GPU clusters

GPU clusters

Many cloud providers have GPU offerings and so it is possible to launch GPU enabled Dask clusters with Dask Cloudprovider.

Each cluster manager handles this differently but generally you will need to configure the following settings:

  • Configure the hardware to include GPUs. This may be by changing the hardware type or adding accelerators.

  • Ensure the OS/Docker image has the NVIDIA drivers. For Docker images it is recommended to use the [RAPIDS images](https://hub.docker.com/r/rapidsai/rapidsai/).

  • Set the worker_module config option to dask_cuda.cli.dask_cuda_worker or worker_command option to dask-cuda-worker.

In the following AWS dask_cloudprovider.aws.EC2Cluster example we set the ami to be a Deep Learning AMI with NVIDIA drivers, the docker_image to RAPIDS, the instance_type to p3.2xlarge which has one NVIDIA Tesla V100 and the worker_module to dask_cuda.cli.dask_cuda_worker.

>>> cluster = EC2Cluster(ami="ami-0c7c7d78f752f8f17",  # Example Deep Learning AMI (Ubuntu 18.04)
                         docker_image="rapidsai/rapidsai:cuda10.1-runtime-ubuntu18.04",
                         instance_type="p3.2xlarge",
                         worker_module="dask_cuda.cli.dask_cuda_worker",
                         bootstrap=False,
                         filesystem_size=120)

See each cluster manager’s example sections for info on starting a GPU cluster.

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