in s3
return cleaned_data, scaler, monthdummies, compadummies, parceldummies
def cleaning_test(data, scaler, monthdummies, compadummies, parceldummies):
cleaning on data without labels
return cleaned_data
def model_fn(model_dir):
clf = joblib.load(os.path.join(model_dir, "model.joblib"))
return clf
def input_fn(request_body, request_content_type):
if request_content_type == "application/json":
data = json.loads(request_body)
df = pd.DataFrame(data, index = [0])
input_data = cleaning_test(df, scaler, monthdummies, compadummies, parceldummies)
else:
pass
return input_data
def predict_fn(input_data, model):
return model.predict_proba(input_data)
if __name__ =='__main__':
print('extracting arguments')
parser = argparse.ArgumentParser()
# hyperparameters sent by the client are passed as command-line arguments to the script.
parser.add_argument('--n_estimators', type=int, default=10)
parser.add_argument('--min-samples-leaf', type=int, default=3)
# Data, model, and output directories
parser.add_argument('--model-dir', type=str, default=os.environ.get('SM_MODEL_DIR'))
parser.add_argument('--train', type=str, default=os.environ.get('SM_CHANNEL_TRAIN'))
#parser.add_argument('--test', type=str, default=os.environ.get('SM_CHANNEL_TEST'))
parser.add_argument('--train-file', type=str, default='fp_train.csv')
#parser.add_argument('--test-file', type=str, default='fp_test.csv')
args, _ = parser.parse_known_args()
print('reading data')
train_df = pd.read_csv(os.path.join(args.train, args.train_file))
#test_df = pd.read_csv(os.path.join(args.test, args.test_file))
print("cleaning")
train_df, scaler, monthdummies, compadummies, parceldummies = cleaning(train_df)
#test_df, scaler1, monthdummies1, compadummies1, parceldummies1 = cleaning(test_df)
print("splitting")
y = train_df.loc[:,"event"]
X = train_df.loc[:, train_df.columns != 'event']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
"""print('building training and testing datasets')
X_train = train_df.loc[:, train_df.columns != 'event']
X_test = test_df.loc[:, test_df.columns != 'event']
y_train = train_df.loc[:,"event"]
y_test = test_df.loc[:,"event"]"""
print(X_train.columns)
print(X_test.columns)
# train
print('training model')
model = RandomForestClassifier(
n_estimators=args.n_estimators,
min_samples_leaf=args.min_samples_leaf,
n_jobs=-1)
model.fit(X_train, y_train)
# print abs error
print('validating model')
proba = model.predict_proba(X_test)
# persist model
path = os.path.join(args.model_dir, "model.joblib")
joblib.dump(model, path)
print('model persisted at ' + path)
That I run through:
sklearn_estimator = SKLearn(
entry_point='script.py',
role = get_execution_role(),
train_instance_count=1,
train_instance_type='ml.c5.xlarge',
framework_version='0.20.0',
base_job_name='rf-scikit',
hyperparameters = {'n_estimators': 15})
sklearn_estimator.fit({'train':trainpath})
sklearn_estimator.latest_training_job.wait(logs='None')
artifact = sm_boto3.describe_training_job(
TrainingJobName=sklearn_estimator.latest_training_job.name)['ModelArtifacts']['S3ModelArtifacts']
predictor = sklearn_estimator.deploy(
instance_type='ml.c5.large',
initial_instance_count=1)
The question is, how can I "store" the variables given by the cleaning() function during the training process, in order to use them in the input_fn() function, making cleaning_test() work fine?
Thanks!