def _try_to_eval(self, epoch=0):
if epoch % self.save_extra_data_interval == 0:
logger.save_extra_data(self.get_extra_data_to_save(epoch), epoch)
if self._can_evaluate():
self.evaluate(epoch)
params = self.get_epoch_snapshot(epoch)
logger.save_itr_params(epoch, params)
table_keys = logger.get_table_key_set()
if self._old_table_keys is not None:
assert table_keys == self._old_table_keys, (
"Table keys cannot change from iteration to iteration.")
self._old_table_keys = table_keys
logger.record_tabular(
"Number of train steps total",
self._n_train_steps_total,
)
logger.record_tabular(
"Number of env steps total",
self._n_env_steps_total,
)
logger.record_tabular(
"Number of rollouts total",
self._n_rollouts_total,
)
logger.record_tabular("Epoch", epoch)
logger.dump_tabular(with_prefix=False, with_timestamp=False)
else:
logger.log("Skipping eval for now.")
def _try_to_eval(self, epoch):
#logger.save_extra_data(self.get_extra_data_to_save(epoch))
if self._can_evaluate():
self.evaluate(epoch)
#params = self.get_epoch_snapshot(epoch)
#logger.save_itr_params(epoch, params)
table_keys = logger.get_table_key_set()
if self._old_table_keys is not None:
assert table_keys == self._old_table_keys, (
"Table keys cannot change from iteration to iteration.")
self._old_table_keys = table_keys
logger.record_tabular("Number of train steps total",
self._n_train_steps_total)
logger.record_tabular("Number of env steps total",
self._n_env_steps_total)
logger.record_tabular("Number of rollouts total",
self._n_rollouts_total)
times_itrs = gt.get_times().stamps.itrs
train_time = times_itrs['train'][-1]
sample_time = times_itrs['sample'][-1]
eval_time = times_itrs['eval'][-1] if epoch > 0 else 0
epoch_time = train_time + sample_time + eval_time
total_time = gt.get_times().total
logger.record_tabular('Train Time (s)', train_time)
logger.record_tabular('(Previous) Eval Time (s)', eval_time)
logger.record_tabular('Sample Time (s)', sample_time)
logger.record_tabular('Epoch Time (s)', epoch_time)
logger.record_tabular('Total Train Time (s)', total_time)
logger.record_tabular("Epoch", epoch)
logger.dump_tabular(with_prefix=False, with_timestamp=False)
else:
logger.log("Skipping eval for now.")
def _try_to_eval(self, epoch):
logger.save_extra_data(self.get_extra_data_to_save(epoch))
if self._can_evaluate():
if self.environment_farming:
# Create new new eval_sampler each evaluation time in order to avoid relesed environment problem
env_for_eval_sampler = self.farmer.force_acq_env()
print(env_for_eval_sampler)
self.eval_sampler = InPlacePathSampler(
env=env_for_eval_sampler,
policy=self.eval_policy,
max_samples=self.num_steps_per_eval + self.max_path_length,
max_path_length=self.max_path_length,
)
self.evaluate(epoch)
# Adding env back to free_env list
self.farmer.add_free_env(env_for_eval_sampler)
params = self.get_epoch_snapshot(epoch)
logger.save_itr_params(epoch, params)
table_keys = logger.get_table_key_set()
if self._old_table_keys is not None:
assert table_keys == self._old_table_keys, (
"Table keys cannot change from iteration to iteration.")
self._old_table_keys = table_keys
logger.record_tabular(
"Number of train steps total",
self._n_train_steps_total,
)
logger.record_tabular(
"Number of env steps total",
self._n_env_steps_total,
)
logger.record_tabular(
"Number of rollouts total",
self._n_rollouts_total,
)
times_itrs = gt.get_times().stamps.itrs
train_time = times_itrs['train'][-1]
sample_time = times_itrs['sample'][-1]
eval_time = times_itrs['eval'][-1] if epoch > 0 else 0
epoch_time = train_time + sample_time + eval_time
total_time = gt.get_times().total
logger.record_tabular('Train Time (s)', train_time)
logger.record_tabular('(Previous) Eval Time (s)', eval_time)
logger.record_tabular('Sample Time (s)', sample_time)
logger.record_tabular('Epoch Time (s)', epoch_time)
logger.record_tabular('Total Train Time (s)', total_time)
logger.record_tabular("Epoch", epoch)
logger.dump_tabular(with_prefix=False, with_timestamp=False)
else:
logger.log("Skipping eval for now.")
