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Functions460 in github.com/pytorch/examples

⨍ Functions 460◇ Types & classes 108

↓ 1 callersMethodrun_episode

r""" Run one episode. The agent will tell each oberser to run one episode with n_steps. Then it collects all actions and rewards, and

distributed/rpc/batch/reinforce.py:184

↓ 1 callersFunctionrun_master

distributed/rpc/pipeline/main.py:204

↓ 1 callersFunctionrun_ps

distributed/rpc/batch/parameter_server.py:106

↓ 1 callersFunctionrun_training_loop

(rank, num_gpus, train_loader, test_loader)

distributed/rpc/parameter_server/rpc_parameter_server.py:171

↓ 1 callersFunctionsave_checkpoint

(state, is_best, filename='checkpoint.pth.tar')

imagenet/main.py:429

↓ 1 callersFunctionselect_action

reinforcement_learning/actor_critic.py:78

↓ 1 callersFunctionselect_action

reinforcement_learning/reinforce.py:54

↓ 1 callersFunctionset_modules_to_backward_prefetch

(model, num_to_backward_prefetch)

distributed/FSDP2/example.py:26

↓ 1 callersFunctionset_modules_to_forward_prefetch

(model, num_to_forward_prefetch)

distributed/FSDP2/example.py:16

↓ 1 callersFunctionsetup

distributed/FSDP/utils/train_utils.py:11

↓ 1 callersFunctionsetup_model

distributed/FSDP/utils/train_utils.py:99

↓ 1 callersFunctionstylize

fast_neural_style/neural_style/neural_style.py:127

↓ 1 callersFunctionstylize_onnx

Read ONNX model and run it using onnxruntime

fast_neural_style/neural_style/neural_style.py:166

↓ 1 callersMethodsummary

imagenet/main.py:475

↓ 1 callersFunctiontest

vae/main.py:113

↓ 1 callersFunctiontest

(model, device, test_loader)

mnist/main.py:53

↓ 1 callersFunctiontest

(args, model, device, test_loader)

mnist_rnn/main.py:60

↓ 1 callersFunctiontest

super_resolution/main.py:63

↓ 1 callersFunctiontest

(args, model, device, dataset, dataloader_kwargs)

mnist_hogwild/train.py:17

↓ 1 callersFunctiontest

(model, device, test_loader)

siamese_network/main.py:211

↓ 1 callersFunctiontest_epoch

(model, device, data_loader)

mnist_hogwild/train.py:42

↓ 1 callersFunctionto_numpy

fast_neural_style/neural_style/neural_style.py:177

↓ 1 callersFunctiontrain

↓ 1 callersFunctiontrain

word_language_model/main.py:163

↓ 1 callersFunctiontrain

(args, model, device, train_loader, optimizer, epoch)

mnist/main.py:36

↓ 1 callersFunctiontrain

(train_loader, model, criterion, optimizer, epoch, device, args)

imagenet/main.py:309

↓ 1 callersFunctiontrain

(args, model, rank, world_size, train_loader, optimizer, epoch, sampler=None)

distributed/FSDP/utils/train_utils.py:35

↓ 1 callersFunctiontrain

(args, model, device, train_loader, optimizer, epoch)

mnist_rnn/main.py:43

↓ 1 callersFunctiontrain

super_resolution/main.py:47

↓ 1 callersFunctiontrain

(model, train_dl, loss_fn, optim, special_symbols, opts)

language_translation/main.py:109

↓ 1 callersFunctiontrain

(args, model, device, train_loader, optimizer, epoch)

siamese_network/main.py:190

↓ 1 callersFunctiontrain

fast_neural_style/neural_style/neural_style.py:31

↓ 1 callersMethodtrain

distributed/rpc/batch/parameter_server.py:86

↓ 1 callersMethodtrain

(self, max_epochs: int)

distributed/ddp-tutorial-series/single_gpu.py:43

↓ 1 callersMethodtrain

(self, max_epochs: int)

distributed/ddp-tutorial-series/multigpu.py:62

↓ 1 callersMethodtrain

(self, max_epochs: int)

distributed/ddp-tutorial-series/multigpu_torchrun.py:70

↓ 1 callersMethodtrain

(self, max_epochs: int)

distributed/ddp-tutorial-series/multinode.py:71

↓ 1 callersFunctiontrain_epoch

(epoch, args, model, device, data_loader, optimizer)

mnist_hogwild/train.py:25

↓ 1 callersFunctiontrain_iter

(epoch, model, optimizer, criterion, input, target, mask_train, mask_val, print_every=10)

gat/main.py:256

↓ 1 callersFunctiontrain_iter

(epoch, model, optimizer, criterion, input, target, mask_train, mask_val, print_every=10)

gcn/main.py:168

↓ 1 callersFunctionunzip

(zipped_path, quiet)

cpp/tools/download_mnist.py:49

↓ 1 callersFunctionunzip

(source_filename, dest_dir)

fast_neural_style/download_saved_models.py:21

↓ 1 callersFunctionupload_to_s3

distributed/minGPT-ddp/mingpt/trainer.py:37

↓ 1 callersFunctionvalidate

(model, valid_dl, loss_fn, special_symbols)

