diff --git a/python/paddle/distributed/fleet/elastic.py b/python/paddle/distributed/fleet/elastic.py
new file mode 100644
index 0000000000000000000000000000000000000000..b919c4737576d5947265004854a8d1f66450a8fc
--- /dev/null
+++ b/python/paddle/distributed/fleet/elastic.py
@@ -0,0 +1,312 @@
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import time
+import socket
+import os
+import six
+import logging
+import signal
+
+logging.basicConfig(level=os.environ.get('LOGLEVEL', 'INFO').upper())
+logger = logging.getLogger("ELASTIC")
+
+ELASTIC_EXIT_CODE = 101
+
+
+class ElasticStatus:
+    COMPLETED = "completed"
+    ERROR = "error"
+    HOLD = "hold"
+    RESTART = "restart"
+    EXIT = "exit"
+
+
+class LauncherInterface(object):
+    def __init__(self, args):
+        self.args = args
+        self.procs = []
+
+    def _terminate_procs(self):
+        for p in self.procs:
+            if p.proc.poll() is None:
+                p.proc.terminate()
+                if p.log_fn:
+                    p.log_fn.close()
+                logger.info("terminate process id:{}".format(p.proc.pid))
+
+        for step in range(0, 50):
+            alive = False
+            for p in self.procs:
+                if p.proc.poll() is None:  # not termniate
+                    os.kill(p.proc.pid, signal.SIGKILL)
+                    alive = True
+
+            if not alive:
+                logger.info("terminate all the procs")
+                return True
+
+            time.sleep(1)
+        return False
+
+    def _check_procs(self):
+        alive = False
+        result = None
+        for p in self.procs:
+            ret = p.proc.poll()
+            if ret is None:
+                alive = True
+            elif ret != 0:
+                logger.error("ERROR rank {} error with code {}".format(p.rank,
+                                                                       ret))
+                result = ret
+        if not alive and result is None:
+            return 0
+        else:
+            return result
+
+    def launch(self):
+        raise NotImplementedError
+
+    def stop(self):
+        raise NotImplementedError
+
+    def watch(self):
+        raise NotImplementedError
+
+
+class ElasticManager(object):
+    def __init__(self, args):
+
+        self.args = args
+        server = args.elastic_server or os.getenv('PADDLE_ELASTIC_SERVER')
+        name = args.job_id or os.getenv('PADDLE_ELASTIC_JOB_ID')
+        np = args.np or int(os.getenv('PADDLE_ELASTIC_NP', 0))
+        host = args.host or os.getenv('POD_IP')
+        scale = args.scale or int(os.getenv('PADDLE_ELASTIC_SCALE', 0))
+        force = args.force or os.getenv('PADDLE_ELASTIC_FORCE')
+
+        self.endpoints = os.getenv('DISTRIBUTED_TRAINER_ENDPOINTS', '')
+        self.trainers = os.getenv('PADDLE_TRAINERS', '')
+
+        self.elastic_level = int(
+            os.getenv('PADDLE_ELASTIC_FAULT_TOLERANC_LEVEL', 1))
+
+        #elastic_timeout = os.getenv('PADDLE_ELASTIC_TIMEOUT',1)
+
+        logger.debug('init with server {} host {}'.format(server, host))
+
+        self.hosts = []
+        self.stopped = False
+
+        self.sigint = 0
+
+        if not server or ':' not in server or not name or not np:
+            logger.info(
+                'Elastic is not enabled with server {} name {} and np {}'.
+                format(server, name, np))
+            self.enable = False
+            return
+        else:
+            self.enable = True
+
+        import etcd3
+
+        srv, port = server.split(':')
+        self.etcd = etcd3.client(host=srv, port=port)
+        self.host = host if host else self._get_host()
+
+        # etcd data
+        self.prefix = "/paddle/" + name
+        self.node_prefix = self.prefix + '/nodes/'
+        self.np_path = self.prefix + '/np'
+        self.endpoints_path = self.prefix + '/endpoints'
+        self.host_path = '{}{}'.format(self.node_prefix, time.time())
+
+        self.np = np + scale
+        '''
+        0 group mode, be aware of healthy status of other workers
+        1 decouple mode, check own status only
+        '''
+        self.etcd.put(self.prefix, b'0')
+
+        # host
+        # register self host to etcd
+        # register watch to reset host after host been deleted
+        self.etcd.delete_prefix(self.node_prefix)
+
+        def host_call_back(event):
+            if self.etcd.get(self.host_path)[0] == None:
+                # ensure unmatch trigger
+                logger.info('register host again {}'.format(self.host))
+                time.sleep(5)
+
+                self.etcd.put(self.host_path, six.b(self.host))
+
+        host_watch = self.etcd.add_watch_callback(self.host_path,
+                                                  host_call_back)
+        self.etcd.put(self.host_path, six.b(self.host))
+
+        # np describes the exact number of nodes to run the job
+        inp = int(self.etcd.get(self.np_path)[0] or 0)
+        if scale == 0 and not force:
+            assert inp == np or inp == 0, "np {} is not consistent with np in etcd {}".format(
+                np, inp)
+        else:
+            assert inp == np or inp == self.np, "np {} scale to {} by {} is not allowed".format(
+                inp, self.np, scale)
+
+        self.etcd.put(self.np_path, six.b("%d" % (self.np)))