def _try_to_eval(self, epoch):
if epoch % self.freq_saving == 0:
logger.save_extra_data(self.get_extra_data_to_save(epoch))
if self._can_evaluate():
self.evaluate(epoch)
if epoch % self.freq_saving == 0:
params = self.get_epoch_snapshot(epoch)
logger.save_itr_params(epoch, params)
table_keys = logger.get_table_key_set()
# logger.record_tabular(
# "Number of train steps total",
# self._n_policy_train_steps_total,
# )
logger.record_tabular(
"Number of env steps total",
self._n_env_steps_total,
)
logger.record_tabular(
"Number of rollouts total",
self._n_rollouts_total,
)
times_itrs = gt.get_times().stamps.itrs
train_time = times_itrs['train'][-1]
sample_time = times_itrs['sample'][-1]
eval_time = times_itrs['eval'][-1] if epoch > 0 else 0
epoch_time = train_time + sample_time + eval_time
total_time = gt.get_times().total
logger.record_tabular('Train Time (s)', train_time)
logger.record_tabular('(Previous) Eval Time (s)', eval_time)
logger.record_tabular('Sample Time (s)', sample_time)
logger.record_tabular('Epoch Time (s)', epoch_time)
logger.record_tabular('Total Train Time (s)', total_time)
logger.record_tabular("Epoch", epoch)
logger.dump_tabular(with_prefix=False, with_timestamp=False)
else:
logger.log("Skipping eval for now.")
def _try_to_eval(self, epoch):
logger.save_extra_data(self.get_extra_data_to_save(epoch))
if self._can_evaluate():
self.evaluate(epoch)
params = self.get_epoch_snapshot(epoch)
logger.save_itr_params(epoch, params)
table_keys = logger.get_table_key_set()
#print("TABLE KEYS")
#print(table_keys)
#if self._old_table_keys is not None:
# assert table_keys == self._old_table_keys, (
# "Table keys cannot change from iteration to iteration."
# )
self._old_table_keys = table_keys
logger.record_tabular(
"Number of train steps total",
self._n_train_steps_total,
)
logger.record_tabular(
"Number of env steps total",
self._n_env_steps_total,
)
logger.record_tabular(
"Number of rollouts total",
self._n_rollouts_total,
)
times_itrs = gt.get_times().stamps.itrs
train_time = times_itrs['train'][-1]
sample_time = times_itrs['sample'][-1]
eval_time = times_itrs['eval'][-1] if epoch > 0 else 0
epoch_time = train_time + sample_time + eval_time
total_time = gt.get_times().total
logger.record_tabular('Train Time (s)', train_time)
logger.record_tabular('(Previous) Eval Time (s)', eval_time)
logger.record_tabular('Sample Time (s)', sample_time)
logger.record_tabular('Epoch Time (s)', epoch_time)
logger.record_tabular('Total Train Time (s)', total_time)
logger.record_tabular("Epoch", epoch)
# tensorboard stuff
_writer = self._writer
for k, v_str in logger._tabular:
if k == 'Epoch': continue
v = float(v_str)
if k.endswith('Loss'):
_writer.add_scalar('Loss/{}'.format(k), v, epoch)
elif k.endswith('Max'):
prefix = k[:-4]
_writer.add_scalar('{}/{}'.format(prefix, k), v, epoch)
elif k.endswith('Min'):
prefix = k[:-4]
_writer.add_scalar('{}/{}'.format(prefix, k), v, epoch)
elif k.endswith('Std'):
prefix = k[:-4]
_writer.add_scalar('{}/{}'.format(prefix, k), v, epoch)
elif k.endswith('Mean'):
prefix = k[:-5]
_writer.add_scalar('{}/{}'.format(prefix, k), v, epoch)
elif 'Time' in k:
_writer.add_scalar('Time/{}'.format(k), v, epoch)
elif k.startswith('Num'):
_writer.add_scalar('Number/{}'.format(k), v, epoch)
elif k.startswith('Exploration'):
_writer.add_scalar('Exploration/{}'.format(k), v, epoch)
elif k.startswith('Test'):
_writer.add_scalar('Test/{}'.format(k), v, epoch)
else:
_writer.add_scalar(k, v, epoch)
_writer.file_writer.flush()
logger.dump_tabular(with_prefix=False, with_timestamp=False)
else:
logger.log("Skipping eval for now.")
def _try_to_eval(self, epoch, eval_paths=None):
if MPI and MPI.COMM_WORLD.Get_rank() == 0:
if epoch % self.save_extra_data_interval == 0:
logger.save_extra_data(self.get_extra_data_to_save(epoch))
if epoch % self.num_epochs_per_param_save == 0:
print("Attemping itr param save...")
params = self.get_epoch_snapshot(epoch)
logger.save_itr_params(epoch, params)
print(F"Itr{epoch} param saved!")
