Launching the Tensorboard

Here we present how to launch the tensorboard within the EasyTPP framework.

Step 1: Activate the usage of tensorboard in Config file

As shown in Training Pipeline, we need to firstly initialize the ‘model_config.yaml’ file to setup the running config before training or evaluating the model.

In the model config (modeling attribute of the config), one needs to set use_tfb to True in trainer. Then before the running process, summary writers tracking the performance on training and valid sets are both initialized.

NHP_train:
  base_config:
    stage: train
    backend: torch
    dataset_id: taxi
    runner_id: std_tpp
    model_id: NHP # model name
    base_dir: './checkpoints/'
  trainer_config:
    batch_size: 256
    max_epoch: 200
    shuffle: False
    optimizer: adam
    learning_rate: 1.e-3
    valid_freq: 1
    use_tfb: True  # Activate the tensorboard
    metrics: [ 'acc', 'rmse' ]
    seed: 2019
    gpu: -1
  model_config:
    hidden_size: 64
    loss_integral_num_sample_per_step: 20
#    pretrained_model_dir: ./checkpoints/75518_4377527680_230530-132355/models/saved_model
    thinning:
      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
      num_step_gen: 1

Step 2: Launching the tensorboard

We simply go to the output file of the training runner (its directory is specified in base_dir of base_config), find out the tensorboard file address and launch it.

A complete example of using tensorboard can be seen at examples/run_tensorboard.py.

import os

def main():
    # one can find this dir in the config out file
    log_dir = './checkpoints/NHP_train_taxi_20220527-20:18:30/tfb_train'
    os.system('tensorboard --logdir={}'.format(log_dir))
    return


if __name__ == '__main__':
    main()