+
+        def np_call_back(event):
+            gnp = int(self.etcd.get(self.np_path)[0])
+            if gnp != self.np:
+                logger.info("scale np {} to {} ".format(self.np, gnp))
+                self.np = gnp
+
+        np_watch = self.etcd.add_watch_callback(self.np_path, np_call_back)
+
+        # endpoints handle DISTRIBUTED_TRAINER_ENDPOINTS and PADDLE_TRAINERS
+        self.etcd.put(self.endpoints_path,
+                      six.b('{}|{}'.format(self.endpoints, self.trainers)))
+
+        def endpoints_call_back(event):
+            if not self.endpoints:
+                return
+            edps = six.ensure_str(self.etcd.get(self.endpoints_path)[0] or '')
+            self.endpoints, self.trainers = edps.split('|')
+            logger.info("set DISTRIBUTED_TRAINER_ENDPOINTS {} ".format(
+                self.endpoints))
+            logger.info("set PADDLE_TRAINERS {} ".format(self.trainers))
+
+        endpoints_watch = self.etcd.add_watch_callback(self.endpoints_path,
+                                                       endpoints_call_back)
+
+        self.watches = [host_watch, np_watch, endpoints_watch]
+
+    def exit(self, completed=False):
+        logger.info('manager exist completed {}'.format(completed))
+
+        if not self.enable:
+            return
+
+        if completed:
+            self.etcd.put(self.prefix, b'1')
+
+        for watch in self.watches:
+            self.etcd.cancel_watch(watch)
+        self.etcd.delete(self.host_path)
+
+        hosts = [i for i in self.etcd.get_prefix(self.node_prefix)]
+        if len(hosts) == 0:
+            self.etcd.delete_prefix(self.prefix)
+
+    def _get_host(self):
+        try:
+            return socket.gethostbyname(socket.getfqdn(socket.gethostname()))
+        except:
+            return '127.0.0.1'
+
+    def _completed(self):
+        if not self.enable:
+            return True
+
+        return int(self.etcd.get(self.prefix)[0]) == 1
+
+    def _match(self):
+        self.hosts = [
+            six.ensure_str(i[0]) for i in self.etcd.get_prefix(self.node_prefix)
+        ]
+        if len(self.hosts) == self.np:
+            return True
+        else:
+            return False
+
+    def _update_hosts(self):
+        assert len(self.hosts) != 0, 'hosts empty'
+
+        if self.host in self.endpoints:
+            os.environ['DISTRIBUTED_TRAINER_ENDPOINTS'] = self.endpoints
+            os.environ['PADDLE_TRAINERS'] = self.trainers
+            logger.info("update env DISTRIBUTED_TRAINER_ENDPOINTS {} ".format(
+                self.endpoints))
+            logger.info("update env PADDLE_TRAINERS {} ".format(self.trainers))
+            return
+
+        rank = int(os.getenv('PADDLE_TRAINER_ID', -1))
+        idx = self.hosts.index(self.host)
+
+        # swap if self.host not in the right position
+        if rank >= 0:
+            self.hosts[idx] = self.hosts[rank]
+            self.hosts[rank] = self.host
+        else:
+            os.environ['PADDLE_TRAINER_ID'] = '{}'.format(idx)
+
+        hosts = ','.join(self.hosts)
+        self.args.ips = hosts
+        os.environ['PADDLE_TRAINERS'] = hosts
+
+    def wait(self):
+        if not self.enable:
+            return
+
+        while not self.stopped:
+            if self._match():
+                logger.info('ready with hosts {}'.format(self.hosts))
+                self._update_hosts()
+                return
+            logger.info('not ready for np {} with hosts {}'.format(self.np,
+                                                                   self.hosts))
+            time.sleep(3)
+        return
+
+    def run(self, launcher):
+        if self.stopped:
+            return
+
+        self.launcher = launcher(self.args)
+        self.launcher.launch()
+
+    def watch(self):
+
+        while not self.stopped:
+            ret = self.launcher.watch()
+
+            if ret is not None:  # self terminated
+                logger.info('job exit with code {}'.format(ret))
+                # process is completed if ret >= 0 or error else
+                completed = True if ret == 0 else False
+                self.launcher.stop()
+                self.exit(completed=completed)
+                if completed:
+                    return ElasticStatus.COMPLETED
+                if self.elastic_level == 1:
+                    return ElasticStatus.RESTART
+                else:
+                    return ElasticStatus.ERROR
+
+            if not self._completed() and not self._match():
+                self.launcher.stop()
+                return ElasticStatus.HOLD
+
+            time.sleep(3)
+
+        return ElasticStatus.EXIT
+
+    def signal_handler(self, sigint, frame):
+        if self.enable:
+            self.exit()
+        self.sigint = sigint
+        self.stopped = True
diff --git a/python/paddle/distributed/fleet/launch.py b/python/paddle/distributed/fleet/launch.py
index 25b10133191788cd85085fd4612ae7cea0f122f3..07862a07c92c419c88869e2544414ba4e63141e0 100644
--- a/python/paddle/distributed/fleet/launch.py
+++ b/python/paddle/distributed/fleet/launch.py
@@ -69,12 +69,18 @@ from argparse import ArgumentParser, REMAINDER
 import paddle
 import paddle.fluid as fluid
 from paddle.distributed.fleet import launch_utils
+import signal
 
