问Sagemaker没有这样的文件或目录

EN

ErrorMessage "FileNotFoundError: [Errno 2] No such file or directory: '/opt/ml/input/data/training/train/data.csv 

bucket = sagemaker_session.default_bucket()
prefix = 'sagemaker_forecasting_ml' 

train_1d.to_csv('train_1d.csv', sep=',', index=False, header=False)
boto3.Session().resource('s3').Bucket(bucket).Object(os.path.join(prefix, 'data/train_1d.csv')).upload_file('train_1d.csv') 

training_1d_s3_path = TrainingInput(
    "s3://{}/{}/{}".format(bucket, prefix, "data/train_1d.csv"), content_type="csv"
)

from sagemaker import hyperparameters
from sagemaker.session import TrainingInput
from sagemaker.estimator import Estimator
from sagemaker.utils import name_from_base


train_model_id, train_model_version, train_scope = "lightgbm-regression-model", "*", "training"
training_instance_type = "ml.g4dn.xlarge"
region = sagemaker.Session().boto_region_name

# Retrieve the docker image
train_image_uri = image_uris.retrieve(
    region=region,
    framework=None,
    model_id=train_model_id,
    model_version=train_model_version,
    image_scope=train_scope,
    instance_type=training_instance_type
)

# Retrieve the training script
train_source_uri = script_uris.retrieve(
    model_id=train_model_id, model_version=train_model_version, script_scope=train_scope
)

train_model_uri = model_uris.retrieve(
    model_id=train_model_id, model_version=train_model_version, model_scope=train_scope
)



training_1d_s3_path = TrainingInput(
    "s3://{}/{}/{}".format(bucket, prefix, "data/train.csv"), content_type="csv"
)

s3_output_loc_1d = f"s3://{bucket}/{prefix}/output"

# Retrieve the default hyper-parameters for training the model
hyperparameters = hyperparameters.retrieve_default(
    model_id=train_model_id, model_version=train_model_version
)

# Override default hyperparameters with custom values
hyperparameters["num_boost_round"] = "500"
hyperparameters["n_estimators"] = "600"
hyperparameters["boosting_type"] = "gbdt"
hyperparameters["learning_rate"] = "0.01"
hyperparameters["objective"] = "tweedie"
hyperparameters["tweedie_variance_power"] = "1"



aws_role = sagemaker.get_execution_role()

training_job_name = name_from_base(f"built-in-algo-{train_model_id}-training")

# Create SageMaker Estimator instance
tabular_estimator = Estimator(
    role=aws_role,
    image_uri=train_image_uri,
    source_dir=train_source_uri,
    model_uri=train_model_uri,
    entry_point="transfer_learning.py",
    instance_count=1,
    instance_type=training_instance_type,
    max_run=360000,
    hyperparameters=hyperparameters,
    output_path=s3_output_loc_1d
)

# Launch a SageMaker Training job by passing the S3 path of the training data
tabular_estimator.fit(
    {"training": training_1d_s3_path}, logs=True, job_name=training_job_name
)