问题:使用llamafactory预训练模型时,max_token=8192的情况下,为什么perdevice_batchsize =2 ,会超出4卡A100 40G的显存,而perdevice_batchsize = 1 的时候只会占用每卡20G。 Others No response Activity github-actionsadded pendingThis problem is yet to be addressed on Dec 2, 2024 CiaranZhou commented ...
在image_classification_timm_peft_lora模型微调任务时,训练这一步报错:KeyError: 'per_gpu_train_batch_size',但是在args中两句代码是这样的:per_device_train_batch_size=batch_size,per_device_eval_batch_size=batch_size并没有问题。 Environment / 环境信息 (Mandatory / 必填) -- MindSpore version : 2.3....
--per_device_train_batch_size 4 --per_device_eval_batch_size 4 --gradient_accumulation_steps 1 --evaluation_strategy "no" --save_strategy "steps" --save_steps 50000 --save_total_limit 1 --learning_rate 2e-5 --weight_decay 0. --warmup_ratio 0.03 --lr_scheduler_type "cosine" --l...
random.randint(0, high=len(data), size=None, dtype=int) seq, label = data[ind] seq = seq.to(args.device) label = label.to(args.device) y_pred = model(seq) optimizer = torch.optim.Adam(model.parameters(), lr=lr) loss_function = nn.MSELoss().to(args.device) loss = loss_...
解决办法就是让batch_size>1.14./pytorch/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.import warningswarnings.filterwarnings("ignore", category=UserWarning)15.RuntimeError: zero-dimensional tensor (at...
device = 'cuda:0' model = init_detector(config_file, checkpoint=checkpoint_file, device=device) # 此队列用于并行推理多张图像 streamqueue = asyncio.Queue() # 队列大小定义了并行的数量 streamqueue_size = 3 for _ in range(streamqueue_size): ...
# TorchServe front-end parameters minWorkers: 1 maxWorkers: 1 maxBatchDelay: 100 responseTimeout: 1200 parallelType: "tp" deviceType: "gpu" # example of user specified GPU deviceIds deviceIds: [0,1,2,3] # sets CUDA_VISIBLE_DEVICES torchrun: nproc-per-node: 4 # TorchServe back-end...
parser.add_argument('--start-epoch', default=0, type=int, help='manual epoch number')parser.add_argument('--batch-size', default=128, type=int, help='mini-batch size')parser.add_argument('--optimizer', default='sgd', help='optimizer function used')parser.add_argument('--lr', ...
Le dimensioni del batch (due) e il numero massimo di iterazioni di training (10.000) è anche degli iperparametri. Corsi di formazione viene eseguito come segue: XML Copia for i in range(0, max_epochs): rows = np.random.choice(N, bat_size, replace=False) trainer....
Cannot open backup device 'C:\TEMP\Demo.bak'. Operating system error 2(The system cannot find the file specified.). Cannot parse using OPENXML with namespace Cannot promote the transaction to a distributed transaction because there is an active save point in this transaction Cannot resolve colla...