diff --git a/pytorch_lightning/accelerators/ddp_backend.py b/pytorch_lightning/accelerators/ddp_backend.py
index 4b63b69c6b..614d38bc4d 100644
--- a/pytorch_lightning/accelerators/ddp_backend.py
+++ b/pytorch_lightning/accelerators/ddp_backend.py
@@ -133,124 +133,11 @@ class DDPBackend(DDPBase):
     def train(self):
         model = self.trainer.model
         if self.mode == 'ddp':
-            results = self.ddp_train(process_idx=self.task_idx, mp_queue=None, model=model, is_master=True)
+            results = self.ddp_train_tmp(process_idx=self.task_idx, mp_queue=None, model=model, is_master=True)
             del os.environ['WORLD_SIZE']
             return results
         else:
-            self.ddp_train(process_idx=self.task_idx, mp_queue=None, model=model)
-
-    def ddp_train(self, process_idx, mp_queue, model, is_master=False, proc_offset=0):
-        """
-        Entry point for ddp
-
-        Args:
-            process_idx:
-            mp_queue: multiprocessing queue
-            model:
-            is_master:
-            proc_offset:
-
-        Returns:
-
-        """
-        # offset the process id if requested
-        process_idx = process_idx + proc_offset
-
-        # show progressbar only on progress_rank 0
-        if (self.trainer.node_rank != 0 or process_idx != 0) and self.trainer.progress_bar_callback is not None:
-            self.trainer.progress_bar_callback.disable()
-
-        # determine which process we are and world size
-        self.trainer.local_rank = process_idx
-        self.trainer.global_rank = self.trainer.node_rank * self.trainer.num_processes + process_idx
-        self.trainer.world_size = self.trainer.num_nodes * self.trainer.num_processes
-
-        # set warning rank
-        rank_zero_only.rank = self.trainer.global_rank
-
-        # set up server using proc 0's ip address
-        # try to init for 20 times at max in case ports are taken
-        # where to store ip_table
-        model.trainer = self.trainer
-        model.init_ddp_connection(
-            self.trainer.global_rank,
-            self.trainer.world_size,
-            self.trainer.is_slurm_managing_tasks
-        )
-
-        # call setup after the ddp process has connected
-        self.trainer.call_setup_hook(model)
-
-        # on world_size=0 let everyone know training is starting
-        if self.trainer.is_global_zero:
-            log.info('-' * 100)
-            log.info(f'distributed_backend={self.trainer.distributed_backend}')
-            log.info(f'All DDP processes registered. Starting ddp with {self.trainer.world_size} processes')
-            log.info('-' * 100)
-
-        # call sync_bn before .cuda(), configure_apex and configure_ddp
-        if self.trainer.sync_batchnorm:
-            model = model.configure_sync_batchnorm(model)
-
-        # MODEL
-        # copy model to each gpu
-        if self.trainer.on_gpu:
-            gpu_idx = process_idx
-
-            # when using ddp, the master process (proc 0) continues running as the main one
-            # this means that the local rank will always be 0
-            # (even if cuda visible devices has other visible gpus)
-            # this means that the master process needs to pull the 0th visible index as the device number
-            if is_master:
-                available_gpus = os.environ['CUDA_VISIBLE_DEVICES'].split(',')
-                gpu_idx = int(available_gpus[self.trainer.local_rank])
-
-            self.trainer.root_gpu = gpu_idx
-            torch.cuda.set_device(self.trainer.root_gpu)
-            model.cuda(self.trainer.root_gpu)
-
-        # CHOOSE OPTIMIZER
-        # allow for lr schedulers as well
-        optimizers, lr_schedulers, optimizer_frequencies = self.trainer.init_optimizers(model)
-        self.trainer.optimizers = optimizers
-        self.trainer.lr_schedulers = lr_schedulers
-        self.trainer.optimizer_frequencies = optimizer_frequencies
-
-        # set model properties before going into wrapper
-        self.trainer.model_connector.copy_trainer_model_properties(model)
-
-        # AMP - run through amp wrapper before going to distributed DP
-        if self.trainer.amp_backend == AMPType.APEX:
-            model, optimizers = model.configure_apex(amp, model, self.trainer.optimizers, self.trainer.amp_level)
-            self.trainer.optimizers = optimizers
-            self.trainer.reinit_scheduler_properties(self.trainer.optimizers, self.trainer.lr_schedulers)
-
-        # DDP2 uses all GPUs on the machine
-        if self.trainer.distributed_backend == 'ddp' or self.trainer.distributed_backend == 'ddp_spawn':
-            device_ids = [self.trainer.root_gpu]
-        else:  # includes ddp_cpu
-            device_ids = None
-
-        # allow user to configure ddp
-        model = model.configure_ddp(model, device_ids)
-
-        # set up training routine
-        self.trainer.train_loop.setup_training(model)
-
-        # train or test
-        results = self.train_or_test()
-
-        # get original model
-        model = self.trainer.get_model()
-
-        # persist info in ddp_spawn
-        self.transfer_distrib_spawn_state_on_fit_end(model, mp_queue, results)
-
-        # clean up memory
-        torch.cuda.empty_cache()
-
-        if self.trainer.global_rank == 0 and self.trainer.distributed_backend not in ['ddp_spawn', 'ddp_cpu']:
-            return results
+            self.ddp_train_tmp(process_idx=self.task_idx, mp_queue=None, model=model)
 
