以下是我的手机代码：
from transformers import TrainingArguments
training_args = TrainingArguments(
output_dir="wav2vec2-medical",
group_by_length=True,
per_device_train_batch_size=32,
evaluation_strategy="steps",
num_train_epochs=30,
fp16=True,
save_steps=500,
eval_steps=500,
logging_steps=500,
learning_rate=1e-4,
weight_decay=0.005,
warmup_steps=1000,
save_total_limit=2,
)
这就是我犯的错误。我不知道该从中得到什么。
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-26-f9014a6221db> in <module>
1 from transformers import TrainingArguments
2
----> 3 training_args = TrainingArguments(
4 # output_dir="/content/gdrive/MyDrive/wav2vec2-base-timit-demo",
5 output_dir="./wav2vec2-medical",
~/Library/Python/3.8/lib/python/site-packages/transformers/training_args.py in __init__(self, output_dir, overwrite_output_dir, do_train, do_eval, do_predict, evaluation_strategy, prediction_loss_only, per_device_train_batch_size, per_device_eval_batch_size, per_gpu_train_batch_size, per_gpu_eval_batch_size, gradient_accumulation_steps, eval_accumulation_steps, learning_rate, weight_decay, adam_beta1, adam_beta2, adam_epsilon, max_grad_norm, num_train_epochs, max_steps, lr_scheduler_type, warmup_ratio, warmup_steps, logging_dir, logging_strategy, logging_first_step, logging_steps, save_strategy, save_steps, save_total_limit, no_cuda, seed, fp16, fp16_opt_level, fp16_backend, fp16_full_eval, local_rank, tpu_num_cores, tpu_metrics_debug, debug, dataloader_drop_last, eval_steps, dataloader_num_workers, past_index, run_name, disable_tqdm, remove_unused_columns, label_names, load_best_model_at_end, metric_for_best_model, greater_is_better, ignore_data_skip, sharded_ddp, deepspeed, label_smoothing_factor, adafactor, group_by_length, length_column_name, report_to, ddp_find_unused_parameters, dataloader_pin_memory, skip_memory_metrics, use_legacy_prediction_loop, push_to_hub, resume_from_checkpoint, mp_parameters)
~/Library/Python/3.8/lib/python/site-packages/transformers/training_args.py in __post_init__(self)
609
610 if is_torch_available() and self.device.type != "cuda" and (self.fp16 or self.fp16_full_eval):
--> 611 raise ValueError(
612 "Mixed precision training with AMP or APEX (`--fp16`) and FP16 evaluation can only be used on CUDA devices."
613 )
ValueError: Mixed precision training with AMP or APEX (`--fp16`) and FP16 evaluation can only be used on CUDA devices.
我试着在本地设备上的Jupyter笔记本电脑上运行它，也在谷歌Colab上运行，但我仍然得到了相同的错误
您应该删除fp16=True或在GPU上运行，这是仅限GPU的参数