【发布时间】：2022-08-22 18:31:31
【问题描述】：
我是 PyTorch 的初学者，我一直在尝试在 Azure ML Studio 中设置 PyTorch 模型。该代码在 Google Colab 中运行良好，但在 Azure ML 笔记本中，我收到此错误：
File /anaconda/envs/azureml_py38_PT_TF/lib/python3.8/site-packages/pytorch_lightning/trainer/training_io.py:268, in TrainerIOMixin.save_checkpoint(self, filepath, weights_only)
267 def save_checkpoint(self, filepath, weights_only: bool = False):
--> 268 checkpoint = self.dump_checkpoint(weights_only)
270 if self.is_global_zero:
271 # do the actual save
272 try:
File /anaconda/envs/azureml_py38_PT_TF/lib/python3.8/site-packages/pytorch_lightning/trainer/training_io.py:362, in TrainerIOMixin.dump_checkpoint(self, weights_only)
360 # save native amp scaling
361 if self.use_amp and NATIVE_AMP_AVALAIBLE and not self.use_tpu:
--> 362 checkpoint[\'native_amp_scaling_state\'] = self.scaler.state_dict()
364 # add the module_arguments and state_dict from the model
365 model = self.get_model()
AttributeError: \'NoneType\' object has no attribute \'state_dict\'
型号代码：
class EvaluationModel(pl.LightningModule):
def __init__(self,learning_rate=1e-3,batch_size=1024,layer_count=10):
super().__init__()
self.batch_size = batch_size
self.learning_rate = learning_rate
layers = []
for i in range(layer_count-1):
layers.append((f\"linear-{i}\", nn.Linear(808, 808)))
layers.append((f\"relu-{i}\", nn.ReLU()))
layers.append((f\"linear-{layer_count-1}\", nn.Linear(808, 1)))
self.seq = nn.Sequential(OrderedDict(layers))
def forward(self, x):
return self.seq(x)
def training_step(self, batch, batch_idx):
x, y = batch[\'binary\'], batch[\'eval\']
y_hat = self(x)
loss = F.l1_loss(y_hat, y)
self.log(\"train_loss\", loss)
return loss
def configure_optimizers(self):
return torch.optim.Adam(self.parameters(), lr=self.learning_rate)
def train_dataloader(self):
dataset = EvaluationDataset(count=LABEL_COUNT)
return DataLoader(dataset, batch_size=self.batch_size, num_workers=2, pin_memory=True)
configs = [
{\"layer_count\": 4, \"batch_size\": 512},
# {\"layer_count\": 6, \"batch_size\": 1024},
]
for config in configs:
version_name = f\'{int(time.time())}-batch_size-{config[\"batch_size\"]}-layer_count-{config[\"layer_count\"]}\'
logger = pl.loggers.TensorBoardLogger(\"lightning_logs\", name=\"chessml\", version=version_name)
trainer = pl.Trainer(gpus=1,precision=16,max_epochs=1,auto_lr_find=True,logger=logger)
model = EvaluationModel(layer_count=config[\"layer_count\"],batch_size=config[\"batch_size\"],learning_rate=1e-3)
# trainer.tune(model)
# lr_finder = trainer.tuner.lr_find(model, min_lr=1e-6, max_lr=1e-3, num_training=25)
# fig = lr_finder.plot(suggest=True)
# fig.show()
trainer.fit(model)
break
型号代码来自https://towardsdatascience.com/train-your-own-chess-ai-66b9ca8d71e4
标签： python tensorflow machine-learning pytorch pytorch-lightning