[docs]defdistributed_transpose(tensor,dim0,dim1,group=None,async_op=False):"""Perform distributed transpose of tensor to switch sharding dimension"""# get input formatinput_format=get_memory_format(tensor)# get comm paramscomm_size=dist.get_world_size(group=group)# split and local transposi...
Namespace/Package: distributedutils_testMethod/Function: make_hdfs导入包: distributedutils_test每个示例代码都附有代码来源和完整的源代码,希望对您的程序开发有帮助。示例1def dont_test_dataframes(s, a): # slow pytest.importorskip('pandas') n = 3000000 fn = '/tmp/test/file.csv' with make_...
ModuleNotFoundError: No module named 'fairseq.distributed_utils' fairseq Version (e.g., 1.0 or main): PyTorch Version: 1.10.0a OS (e.g., Linux): Ubuntu 20.04 How you installed fairseq (pip, source): https://github.com/fairseq/Megatron-LM Build command you used (if compiling from sour...
问不断收到"distributed.utils_perf - WARNING - full垃圾回收占用了19%的CPU时间...“EN垃圾回收（G...
preload = ['distributed'] if 'pkg_resources' in sys.modules: preload.append('pkg_resources') ctx.set_forkserver_preload(preload) else: ctx = multiprocessingreturn ctxmp_context = _initialize_mp_context()def funcname(func): """Get the name of a function.""" ...
然而,实际上,DistributedSampler 是专为分布式训练设计的,它能够在多个进程之间协调数据划分,确保每个进程处理不同的数据子集。 DistributedSampler 的正确使用方法和场景 DistributedSampler 通常与 torch.utils.data.DataLoader 结合使用,在分布式训练场景中加载数据。以下是使用 DistributedSampler 的一个基本示例:...
ifnottorch.distributed.is_initialized(): # Setup some reasonable defaults for env-based distributed init if # not set by the running environment. if'MASTER_ADDR'notinos.environ: os.environ['MASTER_ADDR']='localhost' if'MASTER_PORT'notinos.environ: ...
path.join(_DATA_DIR, _PATHS[dataset_name]) # Construct the dataset dataset = _DATASETS[dataset_name](data_path, split) # Create a sampler for multi-process training sampler = DistributedSampler(dataset) if cfg.NUM_GPUS > 1 else None # Create a loader loader = torch.utils.data.DataLoader...
distributed: train_dist_sampler = DistributedSampler(train_dataset) #valid_sampler_dist = DistributedSampler(valid_dataset) else: train_dist_sampler = None train_queue = torch.utils.data.DataLoader(train_dataset, batch_size = config.train.batchsize, num_workers = num_workers , pin_memory=pin_...
by default TrueRaises---ExceptionIf DistributedManager has yet to be initialized"""ifnotDistributedManager.is_initialized():raiseException("Distributed manager should be initialized when using gather_loss")distmng=DistributedManager()loss=torch.Tensor([loss])# For serial runs, just return the cur...