CUDA initialization

### System Info

```Shell
Hello everybody. I keep encountering the same issue: I use '1.12.1+cu102'and FastAI '2.7.9'.
I need to use the multiple GPUs in our server to train deeper networks with more images.
___
accelerate env

Traceback (most recent call last):
File "/home/andrea/anaconda3/bin/accelerate", line 8, in
sys.exit(main())
File "/home/andrea/anaconda3/lib/python3.9/site-packages/accelerate/commands/accelerate_cli.py", line 43, in main
args.func(args)
File "/home/andrea/anaconda3/lib/python3.9/site-packages/accelerate/commands/env.py", line 34, in env_command
accelerate_config = load_config_from_file(args.config_file).to_dict()
File "/home/andrea/anaconda3/lib/python3.9/site-packages/accelerate/commands/config/config_args.py", line 63, in load_config_from_file
return config_class.from_yaml_file(yaml_file=config_file)
File "/home/andrea/anaconda3/lib/python3.9/site-packages/accelerate/commands/config/config_args.py", line 116, in from_yaml_file
return cls(**config_dict)
TypeError: __init__() got an unexpected keyword argument 'command_file'
```

### Information

- [ ] The official example scripts
- [X] My own modified scripts

### Tasks

- [ ] One of the scripts in the examples/ folder of Accelerate or an officially supported `no_trainer` script in the `examples` folder of the `transformers` repo (such as `run_no_trainer_glue.py`)
- [X] My own task or dataset (give details below)

### Reproduction

Here is the script that I am using:
____________________________________
from fastai.vision.all import *
from fastai.distributed import *
from fastai.vision.models.xresnet import *

from accelerate import Accelerator
from accelerate.utils import set_seed
from timm import create_model
from accelerate import notebook_launcher

def get_msk(o):
return path_Rflbl+fr'/RfM_{o.stem}{o.suffix.lower()}___fuse{o.suffix.lower()}'

numeral_codes=[i for i in range(0,16)] #as I am labeling 16 categories in the data
print('numeral codes ', numeral_codes)
file = open(path+'/codes.txt', "w+")

# Saving the array in a text file
content = str(numeral_codes)
file.write(content)
file.close()

def train():
dls = SegmentationDataLoaders.from_label_func(
path, bs=8, fnames = get_image_files(path+'/Impng'),
label_func = get_msk,
codes = np.loadtxt(path+'/codes.txt', dtype=str)
)
learn = unet_learner(dls, resnet34)
with learn.distrib_ctx(in_notebook=True, sync_bn=False):
learn.fit(10)

notebook_launcher(train, num_processes=4)

________________
It all works until I use notebook launcher. then it comes up with:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Input In [46], in ()
19 with learn.distrib_ctx(in_notebook=True, sync_bn=False):
20 learn.fit(10)
---> 24 notebook_launcher(train, num_processes=4)

File ~/anaconda3/lib/python3.9/site-packages/accelerate/launchers.py:102, in notebook_launcher(function, args, num_processes, use_fp16, mixed_precision, use_port)
95 raise ValueError(
96 "To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized "
97 "inside your training function. Restart your notebook and make sure no cells initializes an "
98 "`Accelerator`."
99 )
101 if torch.cuda.is_initialized():
--> 102 raise ValueError(
103 "To launch a multi-GPU training from your notebook, you need to avoid running any instruction "
104 "using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA "
105 "function."
106 )
108 try:
109 mixed_precision = PrecisionType(mixed_precision.lower())

ValueError: To launch a multi-GPU training from your notebook, you need to avoid running any instruction using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA function.
____________
Yet, I have no CUDA instructions. And I need the notebook launcher in order to train on multiple GPUs (I would have 6).

Do you have any ideas?
Do I need to update some version of something?

### Expected behavior

```Shell
if, instead of my data, I use
path = untar_data(URLs.CAMVID_TINY)

I can train up to 4 GPUs, independently and also using xresnet50. The processes seem to run on 4 independent GPUs, but I am not sure yet that each is a chunk of the total and it tries to execute the calculation in parallel as intended (by me). For instance I am not sure that the memory it uses for the whole calculation is the sum of the GPUs memory.

Anyhow, could you please help me in executing this calculation on multiple GPUs?
```