============================================ Training a Model & Configuration Explanation ============================================ This tutorial shows how one can use ``EasyTPP`` to train the implemented models. In principle, firstly we need to initialize a config yaml file, containing all the input configuration to guide the training and eval process. The overall structure of a config file is shown as below: .. code-block:: yaml pipeline_config_id: .. # name of the config for guiding the pipeline data: [Dataset ID]: # name of the dataset, e.g, taxi .... [EXPERIMENT ID]: # name of the experiment to run base_config: .... model_config: ... After the config file is setup, we can run the script, by specifying the `config directory` and `experiment id`, to start the pipeline. We currently provide a preset script at `examples/train_nhp.py`. Step 1: Setup the config file containing data and model configs ================================================================ To be specific, one needs to define the following entries in the config file: - **pipeline_config_id**: registered name of EasyTPP.Config objects, such as `runner_config` or `hpo_runner_config`. By reading this, the corresponding configuration class will be loaded for constructing the pipeline. .. code-block:: yaml pipeline_config_id: runner_config - **data**: dataset specifics. One can put multiple dataset specifics in the config file, but only one will be used in one experiment. - *[DATASET ID]*: name of the dataset, e.g., taxi. - *train_dir, valid_dir, test_dir*: directory of the datafile. For the moment we only accept pkl file (please see `Dataset <./dataset.html>`_ for details) - *data_spec*: define the event type information. .. code-block:: yaml data: taxi: data_format: pkl train_dir: ../data/taxi/train.pkl valid_dir: ../data/taxi/dev.pkl test_dir: ../data/taxi/test.pkl data_spec： num_event_types: 7 # num of types excluding pad events. pad_token_id: 6 # event type index for pad events padding_side: right # pad at the right end of the sequence truncation_side: right # truncate at the right end of the sequence max_len: 100 # max sequence length used as model input - **[EXPERIMENT ID]**: name of the experiment to run in the pipeline. It contains two blocks of configs: *base_config* contains the pipeline framework related specifications. .. code-block:: yaml base_config: stage: train # train, eval and generate backend: tensorflow # tensorflow and torch dataset_id: conttime # name of the dataset runner_id: std_tpp # registered name of the pipeline runner model_id: RMTPP # model name # registered name of the implemented model base_dir: './checkpoints/' # base dir to save the logs and models. *model_config* contains the model related specifications. .. code-block:: yaml model_config: hidden_size: 32 time_emb_size: 16 num_layers: 2 num_heads: 2 mc_num_sample_per_step: 20 sharing_param_layer: False loss_integral_num_sample_per_step: 20 dropout: 0.0 use_ln: False thinning_params: # thinning algorithm for event sampling num_seq: 10 num_sample: 1 num_exp: 500 # number of i.i.d. Exp(intensity_bound) draws at one time in thinning algorithm look_ahead_time: 10 patience_counter: 5 # the maximum iteration used in adaptive thinning over_sample_rate: 5 num_samples_boundary: 5 dtime_max: 5 *trainer_config* contains the training related specifications. .. code-block:: yaml trainer_config: # trainer arguments seed: 2019 gpu: 0 batch_size: 256 max_epoch: 10 shuffle: False optimizer: adam learning_rate: 1.e-3 valid_freq: 1 use_tfb: False metrics: ['acc', 'rmse'] A complete example of these files can be seen at *examples/example_config*. Step 2: Run the training script =============================================== To run the training process, we simply need to call two functions: 1. ``Config``: it reads the directory of the configs specified in Step 1 and do some processing to form a complete configuration. 2. ``Runner``: it reads the configuration and setups the whole pipeline for training, evaluation and generation. The following code is an example, which is a copy from *examples/train_nhp.py*. .. code-block:: python import argparse from easy_tpp.config_factory import Config from easy_tpp.runner import Runner def main(): parser = argparse.ArgumentParser() parser.add_argument('--config_dir', type=str, required=False, default='configs/experiment_config.yaml', help='Dir of configuration yaml to train and evaluate the model.') parser.add_argument('--experiment_id', type=str, required=False, default='RMTPP_train', help='Experiment id in the config file.') args = parser.parse_args() config = Config.build_from_yaml_file(args.config_dir, experiment_id=args.experiment_id) model_runner = Runner.build_from_config(config) model_runner.run() if __name__ == '__main__': main() Checkout the output ======================== During training, the log, the best model based on valid set performance, the complete configuration file are all saved. The directory of the saved files is specified in 'base' of ``model_config.yaml``, i.e., In the `./checkpoints/` folder, one find the correct subfolder by concatenating the 'experiment_id' and running timestamps. Inside that subfolder, there is a complete configuration file, e.g., ``NHP_train_output.yaml`` that records all the information used in the pipeline. The .. code-block:: yaml data_config: train_dir: ../data/conttime/train.pkl valid_dir: ../data/conttime/dev.pkl test_dir: ../data/conttime/test.pkl specs: num_event_types_pad: 6 num_event_types: 5 event_pad_index: 5 data_format: pkl base_config: stage: train backend: tensorflow dataset_id: conttime runner_id: std_tpp model_id: RMTPP base_dir: ./checkpoints/ exp_id: RMTPP_train log_folder: ./checkpoints/98888_4299965824_221205-153425 saved_model_dir: ./checkpoints/98888_4299965824_221205-153425/models/saved_model saved_log_dir: ./checkpoints/98888_4299965824_221205-153425/log output_config_dir: ./checkpoints/98888_4299965824_221205-153425/RMTPP_train_output.yaml model_config: hidden_size: 32 time_emb_size: 16 num_layers: 2 num_heads: 2 mc_num_sample_per_step: 20 sharing_param_layer: false loss_integral_num_sample_per_step: 20 dropout: 0.0 use_ln: false seed: 2019 gpu: 0 thinning_params: num_seq: 10 num_sample: 1 num_exp: 500 look_ahead_time: 10 patience_counter: 5 over_sample_rate: 5 num_samples_boundary: 5 dtime_max: 5 num_step_gen: 1 trainer: batch_size: 256 max_epoch: 10 shuffle: false optimizer: adam learning_rate: 0.001 valid_freq: 1 use_tfb: false metrics: - acc - rmse seq_pad_end: true is_training: true num_event_types_pad: 6 num_event_types: 5 event_pad_index: 5 model_id: RMTPP If we set ``use_tfb`` to ``true``, it means we can launch the tensorboard to track the training process, one can see `Running Tensorboard <../advanced/tensorboard.html>`_ for details.