trainer.py

"""Trainer
"""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import argparse
import importlib

import tensorflow as tf

if __name__ == '__main__':
  parser = argparse.ArgumentParser()
  parser.add_argument(
      '--dataset',
      help='Dataset name',
      default=None,
      type=str,
  )
  parser.add_argument(
      '--model',
      help='Model name',
      default=None,
      type=str,
  )
  parser.add_argument(
      '--job-dir',
      help='GCS location to write checkpoints and export models',
      required=True)

  # Experiment arguments
  parser.add_argument(
      '--save-checkpoints-steps',
      help='Number of steps to save checkpoint',
      default=1000,
      type=int)
  parser.add_argument(
      '--train-steps',
      help='Number of steps to run training totally',
      default=None,
      type=int)
  parser.add_argument(
      '--eval-steps',
      help='Number of steps to run evaluation for at each checkpoint',
      default=None,
      type=int)
  parser.add_argument(
      '--save-summary-steps',
      help='Number of steps to save summary',
      default=100,
      type=int)
  parser.add_argument(
      '--random-seed',
      help='Random seed for TensorFlow',
      default=None,
      type=int)
  # Performance tuning parameters
  parser.add_argument(
      '--allow-growth',
      help='Whether to enable allow_growth in GPU_Options',
      default=False,
      action='store_true')
  parser.add_argument(
      '--xla',
      help='Whether to enable XLA auto-jit compilation',
      default=False,
      action='store_true')
  parser.add_argument(
      '--save-profiling-steps',
      help='Number of steps to save profiling',
      default=None,
      type=int)
  # Argument to turn on all logging
  parser.add_argument(
      '--verbosity',
      choices=['DEBUG', 'ERROR', 'FATAL', 'INFO', 'WARN'],
      default='INFO',
      help='Set logging verbosity')

  # Parse arguments
  args, _ = parser.parse_known_args()
  dataset_module = importlib.import_module('datasets.' + args.dataset
                                           if args.dataset else 'datasets')
  dataset_module.update_argparser(parser)
  model_module = importlib.import_module('models.' + args.model
                                         if args.model else 'models')
  model_module.update_argparser(parser)
  hparams = parser.parse_args()
  print(hparams)

  # Set python level verbosity
  tf.logging.set_verbosity(hparams.verbosity)
  # Set C++ Graph Execution level verbosity
  os.environ['TF_CPP_MIN_LOG_LEVEL'] = str(
      tf.logging.__dict__[hparams.verbosity] / 10)

  # Run the training job
  model_fn = getattr(model_module, 'model_fn')
  input_fn = getattr(dataset_module, 'input_fn')
  train_input_fn = lambda: input_fn(
      mode=tf.estimator.ModeKeys.TRAIN,
      params=hparams,
  )
  eval_input_fn = lambda: input_fn(
      mode=tf.estimator.ModeKeys.EVAL,
      params=hparams,
  )
  predict_input_fn = getattr(dataset_module, 'predict_input_fn', None)

  session_config = tf.ConfigProto()
  session_config.gpu_options.allow_growth = hparams.allow_growth
  if hparams.xla:
    session_config.graph_options.optimizer_options.global_jit_level = (tf.OptimizerOptions.ON_1)
  run_config = tf.estimator.RunConfig( model_dir=hparams.job_dir, tf_random_seed=hparams.random_seed, save_summary_steps=hparams.save_summary_steps,  save_checkpoints_steps=hparams.save_checkpoints_steps,  session_config=session_config)
  estimator = tf.estimator.Estimator( model_fn=model_fn, config=run_config, params=hparams)
  hooks = []
  if hparams.save_profiling_steps:
    hooks.append( tf.train.ProfilerHook( save_steps=hparams.save_profiling_steps,  output_dir=hparams.job_dir ))
  train_spec = tf.estimator.TrainSpec( input_fn=train_input_fn, max_steps=hparams.train_steps, hooks=hooks)
  eval_spec = tf.estimator.EvalSpec(input_fn=eval_input_fn, steps=hparams.eval_steps, exporters=tf.estimator.LatestExporter(name='Servo', serving_input_receiver_fn=predict_input_fn) if predict_input_fn else None)
  tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)