本地编译启动，linux环境下 about primihub

[phoenix20162016]

执行make clean
然后执行make
从错误日志中看是对应的依赖没有拉取到，检查下是不是在拉取对应包的过程中出现了网络超时现象

[Mash2065]

执行make clean 然后执行make 从错误日志中看是对应的依赖没有拉取到，检查下是不是在拉取对应包的过程中出现了网络超时现象

原因是WORKSPACE里的workspace未被定义，将WORKSPACE_CN中的内容拷贝到WORKSPACE中，然后进行编译。当时编译过程中虚拟机容易卡死，我的虚拟机设置的8C8G，是否需要升级硬件配置呢？

[phoenix20162016]

编译的时候限制下job的数目，修改makefile文件在对应的编译命令后面加--jobs=xxx

[Mash2065]

编译的时候限制下job的数目，修改makefile文件在对应的编译命令后面加--jobs=xxx

编译通过。但是后续在多方安全计算任务中，从本地编译启动，报错如下：

root@ubuntu:/home/mash/Desktop/primihub# ./bazel-bin/cli --server="192.168.14.129:50050" --task_lang=proto --task_type=0 --task_code="logistic_regression" --params="BatchSize:INT32:0:128,NumIters:INT32:0:100,Data_File:STRING:0:train_party_0;train_party_1;train_party_2,modelName:STRING:0:/data/result/lr_mode.csv" --input_datasets="Data_File"
I20230419 07:24:51.783396 142444 :468] use tls: 0
I20230419 07:24:51.783684 142444 cli.cc:478] SDK SubmitTask to: 192.168.14.129:50050
I20230419 07:24:51.785651 142444 cli.cc:382] SubmitTask...
E20230419 07:24:51.787084 142444 grpc_link_context.cc:227] submitTask to Node [defalut:192.168.14.129:50050:0:default] rpc failed. 14: failed to connect to all addresses
I20230419 07:24:51.787552 142444 cli.cc:491] SubmitTask time cost(ms): 2

rpc无法连接到地址
查找到src/primihub目录下源代码，cli.cc和grpc_link_context.cc如下：

[fuxingbit]

检查下是不是本地有代理，把内网地址的代理去掉试一下

[Mash2065]

检查下是不是本地有代理，把内网地址的代理去掉试一下

关了内外网所有代理之后还是报一样的错

[fuxingbit]

把 config/node*.yaml 中location 配置的127.0.0.1改成机器的IP地址在再试试

[Mash2065]

把 config/node*.yaml 中location 配置的127.0.0.1改成机器的IP地址在再试试

报一样的错，包括我这里让三个节点共同执行一个MPC的逻辑回归任务也是同样的报错（修改未修改地址都一样）
root@ubuntu:/home/mash/Desktop/primihub# docker run --network=host -it primihub/primihub-node:latest ./primihub-cli --server="192.168.14.129:8050"
I20230420 21:01:27.604171 1 cli.cc:373] use tls: 0
I20230420 21:01:27.604892 1 cli.cc:383] SDK SubmitTask to: 192.168.14.129:8050
I20230420 21:01:27.606652 1 cli.cc:246] SubmitTask...
E20230420 21:01:47.608011 1 grpc_link_context.cc:222] submitTask to Node [defalut:192.168.14.129:8050:0:default] rpc failed. 14: failed to connect to all addresses
I20230420 21:01:47.608525 1 cli.cc:396] SubmitTask time cost(ms): 20002

[fuxingbit]

telnet下看端口通吗？

[Mash2065]

telnet下看端口通吗？

telnet 127.0.0.1 和 telnet [机器ip地址] 都能通

[fuxingbit]

以往遇到这个问题的可能情况，一个是把配置里的127.0.0.1改成本机IP，一个是机器设置了http_proxy，去掉后就可以了，你现在两种都不行，确实不好确定什么原因，你系统是ubuntu哪个版本？

