def _summarize_training_setting(self):
# We need to wait for one iteration to get the operative args
# Right just give a fixed gin file name to store operative args
common.write_gin_configs(self._root_dir, "configured.gin")
with alf.summary.record_if(lambda: True):
def _markdownify(paragraph):
return " ".join(
(os.linesep + paragraph).splitlines(keepends=True))
common.summarize_gin_config()
alf.summary.text('commandline', ' '.join(sys.argv))
alf.summary.text(
'optimizers',
_markdownify(self._algorithm.get_optimizer_info()))
alf.summary.text('revision', git_utils.get_revision())
alf.summary.text('diff', _markdownify(git_utils.get_diff()))
alf.summary.text('seed', str(self._random_seed))
if self._config.code_snapshots is not None:
for f in self._config.code_snapshots:
path = os.path.join(
os.path.abspath(os.path.dirname(__file__)), "..", f)
if not os.path.isfile(path):
common.warning_once(
"The code file '%s' for summary is invalid" % path)
continue
with open(path, 'r') as fin:
code = fin.read()
# adding "<pre>" will make TB show raw text instead of MD
alf.summary.text('code/%s' % f,
"<pre>" + code + "</pre>")
def _train(self):
for env in self._envs:
env.reset()
time_step = self._driver.get_initial_time_step()
policy_state = self._driver.get_initial_policy_state()
iter_num = 0
while True:
t0 = time.time()
with record_time("time/train_iter"):
time_step, policy_state, train_steps = self._train_iter(
iter_num=iter_num,
policy_state=policy_state,
time_step=time_step)
t = time.time() - t0
logging.log_every_n_seconds(logging.INFO,
'%s time=%.3f throughput=%0.2f' %
(iter_num, t, int(train_steps) / t),
n_seconds=1)
if (iter_num + 1) % self._checkpoint_interval == 0:
self._save_checkpoint()
if self._evaluate and (iter_num + 1) % self._eval_interval == 0:
self._eval()
if iter_num == 0:
# We need to wait for one iteration to get the operative args
# Right just give a fixed gin file name to store operative args
common.write_gin_configs(self._root_dir, "configured.gin")
with tf.summary.record_if(True):
def _markdownify(paragraph):
return " ".join(
(os.linesep + paragraph).splitlines(keepends=True))
common.summarize_gin_config()
tf.summary.text('commandline', ' '.join(sys.argv))
tf.summary.text(
'optimizers',
_markdownify(self._algorithm.get_optimizer_info()))
tf.summary.text('revision', git_utils.get_revision())
tf.summary.text('diff', _markdownify(git_utils.get_diff()))
tf.summary.text('seed', str(self._random_seed))
# check termination
env_steps_metric = self._driver.get_step_metrics()[1]
total_time_steps = env_steps_metric.result().numpy()
iter_num += 1
if (self._num_iterations and iter_num >= self._num_iterations) \
or (self._num_env_steps and total_time_steps >= self._num_env_steps):
break
def _summarize_training_setting(self):
# We need to wait for one iteration to get the operative args
# Right just give a fixed gin file name to store operative args
common.write_gin_configs(self._root_dir, "configured.gin")
with alf.summary.record_if(lambda: True):
def _markdownify(paragraph):
return " ".join(
(os.linesep + paragraph).splitlines(keepends=True))
common.summarize_gin_config()
alf.summary.text('commandline', ' '.join(sys.argv))
alf.summary.text(
'optimizers',
_markdownify(self._algorithm.get_optimizer_info()))
alf.summary.text('revision', git_utils.get_revision())
alf.summary.text('diff', _markdownify(git_utils.get_diff()))
alf.summary.text('seed', str(self._random_seed))
def _train(self):
self._env.reset()
time_step = self._driver.get_initial_time_step()
policy_state = self._driver.get_initial_policy_state()
for iter_num in range(self._num_iterations):
t0 = time.time()
time_step, policy_state, steps = self.train_iter(
iter_num=iter_num,
policy_state=policy_state,
time_step=time_step)
logging.info('%s time=%.3f throughput=%0.2f' %
(iter_num, time.time() - t0, int(steps) /
(time.time() - t0)))
if (iter_num + 1) % self._checkpoint_interval == 0:
self._save_checkpoint()
if self._evaluate and (iter_num + 1) % self._eval_interval == 0:
self._eval()
if iter_num == 0:
with tf.summary.record_if(True):
common.summarize_gin_config()
tf.summary.text('commandline', ' '.join(sys.argv))
def _train(self):
for env in self._envs:
env.reset()
time_step = self._driver.get_initial_time_step()
policy_state = self._driver.get_initial_policy_state()
iter_num = 0
while True:
t0 = time.time()
time_step, policy_state, train_steps = self.train_iter(
iter_num=iter_num,
policy_state=policy_state,
time_step=time_step)
t = time.time() - t0
logging.log_every_n_seconds(logging.INFO,
'%s time=%.3f throughput=%0.2f' %
(iter_num, t, int(train_steps) / t),
n_seconds=1)
tf.summary.scalar("time/train_iter", t)
if (iter_num + 1) % self._checkpoint_interval == 0:
self._save_checkpoint()
if self._evaluate and (iter_num + 1) % self._eval_interval == 0:
self._eval()
if iter_num == 0:
# We need to wait for one iteration to get the operative args
# Right just give a fixed gin file name to store operative args
common.write_gin_configs(self._root_dir, "configured.gin")
with tf.summary.record_if(True):
common.summarize_gin_config()
tf.summary.text('commandline', ' '.join(sys.argv))
tf.summary.text('optimizers',
self._algorithm.get_optimizer_info())
# check termination
env_steps_metric = self._driver.get_step_metrics()[1]
total_time_steps = env_steps_metric.result().numpy()
iter_num += 1
if (self._num_iterations and iter_num >= self._num_iterations) \
or (self._num_env_steps and total_time_steps >= self._num_env_steps):
break
