def build(self, data, stop_batch=0):
self.ntypes = self.model.get_ntypes()
# Usually, the type number of the model should be equal to that of the data
# However, nt_model > nt_data should be allowed, since users may only want to
# train using a dataset that only have some of elements
assert (self.ntypes >=
data.get_ntypes()), "ntypes should match that found in data"
self.stop_batch = stop_batch
self.batch_size = data.get_batch_size()
if self.numb_fparam > 0:
self._message("training with %d frame parameter(s)" %
self.numb_fparam)
else:
self._message("training without frame parameter")
self.type_map = data.get_type_map()
self.model.data_stat(data)
worker_device = "/job:%s/task:%d/%s" % (self.run_opt.my_job_name,
self.run_opt.my_task_index,
self.run_opt.my_device)
with tf.device(
tf.train.replica_device_setter(
worker_device=worker_device,
cluster=self.run_opt.cluster_spec)):
self._build_lr()
self._build_network(data)
self._build_training()
def build(self, data, stop_batch=0):
self.ntypes = self.model.get_ntypes()
assert (self.ntypes == data.get_ntypes()
), "ntypes should match that found in data"
self.stop_batch = stop_batch
self.batch_size = data.get_batch_size()
if self.numb_fparam > 0:
self._message("training with %d frame parameter(s)" %
self.numb_fparam)
else:
self._message("training without frame parameter")
self.type_map = data.get_type_map()
self.model.data_stat(data)
worker_device = "/job:%s/task:%d/%s" % (self.run_opt.my_job_name,
self.run_opt.my_task_index,
self.run_opt.my_device)
with tf.device(
tf.train.replica_device_setter(
worker_device=worker_device,
cluster=self.run_opt.cluster_spec)):
self._build_lr()
self._build_network(data)
self._build_training()
def connect_done_queue(cluster_spec, task_index):
done_ops = []
for i in range(cluster_spec.num_tasks("ps")):
with tf.device("/job:ps/task:%d" % i):
queue = tf.FIFOQueue(cluster_spec.num_tasks('worker'), tf.int32,
shared_name='done_queue' + str(i))
done_ops.append(queue.enqueue(task_index))
return done_ops
def create_done_queue(cluster_spec, task_index):
with tf.device("/job:ps/task:%d" % (task_index)):
queue = tf.FIFOQueue(cluster_spec.num_tasks("worker"),
tf.int32,
shared_name="done_queue" + str(task_index))
return queue
