dask-cuda

Dask-CUDA is a library extending LocalCluster from dask.distributed to enable multi-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.

from dask_cuda import LocalCUDACluster
from dask.distributed import Client

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

The Dask scheduler coordinates the execution of tasks, whereas the 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.

def get_gpu_model():
    import pynvml

    pynvml.nvmlInit()
    return pynvml.nvmlDeviceGetName(pynvml.nvmlDeviceGetHandleByIndex(0))


result = client.submit(get_gpu_model).result()

print(result)
# b'Tesla V100-SXM2-16GB