RuntimeError when training on multiple GPUs

Nobody has claimed this yet.

Hey! I'm trying to train on multiple GPUs and consistently getting the following RuntimeError. Here's the modified line in `tutorial_train.py`:

```
trainer = pl.Trainer(gpus=8, precision=32, callbacks=[logger])
```

As soon as I change `gpus` to `1`, training works fine. Anyone have ideas?

## The error when training on >1 GPU
```
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
/home/ubuntu/anaconda3/envs/control/lib/python3.8/site-packages/pytorch_lightning/trainer/configuration_validator.py:118: UserWarning: You defined a `validation_step` but have no `val_dataloader`. Skipping val loop.
rank_zero_warn("You defined a `validation_step` but have no `val_dataloader`. Skipping val loop.")
/home/ubuntu/anaconda3/envs/control/lib/python3.8/site-packages/pytorch_lightning/trainer/configuration_validator.py:280: LightningDeprecationWarning: Base `LightningModule.on_train_batch_start` hook signature has changed in v1.5. The `dataloader_idx` argument will be removed in v1.7.
rank_zero_deprecation(
/home/ubuntu/anaconda3/envs/control/lib/python3.8/site-packages/pytorch_lightning/trainer/configuration_validator.py:287: LightningDeprecationWarning: Base `Callback.on_train_batch_end` hook signature has changed in v1.5. The `dataloader_idx` argument will be removed in v1.7.
rank_zero_deprecation(
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]
Traceback (most recent call last):
File "", line 1, in
File "/home/ubuntu/anaconda3/envs/control/lib/python3.8/multiprocessing/spawn.py", line 116, in spawn_main
exitcode = _main(fd, parent_sentinel)
File "/home/ubuntu/anaconda3/envs/control/lib/python3.8/multiprocessing/spawn.py", line 125, in _main
prepare(preparation_data)
File "/home/ubuntu/anaconda3/envs/control/lib/python3.8/multiprocessing/spawn.py", line 236, in prepare
_fixup_main_from_path(data['init_main_from_path'])
File "/home/ubuntu/anaconda3/envs/control/lib/python3.8/multiprocessing/spawn.py", line 287, in _fixup_main_from_path
main_content = runpy.run_path(main_path,
File "/home/ubuntu/anaconda3/envs/control/lib/python3.8/runpy.py", line 265, in run_path
return _run_module_code(code, init_globals, run_name,
File "/home/ubuntu/anaconda3/envs/control/lib/python3.8/runpy.py", line 97, in _run_module_code
_run_code(code, mod_globals, init_globals,
File "/home/ubuntu/anaconda3/envs/control/lib/python3.8/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/home/ubuntu/ControlNet/tutorial_train.py", line 35, in
trainer.fit(model, dataloader)
File "/home/ubuntu/anaconda3/envs/control/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 735, in fit
self._call_and_handle_interrupt(
File "/home/ubuntu/anaconda3/envs/control/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 682, in _call_and_handle_interrupt
return trainer_fn(*args, **kwargs)
File "/home/ubuntu/anaconda3/envs/control/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 770, in _fit_impl
self._run(model, ckpt_path=ckpt_path)
File "/home/ubuntu/anaconda3/envs/control/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1193, in _run
self._dispatch()
File "/home/ubuntu/anaconda3/envs/control/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1272, in _dispatch
self.training_type_plugin.start_training(self)
File "/home/ubuntu/anaconda3/envs/control/lib/python3.8/site-packages/pytorch_lightning/plugins/training_type/ddp_spawn.py", line 173, in start_training
self.spawn(self.new_process, trainer, self.mp_queue, return_result=False)
File "/home/ubuntu/anaconda3/envs/control/lib/python3.8/site-packages/pytorch_lightning/plugins/training_type/ddp_spawn.py", line 201, in spawn
mp.spawn(self._wrapped_function, args=(function, args, kwargs, return_queue), nprocs=self.num_processes)
File "/home/ubuntu/.local/lib/python3.8/site-packages/torch/multiprocessing/spawn.py", line 240, in spawn
return start_processes(fn, args, nprocs, join, daemon, start_method='spawn')
File "/home/ubuntu/.local/lib/python3.8/site-packages/torch/multiprocessing/spawn.py", line 189, in start_processes
process.start()
File "/home/ubuntu/anaconda3/envs/control/lib/python3.8/multiprocessing/process.py", line 121, in start
self._popen = self._Popen(self)
File "/home/ubuntu/anaconda3/envs/control/lib/python3.8/multiprocessing/context.py", line 284, in _Popen
return Popen(process_obj)
File "/home/ubuntu/anaconda3/envs/control/lib/python3.8/multiprocessing/popen_spawn_posix.py", line 32, in __init__
super().__init__(process_obj)
File "/home/ubuntu/anaconda3/envs/control/lib/python3.8/multiprocessing/popen_fork.py", line 19, in __init__
self._launch(process_obj)
File "/home/ubuntu/anaconda3/envs/control/lib/python3.8/multiprocessing/popen_spawn_posix.py", line 42, in _launch
prep_data = spawn.get_preparation_data(process_obj._name)
File "/home/ubuntu/anaconda3/envs/control/lib/python3.8/multiprocessing/spawn.py", line 154, in get_preparation_data
_check_not_importing_main()
File "/home/ubuntu/anaconda3/envs/control/lib/python3.8/multiprocessing/spawn.py", line 134, in _check_not_importing_main
raise RuntimeError('''
RuntimeError:
An attempt has been made to start a new process before the
current process has finished its bootstrapping phase.

This probably means that you are not using fork to start your
child processes and you have forgotten to use the proper idiom
in the main module:

if __name__ == '__main__':
freeze_support()
...

The "freeze_support()" line can be omitted if the program
is not going to be frozen to produce an executable.
```

## What I've tried
- clean-install conda environment
- with/without triton, xformers

Start in tutorial_train.py at the trainer.fit(model, dataloader) entry point and compare the single-GPU and multi-GPU execution paths shown in the traceback. Run the tutorial with multiple GPUs after addressing the process-startup failure, and consider the issue done when training begins without the reported RuntimeError.

Written by the indexing model from the issue text.