gpu-yarn.txt

## start cluster
REGION="us-east1"
CLUSTER_NAME="dask-rapids-test"
NUM_GPUS=2
NUM_WORKERS=2
gcloud dataproc clusters create $CLUSTER_NAME \
                --region $REGION     \
                --image-version=2.0.0-RC22-ubuntu18     \
                --master-machine-type n1-standard-16     \
                --num-workers $NUM_WORKERS     \
                --worker-accelerator type=nvidia-tesla-t4,count=$NUM_GPUS     \
                --worker-machine-type n1-highmem-32    \
                --num-worker-local-ssds 4     \
                --initialization-actions gs://goog-dataproc-initialization-actions-${REGION}/gpu/install_gpu_driver.sh,gs://goog-dataproc-initialization-actions-${REGION}/rapids/rapids.sh    \
                --optional-components=JUPYTER,ZEPPELIN     \
                --metadata gpu-driver-provider="NVIDIA"     \
                --metadata rapids-runtime=DASK    \
                --metadata cuda-version=11.0 \
                --metadata rapids-version=0.18 \
                --enable-component-gateway     \
                --properties="^#^spark:spark.yarn.unmanagedAM.enabled=false"

## Restart Yarn on Master
sudo systemctl restart hadoop-yarn-resourcemanager.service

## confirm setup on master node
yarn jar /usr/lib/hadoop-yarn/hadoop-yarn-applications-distributedshell.jar -jar /usr/lib/hadoop-yarn/hadoop-yarn-applications-distributedshell.jar -shell_command /usr/bin/nvidia-smi   -container_resources memory-mb=3072,vcores=1,yarn.io/gpu=2   -num_containers 2

yarn logs -applicationId application_1615261846070_0001

## ssh to master node and start skein
skein driver start

```
# gpu-yarn.yml
name: gpu-yarn
queue: default

services:
  dask.scheduler:
    instances: 1
    resources:
      memory: 4 GiB
      vcores: 1
      gpus: 1
    script: |
      echo "HELLO WORLD"
      hostname
      which python
      /usr/bin/nvidia-smi
      /opt/conda/bin/python -c 'import numba.cuda; print(numba.cuda.gpus)'
```
skein application submit gpu-yarn.yml