 # TODO(danleifeng): Don't import * from a module
 from paddle.distributed.fleet.launch_utils import *
 import paddle.distributed.fleet.cloud_utils as cloud_utils
 import paddle.distributed.fleet.ascend_utils as ascend_utils
 
+from paddle.distributed.fleet.elastic import ElasticManager
+from paddle.distributed.fleet.elastic import LauncherInterface
+from paddle.distributed.fleet.elastic import ElasticStatus
+from paddle.distributed.fleet.elastic import ELASTIC_EXIT_CODE
+
 __all__ = []
 
 
@@ -175,6 +181,18 @@ see: http://www.paddlepaddle.org/documentation/docs/zh/1.6/user_guides/howto/tra
         "--heter_worker_num", type=int, help="number of heter_workers")
     ps_group.add_argument("--http_port", type=int, help="Gloo http Port")
 
+    # parameter elastic mode
+    elastic_group = parser.add_argument_group("Elastic Parameters")
+    elastic_group.add_argument(
+        "--elastic_server", type=str, help="etcd server host:port")
+    elastic_group.add_argument("--job_id", type=str, help="job unique id")
+    elastic_group.add_argument("--np", type=int, help="job pod/node number")
+    elastic_group.add_argument("--scale", type=int, default=0, help="scale np")
+    elastic_group.add_argument(
+        "--host", type=str, help="bind host, default to POD_IP env")
+    elastic_group.add_argument(
+        "--force", type=bool, default=False, help="update np force")
+
     return parser.parse_args()
 
