# 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.

# workspace
workspace: "paddlerec.models.match.multiview-simnet"

# list of dataset
dataset:
- name: dataset_train # name of dataset to distinguish different datasets
  batch_size: 2
  type: DataLoader # or QueueDataset
  data_path: "{workspace}/data/train"
  sparse_slots: "1 2 3"
- name: dataset_infer # name
  batch_size: 2
  type: DataLoader # or QueueDataset
  data_path: "{workspace}/data/test"
  sparse_slots: "1 2"

# hyper parameters of user-defined network
hyper_parameters:
  optimizer:
    class: Adam
    learning_rate: 0.0001
    strategy: async
  query_encoder: "bow"
  title_encoder: "bow"
  query_encode_dim: 128
  title_encode_dim: 128
  sparse_feature_dim: 1000001
  embedding_dim: 128
  hidden_size: 128
  margin: 0.1

# select runner by name
mode: train_runner
# config of each runner.
# runner is a kind of paddle training class, which wraps the train/infer process.
runner:
- name: train_runner
  class: single_train
  # num of epochs
  epochs: 2
  # device to run training or infer
  device: cpu
  save_checkpoint_interval: 1 # save model interval of epochs
  save_inference_interval: 1 # save inference
  save_checkpoint_path: "increment" # save checkpoint path
  save_inference_path: "inference" # save inference path
  save_inference_feed_varnames: [] # feed vars of save inference
  save_inference_fetch_varnames: [] # fetch vars of save inference
  init_model_path: "" # load model path
  fetch_period: 1
- name: infer_runner
  class: single_infer
  # num of epochs
  epochs: 1
  # device to run training or infer
  device: cpu
  fetch_period: 1
  init_model_path: "increment/0" # load model path

# runner will run all the phase in each epoch
phase:
- name: phase1
  model: "{workspace}/model.py" # user-defined model
  dataset_name: dataset_train # select dataset by name
  thread_num: 1
#- name: phase2
#  model: "{workspace}/model.py" # user-defined model
#  dataset_name: dataset_infer # select dataset by name
#  thread_num: 1