[Mash2065]

ubuntu20.04.6。我现在直接通过docker-compose启动一个MPC的逻辑回归任务。请问primihub-cli 的公共参数到哪里手动设置，找不到--task_config_file。
root@ubuntu:/home/mash/Desktop/primihub# docker exec -it primihub-node0 bash
root@1ce1ed164c35:/app# ./primihub-cli --task_lang=proto --task_type=0 --task_code="logistic_regression" --params="BatchSize:INT32:0:128,NumIters:INT32:0:100,Data_File:STRING:0:train_party_0;train_party_1;train_party_2,modelName:STRING:0:/data/result/lr_mode.csv" --input_datasets="Data_File"
I20230421 23:19:30.795068 110 cli.cc:373] use tls: 0
I20230421 23:19:30.795435 110 cli.cc:383] SDK SubmitTask to: 127.0.0.1:50050
I20230421 23:19:30.797150 110 cli.cc:246] SubmitTask...
I20230421 23:19:33.859876 110 cli.cc:396] SubmitTask time cost(ms): 3063
root@1ce1ed164c35:/app# ./primihub-cli --task_config_file="example/mpc_lr_task_conf.json"
ERROR: Unknown command line flag 'task_config_file'

[fuxingbit]

task_config_file 这个问题我们确认下，文档有点问题，这两个命令是两种提交方式，执行任意一种都可以，你这个mpc任务已经运行成功了。

[phoenix20162016]

ubuntu20.04.6。我现在直接通过docker-compose启动一个MPC的逻辑回归任务。请问primihub-cli 的公共参数到哪里手动设置，找不到--task_config_file。 root@ubuntu:/home/mash/Desktop/primihub# docker exec -it primihub-node0 bash root@1ce1ed164c35:/app# ./primihub-cli --task_lang=proto --task_type=0 --task_code="logistic_regression" --params="BatchSize:INT32:0:128,NumIters:INT32:0:100,Data_File:STRING:0:train_party_0;train_party_1;train_party_2,modelName:STRING:0:/data/result/lr_mode.csv" --input_datasets="Data_File" I20230421 23:19:30.795068 110 cli.cc:373] use tls: 0 I20230421 23:19:30.795435 110 cli.cc:383] SDK SubmitTask to: 127.0.0.1:50050 I20230421 23:19:30.797150 110 cli.cc:246] SubmitTask... I20230421 23:19:33.859876 110 cli.cc:396] SubmitTask time cost(ms): 3063 root@1ce1ed164c35:/app# ./primihub-cli --task_config_file="example/mpc_lr_task_conf.json" ERROR: Unknown command line flag 'task_config_file'

确保代码编译是用的最新的版本，这个flag是最近几个版本才加入的

[Mash2065]

ubuntu20.04.6。我现在直接通过docker-compose启动一个MPC的逻辑回归任务。请问primihub-cli 的公共参数到哪里手动设置，找不到--task_config_file。 root@ubuntu:/home/mash/Desktop/primihub# docker exec -it primihub-node0 bash root@1ce1ed164c35:/app# ./primihub-cli --task_lang=proto --task_type=0 --task_code="logistic_regression" --params="BatchSize:INT32:0:128,NumIters:INT32:0:100,Data_File:STRING:0:train_party_0;train_party_1;train_party_2,modelName:STRING:0:/data/result/lr_mode.csv" --input_datasets="Data_File" I20230421 23:19:30.795068 110 cli.cc:373] use tls: 0 I20230421 23:19:30.795435 110 cli.cc:383] SDK SubmitTask to: 127.0.0.1:50050 I20230421 23:19:30.797150 110 cli.cc:246] SubmitTask... I20230421 23:19:33.859876 110 cli.cc:396] SubmitTask time cost(ms): 3063 root@1ce1ed164c35:/app# ./primihub-cli --task_config_file="example/mpc_lr_task_conf.json" ERROR: Unknown command line flag 'task_config_file'

