I'm encountering a CUDA out of memory error when using the compute_metrics function with the Hugging Face Trainer during model evaluation. My GPU is running out of memory while trying to compute the ROUGE scores. Below is a summary of my setup and the error message:...
xxxxxxxxxx export CUDA_VISIBLE_DEVICES=1,0 e. Trainer 集成 Trainer 已经被扩展到支持一些库,这些库可能会极大地改善你的训练时间并适应更大的模型。 目前,它支持第三方解决方案,如 DeepSpeed, PyTorch FSDP, FairScale ,它们实现了论文 《ZeRO: Memory Optimizations Toward Training Trillion Parameter Models》 ...
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🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX. - transformers/tests/trainer/test_trainer.py at main · huggingface/transformers
OutOfMemoryError: CUDA out of memory. Tried to allocate 62.00 MiB (GPU 0; 11.76 GiB total capacity; 10.77 GiB already allocated; 61.69 MiB free; 10.88 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See doc...
cuda.manual_seed(config.random_seed) kwargs = {'num_workers': 1, 'pin_memory': True} else: torch.manual_seed(config.random_seed) kwargs = {} # instantiate data loaders if config.is_train: data_loader = get_train_valid_loader(config.data_dir, config.dataset, config.batch_size, ...
而且是一旦我们的dataset 过大，无法放在 RAM 中，那么这样子的做法会导致 Out of Memory 的异常。
def main(FLAGS): # import data kwargs = {'num_workers': 1, 'pin_memory': True} if FLAGS.cuda else {} if FLAGS.dataset == "cifar10": proj_dst = datasets.CIFAR10 num_classes = 10 elif FLAGS.dataset == "cifar100": proj_dst = datasets.CIFAR100 num_classes = 100 elif FLAGS.dat...
("*** Evaluate ***") metrics = trainer.evaluate( max_length=data_args.val_max_target_length, num_beams=data_args.num_beams, metric_key_prefix="val" ) metrics = {k: round(v, 4) for k, v in metrics.items()} max_val_samples = data_args.max_val_samples if data_args....
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