/jddlmZddlZddlZddlZddlZddlmZddlm Z ddl m Z erddl m Z dd ZddZddZddZdS)) annotationsN) TYPE_CHECKING)USER_CONFIG_DIR) TORCH_1_9) BaseTrainerreturnintcddl}||j|j5}|d|dcdddS#1swxYwYdS)zFind 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)r ss [/home/longshao/multi-rider-rag/.venv/lib/python3.11/site-packages/ultralytics/utils/dist.pyfind_free_network_portrsMMM v~v'9 : :"a    }}q!""""""""""""""""""s/A!!A%(A%trainerrstrc||jjd|jjdd\}}t |j}|dddlfd|dD|d<d|d|d |d |d t|j d |jj d }tdz dtjdt!|dddtdz d5}||dddn #1swxYwY|jS)a1Generate 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 .r augmentationsNrc:g|]}|S)to_dict).0tAs r z%generate_ddp_file..=s#%W%W%Wqaiill%W%W%Wz # Ultralytics Multi-GPU training temp file (should be automatically deleted after use) from pathlib import Path, PosixPath # For model arguments stored as Path instead of str overrides = z& if __name__ == "__main__": from z import a from ultralytics.utils import DEFAULT_CFG_DICT # Deserialize augmentations from dicts back to Albumentations transform objects if overrides.get("augmentations") is not None: import albumentations as A overrides["augmentations"] = [A.from_dict(t) for t in overrides["augmentations"]] 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)prefixsuffixmodeencodingdirdelete) __class__ __module____name__rsplitvarsargscopygetalbumentationsgetattr hub_sessionmodelrmkdirtempfileNamedTemporaryFileidwritename)rmoduler= overridescontentfilers @rgenerate_ddp_filerB"s('2QQW5F5OQQXXY\^_``LFDW\""''))I}}_%%1""""%W%W%W%WIo( g&''' W % %D*3S&&9SH ! # #D        C 9r rANonecZt|d|vrtj|dSdS)aDDelete 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) r%N)r;osremove)rrAs r ddp_cleanuprU~s7 W++d"" $#"r )r r )rrr r)rrr rC)rrrArr rQ) __future__rrSrGrJr9typingrr torch_utilsrultralytics.engine.trainerrrrBrPrUrr rr[s#"""""  """"""7666666 " " " " ;;;;|<r