parser = LitModel.add_model_specific_args(parser) # 将所有可用的trainer选项添加到argparse parser = Trainer.add_argparse_args(parser) args = parser.parse_args() 现在,你可以这样调用运行程序: python trainer_main.py --gpus 2 --num_nodes 2 --conda_env 'my_env' --encoder_layers 12 最后,确...
Trainer可以接受的参数可以直接使用Trainer.add_argparse_args来添加,免去手动去写一条条的argparse 在实例化Trainer时,使用Trainer.from_argparse_args(args)来导入接收到的args from argparse import ArgumentParser def main(args): model = MyModule() data = MyData() trainer = Trainer.from_argparse_args(args...
I had the same "AttributeError: type object 'Trainer' has no attribute 'add_argparse_args'" error with the lastest version of lightning==2.0.0rc1 (Release on Mar 2, 2023), but I tried with lightning==1.9.0 which is the same as https://github.com/hpcaitech/ColossalAI/blob/main/examp...
Please reproduce using the BoringModel To Reproduce in acli.pyfile importargparseimportpytorch_lightningasplparser=argparse.ArgumentParser("")sub_parsers=parser.add_subparsers()train_parser=sub_parsers.add_parser("train")train_parser.add_argument("--seed")train_parser=pl.Trainer.add_argparse_args(tr...
# 需要导入模块: import pytorch_lightning [as 别名]# 或者: from pytorch_lightning importTrainer[as 别名]defmain(args: argparse.Namespace)->None:"""Train the model. Args: args: Model hyper-parameters Note: For the sake of the example, the images dataset will be downloaded ...
args:一个 TrainingArguments,指定用于实例化 Trainer 的训练参数。 state:一个 TrainerState,指定训练器的当前状态。 control:一个 TrainerControl,指定返回给训练器的对象,它可以用来做一些决定。 model:一个 PreTrainedModel 或torch.nn.Module,指定正在训练的模型。 tokenizer:一个 PreTrainedTokenizer,指定用于对数据...
支持argparse命令行指定参数,也支持config.yaml配置文件 支持最优模型保存ModelCheckpoint 支持自定义回调函数Callback 支持NNI模型剪枝(L1/L2-Pruner,FPGM-Pruner Slim-Pruner)nni_pruning 非常轻便,安装简单 ...
from .library.custom_train_functions import apply_masked_loss, add_custom_train_arguments class FluxTrainer: def __init__(self): self.sample_prompts_te_outputs = None def init_train(self, args): train_util.verify_training_args(args) train_util.prepare_dataset_args(args, True) #...
args=parser.parse_args(sys.argv[1:])model=BertForPreTraining.from_pretrained(args.model_name)check_point=torch.load(args.checkpoint_path,map_location='cpu')model.load_state_dict(check_point['model'],strict=False)model.save_pretrained(args.output_saved_model_path,save_c...
""" import argparse import os import json from dataclasses import dataclass import mindspore from mindspore import context, DynamicLossScaleManager from mindspore import load_checkpoint, load_param_into_net from mindspore.common import set_seed ...