h7\SSKrSSKrSSKrSSKrSSKJr SSKJr S\4Sjr Sr Sr S r g) N)USER_CONFIG_DIR) TORCH_1_9returncSSKnURURUR5nURS5 UR 5SsSSS5 $!,(df  g=f)z Find a free port on localhost. It is useful in single-node training when we don't want to connect to a real main node but have to set the `MASTER_PORT` environment variable. Returns: (int): The available network port number. rN)z 127.0.0.1rr)socketAF_INET SOCK_STREAMbind getsockname)rss P/home/james-whalen/.local/lib/python3.13/site-packages/ultralytics/utils/dist.pyfind_free_network_portr sH v~~v'9'9 :a  }}q! ; : :s $A A'c(URRSURR3RSS5upS[ UR 5SUSUSUS[ URSUR R5S 3 n[S - RS S 9 [R"S [U5S3SS[S - SS9nURU5 SSS5 UR$!,(df  WR$=f)a Generate a DDP (Distributed Data Parallel) file for multi-GPU training. This function creates a temporary Python file that enables distributed training across multiple GPUs. The file contains the necessary configuration to initialize the trainer in a distributed environment. Args: trainer (ultralytics.engine.trainer.BaseTrainer): The trainer containing training configuration and arguments. Must have args attribute and be a class instance. Returns: (str): Path to the generated temporary DDP file. Notes: The generated file is saved in the USER_CONFIG_DIR/DDP directory and includes: - Trainer class import - Configuration overrides from the trainer arguments - Model path configuration - Training initialization code .rzd # Ultralytics Multi-GPU training temp file (should be automatically deleted after use) overrides = z& if __name__ == "__main__": from z import z from ultralytics.utils import DEFAULT_CFG_DICT cfg = DEFAULT_CFG_DICT.copy() cfg.update(save_dir='') # handle the extra key 'save_dir' trainer = z9(cfg=cfg, overrides=overrides) trainer.args.model = " model_urlz " results = trainer.train() DDPT)exist_ok_temp_.pyzw+zutf-8F)prefixsuffixmodeencodingdirdeleteN) __class__ __module____name__rsplitvarsargsgetattr hub_sessionmodelrmkdirtempfileNamedTemporaryFileidwritename)trainermoduler+contentfiles rgenerate_ddp_filer0s#*''2231W5F5F5O5O4PQXXY\^_`LF ',,  !  $  f"7#6#6 W\\EWEWXYZ Gu##T#2  $ $W+c"  e #    7  99   99s C88 DcSSKnUR(d [R"UR5 [ U5n[ (aSOSn[5n[RSUSURSUU/nXR4$)a= Generate command for distributed training. Args: trainer (ultralytics.engine.trainer.BaseTrainer): The trainer containing configuration for distributed training. Returns: cmd (list[str]): The command to execute for distributed training. file (str): Path to the temporary file created for DDP training. rNztorch.distributed.runztorch.distributed.launchz-mz--nproc_per_nodez --master_port) __main__resumeshutilrmtreesave_dirr0rrsys executable world_size)r,r2r/dist_cmdportcmds rgenerate_ddp_commandr=Osw >> g&&' W %D*3)&9SH ! #D       &  C 9cV[U5S3U;a[R"U5 gg)a Delete temporary file if created during distributed data parallel (DDP) training. This function checks if the provided file contains the trainer's ID in its name, indicating it was created as a temporary file for DDP training, and deletes it if so. Args: trainer (ultralytics.engine.trainer.BaseTrainer): The trainer used for distributed training. file (str): Path to the file that might need to be deleted. Examples: >>> trainer = YOLOTrainer() >>> file = "/tmp/ddp_temp_123456789.py" >>> ddp_cleanup(trainer, file) rN)r)osremove)r,r/s r ddp_cleanuprBns' W+cd" $#r>) r@r4r7r'r torch_utilsrintrr0r=rBr>rrGs5 """"/d>r>