In addition to configuring resource information through the rank table file, you can also achieve so by combining environment variables described in this section.

This method applies only to communicator initialization of the TensorFlow network on the following products:

Atlas A2 training product/Atlas A2 inference product

Atlas training product

Configuration Description

To configure resource information, set the following environment variables on every AI server node where training is needed. The following is an example:

export CM_CHIEF_IP=192.168.1.1
export CM_CHIEF_PORT=6000
export CM_CHIEF_DEVICE=0
export CM_WORKER_SIZE=8
export CM_WORKER_IP=192.168.0.1
export HCCL_SOCKET_FAMILY=AF_INET

CM_CHIEF_IP: host listening IP address of the master node, that is, the IP address used to communicate with other nodes. The value must be in the IPv4 or IPv6 format.
CM_CHIEF_PORT: listening port of the master node. The value must be an integer ranging from 0 to 65520. Ensure that the port is not occupied by other processes.
CM_CHIEF_DEVICE: logical ID of the device that collects server cluster information on the master node.
The value of this environment variable must be an integer within the range of [0, Maximum number of devices in the server – 1].
CM_WORKER_SIZE: total number of devices involved in cluster training on the network. The value must be an integer ranging from 0 to 32768.
CM_WORKER_IP: IP address of the NIC used by the current node to communicate with the master node. The value must be in the IPv4 or IPv6 format.
HCCL_SOCKET_FAMILY: (Optional) IP version used by the communication NIC on the device. AF_INET indicates that IPv4 is used, and AF_INET6 indicates that IPv6 is used. By default, IPv4 is used preferentially.

If the IP specified by the environment variable HCCL_SOCKET_FAMILY does not match the obtained NIC information, the actual NIC information will be used.
For example, if HCCL_SOCKET_FAMILY is set to AF_INET6 but only IPv4 NICs are available on the device, IPv4 will be used instead.
When the preceding environment variables are used to configure cluster information, RANK_TABLE_FILE, RANK_ID, and RANK_SIZE cannot exist.
For Atlas A2 training product/Atlas A2 inference product, if the service is deployed in a single-device multi-process scenario, configure the communication ports used by HCCL on the NPU through the environment variable HCCL_NPU_SOCKET_PORT_RANGE. Otherwise, port conflicts may occur. Running multiple processes may increase resource overhead and affect communication performance. Configuration example:
```
export HCCL_NPU_SOCKET_PORT_RANGE="auto"
```
For details about the environment variable HCCL_NPU_SOCKET_PORT_RANGE, see Collective Communication in Environment Variables.

Example

Assume that there are two AI server nodes and 16 devices (that is, eight on each AI server node) for distributed training. Before starting training processes on each device, configure the following environment variables in the corresponding shells to configure resource information.

Node 0 is used as the master node, responsible for managing cluster information, resource allocation, and scheduling.

export CM_CHIEF_IP=192.168.1.1
export CM_CHIEF_PORT=6000
export CM_CHIEF_DEVICE=0
export CM_WORKER_SIZE=16
export CM_WORKER_IP=192.168.1.1

Node 1

export CM_CHIEF_IP=192.168.1.1
export CM_CHIEF_PORT=6000
export CM_CHIEF_DEVICE=0
export CM_WORKER_SIZE=16
export CM_WORKER_IP=192.168.2.1

Configuring Resource Information Through Environment Variables

Configuration Description

Example