一.上传数据集

1.1Host方操作：

1.1.1进入party10000 client容器

# docker exec -it confs-10000_client_1 bash //进入容器

1.1.2修改examples/upload_host.json

cat > fateflow/examples/upload/upload_host.json <<EOF
{
  "file": "examples/data/train-PartyA.csv", 
  "id_delimiter": ",",
  "head": 1,
  "partition": 10,
  "namespace": "experiment",
  "table_name": "train-PartyA"
}
EOF
file是数据源

1.1.3传输数据：

sudo docker cp 数据绝对路径/train-PartyA.csv 容器名字:放置位置
例如：

sudo docker cp /home/KubeFATE/data/train-PartyA.csv XXXXXXXXXXXX:/data/projects/fate/examples/data

1.1.4上传host数据

docker exec -it confs-9999_client_1 bash

1.2Guest方操作：

1.2.1进入party9999 client容器

docker exec -it confs-9999_client_1 bash

1.2.2如果打不开的话，可以试试：docker start 容器名字

cat > fateflow/examples/upload/upload_guest.json <<EOF 
{   
"file": "examples/data/train-PartyB.csv",   
"id_delimiter": ",",   
"head": 1,   
"partition": 4,   
"namespace": "experiment",   
"table_name": "train-PartyB" 
} EOF

1.2.3还是先传输数据，再上传guest数据：

flow data upload -c fateflow/examples/upload/upload_guest.json

二.进行训练：

2.1修改examples/test_hetero_lr_job_conf.json

cat > fateflow/examples/lr/test_hetero_lr_job_conf.json <<EOF
{
  "dsl_version": "2",
  "initiator": {
    "role": "guest",
    "party_id": 9999
  },
  "role": {
    "guest": [
      9999
    ],
    "host": [
      10000
    ],
    "arbiter": [
      10000
    ]
  },
  "job_parameters": {
    "common": {
      "task_parallelism": 2,
      "computing_partitions": 8,
      "task_cores": 4,
      "auto_retries": 1
    }
  },
  "component_parameters": {
    "common": {
      "intersection_0": {
        "intersect_method": "raw",
        "sync_intersect_ids": true,
        "only_output_key": false
      },
      "hetero_lr_0": {
        "penalty": "L2",
        "optimizer": "rmsprop",
        "alpha": 0.01,
        "max_iter": 3,
        "batch_size": 320,
        "learning_rate": 0.15,
        "init_param": {
          "init_method": "random_uniform"
        }
      }
    },
    "role": {
      "guest": {
        "0": {
          "reader_0": {
            "table": {
              "name": "train-PartyB",
              "namespace": "experiment"
            }
          },
          "dataio_0": {
            "with_label": true,
            "label_name": "y",
            "label_type": "int",
            "output_format": "dense"
          }
        }
      },
      "host": {
        "0": {
          "reader_0": {
            "table": {
              "name": "train-PartyA",
              "namespace": "experiment"
            }
          },
          "dataio_0": {
            "with_label": false,
            "output_format": "dense"
          },
          "evaluation_0": {
            "need_run": false
          }
        }
      }
    }
  }
}
EOF

2.2修改examples/test_hetero_lr_job_dsl.json

cat > fateflow/examples/lr/test_hetero_lr_job_dsl.json <<EOF
{
  "components": {
    "reader_0": {
      "module": "Reader",
      "output": {
        "data": [
          "table"
        ]
      }
    },
    "dataio_0": {
      "module": "DataIO",
      "input": {
        "data": {
          "data": [
            "reader_0.table"
          ]
        }
      },
      "output": {
        "data": [
          "train"
        ],
        "model": [
          "dataio"
        ]
      },
      "need_deploy": true
    },
    "intersection_0": {
      "module": "Intersection",
      "input": {
        "data": {
          "data": [
            "dataio_0.train"
          ]
        }
      },
      "output": {
        "data": [
          "train"
        ]
      }
    },
    "hetero_feature_binning_0": {
      "module": "HeteroFeatureBinning",
      "input": {
        "data": {
          "data": [
            "intersection_0.train"
          ]
        }
      },
      "output": {
        "data": [
          "train"
        ],
        "model": [
          "hetero_feature_binning"
        ]
      }
    },
    "hetero_feature_selection_0": {
      "module": "HeteroFeatureSelection",
      "input": {
        "data": {
          "data": [
            "hetero_feature_binning_0.train"
          ]
        },
        "isometric_model": [
          "hetero_feature_binning_0.hetero_feature_binning"
        ]
      },
      "output": {
        "data": [
          "train"
        ],
        "model": [
          "selected"
        ]
      }
    },
    "hetero_lr_0": {
      "module": "HeteroLR",
      "input": {
        "data": {
          "train_data": [
            "hetero_feature_selection_0.train"
          ]
        }
      },
      "output": {
        "data": [
          "train"
        ],
        "model": [
          "hetero_lr"
        ]
      }
    },
    "evaluation_0": {
      "module": "Evaluation",
      "input": {
        "data": {
          "data": [
            "hetero_lr_0.train"
          ]
        }
      },
      "output": {
        "data": [
          "evaluate"
        ]
      }
    }
  }
}
EOF

2.3提交任务

flow job submit -d fateflow/examples/lr/test_hetero_lr_job_dsl.json -c fateflow/examples/lr/test_hetero_lr_job_conf.json

output：

{
    "data": {
        "board_url": "http://fateboard:8080/index.html#/dashboard?job_id=202111230933232084530&role=guest&party_id=9999",
        "code": 0,
        "dsl_path": "/data/projects/fate/fate_flow/jobs/202111230933232084530/job_dsl.json",
        "job_id": "202111230933232084530",
        "logs_directory": "/data/projects/fate/fate_flow/logs/202111230933232084530",
        "message": "success",
        "model_info": {
            "model_id": "arbiter-10000#guest-9999#host-10000#model",
            "model_version": "202111230933232084530"
        },
        "pipeline_dsl_path": "/data/projects/fate/fate_flow/jobs/202111230933232084530/pipeline_dsl.json",
        "runtime_conf_on_party_path": "/data/projects/fate/fate_flow/jobs/202111230933232084530/guest/9999/job_runtime_on_party_conf.json",
        "runtime_conf_path": "/data/projects/fate/fate_flow/jobs/202111230933232084530/job_runtime_conf.json",
        "train_runtime_conf_path": "/data/projects/fate/fate_flow/jobs/202111230933232084530/train_runtime_conf.json"
    },
    "jobId": "202111230933232084530",
    "retcode": 0,
    "retmsg": "success"
}

查看训练任务状态

flow task query -r guest -j 202111230933232084530 | grep -w f_status

未完待续….