wandb运行时错误：CUDA内存不足

Question

我试图运行simple transformers文档中的hyperparameter optimization example中给出的模型，但在经过一定次数的迭代后搜索hyperparameters时，出现CUDA内存不足错误。此外，在搜索超参数的过程中，分配的GPU内存不断增加

以下是内存分配图：

这是我的密码。我在Google Collab上发布的代码。如何修复此错误

import logging

import pandas as pd
import sklearn

import wandb
from simpletransformers.classification import (
    ClassificationArgs,
    ClassificationModel,
)

sweep_config = {
    "method": "bayes",  # grid, random
    "metric": {"name": "train_loss", "goal": "minimize"},
    "parameters": {
        "num_train_epochs": {"values": [2, 3, 5]},
        "learning_rate": {"min": 5e-5, "max": 4e-4},
    },
}

sweep_id = wandb.sweep(sweep_config, project="Simple Sweep")

logging.basicConfig(level=logging.INFO)
transformers_logger = logging.getLogger("transformers")
transformers_logger.setLevel(logging.WARNING)

# Preparing train data
train_data = [
    ["Aragorn was the heir of Isildur", "true"],
    ["Frodo was the heir of Isildur", "false"],
]
train_df = pd.DataFrame(train_data)
train_df.columns = ["text", "labels"]

# Preparing eval data
eval_data = [
    ["Theoden was the king of Rohan", "true"],
    ["Merry was the king of Rohan", "false"],
]
eval_df = pd.DataFrame(eval_data)
eval_df.columns = ["text", "labels"]

model_args = ClassificationArgs()
model_args.reprocess_input_data = True
model_args.overwrite_output_dir = True
model_args.evaluate_during_training = True
model_args.manual_seed = 4
model_args.use_multiprocessing = True
model_args.train_batch_size = 16
model_args.eval_batch_size = 8
model_args.labels_list = ["true", "false"]
model_args.wandb_project = "Simple Sweep"

def train():
    # Initialize a new wandb run
    wandb.init()

    # Create a TransformerModel
    model = ClassificationModel(
        "roberta",
        "roberta-base",
        use_cuda=True,
        args=model_args,
        sweep_config=wandb.config,
    )

    # Train the model
    model.train_model(train_df, eval_df=eval_df)

    # Evaluate the model
    model.eval_model(eval_df)

    # Sync wandb
    wandb.join()


wandb.agent(sweep_id, train)