No such file or directory: '/opt/ml/input/data/test/revenue_train.csv' Sagemaker [SM_CHANNEL_TRAIN]


I am trying to deploy my RandomForestClassifier on Amazon Sagemaker using Python SDK. I have been following this example but keep getting an error that the train file was not found. I think the file were not uploaded to the correct channel. When I run the script as follows it works fine.

! python --model-dir ./ \
                   --train ./ \
                   --test ./ \

This is my script code:

# inference functions ---------------
def model_fn(model_dir):
    clf = joblib.load(os.path.join(model_dir, "model.joblib"))
    return clf

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('--max_depth', type=int, default=2)
    parser.add_argument('--n_estimators', type=int, default=100)
    parser.add_argument('--random_state', type=int, default=0)

    # 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_TEST'))
    parser.add_argument('--test', type=str, default=os.environ.get('SM_CHANNEL_TEST'))
    parser.add_argument('--train-file', type=str, default='revenue_train.csv')
    parser.add_argument('--test-file', type=str, default='revenue_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))
    if len(train_df) == 0:
        raise ValueError(('There are no files in {}.\n').format(args.train, "train"))

    print('building training and testing datasets')
    attributes = ['available_minutes_100','ampido_slots_amount','ampido_slots_amount_100','ampido_slots_amount_200','ampido_slots_amount_300','min_dist_loc','count_event','min_dist_phouses','count_phouses','min_dist_stops','count_stops','min_dist_tickets','count_tickets','min_dist_google','min_dist_psa','count_psa']
    X_train = train_df[attributes]
    X_test = test_df[attributes]
    y_train = train_df['target']
    y_test = test_df['target']
    # train
    print('training model')
    model = RandomForestClassifier(
        max_depth =args.max_depth, n_estimators = args.n_estimators), y_train)
    # persist model
    path = os.path.join(args.model_dir, "model_rf.joblib")
    joblib.dump(model, path)
    print('model persisted at ' + path)
    # print accuracy and confusion matrix 
    print('validating model')
    print('Confusion Matrix:')
    result = confusion_matrix(y_test, y_pred)
    result2 = accuracy_score(y_test, y_pred)

the error is raised in the train_df line of the script (FileNotFoundError: [Errno 2] No such file or directory: '/opt/ml/input/data/test/revenue_train.csv').

I tried specifying the input parameters:

# change channel input dirs 
inputs = {
    "train": "ampido-exports/production/revenue_train",
    "test": "ampido-exports/production/revenue_test",
from sagemaker.sklearn.estimator import SKLearn
enable_local_mode_training = False

hyperparameters = {"max_depth": 2, 'random_state':0, "n_estimators": 100}

if enable_local_mode_training:
    train_instance_type = "local"
    inputs = {"train": trainpath, "test": testpath}

    train_instance_type = "ml.c5.xlarge"
    inputs = {"train": trainpath, "test": testpath}

estimator_parameters = {
    "entry_point": "",
    "framework_version": "1.0-1",
    "py_version": "py3",
    "instance_type": train_instance_type,
    "instance_count": 1,
    "hyperparameters": hyperparameters,
    "role": role,
    "base_job_name": "randomforestclassifier-model",
    'channel_input_dirs' : inputs

estimator = SKLearn(**estimator_parameters)

but i still get the error FileNotFoundError: [Errno 2] No such file or directory: '/opt/ml/input/data/test/revenue_train.csv

1 Answer

Your code has a typo that results in the issue, maybe caused by copy-pasting. Note that instead of a training path you're passing a test path location as the default to args.train:

    parser.add_argument('--train', type=str, default=os.environ.get('SM_CHANNEL_TEST'))
                           ^^^^^                                                ^^^^
    parser.add_argument('--test', type=str, default=os.environ.get('SM_CHANNEL_TEST'))

Later, when you try to access the training file from the test location, the file is obviously not there:

No such file or directory: '/opt/ml/input/data/test/revenue_train.csv
                                               ^^^^         ^^^^^

Changing the default argument for the args.train to SM_CHANNEL_TRAIN should resolve the issue,

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answered 3 months ago
reviewed 3 months ago

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