def session_config(params):
optimizer_options = tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L1,
do_function_inlining=True)
graph_options = tf.GraphOptions(optimizer_options=optimizer_options)
config = tf.ConfigProto(allow_soft_placement=True,
graph_options=graph_options)
if distribute.is_distributed_training_mode():
config.gpu_options.visible_device_list = str(distribute.local_rank())
elif params.device_list:
device_str = ",".join([str(i) for i in params.device_list])
config.gpu_options.visible_device_list = device_str
return config
def session_config(params):
optimizer_options = tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L1,
do_function_inlining=True)
graph_options = tf.GraphOptions(optimizer_options=optimizer_options)
config = tf.ConfigProto(allow_soft_placement=True,
graph_options=graph_options)
from tensorflow.python.client import device_lib
n_gpus = sum(1 for d in device_lib.list_local_devices() if d.device_type == 'GPU')
if n_gpus > 1:
params.device_list = list(range(n_gpus))
if distribute.is_distributed_training_mode():
config.gpu_options.visible_device_list = str(distribute.local_rank())
elif params.device_list:
device_str = ",".join([str(i) for i in params.device_list])
config.gpu_options.visible_device_list = device_str
return config