FiddlmZddlZddlZddlZddlZddlmZddlm Z ddl m Z erddl m Z d dZd d Zdd Zdd Zy)) annotationsN) TYPE_CHECKING)USER_CONFIG_DIR) TORCH_1_9) BaseTrainercddl}|j|j|j5}|jd|j dcdddS#1swYyxYw)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 X/var/www/pod-logistic/pod-ai/venv/lib/python3.12/site-packages/ultralytics/utils/dist.pyfind_free_network_portrsQ v~~v'9'9 :"a  }}q!"""s $AA"c|jjd|jjjdd\}}t |j j }|jd)ddl}|dDcgc]}|j|c}|d<d|d|d|d |d t|jd |j jd }td z jdtj dt#|dddtd z d5}|j%|ddd|j&Scc}w#1swYj&SxYw)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 augmentationsNrz # 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__rsplitvarsargscopygetalbumentationsto_dictgetattr hub_sessionmodelrmkdirtempfileNamedTemporaryFileidwritename)trainermoduler2 overridesAtcontentfiles rgenerate_ddp_filer:"sq(''2231W5F5F5O5O4PQXXY\^_`LFDW\\"'')I}}_%1"> g&&' W %D*3&9SH ! #D       &  C 9cPt|d|vrtj|yy)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) rN)r0osremove)r3r9s r ddp_cleanuprL~s' W+cd" $#rH)returnint)r3rrMstr)r3rrMztuple[list[str], str])r3rr9rOrMNone) __future__rrJr>rAr.typingrr torch_utilsrultralytics.engine.trainerrrr:rGrLrHrrWs:#  "6 " ;|<rH