release v0.3.2

docs/Trainer/Distributed training.md

@@ -3,6 +3,26 @@ Lightning makes multi-gpu training and 16 bit training trivial.
 *Note:*   
 None of the flags below require changing anything about your lightningModel definition. 
 
+---
+#### Choosing a backend  
+Lightning supports two backends. DataParallel and DistributedDataParallel. Both can be used for single-node multi-GPU training.
+For multi-node training you must use DistributedDataParallel.   
+
+You can toggle between each mode by setting this flag.
+``` {.python}
+# DEFAULT uses DataParallel
+trainer = Trainer(distributed_backend='dp')
+
+# change to distributed data parallel
+trainer = Trainer(distributed_backend='ddp')
+```
+
+If you request multiple nodes, the back-end will auto-switch to ddp.
+We recommend you use DistributedDataparallel even for single-node multi-GPU training. It is MUCH faster than DP but *may*
+have configuration issues depending on your cluster.
+
+For a deeper understanding of what lightning is doing, feel free to read [this guide](https://medium.com/@_willfalcon/9-tips-for-training-lightning-fast-neural-networks-in-pytorch-8e63a502f565).   
+
 ---
 #### 16-bit mixed precision
 16 bit precision can cut your memory footprint by half. If using volta architecture GPUs it can give a dramatic training speed-up as well.    
@@ -37,11 +57,17 @@ Make sure you're on a GPU machine. You can set as many GPUs as you want.
 In this setting, the model will run on all 8 GPUs at once using DataParallel under the hood.
 ```python
 # set these flags
-os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
-os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3,4,5,6,7"
+# lightning sets these flags for you automatically
+# no need to set yourself
+# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+# os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3,4,5,6,7"
 
 
-trainer = Trainer(gpus=[0,1,2,3,4,5,6,7])
+# to use DataParallel (default)
+trainer = Trainer(gpus=[0,1,2,3,4,5,6,7], distributed_backend='dp')
+
+# RECOMMENDED use DistributedDataParallel
+trainer = Trainer(gpus=[0,1,2,3,4,5,6,7], distributed_backend='ddp')
 ```
 
 ---
@@ -60,6 +86,20 @@ cluster = SlurmCluster(
     log_path='/some/path/to/save',
 )
 
+# OPTIONAL FLAGS WHICH MAY BE CLUSTER DEPENDENT
+# which interface your nodes use for communication
+cluster.add_command('export NCCL_SOCKET_IFNAME=^docker0,lo')
+
+# see output of the NCCL connection process
+# NCCL is how the nodes talk to each other
+cluster.add_command('export NCCL_DEBUG=INFO')
+
+# setting a master port here is a good idea.
+cluster.add_command(f'export MASTER_PORT={PORT}')
+
+# good to load the latest NCCL version
+cluster.load_modules(['NCCL/2.4.7-1-cuda.10.0'])
+
 # configure cluster
 cluster.per_experiment_nb_nodes = 12 
 cluster.per_experiment_nb_gpus = 8
@@ -67,6 +107,20 @@ cluster.per_experiment_nb_gpus = 8
 cluster.add_slurm_cmd(cmd='ntasks-per-node', value=8, comment='1 task per gpu')
 ```
 
+Finally, make sure to add a distributed sampler to your dataset. The distributed sampler copies a 
+portion of your dataset onto each GPU. (World_size = gpus_per_node * nb_nodes).   
+
+```python
+# ie: this:
+dataset = myDataset()
+dataloader = Dataloader(dataset)
+
+# becomes:
+dataset = myDataset()
+dist_sampler = torch.utils.data.distributed.DistributedSampler(dataset)
+dataloader = Dataloader(dataset, sampler=dist_sampler)
+```
+
 ---
 #### Self-balancing architecture
 Here lightning distributes parts of your module across available GPUs to optimize for speed and memory.   

pytorch_lightning/models/trainer.py

@@ -393,9 +393,10 @@ class Trainer(TrainerIO):
 
             # whenever we have the correct number of tasks, we let slurm manage processes
             # otherwise we launch the required number of processes
+            nb_requested_gpus = len(self.data_parallel_device_ids) * self.nb_gpu_nodes
+            nb_slurm_tasks = 0
             try:
                 nb_slurm_tasks = int(os.environ['SLURM_NTASKS'])
-                nb_requested_gpus = len(self.data_parallel_device_ids) * self.nb_gpu_nodes
                 is_slurm_managing_tasks = nb_slurm_tasks == nb_requested_gpus
             except Exception as e:
                 # likely not on slurm, so set the slurm managed flag to false

setup.py

@@ -7,7 +7,7 @@ from setuptools import setup, find_packages
 # http://blog.ionelmc.ro/2014/05/25/python-packaging/
 setup(
     name="pytorch-lightning",
-    version='0.3.1',
+    version='0.3.2',
     description="The Keras for ML researchers using PyTorch",
     author="William Falcon",
     author_email="waf2107@columbia.edu",