if self._can_evaluate():
self.evaluate(epoch, eval_paths=eval_paths)
logger.record_tabular(
"Number of train steps total",
self._n_train_steps_total,
)
logger.record_tabular(
"Number of env steps total",
self._n_env_steps_total,
)
logger.record_tabular(
"Number of rollouts total",
self._n_rollouts_total,
)
times_itrs = gt.get_times().stamps.itrs
# train_time = times_itrs['train'][-1]
training_loops = ['get_batch', 'update_normalizer', 'forward', 'compute_losses', 'qf1_loop', "policy_loss_forward", 'policy_loop', 'vf_loop']
train_time = sum(times_itrs[loop][-1] for loop in times_itrs.keys())
sample_time = times_itrs['sample'][-1]
if epoch > 0:
eval_time = times_itrs['eval'][-1]
else:
times_itrs['eval'] = [0] # Need to do this so we can do line 343, the list comprehension
eval_time = 0
epoch_time = train_time + sample_time + eval_time
total_time = gt.get_times().total
# logger.record_tabular('Get Batch (s)', times_itrs['get_batch'][-1])
# logger.record_tabular('Update Normalizer (s)', times_itrs['update_normalizer'][-1])
# logger.record_tabular('Forward (s)', times_itrs['forward'][-1])
# logger.record_tabular('Compute Losses (s)', times_itrs['compute_losses'][-1])
# logger.record_tabular('QF1 Loop (s)', times_itrs['qf1_loop'][-1])
# logger.record_tabular('QF2 Loop (s)', times_itrs['qf2_loop'][-1])
# logger.record_tabular("Policy Forward (s)", times_itrs['policy_loss_forward'][-1])
# logger.record_tabular('Policy Loop (s)', times_itrs['policy_loop'][-1])
# logger.record_tabular('VF Loop (s)', times_itrs['vf_loop'][-1])
[logger.record_tabular(key.title(), times_itrs[key][-1]) for key in times_itrs.keys()]
logger.record_tabular('Train Time (s) ---', train_time)
logger.record_tabular('(Previous) Eval Time (s) ---', eval_time)
logger.record_tabular('Sample Time (s) ---', sample_time)
logger.record_tabular('Epoch Time (s)', epoch_time)
logger.record_tabular('Total Train Time (s)', total_time)
logger.record_tabular("Epoch", epoch)
table_keys = logger.get_table_key_set()
if self._old_table_keys is not None and table_keys != self._old_table_keys:
# assert table_keys == self._old_table_keys, (
# "Table keys cannot change from iteration to iteration."
# )
print("Table keys have changed. Rewriting header and filling with 0s")
logger.update_header()
raise NotImplementedError
self._old_table_keys = table_keys
logger.dump_tabular(with_prefix=False, with_timestamp=False)
else:
logger.log("Skipping eval for now.")
def _try_to_eval(self, epoch):
if epoch % self.logging_period != 0:
return
if epoch in self.save_extra_manual_epoch_set:
logger.save_extra_data(
self.get_extra_data_to_save(epoch),
file_name='extra_snapshot_itr{}'.format(epoch),
mode='cloudpickle',
)
if self._save_extra_every_epoch:
logger.save_extra_data(self.get_extra_data_to_save(epoch))
gt.stamp('save-extra')
if self._can_evaluate():
self.evaluate(epoch)
gt.stamp('eval')
params = self.get_epoch_snapshot(epoch)
logger.save_itr_params(epoch, params)
gt.stamp('save-snapshot')
table_keys = logger.get_table_key_set()
if self._old_table_keys is not None:
assert table_keys == self._old_table_keys, (
"Table keys cannot change from iteration to iteration.")
self._old_table_keys = table_keys
logger.record_dict(
self.trainer.get_diagnostics(),
prefix='trainer/',
)
logger.record_tabular(
"Number of train steps total",
self._n_train_steps_total,
)
logger.record_tabular(
"Number of env steps total",
self._n_env_steps_total,
)
logger.record_tabular(
"Number of rollouts total",
self._n_rollouts_total,
)
times_itrs = gt.get_times().stamps.itrs
train_time = times_itrs['train'][-1]
sample_time = times_itrs['sample'][-1]
save_extra_time = times_itrs['save-extra'][-1]
save_snapshot_time = times_itrs['save-snapshot'][-1]
eval_time = times_itrs['eval'][-1] if epoch > 0 else 0
epoch_time = train_time + sample_time + save_extra_time + eval_time
total_time = gt.get_times().total
logger.record_tabular('in_unsupervised_model',
float(self.in_unsupervised_phase))
logger.record_tabular('Train Time (s)', train_time)
logger.record_tabular('(Previous) Eval Time (s)', eval_time)
logger.record_tabular('Sample Time (s)', sample_time)
logger.record_tabular('Save Extra Time (s)', save_extra_time)
logger.record_tabular('Save Snapshot Time (s)', save_snapshot_time)
logger.record_tabular('Epoch Time (s)', epoch_time)
logger.record_tabular('Total Train Time (s)', total_time)
logger.record_tabular("Epoch", epoch)
logger.dump_tabular(with_prefix=False, with_timestamp=False)
else:
logger.log("Skipping eval for now.")