 
@@ -183,7 +201,10 @@ def get_cluster_from_args(args, device_mode, devices_per_proc):
     if len(node_ips) == 1:
         node_ip = node_ips[0]
     else:
-        _, node_ip = get_host_name_ip()
+        if args.host:
+            node_ip = args.host
+        else:
+            _, node_ip = get_host_name_ip()
 
     assert node_ip in node_ips, "Can't find your local ip {%s} in node_ips: {%s}" \
         % (node_ip, node_ips)
@@ -214,65 +235,75 @@ def get_cluster_from_args(args, device_mode, devices_per_proc):
                        devices_per_proc)
 
 
-def launch_collective(args):
-    # parse arguments, used for cloud-single-machine and local
-    (device_mode, devices_per_proc) = launch_utils.get_device_proc_info(args)
-    trainers_num = cloud_utils.get_trainers_num()
-    logger.debug("parsed from args trainerss_num:{} mode:{} devices:{}".format(
-        trainers_num, device_mode, devices_per_proc))
-
-    cluster = None
-    pod = None
-
-    start_port = 6170
-    if os.environ.get('FLAGS_START_PORT') is not None:
-        start_port = os.environ.get('FLAGS_START_PORT')
-    if cloud_utils.use_paddlecloud() and trainers_num != 1:
-        cluster, pod = cloud_utils.get_cloud_cluster(
-            args.ips, device_mode, devices_per_proc, start_port)
-        logger.debug("get cluster from cloud:{}".format(cluster))
-    elif device_mode == DeviceMode.ASCEND_NPU:
-        # for ascend
-        cluster, pod = ascend_utils.get_cloud_cluster(
-            rank_table_file=os.getenv("RANK_TABLE_FILE", None),
-            device_mode=device_mode,
-            start_port=start_port)
-    else:
-        # trainers_num = 1 or not use paddlecloud ips="a,b"
-        cluster, pod = get_cluster_from_args(args, device_mode,
-                                             devices_per_proc)
-        logger.debug("get cluster from args:{}".format(cluster))
-
-    global_envs = copy.copy(os.environ.copy())
-    gloo_rendezvous_dir = tempfile.mkdtemp()
-    # add gloo env
-    global_envs["PADDLE_WITH_GLOO"] = str(os.getenv("PADDLE_WITH_GLOO", "0"))
-    global_envs["PADDLE_GLOO_RENDEZVOUS"] = "3"
-    global_envs["PADDLE_GLOO_FS_PATH"] = gloo_rendezvous_dir
-
-    procs = start_local_trainers(
-        cluster,
-        pod,
-        training_script=args.training_script,
-        training_script_args=args.training_script_args,
-        log_dir=args.log_dir,
-        envs=global_envs)
-
-    for idx, proc in enumerate(procs):
-        print("launch proc_id:{} idx:{}".format(proc.proc.pid, idx))
+class CollectiveLauncher(LauncherInterface):
+    def __init__(self, args):
+        self.args = args
+        self.procs = []
 
-    while True:
-        alive = watch_local_trainers(procs, cluster.trainers_nranks())
+    def launch(self):
+        logger.info("collective lauchner launch ...")
+        args = self.args
+        # parse arguments, used for cloud-single-machine and local
+        (device_mode,
+         devices_per_proc) = launch_utils.get_device_proc_info(args)
+        trainers_num = cloud_utils.get_trainers_num()
+        logger.debug("parsed from args trainerss_num:{} mode:{} devices:{}".
+                     format(trainers_num, device_mode, devices_per_proc))
 
-        if not alive:
-            logger.info("Local processes completed.")
-            logger.debug("POD info:{}".format(pod))
-            break
+        cluster = None
+        pod = None
 
