Loading Data From S3 Path in Sagemaker
Thanks @philschmid for the suggestion.
As I mentioned in the previous comment, due to security issues I can not save the data locally.
I need to read it from S3 and process it directly.

I guess that many other people try to train / fit those models on huge datasets (e.g entire Wiki), what is the best practice in those cases?
In Sagemaker Im tring to load the data set from S3 path as follows

`train_path = 's3://xxxxxxxxxx/xxxxxxxxxx/train.csv'
    valid_path = 's3://xxxxxxxxxx/xxxxxxxxxx/validation.csv'
    test_path = 's3://xxxxxxxxxx/xxxxxxxxxx/test.csv'
    
    data_files = {}
    data_files["train"] = train_path
    data_files["validation"] = valid_path
    data_files["test"] = test_path
    extension = train_path.split(".")[-1]
    datasets = load_dataset(extension, data_files=data_files, s3_enabled=True)
    print(datasets)`


I getting an error of

`algo-1-7plil_1  |   File "main.py", line 21, in <module>
algo-1-7plil_1  |     datasets = load_dataset(extension, data_files=data_files)
algo-1-7plil_1  |   File "/opt/conda/lib/python3.6/site-packages/datasets/load.py", line 603, in load_dataset
algo-1-7plil_1  |     **config_kwargs,
algo-1-7plil_1  |   File "/opt/conda/lib/python3.6/site-packages/datasets/builder.py", line 155, in __init__
algo-1-7plil_1  |     **config_kwargs,
algo-1-7plil_1  |   File "/opt/conda/lib/python3.6/site-packages/datasets/builder.py", line 305, in _create_builder_config
algo-1-7plil_1  |     m.update(str(os.path.getmtime(data_file)))
algo-1-7plil_1  |   File "/opt/conda/lib/python3.6/genericpath.py", line 55, in getmtime
algo-1-7plil_1  |     return os.stat(filename).st_mtime
algo-1-7plil_1  | FileNotFoundError: [Errno 2] No such file or directory: 's3://lsmv-sagemaker/pubmedbert/test.csv`

But when im trying with pandas , it is able to load from S3

Does the datasets library support S3 path to load
Loading Data From S3 Path in Sagemaker 
 In Sagemaker Im tring to load the data set from S3 path as follows

`train_path = 's3://xxxxxxxxxx/xxxxxxxxxx/train.csv'
    valid_path = 's3://xxxxxxxxxx/xxxxxxxxxx/validation.csv'
    test_path = 's3://xxxxxxxxxx/xxxxxxxxxx/test.csv'
    
    data_files = {}
    data_files["train"] = train_path
    data_files["validation"] = valid_path
    data_files["test"] = test_path
    extension = train_path.split(".")[-1]
    datasets = load_dataset(extension, data_files=data_files, s3_enabled=True)
    print(datasets)`


I getting an error of

`algo-1-7plil_1  |   File "main.py", line 21, in <module>
algo-1-7plil_1  |     datasets = load_dataset(extension, data_files=data_files)
algo-1-7plil_1  |   File "/opt/conda/lib/python3.6/site-packages/datasets/load.py", line 603, in load_dataset
algo-1-7plil_1  |     **config_kwargs,
algo-1-7plil_1  |   File "/opt/conda/lib/python3.6/site-packages/datasets/builder.py", line 155, in __init__
algo-1-7plil_1  |     **config_kwargs,
algo-1-7plil_1  |   File "/opt/conda/lib/python3.6/site-packages/datasets/builder.py", line 305, in _create_builder_config
algo-1-7plil_1  |     m.update(str(os.path.getmtime(data_file)))
algo-1-7plil_1  |   File "/opt/conda/lib/python3.6/genericpath.py", line 55, in getmtime
algo-1-7plil_1  |     return os.stat(filename).st_mtime
algo-1-7plil_1  | FileNotFoundError: [Errno 2] No such file or directory: 's3://lsmv-sagemaker/pubmedbert/test.csv`

But when im trying with pandas , it is able to load from S3

Does the datasets library support S3 path to load 
 Thanks @philschmid for the suggestion.
As I mentioned in the previous comment, due to security issues I can not save the data locally.
I need to read it from S3 and process it directly.

I guess that many other people try to train / fit those models on huge datasets (e.g entire Wiki), what is the best practice in those cases?