def _init_runtime(self):
if self.idx != 0:
from tensorpack.models._common import disable_layer_logging
disable_layer_logging()
self.func = OfflinePredictor(self.config)
if self.idx == 0:
describe_model()
def _init_runtime(self):
""" Call _init_runtime under different CUDA_VISIBLE_DEVICES, you'll
have workers that run on multiGPUs
"""
if self.idx != 0:
from tensorpack.models._common import disable_layer_logging
disable_layer_logging()
self.func = OfflinePredictor(self.config)
if self.idx == 0:
describe_model()
def _init_runtime(self):
""" Call _init_runtime under different CUDA_VISIBLE_DEVICES, you'll
have workers that run on multiGPUs
"""
if self.idx != 0:
from tensorpack.models._common import disable_layer_logging
disable_layer_logging()
self.func = OfflinePredictor(self.config)
if self.idx == 0:
describe_model()
def _init_runtime(self):
if self.gpuid >= 0:
logger.info("Worker {} uses GPU {}".format(self.idx, self.gpuid))
os.environ['CUDA_VISIBLE_DEVICES'] = str(self.gpuid)
else:
logger.info("Worker {} uses CPU".format(self.idx))
os.environ['CUDA_VISIBLE_DEVICES'] = ''
G = tf.Graph() # build a graph for each process, because they don't need to share anything
with G.as_default():
if self.idx != 0:
from tensorpack.models._common import disable_layer_logging
disable_layer_logging()
self.func = get_predict_func(self.config)
if self.idx == 0:
describe_model()
def _init_runtime(self):
if self.gpuid >= 0:
logger.info("Worker {} uses GPU {}".format(self.idx, self.gpuid))
os.environ['CUDA_VISIBLE_DEVICES'] = str(self.gpuid)
else:
logger.info("Worker {} uses CPU".format(self.idx))
os.environ['CUDA_VISIBLE_DEVICES'] = ''
G = tf.Graph() # build a graph for each process, because they don't need to share anything
with G.as_default():
if self.idx != 0:
from tensorpack.models._common import disable_layer_logging
disable_layer_logging()
self.func = get_predict_func(self.config)
if self.idx == 0:
describe_model()