def main(argv):
tf.logging.set_verbosity(tf.logging.INFO)
trainer_lib.set_random_seed(FLAGS.random_seed)
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
t2t_trainer.maybe_log_registry_and_exit()
if FLAGS.cloud_mlengine:
cloud_mlengine.launch()
return
if FLAGS.generate_data:
t2t_trainer.generate_data()
if cloud_mlengine.job_dir():
FLAGS.output_dir = cloud_mlengine.job_dir()
if argv:
t2t_trainer.set_hparams_from_args(argv[1:])
root_output_dir = FLAGS.output_dir
if FLAGS.teacher_dir:
teacher_dir = FLAGS.teacher_dir
else:
teacher_dir = os.path.join(root_output_dir, "teacher")
# Train Teacher ============
if FLAGS.skip_teacher_training:
tf.logging.info("training teacher skipped")
else:
hparams = t2t_trainer.create_hparams()
hparams.distill_phase = "train"
FLAGS.output_dir = teacher_dir
exp_fn = t2t_trainer.create_experiment_fn()
run_config = t2t_trainer.create_run_config(hparams)
exp = exp_fn(run_config, hparams)
if t2t_trainer.is_chief():
t2t_trainer.save_metadata(hparams)
t2t_trainer.execute_schedule(exp)
# ==========================
# Train Student ============
hparams = t2t_trainer.create_hparams()
hparams.add_hparam("teacher_dir", teacher_dir)
hparams.distill_phase = "distill"
if FLAGS.student_dir:
student_dir = FLAGS.student_dir
else:
student_dir = os.path.join(root_output_dir, "student")
FLAGS.output_dir = student_dir
hparams.add_hparam("student_dir", student_dir)
exp_fn = t2t_trainer.create_experiment_fn()
run_config = t2t_trainer.create_run_config(hparams)
exp = exp_fn(run_config, hparams)
if t2t_trainer.is_chief():
t2t_trainer.save_metadata(hparams)
t2t_trainer.execute_schedule(exp)
def main(argv):
tf.logging.set_verbosity(tf.logging.INFO)
trainer_lib.set_random_seed(FLAGS.random_seed)
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
t2t_trainer.maybe_log_registry_and_exit()
if FLAGS.cloud_mlengine:
cloud_mlengine.launch()
return
if FLAGS.generate_data:
t2t_trainer.generate_data()
if cloud_mlengine.job_dir():
FLAGS.output_dir = cloud_mlengine.job_dir()
if argv:
t2t_trainer.set_hparams_from_args(argv[1:])
with t2t_trainer.maybe_cloud_tpu():
root_output_dir = FLAGS.output_dir
# Train Teacher ============
hparams = t2t_trainer.create_hparams()
hparams.distill_phase = "train"
teacher_dir = os.path.join(root_output_dir, "teacher")
FLAGS.output_dir = teacher_dir
exp_fn = t2t_trainer.create_experiment_fn()
run_config = t2t_trainer.create_run_config(hparams)
exp = exp_fn(run_config, hparams)
if t2t_trainer.is_chief():
t2t_trainer.save_metadata(hparams)
t2t_trainer.execute_schedule(exp)
# ==========================
# Train Student ============
hparams = t2t_trainer.create_hparams()
hparams.add_hparam("teacher_dir", teacher_dir)
hparams.distill_phase = "distill"
student_dir = os.path.join(root_output_dir, "student")
FLAGS.output_dir = student_dir
exp_fn = t2t_trainer.create_experiment_fn()
run_config = t2t_trainer.create_run_config(hparams)
exp = exp_fn(run_config, hparams)
if t2t_trainer.is_chief():
t2t_trainer.save_metadata(hparams)
t2t_trainer.execute_schedule(exp)
def create_student_experiment(run_config, hparams, argv):
"""Creates experiment function."""
tf.logging.info("training student")
tf.logging.set_verbosity(tf.logging.INFO)
trainer_lib.set_random_seed(FLAGS.random_seed)
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
t2t_trainer.maybe_log_registry_and_exit()
if FLAGS.cloud_mlengine:
return cloud_mlengine.launch()
if FLAGS.generate_data:
t2t_trainer.generate_data()
if cloud_mlengine.job_dir():
FLAGS.output_dir = cloud_mlengine.job_dir()
if argv:
t2t_trainer.set_hparams_from_args(argv[1:])
hparams.add_hparam("teacher_dir", FLAGS.teacher_dir)
hparams.add_hparam("student_dir", FLAGS.student_dir)
hparams.distill_phase = "distill"
exp_fn = t2t_trainer.create_experiment_fn()
exp = exp_fn(run_config, hparams)
return exp
def create_teacher_experiment(run_config, hparams, argv):
"""Creates experiment function."""
tf.logging.info("training teacher")
tf.logging.set_verbosity(tf.logging.INFO)
trainer_lib.set_random_seed(FLAGS.random_seed)
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
t2t_trainer.maybe_log_registry_and_exit()
if FLAGS.cloud_mlengine:
return cloud_mlengine.launch()
if FLAGS.generate_data:
t2t_trainer.generate_data()
if cloud_mlengine.job_dir():
FLAGS.output_dir = cloud_mlengine.job_dir()
if argv:
t2t_trainer.set_hparams_from_args(argv[1:])
with t2t_trainer.maybe_cloud_tpu():
hparams.distill_phase = "train"
exp_fn = t2t_trainer.create_experiment_fn()
exp = exp_fn(run_config, hparams)
return exp
