made ddp the default if no backend specified with multiple GPUs

Lightning-AI · May 12, 2020 · 6710f2e · 6710f2e
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commit 6710f2e
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diff --git a/docs/source/multi_gpu.rst b/docs/source/multi_gpu.rst
@@ -130,6 +130,8 @@ Lightning allows multiple ways of training
 - Horovod (`distributed_backend='horovod'`) (multi-machine, multi-gpu, configured at runtime)
 - TPUs (`num_tpu_cores=8|x`) (tpu or TPU pod)
 
+.. note:: If you request multiple GPUs without setting a mode, ddp will be automatically used.
+
 Data Parallel (dp)
 ^^^^^^^^^^^^^^^^^^
 `DataParallel <https://pytorch.org/docs/stable/nn.html#torch.nn.DataParallel>`_ splits a batch across k GPUs. That is, if you have a batch of 32 and use dp with 2 gpus,

diff --git a/pytorch_lightning/trainer/distrib_data_parallel.py b/pytorch_lightning/trainer/distrib_data_parallel.py
@@ -203,8 +203,8 @@ def set_distributed_mode(self, distributed_backend):
             elif self.num_gpus > 1:
                 rank_zero_warn('You requested multiple GPUs but did not specify a backend, e.g.'
                                ' Trainer(distributed_backend=dp) (or ddp, ddp2).'
-                               ' Setting distributed_backend=dp for you.')
-                self.use_dp = True
+                               ' Setting distributed_backend=ddp for you.')
+                self.use_ddp = True
         elif distributed_backend == "dp":
             # do nothing if num_gpus == 0
             if self.num_gpus == 1: