enable continuous log; update doc

python/paddle/distributed/__init__.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 from .spawn import spawn  # noqa: F401
-from .fleet.launch import launch  # noqa: F401
+from .launch.main import launch  # noqa: F401
 
 from .parallel import init_parallel_env  # noqa: F401
 from .parallel import get_rank  # noqa: F401

python/paddle/distributed/launch/__init__.py

@@ -13,69 +13,3 @@
 # limitations under the License.
 
 __all__ = []
-'''
-Paddle distributed training entry ``python -m paddle.distributed.launch``.
-
-Help
-
-# for arg usage and explanation, try the following command
-# python -m paddle.distributed.launch -h
-
-Collective Mode
-
-Case 1: 1 node
-
-use all visible devices
-# python -m paddle.distributed.launch train.py
-
-use specified devices
-# python -m paddle.distributed.launch --devices=0,1,2,3 train.py
-
-Case 2: multi-node, auto detect ip/port
-
-# python -m paddle.distributed.launch --nnodes 2 train.py
-# auto print following command
-# python -m paddle.distributed.launch --master 10.0.0.1:13538 --nnodes 2 demo.py
-# then copy and paste above command to other nodes
-
-Case 3: multi-node, specified master/rendezvous server
-
-# python -m paddle.distributed.launch --nnodes 2 --master 10.0.0.1:2379 train.py
-# the master ip must be one of the node and the port must available
-
-Parameter Server Mode
-
-Case 1.1: 1 node, 1 ps, 1 trainer
-
-# python -m paddle.distributed.launch --mode ps train.py
-# python -m paddle.distributed.launch --server_num=1 --trainer_num=1 train.py
-
-Case 1.2: 1 node, 2 ps, 2 trainer
-
-# python -m paddle.distributed.launch --server_num=2 --trainer_num=2 train.py
-
-Case 2: 2 node, 2 ps, 2 trainer per node
-
-# python -m paddle.distributed.launch --server_num=2 --trainer_num=2 --nnodes 2 train.py
-# auto print following command
-# python -m paddle.distributed.launch --master 10.0.0.1:13538 --server_num=2 --trainer_num=2 --nnodes 2 train.py
-# then copy and paste above command to other nodes
-
-Case 3: multi-node, specified master/rendezvous server
-
-# python -m paddle.distributed.launch --master 10.0.0.1:13538 --server_num=2 --trainer_num=2 --nnodes 2 train.py
-# the master ip must be one of the node and the port must available
-
-Case 4: specified servers and trainers in each node
-
-python -m paddle.distributed.launch --servers 127.0.0.1:8900,127.0.0.1:8901 --trainers 127.0.0.1:8902,127.0.0.1:8903 train.py
-
-
-Elastic Mode
-
-# run following command in 3 node to run immediately, or in 2 node to run after elastic_timeout
-# python -m paddle.distributed.launch --master etcd://10.0.0.1:2379 --nnodes 2:3 train.py
-
-# once the peer number changes between 2:3, the strategy holds
-
-'''

python/paddle/distributed/launch/__main__.py

@@ -12,31 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from .context import Context
-from . import controllers
+from .main import launch
 
-
-def launch():
-    # initialize the context to run
-    ctx = Context()
-
-    if ctx.is_legacy_mode():
-
-        # legacy mode
-        from paddle.distributed.fleet import launch
-        launch.launch()
-
-    else:
-
-        # initialize the selected controller
-        c = controllers.init(ctx)
-
-        # run the pods
-        c.run()
-
-        # manager or just wait pod
-        c.finalize()
-
-
-if __name__ == "__main__":
-    launch()
+launch()

python/paddle/distributed/launch/context/__init__.py

@@ -82,6 +82,12 @@ def get_logger(self, level=logging.INFO):
         logger.addHandler(ch)
         return logger
 
