MPC Task

MPC Task Parameters Description

Example: launching an MPC logistic regression task：

If starting locally, run the following command from the compiled root directory. If starting with docker-compose, enter the primihub-node0 container by running docker exec -it primihub-node0 bash, then run the following command.

./primihub-cli --task_config_file="example/mpc_lr_task_conf.json"

Observe the logs of node0、node1andnode2 respectively,and the following output means that the task runs successfully. Refer to the result file path in the parameter description to verify whether the generated result file is correct.

node0:
···
I20230614 17:46:15.512172    48 node.cc:749] start to schedule task, task_type: 0
I20230614 17:46:15.512256    48 node.cc:1069]  🤖️ Start create worker node0 worker id: 28ca8b00-c1bf-414d-a9f3-0752d2f1a55c
I20230614 17:46:15.512315    48 node.cc:1073]  🤖️ Fininsh create worker node0 worker id: 28ca8b00-c1bf-414d-a9f3-0752d2f1a55c
E20230614 17:46:15.512459    48 proto_parser.cc:56] party_datasets: 3
W20230614 17:46:15.541919    48 parser.cc:192] update access info for party_name: PARTY0
W20230614 17:46:15.542023    48 parser.cc:192] update access info for party_name: PARTY1
W20230614 17:46:15.542083    48 parser.cc:192] update access info for party_name: PARTY2
I20230614 17:46:15.542430    48 aby3_scheduler.cc:191] Dispatch SubmitTask to 3 node
I20230614 17:46:15.542496    48 aby3_scheduler.cc:203] Dispatch Task to party: node1:primihub-node1:50051:0: party_name: PARTY1
I20230614 17:46:15.542651    48 aby3_scheduler.cc:203] Dispatch Task to party: node2:primihub-node2:50052:0: party_name: PARTY2
I20230614 17:46:15.542924    48 aby3_scheduler.cc:203] Dispatch Task to party: node0:primihub-node0:50050:0: party_name: PARTY0
I20230614 17:46:15.542958    62 aby3_scheduler.cc:49] dest node node1:primihub-node1:50051:0:
I20230614 17:46:15.543244    63 aby3_scheduler.cc:49] dest node node2:primihub-node2:50052:0:
I20230614 17:46:15.543543    64 aby3_scheduler.cc:49] dest node node0:primihub-node0:50050:0:
I20230614 17:46:15.549181    61 node.cc:848] start to create worker for task: job_id : 100 task_id: 200 request id: 28ca8b00-c1bf-414d-a9f3-0752d2f1a55c
I20230614 17:46:15.549252    61 node.cc:1069]  🤖️ Start create worker node0 worker id: 28ca8b00-c1bf-414d-a9f3-0752d2f1a55c
I20230614 17:46:15.549294    61 node.cc:1073]  🤖️ Fininsh create worker node0 worker id: 28ca8b00-c1bf-414d-a9f3-0752d2f1a55c
I20230614 17:46:15.549533    61 node.cc:858] create worker thread future for task: job_id : 100 task_id: 200 request id: 28ca8b00-c1bf-414d-a9f3-0752d2f1a55c finished
I20230614 17:46:15.549640    67 node.cc:821] begin to execute task
I20230614 17:46:15.552394    66 worker.cc:122] Request id: 28ca8b00-c1bf-414d-a9f3-0752d2f1a55c update party: PARTY0 status to: RUNNING
I20230614 17:46:15.553223    67 worker.cc:70] Worker start execute task 
I20230614 17:46:15.553269    67 logistic.cc:131] party_name: PARTY0
I20230614 17:46:15.553288    61 node.cc:875] create worker thread for task: job_id : 100 task_id: 200 request id: 28ca8b00-c1bf-414d-a9f3-0752d2f1a55c finished
I20230614 17:46:15.553335    67 logistic.cc:158] Train data train_party_0, test data .
I20230614 17:46:15.554432    68 worker.cc:122] Request id: 28ca8b00-c1bf-414d-a9f3-0752d2f1a55c update party: PARTY2 status to: RUNNING
I20230614 17:46:15.554865    68 worker.cc:122] Request id: 28ca8b00-c1bf-414d-a9f3-0752d2f1a55c update party: PARTY1 status to: RUNNING
I20230614 17:46:15.565569    67 logistic.cc:430] local_id_local_id_: 2
I20230614 17:46:15.565621    67 logistic.cc:431] next_party: PARTY1 detail: node1:primihub-node1:50051:0:
I20230614 17:46:15.565647    67 logistic.cc:432] prev_party: PARTY2 detail: node2:primihub-node2:50052:0:
