config.yaml

# 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: "models/treebased/tdm"

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

# hyper parameters of user-defined network
hyper_parameters:
  # optimizer config
  optimizer:
    class: Adam
    learning_rate: 0.001
    strategy: async
  # user-defined <key, value> pairs
  max_layers: 4
  node_nums: 26
  leaf_node_nums: 13
  layer_node_num_list: [2, 4, 7, 12]
  child_nums: 2
  node_emb_size: 64
  input_emb_size: 768
  neg_sampling_list: [1, 2, 3, 4]
  output_positive: True
  topK: 1
  learning_rate: 0.0001
  act: tanh
  tree:
    # 单机训练建议tree只load一次，保存为paddle tensor，之后从paddle模型热启
    # 分布式训练trainer需要独立load 
    # 预测时也改为从paddle模型加载
    load_tree_from_numpy: True # only once
    load_paddle_model: False # train & infer need
    tree_layer_path: "{workspace}/tree/layer_list.txt"
    tree_travel_path: "{workspace}/tree/travel_list.npy"
    tree_info_path: "{workspace}/tree/tree_info.npy"
    tree_emb_path: "{workspace}/tree/tree_emb.npy"

# select runner by name
mode: [runner1]

runner:
- name: runner1
  class: train
  startup_class_path: "{workspace}/tdm_startup.py"
  epochs: 10
  device: cpu
  save_checkpoint_interval: 2 # save model interval of epochs
  save_checkpoint_path: "increment" # save checkpoint path
  init_model_path: "" # load model path
  print_interval: 10
  phases: [phase1]

- name: runner2
  class: infer
  startup_class_path: "{workspace}/tdm_startup.py"
  device: cpu
  init_model_path: "increment/0" # load model path
  print_interval: 1
  phases: [phase2]
  
- name: runner3
  class: local_cluster_train
  startup_class_path: "{workspace}/tdm_startup.py"
  fleet_mode: ps
  epochs: 10
  device: cpu
  save_checkpoint_interval: 2 # save model interval of epochs
  save_checkpoint_path: "increment" # save checkpoint path
  init_model_path: "init_model" # load model path
  print_interval: 10
  phases: [phase1]

# 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"
  dataset_name: dataset_infer
  thread_num: 2