+    def continous_log(self) -> bool:
+        if self.args.log_level.upper() in ['DEBUG', 'ERROR']:
+            return True
+        else:
+            return False
+
     def set_env_in_args(self):
         for k, v in env_args_mapping.items():
             if k in self.envs:

python/paddle/distributed/launch/context/args_envs.py

@@ -20,7 +20,7 @@
     'PADDLE_MASTER': 'master',
     'PADDLE_DEVICES': 'devices',
     'PADDLE_NNODES': 'nnodes',
-    'PADDLE_MODE': 'mode',
+    'PADDLE_RUN_MODE': 'run_mode',
     'PADDLE_LOG_LEVEL': 'log_level',
     'PADDLE_NPROC_PER_NODE': 'nproc_per_node',
     'PADDLE_JOB_ID': 'job_id',
@@ -60,7 +60,7 @@ def parse_args():
         "--legacy", type=bool, default=False, help="use legacy launch")
 
     base_group.add_argument(
-        "--rank", type=int, default=-1, help="the peer rank")
+        "--rank", type=int, default=-1, help="the node rank")
 
     base_group.add_argument(
         "--log_level", type=str, default="INFO", help="log level. Default INFO")
@@ -69,7 +69,7 @@ def parse_args():
         "--nnodes",
         type=str,
         default="1",
-        help="the number of peers, i.e. pod/node number")
+        help="the number of nodes, i.e. pod/node number")
 
     base_group.add_argument(
         "--nproc_per_node",
@@ -83,7 +83,7 @@ def parse_args():
         default="log",
         help="the path for each process's log. Default ./log")
     base_group.add_argument(
-        "--mode",
+        "--run_mode",
         type=str,
         default="collective",
         help="run mode of the job, collective/ps/ps-heter")
@@ -146,6 +146,6 @@ def parse_args():
         "--elastic_timeout",
         type=int,
         default=30,
-        help="seconds to wait before elastic perform training")
+        help="seconds to wait before elastic job begin to train")
 
     return parser.parse_known_args()

python/paddle/distributed/launch/controllers/collective.py

@@ -115,46 +115,6 @@ def register(self):
 
         self.master.register_heartbeat(self.job.id, self.pod.name)
 
-    def watch(self) -> bool:
-        '''
-        watch self and peer status, return true to exit
-        '''
-
-        self.ctx.logger.info("Watching {}".format(self.pod))
-        while not self.ctx.status.is_done():
-            # self status
-            status = self.pod.watch(timeout=2)
-            self.ctx.logger.debug("Pod status {}, Ctx status {}".format(
-                status, self.ctx.status.current()))
-
-            # completed
-            if status == self.ctx.status.COMPLETED:
-                self.master.set_status(status)
-                self.ctx.status.complete()
-                self.ctx.logger.info("Pod complete {}".format(status))
-                return True
-
-            # self failure
-            elif status == self.ctx.status.FAILED:
-                self.master.set_status(status)
-                self.master.restart_peer()
-                self.ctx.logger.info("Pod failed {}".format(status))
-                self.pod.stop()
-
-                if self.ctx.args.elastic_level <= 0:
-                    return True
-                else:
-                    return False
-
-            # peer failure
-            if self.ctx.status.is_restarting() and self.master.get_status(
-            ) != self.ctx.status.COMPLETED:
-                self.pod.stop()
-                return False
-
-            #peers = self.master.fetch_peer_alive()
-            #print("peers {}".format(peers))
-
     def run(self):
 
         timeout = self.ctx.args.elastic_timeout if self.job.elastic else self.ctx.args.elastic_timeout * 10
@@ -164,6 +124,8 @@ def run(self):
 
             self.build_job()
 
+            self.ctx.logger.info("Waiting peer ready...")
+
             ok, replicas = self.master.wait_peer_ready(
                 self.job.replicas_min, self.job.replicas_max, timeout)
             if ok:

python/paddle/distributed/launch/controllers/controller.py

@@ -40,7 +40,7 @@ def __init__(self, ctx):
         self.master = Master.factory(self.ctx)
 
         self.job = Job(nnodes=self.ctx.args.nnodes,
-                       mode=self.ctx.args.mode,
+                       mode=self.ctx.args.run_mode,
                        jid=self.ctx.args.job_id)
         self.pod = Pod()
 
