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dsl_v1_predict_tutorial.md

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DSL version 1 predict tutorial

This documentation will give a breif tutorial of how to run a predict task after a trainning task. We will take hetero-secureboost as an example.

Submit a training task

We can start a training job by submitting conf & dsl through flow client, Here we submit a hetero-secureboost binary classification task, whose conf and dsl are in hetero secureboost example folder.

>> flow job submit -c ./examples/dsl/v1/hetero_secureboost/test_secureboost_train_binary_conf.json -d ./examples/dsl/v1/hetero_secureboost/test_secureboost_train_dsl.json
>> {
        "data": {
            "board_url": "http://127.0.0.1:8080/index.html#/dashboard?job_id=2020103015490073208469&role=guest&party_id=10000",
            "job_dsl_path": "fate/jobs/2020103015490073208469/job_dsl.json",
            "job_runtime_conf_path": "/fate/jobs/2020103015490073208469/job_runtime_conf.json",
            "logs_directory": "/fate/logs/2020103015490073208469",
            "model_info": {
                "model_id": "guest-10000#host-10000#model",  <<- model_id needed for prediction tasks
                "model_version": "2020103015490073208469"  <<- model_version needed for prediction tasks
            }
        },
        "jobId": "2020103015490073208469",
        "retcode": 0,
        "retmsg": "success"
    }

Then we can get a return message contains model_id and model_version. They are needed for making a prediction conf.

Retrieve model_id and model_version

Forget to save model_id and model_version in the returned message? No worry. You can query the corresponding model_id and model_version of a job using the "flow job config" command.

>> flow job config -j 2020103015490073208469 -r guest -p 9999 -o ./
>> {
        "data": {
            "job_id": "2020103015490073208469",
            "model_info": {
                "model_id": "guest-10000#host-10000#model", <<- model_id needed for prediction tasks
                "model_version": "2020103015490073208469" <<- model_version needed for prediction tasks
            },
            "train_runtime_conf": {}
        },
        "retcode": 0,
        "retmsg": "download successfully, please check /fate/job_2020103015490073208469_config directory",
        "directory": "/fate/job_2020103015490073208469_config"
    }

Make a predict conf

We can modify existing predict conf by replacing model_id, model_version and data set name with yours to make a new predict conf. Here we replace model_id and model_version in predict conf with model_id and model_version returned by training job.

{
    "initiator": {
        "role": "guest",
        "party_id": 10000
    },
    "job_parameters": {
        "work_mode": 0,
        "job_type": "predict",
        "model_id": "guest-10000#host-10000#model",  <<-- to replace 
        "model_version": "2020103015490073208469"  <<-- to replace
    },
    "role": {
        "guest": [
            10000
        ],
        "host": [
            10000
        ]
    },
    "role_parameters": {
        "guest": {
            "args": {
                "data": {
                    "eval_data": [
                        {
                            "name": "breast_hetero_guest",   <<-- you can set predict dataset here
                            "namespace": "experiment"
                        }
                    ]
                }
            }
        },
        "host": {
            "args": {
                "data": {
                    "eval_data": [
                        {
                            "name": "breast_hetero_host",  <<-- you can set predict dataset here
                            "namespace": "experiment"
                        }
                    ]
                }
            }
        }
    }
}

Submit a predict job

Then we can submit a new predict job:

>> flow job submit -c ./examples/dsl/v1/hetero_secureboost/test_predict_conf.json