因此我们需要创建一个deespeed_config.json。DeepSpeed 配置定义了要使用的 ZeRO 策略以及是否要使用混合精度训练等配置项。 Hugging Face Trainer 允许我们从deepspeed_config.json中的TrainingArguments继承相关配置以避免重复设置,查看文档了解更多信息。 我们创建了 4 组 deepspeed 配置文件用于实验,包括CPU 卸载和混合精...
ok,回到trainer。 data_collator(DataCollator,optional) – The function to use to form a batch from a list of elements oftrain_datasetoreval_dataset. Will default todefault_data_collator()if notokenizeris provided, an instance ofDataCollatorWithPadding()otherwise. data_collator是huggingface自定义的...
train()Trainer训练-2.CPU模式 exportCUDA_VISIBLE_DEVICES='cpu'python trainer.py\--output_dir"./r...
跳过分词部分代码,下面这段代码是每个worker节点上运行的核心代码: def trainer_init_per_worker(train_dataset, eval_dataset=None,**config): # Use the actual number of CPUs assigned by Ray model = GPTJForCausalLM.from_pretrained(model_name, use_cache=False) model.resize_token_embeddings(len(tokeni...
use_auth_token=True if model_args.use_auth_token else None ) return modelargs: 超参数的定义,这部分也是trainer的重要功能,大部分训练相关的参数都是这里设置的,非常的方便:Trainerhuggingface.coclasstransformers.TrainingArguments(output_dir: str,overwrite_output_dir: bool = False,do_train: bool =...
我认为Hugging Face transformers库中的默认Trainer类构建在PyTorch之上。当您创建Trainer类的示例时，它会...
Launching multi-CPU run using MPI 🤗 Here is another way to launch multi-CPU run using MPI. You can learn how to install Open MPI onthis page. You can use Intel MPI or MVAPICH as well. Once you have MPI setup on your cluster, just run: ...
use_cpu:false Multi-GPU FSDP Here, we experiment on the Single-Node Multi-GPU setting. We compare the performance of Distributed Data Parallel (DDP) and FSDP in various configurations. First, GPT-2 Large(762M) model is used wherein DDP works with certain batch sizes without thro...
# Create Trainer instance trainer = Seq2SeqTrainer( model=model, args=training_args, data_collator=data_collator, train_dataset=tokenized_dataset["train"], ) model.config.use_cache = False # silence the warnings. Please re-enable for inference! 运行下面的代码,开始训练模型。请注意,对于 T5,出...
现在我们已经在德语上对XLM-R进行了微调,我们可以通过Trainer的predict()方法来评估它转移到其他语言的能力。由于我们计划评估多种语言,让我们创建一个简单的函数,为我们做这件事: def get_f1_score(trainer, dataset): return trainer.predict(dataset).metrics["test_f1"] 我们可以用这个函数来检查测试集的...