def main_train(trainer):
from tartist.plugins.trainer_enhancer import summary
summary.enable_summary_history(trainer,
extra_summary_types={
'async/train/score': 'async_scalar',
'async/inference/score': 'async_scalar'
})
summary.enable_echo_summary_scalar(trainer,
summary_spec={
'async/train/score': ['avg', 'max'],
'async/inference/score':
['avg', 'max']
})
from tartist.plugins.trainer_enhancer import progress
progress.enable_epoch_progress(trainer)
from tartist.plugins.trainer_enhancer import snapshot
snapshot.enable_snapshot_saver(trainer)
from tartist.core import register_event
from common_hp_a3c import main_inference_play_multithread
def on_epoch_after(trainer):
if trainer.epoch > 0 and trainer.epoch % 2 == 0:
main_inference_play_multithread(trainer, make_player=make_player)
register_event(trainer, 'epoch:after', on_epoch_after, priority=5)
trainer.train()
def main_train(trainer):
from tartist.app.rl.utils.adv import GAEComputer
from tartist.random.sampler import SimpleBatchSampler
trainer.set_adv_computer(
GAEComputer(get_env('ppo.gamma'), get_env('ppo.gae.lambda')))
trainer.set_batch_sampler(
SimpleBatchSampler(get_env('trainer.batch_size'),
get_env('trainer.data_repeat')))
# Register plugins.
from tartist.plugins.trainer_enhancer import summary
summary.enable_summary_history(trainer,
extra_summary_types={
'inference/score': 'async_scalar',
})
summary.enable_echo_summary_scalar(
trainer, summary_spec={'inference/score': ['avg', 'max']})
from tartist.plugins.trainer_enhancer import progress
progress.enable_epoch_progress(trainer)
from tartist.plugins.trainer_enhancer import snapshot
snapshot.enable_snapshot_saver(trainer, save_interval=1)
def on_epoch_after(trainer):
if trainer.epoch > 0 and trainer.epoch % 2 == 0:
main_inference_play_multithread(trainer)
# This one should run before monitor.
trainer.register_event('epoch:after', on_epoch_after, priority=5)
trainer.train()
def main_train(trainer):
from tartist.plugins.trainer_enhancer import summary
summary.enable_summary_history(trainer)
summary.enable_echo_summary_scalar(trainer)
summary.set_error_summary_key(trainer, 'error')
from tartist.plugins.trainer_enhancer import progress
progress.enable_epoch_progress(trainer)
from tartist.plugins.trainer_enhancer import snapshot
snapshot.enable_snapshot_saver(trainer)
from tartist.plugins.trainer_enhancer import inference
inference.enable_inference_runner(trainer, make_dataflow_inference)
from tartist.core import register_event
def on_epoch_after(trainer):
if trainer.epoch == 5:
trainer.optimizer.set_learning_rate(
trainer.optimizer.learning_rate * 0.1)
register_event(trainer, 'epoch:after', on_epoch_after)
trainer.train()
def main_train(trainer):
from tartist.plugins.trainer_enhancer import summary
summary.enable_summary_history(trainer)
summary.enable_echo_summary_scalar(trainer)
from tartist.plugins.trainer_enhancer import progress
progress.enable_epoch_progress(trainer)
from tartist.plugins.trainer_enhancer import snapshot
snapshot.enable_snapshot_saver(trainer)
trainer.train()
def _default_main_train(trainer):
# TODO:: Early stop
def on_optimization_before(trainer):
# clear the validation loss history
trainer.runtime['validation_losses'] = []
# compile the function for inference
trainer.inference_func = trainer.env.make_func()
trainer.inference_func.compile(trainer.network.loss)
def on_epoch_after(trainer):
# compute the validation loss
sum_loss, nr_data = 0, 0
for data in trainer.dataflow_validation:
sum_loss += trainer.inference_func(**data)
nr_data += 1
avg_loss = sum_loss / nr_data
logger.info('Epoch: {}: average validation loss = {}.'.format(
trainer.epoch, avg_loss))
trainer.runtime['validation_losses'].append(avg_loss)
# test whether early stop
losses = trainer.runtime['validation_losses'][-6:]
if len(losses) <= 1:
return
# 2 out of 5
nr_loss_increase = 0
for a, b in zip(losses[:-1], losses[1:]):
if b > a:
nr_loss_increase += 1
if nr_loss_increase >= 2:
# acquire early stop
logger.critical(
'Validation loss is keeping increasing: acquire early stop.')
