# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


# global settings 
debug: false
workspace: "paddlerec.models.rank.dcn"

dataset:
  - name: train_sample
    type: QueueDataset
    batch_size: 5
    data_path: "{workspace}/data/sample_data/train"
    sparse_slots: "label C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20 C21 C22 C23 C24 C25 C26"
    dense_slots: "I1:1 I2:1 I3:1 I4:1 I5:1 I6:1 I7:1 I8:1 I9:1 I10:1 I11:1 I12:1 I13:1"
  - name: infer_sample
    type: QueueDataset
    batch_size: 5
    data_path: "{workspace}/data/sample_data/infer"
    sparse_slots: "label C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20 C21 C22 C23 C24 C25 C26"
    dense_slots: "I1:1 I2:1 I3:1 I4:1 I5:1 I6:1 I7:1 I8:1 I9:1 I10:1 I11:1 I12:1 I13:1"

hyper_parameters:
    optimizer:
        class: Adam
        learning_rate: 0.0001
    # 用户自定义配置
    cross_num: 2
    dnn_hidden_units: [128, 128]
    l2_reg_cross: 0.00005
    dnn_use_bn: False
    clip_by_norm: 100.0
    cat_feat_num: "{workspace}/data/sample_data/cat_feature_num.txt"
    is_sparse: False


mode: train_runner
# if infer, change mode to "infer_runner" and change phase to "infer_phase"

runner:
  - name: train_runner
    class: single_train
    epochs: 1
    device: cpu
    init_model_path: ""
    save_checkpoint_interval: 1
    save_inference_interval: 1
    save_checkpoint_path: "increment"
    save_inference_path: "inference"
    print_interval: 1
  - name: infer_runner
    class: single_infer
    epochs: 1
    device: cpu
    init_model_path: "increment/0"
    print_interval: 1

phase:
- name: phase1
  model: "{workspace}/model.py"
  dataset_name: train_sample
  thread_num: 1
#- name: infer_phase
#  model: "{workspace}/model.py"
#  dataset_name: infer_sample
#  thread_num: 1