Check Pytorch cuda context is valid across GPUs

[VibhuJawa]

Describe the bug

We have had multiple breakages of CUDA context being only used for GPU 0 in a dask+pytorch environment. Sometimes this can occur due to a library creating a cuda context with pytorch before starting the cluster.

What ends up happening is Pytorch models being deployed on GPU-0 and that issue is hard to debug .

[VibhuJawa]

I think a better fix is ensuring we dont fork context if its all ready present for local cuda cluster.

import cupy as cp
cp.cuda.runtime.getDeviceCount()

# import torch
# t = totch.as_tensor([1,2,3])

from dask_cuda import LocalCUDACluster
from distributed import Client 
from distributed.diagnostics.nvml import has_cuda_context
import time
def check_cuda_context():
    _warning_suffix = (
        "This is often the result of a CUDA-enabled library calling a CUDA runtime function before "
        "Dask-CUDA can spawn worker processes. Please make sure any such function calls don't happen "
        "at import time or in the global scope of a program."
    )
    if has_cuda_context().has_context:     
        # If no error was raised, the CUDA context is initialized
        raise RuntimeError(
            f"CUDA context is initialized before the dask-cuda cluster was spun up. {_warning_suffix}"
        )

if __name__ == "__main__":
    check_cuda_context()    
    cluster = LocalCUDACluster(rmm_async=True, rmm_pool_size="2GiB")
    client = Client(cluster)

CC: @ayushdg

[VibhuJawa]

Moving to next release as not a high priority