确保代码编译是用的最新的版本，这个flag是最近几个版本才加入的

请问最新版本是多少，我看了一下，我这边是4.13号clone下来的。

[phoenix20162016]

ubuntu20.04.6。我现在直接通过docker-compose启动一个MPC的逻辑回归任务。请问primihub-cli 的公共参数到哪里手动设置，找不到--task_config_file。 root@ubuntu:/home/mash/Desktop/primihub# docker exec -it primihub-node0 bash root@1ce1ed164c35:/app# ./primihub-cli --task_lang=proto --task_type=0 --task_code="logistic_regression" --params="BatchSize:INT32:0:128,NumIters:INT32:0:100,Data_File:STRING:0:train_party_0;train_party_1;train_party_2,modelName:STRING:0:/data/result/lr_mode.csv" --input_datasets="Data_File" I20230421 23:19:30.795068 110 cli.cc:373] use tls: 0 I20230421 23:19:30.795435 110 cli.cc:383] SDK SubmitTask to: 127.0.0.1:50050 I20230421 23:19:30.797150 110 cli.cc:246] SubmitTask... I20230421 23:19:33.859876 110 cli.cc:396] SubmitTask time cost(ms): 3063 root@1ce1ed164c35:/app# ./primihub-cli --task_config_file="example/mpc_lr_task_conf.json" ERROR: Unknown command line flag 'task_config_file'

确保代码编译是用的最新的版本，这个flag是最近几个版本才加入的

请问最新版本是多少，我看了一下，我这边是4.13号clone下来的。

从这个版本开始的 5372c93
检查下源码src/primihub/cli/cli.cc 中是否有如下定义
ABSL_FLAG(std::string, task_config_file, "", "path of task config file");

[Mash2065]

ubuntu20.04.6。我现在直接通过docker-compose启动一个MPC的逻辑回归任务。请问primihub-cli 的公共参数到哪里手动设置，找不到--task_config_file。 root@ubuntu:/home/mash/Desktop/primihub# docker exec -it primihub-node0 bash root@1ce1ed164c35:/app# ./primihub-cli --task_lang=proto --task_type=0 --task_code="logistic_regression" --params="BatchSize:INT32:0:128,NumIters:INT32:0:100,Data_File:STRING:0:train_party_0;train_party_1;train_party_2,modelName:STRING:0:/data/result/lr_mode.csv" --input_datasets="Data_File" I20230421 23:19:30.795068 110 cli.cc:373] use tls: 0 I20230421 23:19:30.795435 110 cli.cc:383] SDK SubmitTask to: 127.0.0.1:50050 I20230421 23:19:30.797150 110 cli.cc:246] SubmitTask... I20230421 23:19:33.859876 110 cli.cc:396] SubmitTask time cost(ms): 3063 root@1ce1ed164c35:/app# ./primihub-cli --task_config_file="example/mpc_lr_task_conf.json" ERROR: Unknown command line flag 'task_config_file'

确保代码编译是用的最新的版本，这个flag是最近几个版本才加入的

请问最新版本是多少，我看了一下，我这边是4.13号clone下来的。

从这个版本开始的 5372c93 检查下源码src/primihub/cli/cli.cc 中是否有如下定义 ABSL_FLAG(std::string, task_config_file, "", "path of task config file");

我又重新clone 了 5372c93 这个版本从头开始编译，docker方式启动mpc任务，还是报了上面ERROR: Unknown command line flag 'task_config_file'的错误，而从本地源码编译启动mpc任务，则依然是报了上面E20230420 21:01:47.608011 1 grpc_link_context.cc:222] submitTask to Node [defalut:192.168.14.129:8050:0:default] rpc failed. 14: failed to connect to all addresses的错误，同时也修改了本地ip和取消代理。

[fuxingbit]

docker方式运行就和本地编译没关系了，需要更新下最新的镜像，docker pull primihub/primihub-node,docker-compose down停掉原来的，docker-compose up -d再重新启动

[Mash2065]

docker方式运行就和本地编译没关系了，需要更新下最新的镜像，docker pull primihub/primihub-node,docker-compose down停掉原来的，docker-compose up -d再重新启动

查看日志，迭代到Epochs=79就停止了，发现没找到lr_mode.csv
root@20a1abc4e976:/app/log# cat primihub-node.ERROR
Log file created at: 2023/04/24 00:23:00
Running on machine: 20a1abc4e976
Running duration (h:mm:ss): 0:00:00
Log line format: [IWEF]yyyymmdd hh:mm:ss.uuuuuu threadid file:line] msg
E20230424 00:23:00.089043 47 csv_driver.cc:112] Open file /data/result/lr_mode.csv failed.
E20230424 00:23:00.089270 47 logistic.cc:574] Save LR model to file /data/result/lr_mode.csv failed.

