Add SLURM check in ddp_train() and init_ddp_connection()

CHANGELOG.md

@@ -9,10 +9,16 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 ### Added
 
 - Added `auto_select_gpus` flag to trainer that enables automatic selection of available GPUs on exclusive mode systems.
+
+- Added learining rate finder ([#1347](https://github.com/PyTorchLightning/pytorch-lightning/pull/1347))
+
+- Added support for ddp mode in clusters without SLURM ([#1345](https://github.com/PyTorchLightning/pytorch-lightning/issues/1345))
+
 - Added learning rate finder ([#1347](https://github.com/PyTorchLightning/pytorch-lightning/pull/1347))
 
 - Added `terminate_on_nan` flag to trainer that performs a NaN check with each training iteration when set to `True`. ([#1475](https://github.com/PyTorchLightning/pytorch-lightning/pull/1475))
 
+
 ### Changed
 
 - Changed the default behaviour to no longer include a NaN check with each training iteration. ([#1475](https://github.com/PyTorchLightning/pytorch-lightning/pull/1475))

pytorch_lightning/core/lightning.py

@@ -873,53 +873,10 @@ def configure_ddp(self, model, device_ids):
         )
         return model
 
-    def init_ddp_connection(self, proc_rank: int, world_size: int) -> None:
-        r"""
-        Override to define your custom way of setting up a distributed environment.
-
-        Lightning's implementation uses ``env://`` init by default and sets the first node as root.
-
-        Args:
-            proc_rank: The current process rank within the node.
-            world_size: Number of GPUs being use across all nodes (num_nodes * num_gpus).
-
-        Examples:
-            .. code-block:: python
-
-                def init_ddp_connection(self):
-                    # use slurm job id for the port number
-                    # guarantees unique ports across jobs from same grid search
-                    try:
-                        # use the last 4 numbers in the job id as the id
-                        default_port = os.environ['SLURM_JOB_ID']
-                        default_port = default_port[-4:]
-
-                        # all ports should be in the 10k+ range
-                        default_port = int(default_port) + 15000
-
-                    except Exception as e:
-                        default_port = 12910
-
-                    # if user gave a port number, use that one instead
-                    try:
-                        default_port = os.environ['MASTER_PORT']
-                    except Exception:
-                        os.environ['MASTER_PORT'] = str(default_port)
-
-                    # figure out the root node addr
-                    try:
-                        root_node = os.environ['SLURM_NODELIST'].split(' ')[0]
-                    except Exception:
-                        root_node = '127.0.0.2'
-
-                    root_node = self.trainer.resolve_root_node_address(root_node)
-                    os.environ['MASTER_ADDR'] = root_node
-                    dist.init_process_group(
-                        'nccl',
-                        rank=self.proc_rank,
-                        world_size=self.world_size
-                    )
-
+    def _init_slurm_connection(self) -> None:
+        """
+        Sets up environemnt variables necessary for pytorch distributed communications
+        based on slurm environment.
         """
         # use slurm job id for the port number
         # guarantees unique ports across jobs from same grid search
@@ -948,6 +905,40 @@ def init_ddp_connection(self):
 
         root_node = self.trainer.resolve_root_node_address(root_node)
         os.environ['MASTER_ADDR'] = root_node
+
+    def init_ddp_connection(
+            self,
+            proc_rank: int,
+            world_size: int,
+            is_slurm_managing_tasks: bool = True
+    ) -> None:
+        """
+        Override to define your custom way of setting up a distributed environment.
+
+        Lightning's implementation uses env:// init by default and sets the first node as root
+        for SLURM managed cluster.
+
+        Args:
+            proc_rank: The current process rank within the node.
+            world_size: Number of GPUs being use across all nodes. (num_nodes * num_gpus).
+            is_slurm_managing_tasks: is cluster managed by SLURM.
+
+        """
+        if is_slurm_managing_tasks:
+            self._init_slurm_connection()
+
+        if 'MASTER_ADDR' not in os.environ:
+            log.warning("MASTER_ADDR environment variable is not defined. Set as localhost")
+            os.environ['MASTER_ADDR'] = '127.0.0.1'
+
+        if 'MASTER_PORT' not in os.environ:
+            log.warning("MASTER_PORT environment variable is not defined. Set as 12910")
+            os.environ['MASTER_PORT'] = '12910'
+
+        if 'WORLD_SIZE' in os.environ and os.environ['WORLD_SIZE'] != world_size:
+            log.warning("WORLD_SIZE environment variable is not equal to the computed "
+                        "world size. Ignored.")
+
         torch_backend = "nccl" if self.trainer.on_gpu else "gloo"
         torch_distrib.init_process_group(torch_backend, rank=proc_rank, world_size=world_size)
 

pytorch_lightning/trainer/distrib_data_parallel.py

@@ -285,12 +285,13 @@ def ddp_train(self, process_idx, model):
         :param cluster_obj:
         :return:
         """
-        # node rank using relative slurm id
-        # otherwise default to node rank 0
+        # node rank using relative slurm id if under slurm management
+        # otherwise use given node rank or default to node rank 0
         try:
-            node_id = os.environ['SLURM_NODEID']
+            node_id = os.environ['SLURM_NODEID'] if self.is_slurm_managing_tasks else os.environ['NODE_RANK']
             self.node_rank = int(node_id)
-        except Exception:
+        except KeyError:
+            log.warning("SLURM_NODEID or NODE_RANK environment variable is not defined. Set as 0.")
             self.node_rank = 0
 
         # show progressbar only on progress_rank 0
@@ -317,7 +318,7 @@ def ddp_train(self, process_idx, model):
         # try to init for 20 times at max in case ports are taken
         # where to store ip_table
         model.trainer = self
-        model.init_ddp_connection(self.proc_rank, self.world_size)
+        model.init_ddp_connection(self.proc_rank, self.world_size, self.is_slurm_managing_tasks)
 
         # CHOOSE OPTIMIZER
         # allow for lr schedulers as well