-        time.sleep(3)
-
-    if os.path.exists(gloo_rendezvous_dir):
-        shutil.rmtree(gloo_rendezvous_dir)
+        start_port = 6170
+        if os.environ.get('FLAGS_START_PORT') is not None:
+            start_port = os.environ.get('FLAGS_START_PORT')
+        if cloud_utils.use_paddlecloud() and trainers_num != 1:
+            cluster, pod = cloud_utils.get_cloud_cluster(
+                args.ips, device_mode, devices_per_proc, start_port)
+            logger.debug("get cluster from cloud:{}".format(cluster))
+        elif device_mode == DeviceMode.ASCEND_NPU:
+            # for ascend
+            cluster, pod = ascend_utils.get_cloud_cluster(
+                rank_table_file=os.getenv("RANK_TABLE_FILE", None),
+                device_mode=device_mode,
+                start_port=start_port)
+        else:
+            # trainers_num = 1 or not use paddlecloud ips="a,b"
+            cluster, pod = get_cluster_from_args(args, device_mode,
+                                                 devices_per_proc)
+            logger.debug("get cluster from args:{}".format(cluster))
+
+        global_envs = copy.copy(os.environ.copy())
+        self.gloo_rendezvous_dir = tempfile.mkdtemp()
+        # add gloo env
+        global_envs["PADDLE_WITH_GLOO"] = str(
+            os.getenv("PADDLE_WITH_GLOO", "0"))
+        global_envs["PADDLE_GLOO_RENDEZVOUS"] = "3"
+        global_envs["PADDLE_GLOO_FS_PATH"] = self.gloo_rendezvous_dir
+
+        self.procs = start_local_trainers(
+            cluster,
+            pod,
+            training_script=args.training_script,
+            training_script_args=args.training_script_args,
+            log_dir=args.log_dir,
+            envs=global_envs)
+
+        for idx, proc in enumerate(self.procs):
+            logger.info("launch proc_id:{} idx:{}".format(proc.proc.pid, idx))
+
+    def stop(self):
+        logger.info("collective lauchner stop ...")
+        self._terminate_procs()
+        if os.path.exists(self.gloo_rendezvous_dir):
+            shutil.rmtree(self.gloo_rendezvous_dir)
+
+    def watch(self):
+        logger.debug("collective lauchner watch ...")
+        for p in self.procs:
+            if p.log_fn and p.local_rank == 0:
+                pull_worker_log(p)
+        ret = self._check_procs()
+        return ret
 
 
 def launch_ps(args, distribute_mode):
@@ -367,10 +398,42 @@ def launch():
     _print_arguments(args)
 
     distribute_mode = which_distributed_mode(args)
-    if distribute_mode == DistributeMode.COLLECTIVE:
-        launch_collective(args)
-    else:
+    # TODO(kuizhiqing) support ps later
+    if not distribute_mode == DistributeMode.COLLECTIVE:
         launch_ps(args, distribute_mode)
+        return
+
+    elastic = ElasticManager(args)
+
+    signal.signal(signal.SIGTERM, elastic.signal_handler)
+    signal.signal(signal.SIGABRT, elastic.signal_handler)
+    signal.signal(signal.SIGINT, elastic.signal_handler)
+
+    while True:
+
+        # wait for all nodes ready to run
+        elastic.wait()
+
+        # run self with specified launcher
+        elastic.run(CollectiveLauncher)
+
+        # keep wathing the health status of self and being notified for other's failure
+        ret = elastic.watch()
+        if ret == ElasticStatus.COMPLETED:
+            break
+        if ret == ElasticStatus.HOLD:
+            continue
+        if ret == ElasticStatus.EXIT:
+            break
+        if ret == ElasticStatus.ERROR:
+            sys.exit(3)
+        if ret == ElasticStatus.RESTART:
+            sys.exit(ELASTIC_EXIT_CODE)
+
+    if int(elastic.sigint) > 0:
+        sys.exit(128 + int(elastic.sigint))
+    else:
+        sys.exit(0)
 
 
 if __name__ == "__main__":