language_translation/main.py:153

↓ 1 callersFunctionvalidation

(model, rank, world_size, val_loader)

distributed/FSDP/utils/train_utils.py:71

↓ 1 callersFunctionverify_min_gpu_count

verification that we have at least 2 gpus to run dist examples

distributed/ddp/example.py:14

↓ 1 callersFunctionverify_min_gpu_count

verification that we have at least 2 gpus to run dist examples

distributed/rpc/ddp_rpc/main.py:18

↓ 1 callersFunctionverify_min_gpu_count

verification that we have at least 2 gpus to run dist examples

distributed/FSDP2/example.py:10

↓ 1 callersFunctionverify_min_gpu_count

(min_gpus: int = 2)

distributed/minGPT-ddp/mingpt/main.py:12

↓ 1 callersFunctionwrap_in_activation_function

(m: GraphModule, fn: ActivationFunction)

fx/wrap_output_dynamically.py:45

Method__getitem__

distributed/FSDP/summarization_dataset.py:69

Method__getitem__

distributed/minGPT-ddp/mingpt/char_dataset.py:36

Method__getitem__

distributed/ddp-tutorial-series/datautils.py:12

Method__getitem__

super_resolution/dataset.py:26

Method__getitem__

For every example, we will select two images. There are two cases, positive and negative examples. For positive examples, we

siamese_network/main.py:118

(self, in_features: int, out_features: int, n_heads: int, concat: bool = False, dropout: float = 0.4, leaky_re

reinforcement_learning/actor_critic.py:40

reinforcement_learning/reinforce.py:32

word_language_model/data.py:6

word_language_model/data.py:21

(self, d_model, dropout=0.1, max_len=5000)

word_language_model/model.py:81

(self, ntoken, ninp, nhead, nhid, nlayers, dropout=0.5)

word_language_model/model.py:110

mnist/main.py:11

(self, name, use_accel, fmt=':f', summary_type=Summary.AVERAGE)

imagenet/main.py:442

(self, num_batches, meters, prefix="")

imagenet/main.py:492

legacy/snli/model.py:40

(self, tokenizer, type_path, num_samples, input_length, output_length, print_text=False)

distributed/FSDP/summarization_dataset.py:27

distributed/ddp/example.py:21

distributed/tensor_parallelism/tensor_parallel_example.py:60

(self, model_args: ModelArgs)

distributed/tensor_parallelism/llama2_model.py:165

( self, dim: int, hidden_dim: int, multiple_of: int, ffn_dim_multiplie

distributed/tensor_parallelism/llama2_model.py:248

(self, layer_id: int, model_args: ModelArgs)

distributed/tensor_parallelism/llama2_model.py:295

(self, model_args: ModelArgs)

distributed/tensor_parallelism/llama2_model.py:367

distributed/tensor_parallelism/sequence_parallel_example.py:50

(self, remote_emb_module, rank)

distributed/rpc/ddp_rpc/main.py:32

(self, num_gpus=0)

distributed/rpc/parameter_server/rpc_parameter_server.py:85

(self, num_gpus=0)

distributed/rpc/parameter_server/rpc_parameter_server.py:153

distributed/rpc/rl/main.py:56

distributed/rpc/rl/main.py:85

(self, ntoken, ninp, dropout)

distributed/rpc/rnn/rnn.py:42

(self, ps, ntoken, ninp, nhid, nlayers, dropout=0.5)

distributed/rpc/rnn/rnn.py:80

(self, device, *args, **kwargs)

distributed/rpc/pipeline/main.py:89

(self, device, *args, **kwargs)

distributed/rpc/pipeline/main.py:121

(self, split_size, workers, *args, **kwargs)

distributed/rpc/pipeline/main.py:145

(self, batch_update_size=batch_update_size)

distributed/rpc/batch/parameter_server.py:33

(self, ps_rref)

distributed/rpc/batch/parameter_server.py:72

(self, batch=True)

distributed/rpc/batch/reinforce.py:46

(self, batch=True)

distributed/rpc/batch/reinforce.py:74

(self, folder: str, dcp_api: bool)

distributed/FSDP2/checkpoint.py:40

(self, dim, hidden_dim, dropout_p)

distributed/FSDP2/model.py:61

(self, args: ModelArgs)

distributed/FSDP2/model.py:77

(self, args: ModelArgs)

distributed/FSDP2/model.py:101

(self, data_cfg: DataConfig)

distributed/minGPT-ddp/mingpt/char_dataset.py:19

(self, trainer_config: TrainerConfig, model, optimizer, train_dataset, test_dataset=None)

distributed/minGPT-ddp/mingpt/trainer.py:46

(self, config, device="cpu", dtype=torch.float32)

distributed/minGPT-ddp/mingpt/model.py:39

(self, config: GPTConfig)

distributed/minGPT-ddp/mingpt/model.py:63

(self, config: GPTConfig, device="cpu", dtype=torch.float32)

distributed/minGPT-ddp/mingpt/model.py:81

( self, model: torch.nn.Module, train_data: DataLoader, optimizer: torch.optim

distributed/ddp-tutorial-series/single_gpu.py:8

( self, model: torch.nn.Module, train_data: DataLoader, optimizer: torch.optim

distributed/ddp-tutorial-series/multigpu.py:25

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