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July 27, 2023 18:16
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# An unique identifier for the head node and workers of this cluster. | |
cluster_name: gpu-docker | |
# The maximum number of workers nodes to launch in addition to the head | |
# node. | |
max_workers: 2 | |
# The autoscaler will scale up the cluster faster with higher upscaling speed. | |
# E.g., if the task requires adding more nodes then autoscaler will gradually | |
# scale up the cluster in chunks of upscaling_speed*currently_running_nodes. | |
# This number should be > 0. | |
upscaling_speed: 1.0 | |
# This executes all commands on all nodes in the docker container, | |
# and opens all the necessary ports to support the Ray cluster. | |
# Empty string means disabled. | |
docker: | |
image: "rayproject/ray-ml:latest-gpu" | |
# image: rayproject/ray:latest-gpu # use this one if you don't need ML dependencies, it's faster to pull | |
container_name: "ray_nvidia_docker" # e.g. ray_docker | |
# If a node is idle for this many minutes, it will be removed. | |
idle_timeout_minutes: 5 | |
# Cloud-provider specific configuration. | |
provider: | |
type: aws | |
region: us-west-2 | |
# Availability zone(s), comma-separated, that nodes may be launched in. | |
# Nodes will be launched in the first listed availability zone and will | |
# be tried in the subsequent availability zones if launching fails. | |
availability_zone: us-west-2a,us-west-2b | |
# How Ray will authenticate with newly launched nodes. | |
auth: | |
ssh_user: ubuntu | |
# By default Ray creates a new private keypair, but you can also use your own. | |
# If you do so, make sure to also set "KeyName" in the head and worker node | |
# configurations below. | |
# ssh_private_key: /path/to/your/key.pem | |
# Tell the autoscaler the allowed node types and the resources they provide. | |
# The key is the name of the node type, which is just for debugging purposes. | |
# The node config specifies the launch config and physical instance type. | |
available_node_types: | |
# GPU head node. | |
ray.head.gpu: | |
# worker_image: rayproject/ray:latest-gpu # use this one if you don't need ML dependencies, it's faster to pull | |
# The node type's CPU and GPU resources are auto-detected based on AWS instance type. | |
# If desired, you can override the autodetected CPU and GPU resources advertised to the autoscaler. | |
# You can also set custom resources. | |
# For example, to mark a node type as having 1 CPU, 1 GPU, and 5 units of a resource called "custom", set | |
# resources: {"CPU": 1, "GPU": 1, "custom": 5} | |
resources: {} | |
# Provider-specific config for this node type, e.g. instance type. By default | |
# Ray will auto-configure unspecified fields such as SubnetId and KeyName. | |
# For more documentation on available fields, see: | |
# http://boto3.readthedocs.io/en/latest/reference/services/ec2.html#EC2.ServiceResource.create_instances | |
node_config: | |
InstanceType: p3.8xlarge | |
# Default AMI. Uncomment to use a different AMI. | |
# ImageId: | |
# You can provision additional disk space with a conf as follows | |
BlockDeviceMappings: | |
- DeviceName: /dev/sda1 | |
Ebs: | |
VolumeSize: 140 | |
# Additional options in the boto docs. | |
# CPU workers. | |
ray.worker.default: | |
# The minimum number of nodes of this type to launch. | |
# This number should be >= 0. | |
min_workers: 1 | |
# The maximum number of workers nodes of this type to launch. | |
# This takes precedence over min_workers. | |
max_workers: 2 | |
# The node type's CPU and GPU resources are auto-detected based on AWS instance type. | |
# If desired, you can override the autodetected CPU and GPU resources advertised to the autoscaler. | |
# You can also set custom resources. | |
# For example, to mark a node type as having 1 CPU, 1 GPU, and 5 units of a resource called "custom", set | |
# resources: {"CPU": 1, "GPU": 1, "custom": 5} | |
resources: {} | |
# Provider-specific config for this node type, e.g. instance type. By default | |
# Ray will auto-configure unspecified fields such as SubnetId and KeyName. | |
# For more documentation on available fields, see: | |
# http://boto3.readthedocs.io/en/latest/reference/services/ec2.html#EC2.ServiceResource.create_instances | |
node_config: | |
InstanceType: p3.8xlarge | |
# Default AMI. Uncomment to use a different AMI. | |
# ImageId: | |
# Run workers on spot by default. Comment this out to use on-demand. | |
InstanceMarketOptions: | |
MarketType: spot | |
# Additional options can be found in the boto docs, e.g. | |
# SpotOptions: | |
# MaxPrice: MAX_HOURLY_PRICE | |
# Additional options in the boto docs. | |
# Specify the node type of the head node (as configured above). | |
head_node_type: ray.head.gpu | |
# Files or directories to copy to the head and worker nodes. The format is a | |
# dictionary from REMOTE_PATH: LOCAL_PATH, e.g. | |
file_mounts: { | |
# "/path1/on/remote/machine": "/path1/on/local/machine", | |
# "/path2/on/remote/machine": "/path2/on/local/machine", | |
} | |
# List of shell commands to run to set up nodes. | |
# NOTE: rayproject/ray:latest has ray latest bundled | |
setup_commands: [] | |
# - pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-3.0.0.dev0-cp37-cp37m-manylinux2014_x86_64.whl | |
# - pip install -U "ray[default] @ https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-3.0.0.dev0-cp37-cp37m-manylinux2014_x86_64.whl" | |
# Custom commands that will be run on the head node after common setup. | |
head_setup_commands: | |
- pip install boto3>=1.4.8 # 1.4.8 adds InstanceMarketOptions | |
# Custom commands that will be run on worker nodes after common setup. | |
worker_setup_commands: [] | |
# Command to start ray on the head node. You don't need to change this. | |
head_start_ray_commands: | |
- ray stop | |
- ulimit -n 65536; ray start --head --port=6379 --object-manager-port=8076 --autoscaling-config=~/ray_bootstrap_config.yaml | |
# Command to start ray on worker nodes. You don't need to change this. | |
worker_start_ray_commands: | |
- ray stop | |
- ulimit -n 65536; ray start --address=$RAY_HEAD_IP:6379 --object-manager-port=8076 |
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