Usage: ./network_up.sh { start | stop | restart }
./network_up.sh start
./network_up.sh stop
./network_up.sh restart
Usage: ./paddledtx_test.sh {upload_sample_files | start_vl_linear_train | start_vl_linear_predict | start_vl_logistic_train | start_vl_logistic_predict | tasklist | gettaskbyid}
./paddledtx_test.sh upload_sample_files
- vlLinTrainfiles 取值为步骤2.1获取到的 vertical linear train sample files
./paddledtx_test.sh start_vl_linear_train -f $vlLinTrainfiles
./paddledtx_test.sh start_vl_linear_train -f $vlLinTrainfiles -e true
./paddledtx_test.sh start_vl_linear_train -f $vlLinTrainfiles -l true
- vlLinPredictfiles 取值为步骤2.1获取到的 vertical linear predict sample files
- linearModelTaskId 取值为步骤2.2的模型训练任务ID
- 请确保2.2训练任务已经完成
./paddledtx_test.sh start_vl_linear_predict -f $vlLinPredictfiles -m $linearModelTaskId
- vlLogTrainfiles 取值为步骤2.1获取到的 vertical logistic train sample files
./paddledtx_test.sh start_vl_logistic_train -f $vlLogTrainfiles
./paddledtx_test.sh start_vl_logistic_train -f $vlLogTrainfiles -e true
./paddledtx_test.sh start_vl_logistic_train -f $vlLogTrainfiles -l true
- vlLogPredictfiles 取值为步骤2.1获取到的 vertical logistic predict sample files
- logisticModelTaskId 取值为步骤2.4的模型任务ID
- 请确保2.4训练任务已经完成
./paddledtx_test.sh start_vl_logistic_predict -f $vlLogPredictfiles -m $logisticModelTaskId
./paddledtx_test.sh tasklist
docker exec -it executor1.node.com sh -c "
./executor-cli task list --host 127.0.0.1:80 -p 6cb69efc0439032b0d0f52bae1c9aada3f8fb46a5f24fa99065910055b77a1174d4afbac3c0529c8927587bb0e2ad90a85eaa600cfddd6b99f1212112135ef2b
"
- taskID 为目标任务ID
./paddledtx_test.sh gettaskbyid -i $taskID
- taskID 为目标任务ID
docker exec -it executor1.node.com sh -c "./executor-cli task getbyid --host 127.0.0.1:80 -i $taskID"