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_moduleconfig option to
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
p3.2xlarge which has one NVIDIA Tesla V100 and the
>>> 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.