Feature/dcp fix wheels (#264)

Rename `uri` parameter back to `path` and limit number of workers to the same amount as in distributed training

---------

Co-authored-by: Ilya Isaev <[email protected]>

Author: IsaevIlya

s3torchconnector/src/s3torchconnector/dcp/s3_file_system.py

@@ -196,24 +196,24 @@ class S3StorageWriter(FileSystemWriter):
     def __init__(
         self,
         region: str,
-        uri: str,
+        path: str,
         **kwargs,
     ) -> None:
         """
         Initialize an S3 writer for distributed checkpointing.
 
         Args:
             region (str): The AWS region for S3.
-            uri (str): The S3 URI to write checkpoints to.
+            path (str): The S3 URI to write checkpoints to.
             kwargs (dict): Keyword arguments to pass to the parent :class:`FileSystemWriter`.
         """
         super().__init__(
-            path=uri,
+            path=path,
             sync_files=False,  # FIXME: setting this to True makes the run to fail (L#333: `os.fsync(stream.fileno())`)
             **kwargs,
         )
         self.fs = S3FileSystem(region)  # type: ignore
-        self.path = self.fs.init_path(uri)
+        self.path = self.fs.init_path(path)
 
     @classmethod
     def validate_checkpoint_id(cls, checkpoint_id: Union[str, os.PathLike]) -> bool:

s3torchconnector/tst/e2e/dcp/test_e2e_s3_file_system.py

@@ -157,7 +157,7 @@ def test_dcp_when_multi_process(
     checkpoint_directory, tensor_dimensions, thread_count, port_offset
 ):
     multi_process_dcp_save_load(
-        6, thread_count, checkpoint_directory, tensor_dimensions, port_offset
+        3, thread_count, checkpoint_directory, tensor_dimensions, port_offset
     )