From a4045f10c795238d2a6e7780a07dda0569a46dca Mon Sep 17 00:00:00 2001 From: lichenever Date: Sat, 11 Apr 2020 15:27:13 +0800 Subject: [PATCH] fix distributed_training doc --- .../advanced_use/distributed_training.md | 13 +++++++------ .../advanced_use/distributed_training.md | 16 +++++++++------- 2 files changed, 16 insertions(+), 13 deletions(-) diff --git a/tutorials/source_en/advanced_use/distributed_training.md b/tutorials/source_en/advanced_use/distributed_training.md index eefab7b1b4..22079e575c 100644 --- a/tutorials/source_en/advanced_use/distributed_training.md +++ b/tutorials/source_en/advanced_use/distributed_training.md @@ -37,7 +37,7 @@ In this tutorial, we will learn how to train the ResNet-50 network in `DATA_PARA When distributed training is performed in the lab environment, you need to configure the networking information file for the current multi-card environment. If HUAWEI CLOUD is used, skip this section. -The Ascend 910 AI processor and 1980 AIServer are used as an example. The JSON configuration file of a two-card environment is as follows. In this example, the configuration file is named rank_table.json. +The Ascend 910 AI processor and AIServer are used as an example. The JSON configuration file of a two-card environment is as follows. In this example, the configuration file is named rank_table.json. ```json { @@ -67,11 +67,12 @@ The Ascend 910 AI processor and 1980 AIServer are used as an example. The JSON c ``` The following parameters need to be modified based on the actual training environment: -1. `server_num` indicates the number of hosts, and `server_id` indicates the IP address of the local host. -2. `device_num`, `para_plane_nic_num`, and `instance_count` indicate the number of cards. -3. `rank_id` indicates the logical sequence number of a card, which starts from 0 fixedly. `device_id` indicates the physical sequence number of a card, that is, the actual sequence number of the host where the card is located. -4. `device_ip` indicates the IP address of the NIC. You can run the `cat /etc/hccn.conf` command on the current host to obtain the IP address of the NIC. -5. `para_plane_nic_name` indicates the name of the corresponding NIC. +1. `board_id` indicates the environment in which the program runs. +2. `server_num` indicates the number of hosts, and `server_id` indicates the IP address of the local host. +3. `device_num`, `para_plane_nic_num`, and `instance_count` indicate the number of cards. +4. `rank_id` indicates the logical sequence number of a card, which starts from 0 fixedly. `device_id` indicates the physical sequence number of a card, that is, the actual sequence number of the host where the card is located. +5. `device_ip` indicates the IP address of the NIC. You can run the `cat /etc/hccn.conf` command on the current host to obtain the IP address of the NIC. +6. `para_plane_nic_name` indicates the name of the corresponding NIC. After the networking information file is ready, add the file path to the environment variable `MINDSPORE_HCCL_CONFIG_PATH`. In addition, the `device_id` information needs to be transferred to the script. In this example, the information is transferred by configuring the environment variable DEVICE_ID. diff --git a/tutorials/source_zh_cn/advanced_use/distributed_training.md b/tutorials/source_zh_cn/advanced_use/distributed_training.md index 7c2b93fee7..ed6bd7dc93 100644 --- a/tutorials/source_zh_cn/advanced_use/distributed_training.md +++ b/tutorials/source_zh_cn/advanced_use/distributed_training.md @@ -36,7 +36,7 @@ MindSpore支持数据并行及自动并行。自动并行是MindSpore融合了 在实验室环境进行分布式训练时，需要配置当前多卡环境的组网信息文件。如果使用华为云环境，可以跳过本小节。 -以Ascend 910 AI处理器、1980 AIServer为例，一个两卡环境的json配置文件示例如下，本样例将该配置文件命名为rank_table.json。 +以Ascend 910 AI处理器、AIServer为例，一个两卡环境的json配置文件示例如下，本样例将该配置文件命名为rank_table.json。 ```json { @@ -66,11 +66,13 @@ MindSpore支持数据并行及自动并行。自动并行是MindSpore融合了 ``` 其中需要根据实际训练环境修改的参数项有： -1. `server_num`表示机器数量， `server_id`表示本机IP地址。 -2. `device_num`、`para_plane_nic_num`及`instance_count`表示卡的数量。 -3. `rank_id`表示卡逻辑序号，固定从0开始编号，`device_id`表示卡物理序号，即卡所在机器中的实际序号。 -4. `device_ip`表示网卡IP地址，可以在当前机器执行指令`cat /etc/hccn.conf`获取网卡IP地址。 -5. `para_plane_nic_name`对应网卡名称。 + +1. `board_id`表示当前运行的环境。 +2. `server_num`表示机器数量， `server_id`表示本机IP地址。 +3. `device_num`、`para_plane_nic_num`及`instance_count`表示卡的数量。 +4. `rank_id`表示卡逻辑序号，固定从0开始编号，`device_id`表示卡物理序号，即卡所在机器中的实际序号。 +5. `device_ip`表示网卡IP地址，可以在当前机器执行指令`cat /etc/hccn.conf`获取网卡IP地址。 +6. `para_plane_nic_name`对应网卡名称。 组网信息文件准备好后，将文件路径加入环境变量`MINDSPORE_HCCL_CONFIG_PATH`中。此外需要将`device_id`信息传入脚本中，本样例通过配置环境变量DEVICE_ID的方式传入。 @@ -221,7 +223,7 @@ opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, mome `context.set_auto_parallel_context()`是提供给用户设置并行参数的接口。主要参数包括： - `parallel_mode`：分布式并行模式。可选数据并行`ParallelMode.DATA_PARALLEL`及自动并行`ParallelMode.AUTO_PARALLEL`。 -- `mirror_mean`: 反向计算时，框架内部会将数据并行参数分散在多台机器的梯度进行收集，得到全局梯度值后再传入优化器中更新。 +- `mirror_mean`: 反向计算时，框架内部会将数据并行参数分散在多台机器的梯度值进行收集，得到全局梯度值后再传入优化器中更新。 设置为True对应`allreduce_mean`操作，False对应`allreduce_sum`操作。 -- Gitee