defmain():args=parse_args(sys.argv[1:])state=load_checkpoint(args.checkpoint_path)initialize(state)# torch.distributed.run ensures that this will work# by exporting all the env vars needed to initialize the process grouptorch.distributed.init_process_group(backend=args.backend)foriinrange(state...
I originally reproduced this under Pytorch Lightning and distilled that code down to this minimal example. Tested on RTX3090 GPU. import os import torch import torch.distributed as dist import torch.nn as nn import torch.nn.functional as F from torch import Tensor from torch.utils.checkpoint ...
最后，使用 TorchShard 函数保存和加载 checkpoints 非常简单。TorchShard 提供了名为 torchshard.collect_state_dict 基本函数用于保存 checkpoints，torchshard.relocate_state_dict 用于加载 checkpoints。保存检查点：state_dict = model.state_dict()# collect states across all ranksstate_dict = ts.collect_state...
checkpoint_fns = ( thunder.torch.checkpoint, @@ -1715,26 +1715,30 @@ def fn_to_checkpoint(x): for checkpoint_fn in checkpoint_fns: def f(x): return checkpoint_fn(fn_to_checkpoint, x) def f(x, y): return checkpoint_fn(fn_to_checkpoint, x, y) x = make_tensor((2, 2), devi...
除此之外,TorchShard 还支持与 DDP 一起使用时的各种特性,保存和加载 shard checkpoints,初始化 shard 参数,以及跨多台机器和 GPU 处理张量。具体如下: torchshard 包含必要的功能和操作,如 torch 包; torchshard.nn 包含图形的基本构建块,如 torch.nn 包; ...
count=1, instance_type=instance_type, endpoint_name=endpoint_name, volume_size=512, # increase the size to store large model model_data_download_timeout=3600, # increase the timeout to download large model container_startup_health_check_timeout=600, # increase the timeout to load large ...
除此之外,TorchShard 还支持与 DDP 一起使用时的各种特性,保存和加载 shard checkpoints,初始化 shard 参数,以及跨多台机器和 GPU 处理张量。具体如下: torchshard 包含必要的功能和操作,如 torch 包; torchshard.nn 包含图形的基本构建块,如 torch.nn 包; ...
frompytorch_lightning.callbacksimportEarlyStopping,ModelCheckpoint fromtorchgeo.trainersimport( BYOLTask, ChesapeakeCVPRDataModule, CycloneDataModule, LandcoverAIDataModule, NAIPChesapeakeDataModule, RESISC45DataModule, SEN12MSDataModule, So2SatDataModule, ...
# https://github.com/Lightning-AI/pytorch-lightning/issues/19977 "lightning[pytorch-extra]>=2,!=2.3.*,!=2.5.0", # matplotlib 3.6+ required for Python 3.11 wheels "matplotlib>=3.6", # numpy 1.23.2+ required by Python 3.11 wheels
除此之外,TorchShard 还支持与 DDP 一起使用时的各种特性,保存和加载 shard checkpoints,初始化 shard 参数,以及跨多台机器和 GPU 处理张量。具体如下: torchshard 包含必要的功能和操作,如 torch 包; torchshard.nn 包含图形的基本构建块,如 torch.nn 包; ...