class DQNLightning(LightningModule): """ 基本的DQN模型 """ def __init__( self, batch_size: int = 16, lr: float = 1e-2, env: str = "CartPole-v0", gamma: float = 0.99, sync_rate: int = 10, replay_size: int = 1000, warm_start_size: int = 1000, eps_last_frame: int =...
现在可以使用 Lightning Trainer 训练 PyG 模型,如下面代码,更多信息请参阅此处here。 datamodule=Reddit('data/Reddit')model=GraphSAGE(datamodule.num_features,datamodule.num_classes)trainer=Trainer(gpus=2,accelerator='ddp',max_epochs=10)trainer.fit(model,datamodule=datamodule) 新的加速器接口 引入了...
num_frames}S{args.frame_interval}" experiment_dir = os.path.join( # Create an experiment folder args.results_dir, f"{experiment_index:03d}-{model_string_name}-{num_frame_string}-{args.dataset}" ) experiment_dir = get_experiment_dir(experiment_dir, args) checkpoint_dir = os.path.join(...
num_val_batches: int world_size: int fast_dev_run: ... process_output: ... progress_bar_dict: ... @@ -339,6 +341,10 @@ def _evaluate( elif self.is_overridden('validation_epoch_end', model=model): eval_results = model.validation_epoch_end(outputs) # aggregate ddp stats acros...
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,shuffle=True, num_workers=2) testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,shuffle=False, num_workers=2) classes = ('plane', 'car', 'bird', 'cat', ...
valid_dl = DataLoader(valid_data, BATCH_SIZE, pin_memory=True, shuffle=False, num_workers=4, drop_last=False) Model There are two main models, theVisionEncoderand theTextEncoderwhich have resnet18 and distilbert as backbones. In order to make it multi-lingual, we simply choose thedistil...
1__XoPKjwzW3vJKv76bvi2NQ 这种方法将您的研究代码组织在三个不同的存储桶(buckets)中 研究代码(LightningModule)(这是科学)。 工程代码(培训器) 与研究无关的代码(回调) 如何开始 希望本指南向您确切地介绍了Lightning如何入门。最简单的开始方法是运行带有MNIST示例的colab笔记本。
f"Your {mode}_dataloader has `shuffle=True`, it is best practice to turn" Global seed set to 1234 Epoch 0: 11%|##8 | 28/262 [00:04<00:38, 6.05it/s, loss=0.39, v_num=8:00] And you will see a folder created called logs_${your_machine_host_name} with the following ...
gradient_clip=None, # backward compatible, todo: remove in v0.8.0 process_position=0, process_position: int = 0, nb_gpu_nodes=None, # backward compatible, todo: remove in v0.8.0 num_nodes=1, gpus=None, num_tpu_cores=None, log_gpu_memory=None, show_progress_bar=True, overfit_pct=...
@@ -312,7 +311,6 @@ def test_nested_calc_num_data(input_data, compute_func, expected_length): assert calculated_length == expected_length @pytest.mark.skipif(not _TORCH_GREATER_EQUAL_1_7, reason="Requires at least PyTorch 1.7") @mock.patch.dict(os.environ, {"CUDA_VISIBLE_DEVICES"...