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N) superrgr,fsr rr<r=rr>rKsave_idr?) rSrr<r=rr>r?r@rA __class__s rArg_FileSystemWriter.__init__sU0 ,GG%%d+ $8!$(%:"%' "r@rcpU(a URRU5Ul[5UlgrH)rDr rrKrErSrs rAreset_FileSystemWriter.resets% ))-8DI%' r@is_coordinatorcgrHr6)rSrLs rAset_up_storage_writer'_FileSystemWriter.set_up_storage_writerrnr@plancbURRUR5 URRUR5(a_UR (a4[ R"SURSUR <S35 U$[SUR <S35eU$)Nz#Detected an existing checkpoint in z#, overwriting since self.overwrite=z. Past version 2.5 of PyTorch, `overwrite` will default to False. 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Args: path: directory where the checkpoint will be written to. single_file_per_rank: Produce one file per rank instead of one file per tensor/blob. Default to True. sync_files : force files to be synced to permanent storage. Default to True. thread_count: Number of IO threads to use to write. Default to 1. per_thread_copy_ahead: How many bytes to copy from the GPU ahead of saving then. Default 10Mb. cache_staged_state_dict: Whether to cache the staged state_dict. This option decreases staging latency at the cost of increases memory usage. Additionally, if this parameter is set to True, it's the expectation that the stager is maintained and re-used for multiple dcp.async_save calls. Default to False. overwrite: Whether to allow overwriting existing checkpoints. Defaults to True. N. B. 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