然后接着启动节点2和3，两个节点都报了submitTask to Node [defalut:192.168.14.129:8050:0:default] rpc failed. 14: failed to connect to all addresses 这个错误。

[Mash2065]

docker方式运行就和本地编译没关系了，需要更新下最新的镜像，docker pull primihub/primihub-node,docker-compose down停掉原来的，docker-compose up -d再重新启动

停掉了之前的，重新拉了下最新的镜像尝试的

[fuxingbit]

https://docs.primihub.com/docs/advance-usage/create-tasks/mpc-task
看一下文档， 结果文件保存路径可以自定义，上面保存失败是没有result这层目录，你说的然后接着启动节点2和3是什么意思？

[Mash2065]

https://docs.primihub.com/docs/advance-usage/create-tasks/mpc-task 看一下文档， 结果文件保存路径可以自定义，上面保存失败是没有result这层目录，你说的然后接着启动节点2和3是什么意思？

我新建了data文件夹，但是找不到lr_mode.csv数据集无法保存LR模型；
使用docker方式启动第一个节点，启动成功，但是查看log/文件夹下的primihub-node.ERROR日志文件，如下：
root@20a1abc4e976:/app/log# cat primihub-node.ERROR
Log file created at: 2023/04/24 00:23:00
Running on machine: 20a1abc4e976
Running duration (h:mm:ss): 0:00:00
Log line format: [IWEF]yyyymmdd hh:mm:ss.uuuuuu threadid file:line] msg
E20230424 00:23:00.089043 47 csv_driver.cc:112] Open file /data/result/lr_mode.csv failed.
E20230424 00:23:00.089270 47 logistic.cc:574] Save LR model to file /data/result/lr_mode.csv failed.
E20230424 22:05:51.407390 138 redis_helper.cc:26] Connect to redis server failed, No route to host.
E20230424 22:05:51.407670 138 service.cc:501] Connect to redis server redis:6379failed.
E20230424 22:05:51.408107 93 parser.cc:121] no scheduler created to dispatch task
E20230424 22:09:30.862849 168 redis_helper.cc:26] Connect to redis server failed, No route to host.
E20230424 22:09:30.863210 168 service.cc:501] Connect to redis server redis:6379failed.
E20230424 22:09:30.863487 93 parser.cc:121] no scheduler created to dispatch task
E20230424 22:10:15.982421 184 redis_helper.cc:26] Connect to redis server failed, No route to host.
E20230424 22:10:15.982550 184 service.cc:501] Connect to redis server redis:6379failed.
E20230424 22:10:15.982697 96 parser.cc:121] no scheduler created to dispatch task
E20230424 22:11:46.030390 200 redis_helper.cc:26] Connect to redis server failed, No route to host.
E20230424 22:11:46.030558 200 service.cc:501] Connect to redis server redis:6379failed.
E20230424 22:11:46.030736 93 parser.cc:121] no scheduler created to dispatch task
E20230424 22:14:35.598970 209 redis_helper.cc:26] Connect to redis server failed, No route to host.
E20230424 22:14:35.599139 209 service.cc:501] Connect to redis server redis:6379failed.
E20230424 22:14:35.599509 96 parser.cc:121] no scheduler created to dispatch task