I20230614 17:46:15.565676    67 mpc_channel.cc:11] Init channel to PARTY1.
I20230614 17:46:15.565696    67 mpc_channel.cc:11] Init channel to PARTY2.
I20230614 17:46:15.565726    67 runtime.h:48] Init Runtime : 2, 0x7f2a5000a060
I20230614 17:46:15.565752    67 aby3ML.cc:35] Runtime init finish.
I20230614 17:46:15.606828    67 aby3ML.cc:39] Encryptor init finish.
I20230614 17:46:15.624158    67 aby3ML.cc:42] Evaluator init finish.
E20230614 17:46:15.624224    67 logistic.cc:462] Construct shares of train data and train label.
E20230614 17:46:15.718981    67 logistic.cc:470] end Construct shares of train data and train label.
I20230614 17:46:15.757925    67 logistic.cc:584] Train dataset has 558 examples, dimension of each is 11.
I20230614 17:46:15.757994    67 logistic.cc:587] Test dataset has 141 examples, dimension of each is 11.
I20230614 17:46:15.758018    67 logistic.cc:49] (Learning rate):0.0078125.
I20230614 17:46:15.758060    67 logistic.cc:50] (Epoch):100.
I20230614 17:46:15.758076    67 logistic.cc:51] (Batchsize) :128.
I20230614 17:46:15.758090    67 logistic.cc:52] (Train_loader size):4.
I20230614 17:46:15.976900    67 regression.h:249] 0 @  percent:0.326241.
I20230614 17:46:17.106693    67 regression.h:249] 10 @  percent:0.893617.
I20230614 17:46:18.369973    67 regression.h:249] 20 @  percent:0.921986.
I20230614 17:46:19.758834    67 regression.h:249] 30 @  percent:0.929078.
I20230614 17:46:20.961714    67 regression.h:249] 40 @  percent:0.929078.
I20230614 17:46:22.073453    67 regression.h:249] 50 @  percent:0.929078.
I20230614 17:46:23.093506    67 regression.h:249] 60 @  percent:0.929078.
I20230614 17:46:24.239471    67 regression.h:249] 70 @  percent:0.929078.
I20230614 17:46:25.444774    67 regression.h:249] 80 @  percent:0.929078.
I20230614 17:46:26.469460    67 regression.h:249] 90 @  percent:0.93617.
I20230614 17:46:27.379612    67 logistic.cc:607] Party 2 train finish.
I20230614 17:46:27.384567    67 logistic.cc:667] Save model to data/result/mpc_lr_model.csv.
I20230614 17:46:27.420114  3032 worker.cc:119] collected finished party count: 1
I20230614 17:46:27.420164  3032 worker.cc:122] Request id: 28ca8b00-c1bf-414d-a9f3-0752d2f1a55c update party: PARTY2 status to: SUCCESS
I20230614 17:46:27.424995  3032 worker.cc:119] collected finished party count: 2
I20230614 17:46:27.425034  3032 worker.cc:122] Request id: 28ca8b00-c1bf-414d-a9f3-0752d2f1a55c update party: PARTY1 status to: SUCCESS
I20230614 17:46:27.428491    67 logistic.cc:673] Register new dataset finish.
I20230614 17:46:27.429445  3035 worker.cc:119] collected finished party count: 3
I20230614 17:46:27.429504  3035 worker.cc:122] Request id: 28ca8b00-c1bf-414d-a9f3-0752d2f1a55c update party: PARTY0 status to: SUCCESS


node1 and node2 logs are similar to node0 and are omitted.

MPC tasks not only support logistic regression, but also support quadratic operations, average and max-min statistics, missing value outliers, etc. Please refer to the mpc_*.jsonfile in theexample` directory for sample configuration of each task.

Parameter Description

parameter	data type	example	parameter description
task_code.code		logistic_regression	MPC task type
params.BatchSize	INT32	128	data size
params.NumIters	INT32	100	Iteration number
params.modelName	STRING	data/result/mpc_lr_mode.csv	The storage path and file name of the generated model
party_datasets.PARTY0	STRING	test_party_0,train_party_0	training dataset
party_datasets.PARTY1	STRING	test_party_1,train_party_1	training dataset
party_datasets.PARTY2	STRING	test_party_2,train_party_2	training dataset

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MPC Task

Parameter Description​

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Parameter Description