使用自定义数据集的 Sagemaker 训练作业 ErrorMessage“FileNotFoundError：没有此类文件或目录：'/opt/ml/input/data/training/train/data.csv'

Question

算法错误：ExecuteUserScriptError：ExitCode 1 ErrorMessage“FileNotFoundError：[Errno 2]没有这样的文件或目录：'/opt/ml/input/data/training/train/data.csv'”命令“/opt/conda/bin/python3 .8transfer_learning.py --attn_dropout 0.17493582725215484 --batch_size 128 --frac_shared_embed 0.25 --input_dim 32 --learning_rate 0.008165108541098379 --mlp_dropout 0.2870045773619837 --n_blocks 4 --n_纪元 80 --耐心 15"，退出代码：1

from sagemaker.estimator import Estimator
from sagemaker.utils import name_from_base

training_job_name = name_from_base(f"jumpstart-{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_location,
)

if use_amt:

    tuner = HyperparameterTuner(
        tabular_estimator,
        "r2",
        hyperparameter_ranges,
        [{"Name": "r2", "Regex": "metrics={'r2': (\\S+)}"}],
        max_jobs=10,  # increase the max_jobs to achieve better performance from hyperparameter tuning
        max_parallel_jobs=2,
        objective_type="Maximize",
        base_tuning_job_name=training_job_name,
    )

    tuner.fit({"training": training_dataset_s3_path}, logs=True)

else:
    # Launch a SageMaker Training job by passing s3 path of the training data
    tabular_estimator.fit(
        {"training": training_dataset_s3_path}, logs=True, job_name=training_job_name
    )

Answer 1

正如评论中所述，您可能在

S3

中提供了不正确的文件路径。

一些罪魁祸首可能包括：

• 训练数据的格式与

  csv

不同

• data.csv

  被放置在不同的文件夹中，可能位于

  training_dataset_s3_path

  而不是

  training_dataset_s3_path/train

  ？
• 路径或文件名中的拼写错误