     def _check_can_spawn_children(self):
         if self._has_spawned_children:
@@ -258,3 +145,27 @@ class DDPBackend(DDPBase):
                 "You tried to run `.fit` or `.test` multiple times in the same script."
                 " This is not supported in DDP mode, switch to `distributed_backend='ddp_spawn'` instead."
             )
+
+    def set_world_ranks(self, process_idx):
+        self.trainer.local_rank = process_idx
+        self.trainer.global_rank = self.trainer.node_rank * self.trainer.num_processes + process_idx
+        self.trainer.world_size = self.trainer.num_nodes * self.trainer.num_processes
+
+    def model_to_device(self, model, process_idx, is_master):
+        gpu_idx = process_idx
+
+        # when using ddp, the master process (proc 0) continues running as the main one
+        # this means that the local rank will always be 0
+        # (even if cuda visible devices has other visible gpus)
+        # this means that the master process needs to pull the 0th visible index as the device number
+        if is_master:
+            available_gpus = os.environ['CUDA_VISIBLE_DEVICES'].split(',')
+            gpu_idx = int(available_gpus[self.trainer.local_rank])
+
+        self.trainer.root_gpu = gpu_idx
+        torch.cuda.set_device(self.trainer.root_gpu)
+        model.cuda(self.trainer.root_gpu)
+
+    def get_device_ids(self):
+        device_ids = [self.trainer.root_gpu]
+        return device_ids
diff --git a/pytorch_lightning/accelerators/ddp_base_backend.py b/pytorch_lightning/accelerators/ddp_base_backend.py
index e534da0fc5..ff51fbe786 100644
--- a/pytorch_lightning/accelerators/ddp_base_backend.py
+++ b/pytorch_lightning/accelerators/ddp_base_backend.py
@@ -180,6 +180,9 @@ class DDPBase(Accelerator):
         # clean up memory
         torch.cuda.empty_cache()
 
+        if self.trainer.global_rank == 0:
+            return results
+
     def set_world_ranks(self, process_idx):
         raise NotImplementedError('to create a ddp backend, please implement set_world_ranks')
 
diff --git a/pytorch_lightning/accelerators/ddp_cpu_spawn_backend.py b/pytorch_lightning/accelerators/ddp_cpu_spawn_backend.py
index a986aa7960..ec67098dc7 100644
--- a/pytorch_lightning/accelerators/ddp_cpu_spawn_backend.py
+++ b/pytorch_lightning/accelerators/ddp_cpu_spawn_backend.py
@@ -11,68 +11,10 @@
 # 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 os
-
-import torch
-import torch.multiprocessing as mp
-
-from pytorch_lightning.utilities.distributed import find_free_network_port
-from pytorch_lightning.accelerators.ddp_base_backend import DDPBase
-
-try:
-    from apex import amp
-except ImportError:
-    amp = None
+from pytorch_lightning.accelerators.ddp_spawn_backend import DDPSpawnBackend
 
 
-class DDPCPUSpawnBackend(DDPBase):
-
-    def __init__(self, trainer, nprocs):
-        super().__init__(trainer)
-        self.mp_queue = None
-        self.nprocs = nprocs
-
-    def setup(self, model):
-        os.environ['MASTER_PORT'] = os.environ.get('MASTER_PORT', str(find_free_network_port()))
-
-        # pass in a state q
-        smp = mp.get_context('spawn')
-        self.mp_queue = smp.SimpleQueue()
-
-        self.trainer.model = model
-
-    def train(self):
-        model = self.trainer.model
-
-        # train in children process
-        mp.spawn(self.ddp_train_tmp, nprocs=self.nprocs, args=(self.mp_queue, model,))
-
-        # restore main state with best weights
-        best_path = self.mp_queue.get()
-        results = self.mp_queue.get()
-        last_path = self.mp_queue.get()
-
-        # recover the weights of the processes trained in the children
-        self.__recover_child_process_weights(model, best_path, last_path)
-        return results
-
-    def __recover_child_process_weights(self, model, best_path, last_path):
-        # transfer back the best path to the trainer
-        if self.trainer.checkpoint_callback:
-            self.trainer.checkpoint_callback.best_model_path = best_path
-        # todo, pass also best score
-
-        # load last weights
-        if last_path is not None and not self.trainer.testing:
-            ckpt = torch.load(last_path, map_location=lambda storage, loc: storage)
-            model.load_state_dict(ckpt)
-
-        self.trainer.model = model
-
-    def set_world_ranks(self, process_idx):
-        self.trainer.local_rank = process_idx
-        self.trainer.global_rank = self.trainer.node_rank * self.trainer.num_processes + process_idx
-        self.trainer.world_size = self.trainer.num_nodes * self.trainer.num_processes
+class DDPCPUSpawnBackend(DDPSpawnBackend):
 
     def model_to_device(self, model, process_idx, is_master):
         pass