I am quite familiar with Dask distributed for CPUs. I'd like to explore a transition to running my code on GPU cores. When I submit a task to the LocalCUDACluster I get this error:

ValueError: tuple is not allowed for map key

This is my test case:

import cupy as cp
import numpy as np
from dask_cuda import LocalCUDACluster
from dask.distributed import Client

cluster = LocalCUDACluster()
c = Client(cluster)

def test_function(x):
    return(x+1)

sample_np = np.array([0,1])
sample_cp = cp.asarray(sample_np)

test_1 = test_function(sample_cp)
test_2 = c.submit(test_function,sample_cp)
test_2 = test_2.result()

test_1 output:

array([1, 2])

test_2 output:

distributed.protocol.core - CRITICAL - Failed to deserialize
.....
ValueError: tuple is not allowed for map key

How do I correctly distribute tasks on CUDA cores?

UPDATE:

I managed to get it working by first installing the Dask Distributed and Dask CUDA release.

However, I noticed that only 1 worker is available, but I have 600 CUDA cores. How do I distributed individual tasks on these 600 CUDA cores? I'd like to parallelize tasks on these 600 cores.

Versions:

dask 2.17.2
dask-cuda 0.13.0
cupy 7.5.0
cudf 0.13.0
msgpack-python 1.0.0