checkpoint的前向和反向传递的开始和结束处执行get_accelerator().synchronize()。默认为false。如果提供,将覆盖deepspeed_config。 profile – 可选:记录每个deepspeed.checkpointing.checkpoint调用的前向和反向传播时间。如果提供,将覆盖deepspeed_config。 deepspeed.checkpointing.is_configured() 代码语言:javascript ...
import torch from deepspeed.profiling.flops_profiler import get_model_profile from deepspeed.accelerator import get_accelerator with get_accelerator().device(0): model = models.alexnet() batch_size = 256 flops, macs, params = get_model_profile(model=model, # model input_shape=(batch_size, 3,...
_get_data_parallel_world_size() # 2. self._set_distributed_vars(args) # 这个函数的主要作用是 set_device self.local_rank = int(os.environ['LOCAL_RANK']) device_rank = self.local_rank get_accelerator().set_device(device_rank) self.device = torch.device(get_accelerator().device_name()...
如果提供,将覆盖deepspeed_config。 synchronize – 可选:在每次调用deepspeed.checkpointing.checkpoint的前向和反向传递的开始和结束处执行get_accelerator().synchronize()。默认为false。如果提供,将覆盖deepspeed_config。 profile – 可选:记录每个deepspeed.checkpointing.checkpoint调用的前向和反向传播时间。如果提供,...
profiling.flops_profiler import get_model_profile from deepspeed.accelerator import get_accelerator def bert_input_constructor(batch_size, seq_len, tokenizer): fake_seq = "" for _ in range(seq_len - 2): # ignore the two special tokens [CLS] and [SEP] fake_seq += tokenizer.pad_token ...
device(get_accelerator().device_name(), local_rank) # Initializes the distributed backend which will take care of sychronizing nodes/GPUs deepspeed.init_distributed() offload_device = "cpu" if offload else "none" ds_config = { "train_micro_batch_size_per_gpu": per_device_train_...
if torch_available and not get_accelerator().device_name() == 'cuda': # Fix to allow docker builds, similar to https://github.com/NVIDIA/apex/issues/486. print("[WARNING] Torch did not find cuda available, if cross-compiling or running with cpu only " "you can ignore this message...
[real_accelerator.py:110:get_accelerator] Setting ds_accelerator to cuda (auto detect) [2023-07-31 11:06:04,960] [INFO] [launch.py:145:main] WORLD INFO DICT: {'localhost': [1, 2]} [2023-07-31 11:06:04,961] [INFO] [launch.py:151:main] nnodes=1, num_local_procs=2, node...
[2023-12-02 13:57:38,018] [INFO] [real_accelerator.py:158:get_accelerator] Setting ds_accelerator to cuda (auto detect) --- DeepSpeed C++/CUDA extension op report --- NOTE: Ops not installed will be just-in-time (JIT) compiled at runtime if needed. Op compatibility means that your...
inference for a model on the CPU. Device agnostic-interfaces are used to load and run the model. These device agnostic interfaces are accessed throughdeepspeed.accelerator.get_accelerator()as shown in Listing 1. For further details, refer to the DeepSpeed tutorial on DeepSpeed accelerator interfaces...