之后又查看容器primihub-node0的日志文件：docker logs -f primihub-node0，如下：
I20230424 22:14:32.530588 96 node.cc:665] start to schedule task, task_type: 0
I20230424 22:14:32.530601 96 node.cc:1183] 🤖️ Start create worker node0 worker id: abf2c87e-99f8-4f32-bfdd-2153ea6da0ad
I20230424 22:14:32.530615 96 node.cc:1187] 🤖️ Fininsh create worker node0 worker id: abf2c87e-99f8-4f32-bfdd-2153ea6da0ad
I20230424 22:14:32.530701 96 proto_parser.cc:48] dataset: train_party_0
I20230424 22:14:32.530733 96 proto_parser.cc:48] dataset: train_party_1
I20230424 22:14:32.530746 96 proto_parser.cc:48] dataset: train_party_2
I20230424 22:14:32.530758 96 proto_parser.cc:48] dataset: test_party_0
I20230424 22:14:32.530766 96 proto_parser.cc:48] dataset: test_party_1
I20230424 22:14:32.530773 96 proto_parser.cc:48] dataset: test_party_2
I20230424 22:14:32.530946 209 parser.cc:76] 🔍 Proto task finding meta list from datasets...
I20230424 22:14:32.531008 209 redis_helper.cc:20] Redis server: host redis, port 6379.
E20230424 22:14:35.598970 209 redis_helper.cc:26] Connect to redis server failed, No route to host.
E20230424 22:14:35.599139 209 service.cc:501] Connect to redis server redis:6379failed.
E20230424 22:14:35.599509 96 parser.cc:121] no scheduler created to dispatch task

[fuxingbit]

你直接通过项目里的docker-compose.yaml 文件启动，里面有redis，你用docker只启动一个节点没用啊，任务都是需要多个节点才能运行

[fuxingbit]

再看一下文档中的docker-compose启动方式

[fuxingbit]

把你现在启动的这些都停了

[Mash2065]

你直接通过项目里的docker-compose.yaml 文件启动，里面有redis，你用docker只启动一个节点没用啊，任务都是需要多个节点才能运行

嗯呢，节点0启动成功，节点1和2启动还是报下面同样的错。
root@284e36e672a8:/app# ./primihub-cli --task_lang=proto --task_type=0 --task_code="logistic_regression" --params="BatchSize:INT32:0:128,NumIters:INT32:0:100,Data_File:STRING:0:train_party_0;train_party_1;train_party_2,modelName:STRING:0:/data/result/lr_mode.csv" --input_datasets="Data_File"
I20230424 23:57:09.724027 46 cli.cc:468] use tls: 0
I20230424 23:57:09.724349 46 cli.cc:478] SDK SubmitTask to: 127.0.0.1:50050
I20230424 23:57:09.726305 46 cli.cc:382] SubmitTask...
E20230424 23:57:09.727294 46 grpc_link_context.cc:228] submitTask to Node [defalut:127.0.0.1:50050:0:default] rpc failed. 14: failed to connect to all addresses

[fuxingbit]

你是说在node1和node2容器里执行命令报这个错？

[fuxingbit]

任务只在node0容器里运行就可以了，node1和node2的端口是50051和50052

[Mash2065]

任务只在node0容器里运行就可以了，node1和node2的端口是50051和50052

但是看node0的日志，未找到/data/result/lr_mode.csv文件呀。

[fuxingbit]

在node0容器中运行任务，在另一个终端执行docker logs -f primihub-node0，把日志贴一下

[Mash2065]

