dask-cuda#

Dask-CUDA is a library extending Dask.distributed’s single-machine LocalCluster and Worker for use in distributed GPU workloads.

LocalCUDACluster#

You can use LocalCUDACluster to 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 and dask-cuda packages in your local environment

Cluster setup#

Instantiate a LocalCUDACluster object#

The LocalCUDACluster class autodetects the GPUs in your system, so if you create it on a machine with two GPUs it will create a cluster with two workers, each of which is responsible for executing tasks on a separate GPU.

cluster = LocalCUDACluster()

You can also restrict your cluster to use specific GPUs by setting the CUDA_VISIBLE_DEVICES environment variable, or as a keyword argument.

cluster = LocalCUDACluster(CUDA_VISIBLE_DEVICES="0,1")  # Creates one worker for GPUs 0 and 1

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.

client = Client(cluster)

Test RAPIDS#

To test RAPIDS, create a distributed client for the cluster and query for the GPU model.

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()