Datasets库提供了.shuffle()和select()函数来打乱和选择数据,如下所示(随机采样1000个样本)。 drug_sample=drug_dataset["train"].shuffle(seed=42).select(range(1000))# Peek at the first few examplesdrug_sample[:3]{'Unnamed: 0':[87571,178045,80482],'drugName':['Naproxen','Duloxetine','Mobic']...
but they can't run as soon as they arrive locally. This may be because of "operating" System environment, device CPU architecture (x86/ARM) differences", perhaps because of "the Python runtime version is too high or too low
model = AutoModelForCausalLM.from_pretrained("Qwen2-0.5B-Instruct",low_cpu_mem_usage=True) model = model.cuda() ipt = tokenizer("Human: {}\n{}".format("如何关闭华硕主板的xmp功能?","").strip() +"\n\nAssistant: ", return_tensors="pt").to(model.device) re = tokenizer.decode(mod...
learning_rate=2e-5, # per_device_train_batch_size: The batch size per GPU/TPU core/CPU for training. per_device_train_batch_size=16, # per_device_eval_batch_size: The batch size per GPU/TPU core/CPU for evaluation. per_device_eval_batch_size=16, # num_train_epochs (default 3.0)...
per_device_train_batch_size (int, optional, defaults to 8) – The batch size per GPU/TPU core/CPU for training. trainer默认自动开启torch的多gpu模式,这里是设置每个gpu上的样本数量,一般来说,多gpu模式希望多个gpu的性能尽量接近,否则最终多gpu的速度由最慢的gpu决定,比如快gpu 跑一个batch需要5秒,跑...
inputs = processor(text_prompt).to(device) 测量延迟和 GPU 内存占用需要使用特定的 CUDA 函数。我们实现了一个工具函数,用于测量模型的推理延迟及 GPU 内存占用。为了确保结果的准确性,每次测量我们会运行nb_loops次求均值: importtorch fromtransformersimportset_seed ...
import torch device = torch.device("cuda" if torch.cuda.is_available() else "cpu") xlmr_model = (XLMRobertaForTokenClassification .from_pretrained(xlmr_model_name, config=xlmr_config) .to(device)) 作为一个快速检查,我们已经正确地初始化了标记器和模型,让我们在已知实体的小序列上测试预测: ...
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") raw_datasets = load_dataset("glue", "mrpc") checkpoint = "bert-base-uncased" tokenizer = AutoTokenizer.from_pretrained(checkpoint) def tokenize_function(example): return tokenizer(example["sentence1"], ...
device = torch.device("cuda"iftorch.cuda.is_available()else"cpu") print(f'Using device: {device}') 数据 在这里,我们将使用一个非常小的经典数据集 mnist 来进行测试。如果您想在不改变任何其他内容的情况下给模型一个稍微困难一点的挑战,请使用torchvision.dataset,FashionMNIST 应作为替代品。
self._device = torch.device("cpu") import numa Collaborator sywangyi Jul 30, 2024 should add check if the numa package is available optimum/intel/ipex/modeling_base.py Outdated numa.set_membind([0]) print("affinity", numa.get_affinity(0)) print("membind", numa.get_membind(...