def map_fun(context):
print(tf.__version__)
sys.stdout.flush()
tf_context = TFContext(context)
job_name = tf_context.get_role_name()
index = tf_context.get_index()
cluster_json = tf_context.get_tf_cluster()
print(cluster_json)
sys.stdout.flush()
cluster = tf.train.ClusterSpec(cluster=cluster_json)
server = tf.train.Server(cluster, job_name=job_name, task_index=index)
sess_config = tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=False,
device_filters=["/job:ps", "/job:worker/task:%d" % index])
if 'ps' == job_name:
from time import sleep
while True:
sleep(1)
else:
with tf.device(
tf.train.replica_device_setter(
worker_device='/job:worker/task:' + str(index),
cluster=cluster)):
record_defaults = [[9], [tf.constant(value=9, dtype=tf.int64)],
[9.0],
[tf.constant(value=9.0, dtype=tf.float64)],
["9.0"]]
dataset = context.flinkStreamDataSet(buffer_size=0)
dataset = dataset.map(lambda record: tf.decode_csv(
record, record_defaults=record_defaults))
dataset = dataset.batch(3)
iterator = dataset.make_one_shot_iterator()
input_records = iterator.get_next()
global_step = tf.train.get_or_create_global_step()
global_step_inc = tf.assign_add(global_step, 1)
out_list = [input_records[0], input_records[2], input_records[4]]
out = tff_ops.encode_csv(input_list=out_list)
is_chief = (index == 0)
t = time.time()
try:
with tf.train.MonitoredTrainingSession(
master=server.target,
is_chief=is_chief,
config=sess_config,
checkpoint_dir="./target/tmp/input_output/" +
str(t)) as mon_sess:
# while not mon_sess.should_stop():
while True:
print(index, mon_sess.run([global_step_inc, out]))
sys.stdout.flush()
# time.sleep(1)
except Exception as e:
print('traceback.print_exc():')
traceback.print_exc()
sys.stdout.flush()
finally:
SummaryWriterCache.clear()
def map_fun(context):
tf.compat.v1.disable_v2_behavior()
tf_context = TFContext(context)
job_name = tf_context.get_role_name()
index = tf_context.get_index()
cluster_json = tf_context.get_tf_cluster()
print(cluster_json)
sys.stdout.flush()
cluster = tf.compat.v1.train.ClusterSpec(cluster=cluster_json)
server = tf.compat.v1.train.Server(cluster,
job_name=job_name,
task_index=index)
sess_config = tf.compat.v1.ConfigProto(
allow_soft_placement=True,
log_device_placement=False,
device_filters=["/job:ps", "/job:worker/task:%d" % index])
if 'ps' == job_name:
from time import sleep
while True:
sleep(1)
else:
with tf.compat.v1.device(
tf.compat.v1.train.replica_device_setter(
worker_device='/job:worker/task:' + str(index),
cluster=cluster)):
global_step = tf.compat.v1.train.get_or_create_global_step()
global_step_inc = tf.compat.v1.assign_add(global_step, 1)
input_records = [
tf.constant([1, 2, 3]),
tf.constant([1.0, 2.0, 3.0]),
tf.constant(['1.0', '2.0', '3.0'])
]
out = tff_ops.encode_csv(input_list=input_records, field_delim='|')
fw = tff_ops.FlinkTFRecordWriter(address=context.toFlink())
w = fw.write([out])
is_chief = (index == 0)
t = time.time()
try:
hooks = [tf.compat.v1.train.StopAtStepHook(last_step=50)]
with tf.compat.v1.train.MonitoredTrainingSession(
master=server.target,
config=sess_config,
is_chief=is_chief,
checkpoint_dir="./target/tmp/with_output/" + str(t),
hooks=hooks) as mon_sess:
while not mon_sess.should_stop():
print(index, mon_sess.run([global_step_inc, w]))
sys.stdout.flush()
time.sleep(1)
finally:
SummaryWriterCache.clear()