trainer.stop()
from tartist.plugins.trainer_enhancer import summary
summary.enable_summary_history(trainer)
summary.enable_echo_summary_scalar(trainer,
enable_json=False,
enable_tensorboard=False)
from tartist.plugins.trainer_enhancer import progress
progress.enable_epoch_progress(trainer)
# early stop related hooks
trainer.register_event('optimization:before', on_optimization_before)
trainer.register_event('epoch:after', on_epoch_after)
trainer.train()
def main_train(trainer):
# Register plugins.
from tartist.plugins.trainer_enhancer import summary
summary.enable_summary_history(trainer,
extra_summary_types={
'inference/score': 'async_scalar',
'train/exp_epsilon': 'async_scalar'
})
summary.enable_echo_summary_scalar(
trainer, summary_spec={'inference/score': ['avg', 'max']})
from tartist.plugins.trainer_enhancer import progress
progress.enable_epoch_progress(trainer)
from tartist.plugins.trainer_enhancer import snapshot
snapshot.enable_snapshot_saver(trainer, save_interval=2)
def set_exp_epsilon(trainer_or_env, value):
r = trainer_or_env.runtime
if r.get('exp_epsilon', None) != value:
logger.critical('Setting exploration epsilon to {}'.format(value))
r['exp_epsilon'] = value
schedule = [(0, 0.1), (10, 0.1), (250, 0.01), (1e9, 0.01)]
def schedule_exp_epsilon(trainer):
# `trainer.runtime` is synchronous with `trainer.env.runtime`.
last_v = None
for e, v in schedule:
if trainer.epoch < e:
set_exp_epsilon(trainer, last_v)
break
last_v = v
def on_epoch_after(trainer):
if trainer.epoch > 0 and trainer.epoch % 2 == 0:
main_inference_play_multithread(trainer)
# Summarize the exp epsilon.
mgr = trainer.runtime.get('summary_histories', None)
if mgr is not None:
mgr.put_async_scalar('train/exp_epsilon',
trainer.runtime['exp_epsilon'])
# This one should run before monitor.
trainer.register_event('epoch:before', schedule_exp_epsilon, priority=5)
trainer.register_event('epoch:after', on_epoch_after, priority=5)
trainer.train()
def main_train(trainer):
from tartist.plugins.trainer_enhancer import summary
summary.enable_summary_history(trainer)
summary.enable_echo_summary_scalar(trainer)
from tartist.plugins.trainer_enhancer import progress
progress.enable_epoch_progress(trainer)
from tartist.plugins.trainer_enhancer import snapshot
snapshot.enable_snapshot_saver(trainer)
from tartist.plugins.trainer_enhancer import inference
inference.enable_inference_runner(trainer, make_dataflow_inference)
trainer.train()
def main_train(trainer):
# Compose the evaluator
player = make_player()
def evaluate_train(trainer, p=player):
return _evaluate(player=p, func=trainer.pred_func)
trainer.set_evaluator(evaluate_train)
# Register plugins
from tartist.plugins.trainer_enhancer import summary
summary.enable_summary_history(trainer,
extra_summary_types={
'inference/score': 'async_scalar',
})
summary.enable_echo_summary_scalar(
trainer, summary_spec={'inference/score': ['avg', 'max']})
from tartist.plugins.trainer_enhancer import progress
progress.enable_epoch_progress(trainer)
from tartist.plugins.trainer_enhancer import snapshot
snapshot.enable_snapshot_saver(trainer, save_interval=1)
def on_epoch_before(trainer):
v = max(5 - trainer.epoch / 10, 0)
trainer.optimizer.param_std += v
def on_epoch_after(trainer):
if trainer.epoch > 0 and trainer.epoch % 2 == 0:
main_inference_play_multithread(trainer)
trainer.register_event('epoch:before', on_epoch_before, priority=5)
# This one should run before monitor.
trainer.register_event('epoch:after', on_epoch_after, priority=5)
trainer.train()
