From 8d8caafb3e501c5bb5a151c3f1ad0a454c108d7e Mon Sep 17 00:00:00 2001 From: zjun Date: Thu, 30 Apr 2020 09:46:01 +0800 Subject: [PATCH] remove enbale_hccl doc --- .../source_en/advanced_use/distributed_training.md | 4 ++-- .../advanced_use/distributed_training.md | 6 ++---- .../resnet50_distributed_training.py | 1 - tutorials/tutorial_code/resnet/cifar_resnet50.py | 14 ++++---------- 4 files changed, 8 insertions(+), 17 deletions(-) diff --git a/tutorials/source_en/advanced_use/distributed_training.md b/tutorials/source_en/advanced_use/distributed_training.md index 2882b4703b..a3b5225f62 100644 --- a/tutorials/source_en/advanced_use/distributed_training.md +++ b/tutorials/source_en/advanced_use/distributed_training.md @@ -84,7 +84,7 @@ export DEVICE_ID=0 ### Invoking the Collective Communication Library -You need to enable the distributed API `enable_hccl` in the `context.set_context()` API, set the `device_id` parameter, and invoke `init()` to complete the initialization operation. +You need to set the `device_id` parameter, and invoke `init()` to complete the initialization operation. In the sample, the graph mode is used during runtime. On the Ascend AI processor, Huawei Collective Communication Library (HCCL) is used. @@ -94,7 +94,7 @@ from mindspore import context from mindspore.communication.management import init if __name__ == "__main__": - context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", enable_hccl=True, device_id=int(os.environ["DEVICE_ID"])) + context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=int(os.environ["DEVICE_ID"])) init() ... ``` diff --git a/tutorials/source_zh_cn/advanced_use/distributed_training.md b/tutorials/source_zh_cn/advanced_use/distributed_training.md index 890dab6d0c..33effbcb2e 100644 --- a/tutorials/source_zh_cn/advanced_use/distributed_training.md +++ b/tutorials/source_zh_cn/advanced_use/distributed_training.md @@ -97,16 +97,15 @@ from mindspore import context from mindspore.communication.management import init if __name__ == "__main__": - context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", enable_hccl=True, device_id=int(os.environ["DEVICE_ID"])) + context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=int(os.environ["DEVICE_ID"])) init() ... ``` 其中， - `mode=context.GRAPH_MODE`：使用分布式训练需要指定运行模式为图模式（PyNative模式不支持并行）。 -- `enable_hccl=True`：使能HCCL通信。 - `device_id`：卡的物理序号，即卡所在机器中的实际序号。 -- `init()`：完成分布式训练初始化操作。 +- `init()`：使能HCCL通信，并完成分布式训练初始化操作。 ## 数据并行模式加载数据集 @@ -240,7 +239,6 @@ from resnet import resnet50 device_id = int(os.getenv('DEVICE_ID')) context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") context.set_context(enable_task_sink=True, device_id=device_id) # set task_sink and device_id -context.set_context(enable_hccl=True) # set enable_hccl context.set_context(enable_loop_sink=True) def test_train_cifar(num_classes=10, epoch_size=10): diff --git a/tutorials/tutorial_code/distributed_training/resnet50_distributed_training.py b/tutorials/tutorial_code/distributed_training/resnet50_distributed_training.py index 647de17923..fa7b3231d4 100644 --- a/tutorials/tutorial_code/distributed_training/resnet50_distributed_training.py +++ b/tutorials/tutorial_code/distributed_training/resnet50_distributed_training.py @@ -36,7 +36,6 @@ from resnet import resnet50 device_id = int(os.getenv('DEVICE_ID')) context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") context.set_context(enable_task_sink=True, device_id=device_id) # set task_sink and device_id -context.set_context(enable_hccl=True) # set enable_hccl context.set_context(enable_loop_sink=True) context.set_context(enable_mem_reuse=False) init() diff --git a/tutorials/tutorial_code/resnet/cifar_resnet50.py b/tutorials/tutorial_code/resnet/cifar_resnet50.py index 58d2fff005..df890186b2 100644 --- a/tutorials/tutorial_code/resnet/cifar_resnet50.py +++ b/tutorials/tutorial_code/resnet/cifar_resnet50.py @@ -106,16 +106,10 @@ def create_dataset(repeat_num=1, training=True): if __name__ == '__main__': # in this way by judging the mark of args, users will decide which function to use - if args_opt.do_eval: - context.set_context(enable_hccl=False) - else: - if args_opt.run_distribute: - context.set_context(enable_hccl=True) - context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL) - auto_parallel_context().set_all_reduce_fusion_split_indices([140]) - init() - else: - context.set_context(enable_hccl=False) + if not args_opt.do_eval and args_opt.run_distribute: + context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL) + auto_parallel_context().set_all_reduce_fusion_split_indices([140]) + init() epoch_size = args_opt.epoch_size net = resnet50(args_opt.batch_size, args_opt.num_classes) -- Gitee