在node0容器中运行任务，在另一个终端执行docker logs -f primihub-node0，把日志贴一下

I20230424 23:54:07.436573 40 node.cc:854] start to create worker for task: job_id : 100 task_id: 200 request id: 84ca9770-2747-4fe5-a978-a38667b26226
I20230424 23:54:07.436586 40 node.cc:1183] 🤖️ Start create worker node0 worker id: 84ca9770-2747-4fe5-a978-a38667b26226
I20230424 23:54:07.436601 40 node.cc:1187] 🤖️ Fininsh create worker node0 worker id: 84ca9770-2747-4fe5-a978-a38667b26226
I20230424 23:54:07.436733 40 node.cc:865] create worker thread future for task: job_id : 100 task_id: 200 request id: 84ca9770-2747-4fe5-a978-a38667b26226 finished
I20230424 23:54:07.436873 46 node.cc:831] begin to execute task
I20230424 23:54:07.439937 46 logistic.cc:108] 4
I20230424 23:54:07.440155 46 logistic.cc:127] Note party id of this node is 0.
I20230424 23:54:07.440519 46 worker.cc:54] 🚀 Worker start execute task
I20230424 23:54:07.440596 46 logistic.cc:180] Train data train_party_0, test data .
I20230424 23:54:07.440768 40 node.cc:882] create worker thread for task: job_id : 100 task_id: 200 request id: 84ca9770-2747-4fe5-a978-a38667b26226 finished
I20230424 23:54:07.448966 46 logistic.cc:300] [Next] Init server session, party 0, ip 172.28.1.10, port 12120, name sess_0_1.
I20230424 23:54:07.449196 46 logistic.cc:306] [Prev] Init server session, party 0, ip 172.28.1.10, port 12120, name sess_0_2.
I20230424 23:54:07.456758 46 runtime.h:44] Init Runtime : 0, 0x7f21f4014680
I20230424 23:54:07.480000 46 logistic.cc:382] Init party: 0 communication finish.
I20230424 23:54:07.531560 46 logistic.cc:521] Train dataset has 558 examples, dimension of each is 10.
I20230424 23:54:07.531641 46 logistic.cc:524] Test dataset has 141 examples, dimension of each is 10.
I20230424 23:54:07.531927 46 logistic.cc:62] (Learning rate):0.015625.
I20230424 23:54:07.532014 46 logistic.cc:63] (Epoch):100.
I20230424 23:54:07.532033 46 logistic.cc:64] (Batchsize) :128.
I20230424 23:54:07.532042 46 logistic.cc:65] (Train_loader size):4.
I20230424 23:54:07.532048 46 logistic.cc:68] Epochs : ( 0/100 )
I20230424 23:54:07.744144 46 logistic.cc:68] Epochs : ( 1/100 )
I20230424 23:54:07.962257 46 logistic.cc:68] Epochs : ( 2/100 )
I20230424 23:54:08.179220 46 logistic.cc:68] Epochs : ( 3/100 )
I20230424 23:54:08.393626 46 logistic.cc:68] Epochs : ( 4/100 )
I20230424 23:54:08.610514 46 logistic.cc:68] Epochs : ( 5/100 )
I20230424 23:54:08.833190 46 logistic.cc:68] Epochs : ( 6/100 )
I20230424 23:54:09.053086 46 logistic.cc:68] Epochs : ( 7/100 )
I20230424 23:54:09.265569 46 logistic.cc:68] Epochs : ( 8/100 )
I20230424 23:54:09.482378 46 logistic.cc:68] Epochs : ( 9/100 )
I20230424 23:54:09.698501 46 logistic.cc:68] Epochs : ( 10/100 )
I20230424 23:54:09.917053 46 logistic.cc:68] Epochs : ( 11/100 )
I20230424 23:54:10.133777 46 logistic.cc:68] Epochs : ( 12/100 )
I20230424 23:54:10.352149 46 logistic.cc:68] Epochs : ( 13/100 )
I20230424 23:54:10.566813 46 logistic.cc:68] Epochs : ( 14/100 )
I20230424 23:54:10.784725 46 logistic.cc:68] Epochs : ( 15/100 )
