def __init__(self, snapshot_config, max_cpus=1):
self._snapshotter = Snapshotter(snapshot_config.snapshot_dir,
snapshot_config.snapshot_mode,
snapshot_config.snapshot_gap)
if max_cpus > 1:
# pylint: disable=import-outside-toplevel
from garage.sampler import singleton_pool
singleton_pool.initialize(max_cpus)
self._has_setup = False
self._plot = False
self._setup_args = None
self._train_args = None
self._stats = ExperimentStats(total_itr=0,
total_env_steps=0,
total_epoch=0,
last_path=None)
self._algo = None
self._env = None
self._policy = None
self._sampler = None
self._plotter = None
self._start_time = None
self._itr_start_time = None
self.step_itr = None
self.step_path = None
def __init__(self, sess=None, max_cpus=1):
"""Create a new local runner.
Args:
max_cpus: The maximum number of parallel sampler workers.
sess: An optional tensorflow session.
A new session will be created immediately if not provided.
Note:
The local runner will set up a joblib task pool of size max_cpus
possibly later used by BatchSampler. If BatchSampler is not used,
the processes in the pool will remain dormant.
This setup is required to use tensorflow in a multiprocess
environment before a tensorflow session is created
because tensorflow is not fork-safe.
See https://github.com/tensorflow/tensorflow/issues/2448.
"""
if max_cpus > 1:
from garage.sampler import singleton_pool
singleton_pool.initialize(max_cpus)
self.sess = sess or tf.Session()
self.has_setup = False
self.plot = False
def setup_method(self):
self.graph = tf.Graph()
self.sess = tf.Session(graph=self.graph)
self.sess.__enter__()
logger.add_output(NullOutput())
deterministic.set_seed(1)
# initialize global singleton_pool for each test case
from garage.sampler import singleton_pool
singleton_pool.initialize(1)
def __init__(self, snapshot_config, max_cpus=1):
self._snapshotter = Snapshotter(snapshot_config.snapshot_dir,
snapshot_config.snapshot_mode,
snapshot_config.snapshot_gap)
if max_cpus > 1:
from garage.sampler import singleton_pool
singleton_pool.initialize(max_cpus)
self.has_setup = False
self.plot = False
self._setup_args = None
self.train_args = None
def setup_method(self):
self.graph = tf.Graph()
for c in self.graph.collections:
self.graph.clear_collection(c)
self.graph_manager = self.graph.as_default()
self.graph_manager.__enter__()
self.sess = tf.compat.v1.Session(graph=self.graph)
self.sess_manager = self.sess.as_default()
self.sess_manager.__enter__()
self.sess.__enter__()
logger.add_output(NullOutput())
deterministic.set_seed(1)
# initialize global singleton_pool for each test case
from garage.sampler import singleton_pool
singleton_pool.initialize(1)
def __init__(self, snapshot_config=None, sess=None, max_cpus=1):
if snapshot_config:
self._snapshotter = Snapshotter(snapshot_config.snapshot_dir,
snapshot_config.snapshot_mode,
snapshot_config.snapshot_gap)
else:
self._snapshotter = Snapshotter()
if max_cpus > 1:
from garage.sampler import singleton_pool
singleton_pool.initialize(max_cpus)
self.sess = sess or tf.Session()
self.sess_entered = False
self.has_setup = False
self.plot = False
self._setup_args = None
self.train_args = None
def initialize(n_parallel):
singleton_pool.initialize(n_parallel)
singleton_pool.run_each(
_worker_init, [(id, ) for id in range(singleton_pool.n_parallel)])
