How Do I Use Multiple Ascend Cards for Debugging in a Notebook Instance?

An Ascend multi-card training job runs in multi-process, multi-card mode. The number of cards is equal to the number of Python processes. The Ascend underlayer reads the environment variable RANK_TABLE_FILE, which has been configured in the development environment, without requiring manual configuration. For example, to run a job on eight cards, the code is as follows:

 export RANK_SIZE=8
 current_exec_path=$(pwd)
 echo 'start training'
 for((i=0;i<=$RANK_SIZE-1;i++));
 do
 echo 'start rank '$i
 mkdir ${current_exec_path}/device$i
 cd ${current_exec_path}/device$i
 echo $i
 export RANK_ID=$i
 dev=`expr $i + 0`
 echo $dev
 export DEVICE_ID=$dev
 python train.py > train.log 2>&1 &
 done

Set the environment variable DEVICE_ID in train.py.

devid = int(os.getenv('DEVICE_ID'))
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=devid)