I20230424 23:54:11.005990 46 logistic.cc:68] Epochs : ( 16/100 )
I20230424 23:54:11.223676 46 logistic.cc:68] Epochs : ( 17/100 )
I20230424 23:54:11.441133 46 logistic.cc:68] Epochs : ( 18/100 )
I20230424 23:54:11.658236 46 logistic.cc:68] Epochs : ( 19/100 )
I20230424 23:54:11.878443 46 logistic.cc:68] Epochs : ( 20/100 )
I20230424 23:54:12.092494 46 logistic.cc:68] Epochs : ( 21/100 )
I20230424 23:54:12.309180 46 logistic.cc:68] Epochs : ( 22/100 )
I20230424 23:54:12.525719 46 logistic.cc:68] Epochs : ( 23/100 )
I20230424 23:54:12.744263 46 logistic.cc:68] Epochs : ( 24/100 )
I20230424 23:54:12.960798 46 logistic.cc:68] Epochs : ( 25/100 )
I20230424 23:54:13.172833 46 logistic.cc:68] Epochs : ( 26/100 )
I20230424 23:54:13.385586 46 logistic.cc:68] Epochs : ( 27/100 )
I20230424 23:54:13.601778 46 logistic.cc:68] Epochs : ( 28/100 )
I20230424 23:54:13.817140 46 logistic.cc:68] Epochs : ( 29/100 )
I20230424 23:54:14.033438 46 logistic.cc:68] Epochs : ( 30/100 )
I20230424 23:54:14.255892 46 logistic.cc:68] Epochs : ( 31/100 )
I20230424 23:54:14.474695 46 logistic.cc:68] Epochs : ( 32/100 )
I20230424 23:54:14.695394 46 logistic.cc:68] Epochs : ( 33/100 )
I20230424 23:54:14.916321 46 logistic.cc:68] Epochs : ( 34/100 )
I20230424 23:54:15.133008 46 logistic.cc:68] Epochs : ( 35/100 )
I20230424 23:54:15.345700 46 logistic.cc:68] Epochs : ( 36/100 )
I20230424 23:54:15.567945 46 logistic.cc:68] Epochs : ( 37/100 )
I20230424 23:54:15.782415 46 logistic.cc:68] Epochs : ( 38/100 )
I20230424 23:54:16.000154 46 logistic.cc:68] Epochs : ( 39/100 )
I20230424 23:54:16.214078 46 logistic.cc:68] Epochs : ( 40/100 )
I20230424 23:54:16.427541 46 logistic.cc:68] Epochs : ( 41/100 )
I20230424 23:54:16.640097 46 logistic.cc:68] Epochs : ( 42/100 )
I20230424 23:54:16.860771 46 logistic.cc:68] Epochs : ( 43/100 )
I20230424 23:54:17.076203 46 logistic.cc:68] Epochs : ( 44/100 )
I20230424 23:54:17.299577 46 logistic.cc:68] Epochs : ( 45/100 )
I20230424 23:54:17.516425 46 logistic.cc:68] Epochs : ( 46/100 )
I20230424 23:54:17.730170 46 logistic.cc:68] Epochs : ( 47/100 )
I20230424 23:54:17.945080 46 logistic.cc:68] Epochs : ( 48/100 )
I20230424 23:54:18.159049 46 logistic.cc:68] Epochs : ( 49/100 )
I20230424 23:54:18.375844 46 logistic.cc:68] Epochs : ( 50/100 )
I20230424 23:54:18.590790 46 logistic.cc:68] Epochs : ( 51/100 )
I20230424 23:54:18.810873 46 logistic.cc:68] Epochs : ( 52/100 )
I20230424 23:54:19.033954 46 logistic.cc:68] Epochs : ( 53/100 )
I20230424 23:54:19.247550 46 logistic.cc:68] Epochs : ( 54/100 )
I20230424 23:54:19.467563 46 logistic.cc:68] Epochs : ( 55/100 )
I20230424 23:54:19.684513 46 logistic.cc:68] Epochs : ( 56/100 )
I20230424 23:54:19.905318 46 logistic.cc:68] Epochs : ( 57/100 )
I20230424 23:54:20.122334 46 logistic.cc:68] Epochs : ( 58/100 )
I20230424 23:54:20.337607 46 logistic.cc:68] Epochs : ( 59/100 )
I20230424 23:54:20.553450 46 logistic.cc:68] Epochs : ( 60/100 )
I20230424 23:54:20.771946 46 logistic.cc:68] Epochs : ( 61/100 )