@@ -65,18 +65,51 @@ def run(self):
         self.watch()
 
     def watch(self) -> bool:
+        '''
+        watch self and peer status, return true to exit
+        '''
+        #TODO(kuizhiqing) unify ctx.status and master status
+
         self.ctx.logger.info("Watching {}".format(self.pod))
 
-        status = self.pod.watch()
+        while not self.ctx.status.is_done():
+            status = self.pod.watch(timeout=2)
+
+            if self.ctx.continous_log():
+                self.pod.logs()
+
+            # completed
+            if status == self.ctx.status.COMPLETED:
+                self.ctx.status.complete()
+
+                self.master.set_status(status)
+
+                self.ctx.logger.info("Pod {}".format(status))
+                return True
+
+            # self failure
+            elif status == self.ctx.status.FAILED:
+                self.ctx.status.fail()
+
+                self.master.set_status(status)
+                self.master.restart_peer()
+
+                fc = self.pod.failed_container()
+                self.ctx.logger.info("Pod {}".format(status))
+                self.ctx.logger.error("Container failed !!!\n{}".format(fc[0]))
+                fc[0].tail()
+                self.pod.stop()
+
+                if self.ctx.args.elastic_level <= 0:
+                    return True
+                else:
+                    return False
 
-        if status == self.ctx.status.COMPLETED:
-            self.ctx.logger.info("Pod {}".format(status))
-        elif status == self.ctx.status.FAILED:
-            fc = self.pod.failed_container()
-            self.ctx.logger.info("Pod {}".format(status))
-            self.ctx.logger.error("Container failed !!!\n{}".format(fc[0]))
-            fc[0].tail()
-            self.pod.stop()
+            # peer failure
+            if self.ctx.status.is_restarting() and self.master.get_status(
+            ) != self.ctx.status.COMPLETED:
+                self.pod.stop()
+                return False
 
     def stop(self, sigint=None):
         self.ctx.logger.debug("Controller stop")

python/paddle/distributed/launch/controllers/master.py

@@ -43,6 +43,15 @@ def __init__(self, ctx):
     def stop(self):
         raise NotImplementedError
 
+    def set_status(self, status):
+        pass
+
+    def get_status(self):
+        return None
+
+    def restart_peer(self):
+        pass
+
     def sync_peers(self, prefix, key, value, size, rank=-1) -> (list, int):
         raise NotImplementedError
 
@@ -122,7 +131,7 @@ def sync_peers(self, prefix, key, value, size, rank=-1) -> (list, int):
         if size < 2:
             return [value], 0
 
-        self.ctx.logger.info("Waiting peer ready...")
+        self.ctx.logger.info("Waiting peer start...")
 
         self.lazy_init()
 
@@ -184,7 +193,7 @@ def sync_peers(self, prefix, key, value, size, rank=-1) -> (list, int):
         if size < 2:
             return [value], 0
 
-        self.ctx.logger.info("Waiting peer ready...")
+        self.ctx.logger.info("Waiting peer start...")
 
         path = "{}/{}/{}".format(prefix, key, rank)
 

python/paddle/distributed/launch/controllers/ps.py

@@ -21,11 +21,11 @@
 class PSController(Controller):
     @classmethod
     def enable(cls, ctx):
-        if ctx.args.mode == ControleMode.PS or ctx.args.server_num or len(
+        if ctx.args.run_mode == ControleMode.PS or ctx.args.server_num or len(
                 ctx.args.servers) > 0 or ctx.args.trainer_num or len(
                     ctx.args.trainers) > 0:
             ctx.logger.debug("{} enabled".format(cls.__name__))
-            ctx.args.mode = ControleMode.PS
+            ctx.args.run_mode = ControleMode.PS
             return True
         else:
             return False