wehub-resource-sync
137 lines
6.3 KiB
YAML
137 lines
6.3 KiB
YAML
# 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: p2.xlarge
|
|
# 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:
|
|
# Override global docker setting.
|
|
# This node type will run a CPU image,
|
|
# rather than the GPU image specified in the global docker settings.
|
|
docker:
|
|
worker_image: "rayproject/ray-ml:latest-cpu"
|
|
# 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: m5.large
|
|
# 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
|