I20230424 23:54:20.987932 46 logistic.cc:68] Epochs : ( 62/100 )
I20230424 23:54:21.210723 46 logistic.cc:68] Epochs : ( 63/100 )
I20230424 23:54:21.427621 46 logistic.cc:68] Epochs : ( 64/100 )
I20230424 23:54:21.646024 46 logistic.cc:68] Epochs : ( 65/100 )
I20230424 23:54:21.863533 46 logistic.cc:68] Epochs : ( 66/100 )
I20230424 23:54:22.078302 46 logistic.cc:68] Epochs : ( 67/100 )
I20230424 23:54:22.295083 46 logistic.cc:68] Epochs : ( 68/100 )
I20230424 23:54:22.509812 46 logistic.cc:68] Epochs : ( 69/100 )
I20230424 23:54:22.721182 46 logistic.cc:68] Epochs : ( 70/100 )
I20230424 23:54:22.938562 46 logistic.cc:68] Epochs : ( 71/100 )
I20230424 23:54:23.155169 46 logistic.cc:68] Epochs : ( 72/100 )
I20230424 23:54:23.372893 46 logistic.cc:68] Epochs : ( 73/100 )
I20230424 23:54:23.592212 46 logistic.cc:68] Epochs : ( 74/100 )
I20230424 23:54:23.814303 46 logistic.cc:68] Epochs : ( 75/100 )
I20230424 23:54:24.033784 46 logistic.cc:68] Epochs : ( 76/100 )
I20230424 23:54:24.256633 46 logistic.cc:68] Epochs : ( 77/100 )
I20230424 23:54:24.472149 46 logistic.cc:68] Epochs : ( 78/100 )
I20230424 23:54:24.686939 46 logistic.cc:68] Epochs : ( 79/100 )
I20230424 23:54:24.908722 46 logistic.cc:68] Epochs : ( 80/100 )
I20230424 23:54:25.126327 46 logistic.cc:68] Epochs : ( 81/100 )
I20230424 23:54:25.342397 46 logistic.cc:68] Epochs : ( 82/100 )
I20230424 23:54:25.555570 46 logistic.cc:68] Epochs : ( 83/100 )
I20230424 23:54:25.769846 46 logistic.cc:68] Epochs : ( 84/100 )
I20230424 23:54:25.986382 46 logistic.cc:68] Epochs : ( 85/100 )
I20230424 23:54:26.204106 46 logistic.cc:68] Epochs : ( 86/100 )
I20230424 23:54:26.420639 46 logistic.cc:68] Epochs : ( 87/100 )
I20230424 23:54:26.636018 46 logistic.cc:68] Epochs : ( 88/100 )
I20230424 23:54:26.853946 46 logistic.cc:68] Epochs : ( 89/100 )
I20230424 23:54:27.069939 46 logistic.cc:68] Epochs : ( 90/100 )
I20230424 23:54:27.293044 46 logistic.cc:68] Epochs : ( 91/100 )
I20230424 23:54:27.509610 46 logistic.cc:68] Epochs : ( 92/100 )
I20230424 23:54:27.730319 46 logistic.cc:68] Epochs : ( 93/100 )
I20230424 23:54:27.947926 46 logistic.cc:68] Epochs : ( 94/100 )
I20230424 23:54:28.161187 46 logistic.cc:68] Epochs : ( 95/100 )
I20230424 23:54:28.374837 46 logistic.cc:68] Epochs : ( 96/100 )
I20230424 23:54:28.590337 46 logistic.cc:68] Epochs : ( 97/100 )
I20230424 23:54:28.803864 46 logistic.cc:68] Epochs : ( 98/100 )
I20230424 23:54:29.017604 46 logistic.cc:68] Epochs : ( 99/100 )
I20230424 23:54:29.235186 46 logistic.cc:548] Party 0 train finish.
E20230424 23:54:29.236776 46 csv_driver.cc:112] Open file /data/result/lr_mode.csv failed.
E20230424 23:54:29.236980 46 logistic.cc:574] Save LR model to file /data/result/lr_mode.csv failed.

[fuxingbit]

你在node0容器里执行 mkdir /data/result -p，再运行，前面说了一次是缺目录，这个目录会挂载在容器外面的项目目录下的data/result 目录下

[Mash2065]

/

你在node0容器里执行 mkdir /data/result -p，再运行，前面说了一次是缺目录，这个目录会挂载在容器外面的项目目录下的data/result 目录下

成功了，原来docker方式从拉取镜像到创建目录再到启动任务要一次性完成，我之前没有想到是挂载result目录到外面，第一次启动mpc任务失败之后重复启动也是不能成功的。多谢你了。