add docs for localcudacluster

source/guides/cuda/localcluster.md

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+# LocalCUDACluster
+Create a cluster of one or more GPUs on your local machine. You can launch a Dask scheduler on LocalCUDACluster to parallelize and distribute your RAPIDS workflows across multiple GPUs on a single node.
+
+In addition to enabling multi-GPU computation, `LocalCUDACluster` also provides a simple interface for managing the cluster, such as starting and stopping the cluster, querying the status of the nodes, and monitoring the workload distribution.
+
+
+## Pre-requisites
+Before running these instructions, ensure you have installed the [`dask`](https://docs.dask.org/en/stable/install.html) and [`dask_cuda`](https://docs.rapids.ai/api/dask-cuda/nightly/install.html) packages in your local environment 
+
+
+## Cluster setup
+
+### Instantiate a LocalCUDACluster object. 
+In this example, we create a cluster with two workers, each of which is responsible for executing tasks on a separate GPU.
+
+```console
+cluster = LocalCUDACluster(n_workers=2)
+```
+
+### Connecting a Dask client
+Dask scheduler coordinates the execution of tasks, whereas Dask client is the user-facing interface that submits tasks to the scheduler and monitors their progress.
+
+```console
+client = Client(cluster)
+```
+
+## Test RAPIDS
+
+To test RAPIDS, create a distributed client for the cluster and query for the GPU model.
+
+```Python
+from dask_cuda import LocalCUDACluster
+from dask.distributed import Client
+
+cluster = LocalCUDACluster()
+client = Client(cluster)
+
+def get_gpu_model():
+    import pynvml
+
+    pynvml.nvmlInit()
+    return pynvml.nvmlDeviceGetName(pynvml.nvmlDeviceGetHandleByIndex(0))
+
+
+client.submit(get_gpu_model).result()
+```
+
+## Clean up
+Be sure to shut down and clean LocalCUDACluster when you are done:
+
+```console
+client.close()
+cluster.close()
+```
+