Privacy computing fat----- offline prediction

One 、 explain

Fate The model predictions are offline forecast and Online forecasting Two ways , The effect is the same , Mainly the way of use 、 Applicable scenario 、 High availability 、 There are great differences in performance, etc ; This article uses Fate be based on Vertical logistic regression The model trained by the algorithm is used for off-line prediction practice .

Based on the above 《 Privacy computing FATE- model training 》 The model trained in the
About Fate Please refer to the article for the basic overview and installation deployment of 《 Privacy computing FATE- Key concepts and stand-alone Deployment Guide 》

Two 、 Query model information

Execute the following command , Get into Fate In the container ：

docker exec -it $(docker ps -aqf “name=standalone_fate”) bash
First, we need to get the corresponding model_id and model_version Information , Can pass job_id Execute the following command to get ：

flow job config -j 202205070226373055640 -r guest -p 9999 --output-path /data/projects/fate/examples/my_test/
job_id Can be in FATE Board View in .

After successful execution, the corresponding model information will be returned , And generate a folder under the specified directory job_202205070226373055640_config

{
“data”: {
“job_id”: “202205070226373055640”,
“model_info”: {
“model_id”: “arbiter-10000#guest-9999#host-10000#model”,
“model_version”: “202205070226373055640”
},
“train_runtime_conf”: {}
},
“retcode”: 0,
“retmsg”: “download successfully, please check /data/projects/fate/examples/my_test/job_202205070226373055640_config directory”,
“directory”: “/data/projects/fate/examples/my_test/job_202205070226373055640_config”
}
job_202205070226373055640_config It contains 4 File ：

dsl.json： Mission dsl To configure .
model_info.json： Model information .
runtime_conf.json： Task running configuration .
train_runtime_conf.json： empty .

3、 ... and 、 Model deployment

Execute the following command ：

flow model deploy --model-id arbiter-10000#guest-9999#host-10000#model --model-version 202205070226373055640
Pass respectively --model-id And --model-version Specify the... Found in the above steps model_id and model_version

After the deployment is successful, return ：

{
“data”: {
“arbiter”: {
“10000”: 0
},
“detail”: {
“arbiter”: {
“10000”: {
“retcode”: 0,
“retmsg”: “deploy model of role arbiter 10000 success”
}
},
“guest”: {
“9999”: {
“retcode”: 0,
“retmsg”: “deploy model of role guest 9999 success”
}
},
“host”: {
“10000”: {
“retcode”: 0,
“retmsg”: “deploy model of role host 10000 success”
}
}
},
“guest”: {
“9999”: 0
},
“host”: {
“10000”: 0
},
“model_id”: “arbiter-10000#guest-9999#host-10000#model”,
“model_version”: “202205070730131040240”
},
“retcode”: 0,
“retmsg”: “success”
}
After successful deployment, a new model_version

Four 、 Prepare forecast configuration

Execute the following command ：

cp /data/projects/fate/examples/dsl/v2/hetero_logistic_regression/hetero_lr_normal_predict_conf.json /data/projects/fate/examples/my_test/
Put... Directly Fate The built-in vertical logistic regression algorithm prediction configuration example , Copy it to our my_test Under the table of contents .

The predicted configuration file mainly configures three parts ：

The above section is to configure the initiator and participant roles
The middle part needs to be filled with the correct Model information
The following is the data table used in the forecast
The only thing that needs to be modified is the middle one Model information part ; Note that the version number entered here is Model deployment Version number returned after , And need to add job_type by predict Specify the task type as forecast task .

5、 ... and 、 Perform forecast task

Execute the following command ：

flow job submit -c hetero_lr_normal_predict_conf.json
Just like model training, it also uses submit command , adopt -c Specify profile .

Return after successful execution ：

{
“data”: {
“board_url”: “http://127.0.0.1:8080/index.html#/dashboard?job_id=202205070731385067720&role=guest&party_id=9999”,
“code”: 0,
“dsl_path”: “/data/projects/fate/fateflow/jobs/202205070731385067720/job_dsl.json”,
“job_id”: “202205070731385067720”,
“logs_directory”: “/data/projects/fate/fateflow/logs/202205070731385067720”,
“message”: “success”,
“model_info”: {
“model_id”: “arbiter-10000#guest-9999#host-10000#model”,
“model_version”: “202205070730131040240”
},
“pipeline_dsl_path”: “/data/projects/fate/fateflow/jobs/202205070731385067720/pipeline_dsl.json”,
“runtime_conf_on_party_path”: “/data/projects/fate/fateflow/jobs/202205070731385067720/guest/9999/job_runtime_on_party_conf.json”,
“runtime_conf_path”: “/data/projects/fate/fateflow/jobs/202205070731385067720/job_runtime_conf.json”,
“train_runtime_conf_path”: “/data/projects/fate/fateflow/jobs/202205070731385067720/train_runtime_conf.json”
},
“jobId”: “202205070731385067720”,
“retcode”: 0,
“retmsg”: “success”
}

6、 ... and 、 View forecast results

Can be returned by board_url perhaps job_id Go to FATE Board View results in , But the graphical interface can only be viewed at most 100 Bar record ;

We can go through output-data command , Export all data output of the specified component ：

flow tracking output-data -j 202205070731385067720 -r guest -p 9999 -cpn hetero_lr_0 -o /data/projects/fate/examples/my_test/predict
-j： Specify the... Of the forecast task job_id
-cpn： Specify the component name .
-o： Specify the output directory .
Return after successful execution ：

{
“retcode”: 0,
“directory”: “/data/projects/fate/examples/my_test/predict/job_202205070731385067720_hetero_lr_0_guest_9999_output_data”,
“retmsg”: “Download successfully, please check /data/projects/fate/examples/my_test/predict/job_202205070731385067720_hetero_lr_0_guest_9999_output_data directory”
}
In the catalog /data/projects/fate/examples/my_test/predict/job_202205070731385067720_hetero_lr_0_guest_9999_output_data You can see two files in ：

data.csv： For all data output .
data.meta： Is the column header of the data .