@@ -327,7 +327,7 @@ Here are examples of how to use Amazon EFS as input for training:
327327
328328 file_system_input = FileSystemInput(file_system_id = ' fs-1' ,
329329 file_system_type = ' EFS' ,
330- directory_path = ' tensorflow' ,
330+ directory_path = ' / tensorflow' ,
331331 file_system_access_mode = ' ro' )
332332
333333 # Start an Amazon SageMaker training job with EFS using the FileSystemInput class
@@ -347,7 +347,7 @@ Here are examples of how to use Amazon EFS as input for training:
347347
348348 records = FileSystemRecordSet(file_system_id = ' fs-1,
349349 file_system_type = ' EFS' ,
350- directory_path = ' kmeans' ,
350+ directory_path = ' / kmeans' ,
351351 num_records = 784 ,
352352 feature_dim = 784 )
353353
@@ -372,7 +372,7 @@ Here are examples of how to use Amazon FSx for Lustre as input for training:
372372
373373 file_system_input = FileSystemInput(file_system_id = ' fs-2' ,
374374 file_system_type = ' FSxLustre' ,
375- directory_path = ' tensorflow' ,
375+ directory_path = ' /fsx/ tensorflow' ,
376376 file_system_access_mode = ' ro' )
377377
378378 # Start an Amazon SageMaker training job with FSx using the FileSystemInput class
@@ -392,7 +392,7 @@ Here are examples of how to use Amazon FSx for Lustre as input for training:
392392
393393 records = FileSystemRecordSet(file_system_id = ' fs-=2,
394394 file_system_type = ' FSxLustre' ,
395- directory_path = ' kmeans' ,
395+ directory_path = ' /fsx/ kmeans' ,
396396 num_records = 784 ,
397397 feature_dim = 784 )
398398
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