使用dag发起homo_nn 和 hetero_nn训练流程

[lynne0012006]

问题描述1：可以使用dag发起homo_nn 和 hetero_nn的训练流程吗？如果能发起，可不可以传一些dag案例作为参考？
问题描述2：下面dag发起test_nn_binary流程，运行到homo_nn_0组件时就报错“list indices must be integers or slices, not str”，应该怎么修改dag文件呢？

附1：报错内容：
File "/data/projects/fate/fate/python/fate/components/components/nn/loader.py", line 165, in from_dict
166
module_name=data_dict["module_name"],
167
TypeError: list indices must be integers or slices, not str

附2：dag内容
dag:
stage: train
party_tasks:
host_9999:
parties:
- role: host
party_id:
- '9999'
tasks:
reader_0:
parameters:
name: breast_homo_host
namespace: experiment
guest_10000:
parties:
- role: guest
party_id:
- '10000'
tasks:
reader_0:
parameters:
name: breast_homo_guest
namespace: experiment
parties:

• role: guest
  party_id:
  • '10000'
• role: host
  party_id:
  • '9999'
    tasks:
    reader_0:
    stage: default
    parties:
    • role: guest
      party_id:
      • '10000'
    • role: host
      party_id:
      • '9999'
        component_ref: reader
        homo_nn_1:
        inputs:
        data:
        train_data:
        task_output_artifact:
        output_artifact_key: output_data
        producer_task: reader_0
        model:
        warm_start_model:
        task_output_artifact:
        output_artifact_key: output_model
        producer_task: homo_nn_0
        component_ref: homo_nn
        dependent_tasks:
    • homo_nn_0
    • reader_0
      parameters: {}
      homo_nn_0:
      inputs:
      data:
      train_data:
      task_output_artifact:
      output_artifact_key: output_data
      producer_task: reader_0
      model: {}
      component_ref: homo_nn
      dependent_tasks:
    • reader_0
      parameters:
      runner_conf:
      training_args_conf:
      num_train_epochs: 5
      per_device_train_batch_size: 64
      loss_conf: BCELoss
      optimizer_conf:
      lr: 0.01
      type: Adam
      model_conf:
      • layers:
        • in_features: 30
          out_features: 16
          type: Linear
        • type: ReLU
        • in_features: 16
          out_features: 1
          type: Linear
        • type: Sigmoid
          type: Sequential
          task_type: binary
          algo: fedavg
          evaluation_0:
          stage: default
          inputs:
          data:
          input_datas:
          task_output_artifact:
          output_artifact_key: output_data
          producer_task: homo_nn_1
          component_ref: evaluation
          dependent_tasks:
    • homo_nn_1
      parameters:
      metrics:
      • auc