主要功能: 读取输入的device,处理各种格式(列表,字符串,……) 获取所有可用的device 释放显存 Precision 以混合精度训练为例,如果使用lightning库,源码在lightning.pytorch.plugins.precision.MixedPrecision类中,但如果使用pytorch_lightning + lightning fabric/utility, 源码在lightning_fabric.plugins.precision.amp.py中。
最近使用Pytorch_lightning运行多机多卡,发现了一个很奇怪的问题。 我在配置Trainer的时候,使用的是2nodes,2devices,但是在实际运行的时候,却只有2nodes,1device在跑代码。 随后我扒了扒pytorch_lightning的源码,发现在运行过程中实际上是Strategy的set_world_ranks这个方法设置了程序认为的总卡数大小,而这里面最核心的...
lightning_logs/version_10/checkpoints/epoch=8-step=15470.ckpt tensor(0.0376, device='cuda:0') 1. 2. model_clone = Model.load_from_checkpoint(trainer.checkpoint_callback.best_model_path) trainer_clone = pl.Trainer(max_epochs=3,gpus=1) result = trainer_clone.test(model_clone,data_module....
writer = SummaryWriter() model = ResNet18().to(device) optimizer = torch.optim.Adam(model.params, lr=1e-3)forepochinrange(num_epochs):fori, batchinenumerate(train_data): x, y = batch x = x.to(device) output = model(x) loss = criterion(output, y) writer.add_scalar("train_loss...
result = torch.cat([model.forward(t[0].to(model.device)) for t in dl]) return(result.data) result = predict(model,dl_valid) 1. 2. 3. 4. 5. 6. result 1. tensor([[9.8850e-01], [2.3642e-03], [1.2128e-04], ...,
不需要写一大堆的.cuda()和.to(device),Lightning会帮你自动处理。如果要新建一个tensor,可以使用type_as来使得新tensor处于相同的处理器上。 def training_step(self, batch, batch_idx): x, y = batch #把z放在和x一样的处理器上 z = sample_noise() ...
For copy image paths and more information, please view on a desktop device. OverviewTagsLayersSecurity ScanningRelated Collections What is PyTorch Lightning? PyTorch Lightningis a powerful yet lightweight PyTorch wrapper, designed to make high performance AI research simple, allowing you to focus on ...
ckpt tensor(0.0376, device='cuda:0') model_clone = Model.load_from_checkpoint(trainer.checkpoint_callback.best_model_path) trainer_clone = pl.Trainer(max_epochs=3,gpus=1) result = trainer_clone.test(model_clone,data_module.test_dataloader()) print(result) --- DATALOADER:0 TEST RESULTS {...
device:可以使用self.device来构建设备无关型tensor。如:z = torch.rand(2, 3, device=self.device)。 hparams:含有所有前面存下来的输入超参。 precision:精确度。常见32和16。 要点 如果准备使用DataParallel,在写training_step的时候需要调用forward函数,z=self(x) ...
import pytorch_lightning as pl from transformers import ( AutoModelForSequenceClassification, AutoConfig, AutoTokenizer ) class ONNXPredictor: def __init__(self, onnx_client, config): self.device = "cpu" self.client = onnx_client self.tokenizer = AutoTokenizer.from_pretrai...