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cluster.yaml
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cluster.yaml
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# An unique identifier for the head node and workers of this cluster.
cluster_name: ray10cluster
## NOTE: Typically for local clusters, min_workers == max_workers == len(worker_ips).
# The minimum number of workers nodes to launch in addition to the head
# node. This number should be >= 0.
# Typically, min_workers == max_workers == len(worker_ips).
min_workers: 1
# The maximum number of workers nodes to launch in addition to the head node.
# This takes precedence over min_workers.
# Typically, min_workers == max_workers == len(worker_ips).
max_workers: 1
# 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
#idle_timeout_minutes: 5
# 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. Assumes Docker is installed.
#docker:
# image: "rayproject/ray-ml:latest-cpu" # You can change this to latest-cpu if you don't need GPU support and want a faster startup
# # image: rayproject/ray:latest-gpu # use this one if you don't need ML dependencies, it's faster to pull
# container_name: "ray_container"
# # If true, pulls latest version of image. Otherwise, `docker run` will only pull the image
# # if no cached version is present.
# pull_before_run: True
# run_options: [] # Extra options to pass into "docker run"
# Local specific configuration.
provider:
type: local
# head_ip: ray10.cs.upb.de
head_ip: ray09.cs.upb.de
# head_ip: fgcn-tango-4.cs.upb.de
worker_ips:
# - fgcn-tango-5.cs.upb.de
# - ray09.cs.upb.de
- ray08.cs.upb.de
# - ray07.cs.upb.de
# - ray04.cs.upb.de
# [WORKER_NODE_1_HOSTNAME, WORKER_NODE_2_HOSTNAME, ... ]
# Optional when running automatic cluster management on prem. If you use a coordinator server,
# then you can launch multiple autoscaling clusters on the same set of machines, and the coordinator
# will assign individual nodes to clusters as needed.
# coordinator_address: "<host>:<port>"
# How Ray will authenticate with newly launched nodes.
auth:
ssh_user: stefan
# Optional if an ssh private key is necessary to ssh to the cluster.
# ssh_private_key: ~/.ssh/id_ed25519
ssh_private_key: ~/.ssh/id_rsa
# Leave this empty.
head_node: {}
# Leave this empty.
worker_nodes: {}
# 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",
}
# Files or directories to copy from the head node to the worker nodes. The format is a
# list of paths. The same path on the head node will be copied to the worker node.
# This behavior is a subset of the file_mounts behavior. In the vast majority of cases
# you should just use file_mounts. Only use this if you know what you're doing!
cluster_synced_files: []
# Whether changes to directories in file_mounts or cluster_synced_files in the head node
# should sync to the worker node continuously
file_mounts_sync_continuously: False
# Patterns for files to exclude when running rsync up or rsync down
#rsync_exclude:
# - "**/.git"
# - "**/.git/**"
# Pattern files to use for filtering out files when running rsync up or rsync down. The file is searched for
# in the source directory and recursively through all subdirectories. For example, if .gitignore is provided
# as a value, the behavior will match git's behavior for finding and using .gitignore files.
#rsync_filter:
# - ".gitignore"
# List of commands that will be run before `setup_commands`. If docker is
# enabled, these commands will run outside the container and before docker
# is setup.
initialization_commands: []
# List of shell commands to run to set up each nodes.
setup_commands: []
# Note: if you're developing Ray, you probably want to create a Docker image that
# has your Ray repo pre-cloned. Then, you can replace the pip installs
# below with a git checkout <your_sha> (and possibly a recompile).
# Uncomment the following line if you want to run the nightly version of ray (as opposed to the latest)
# - pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-2.0.0.dev0-cp37-cp37m-manylinux2014_x86_64.whl
# Custom commands that will be run on the head node after common setup.
head_setup_commands: []
# 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 -c unlimited && ray start --head --port=6379 --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
- ray start --address=$RAY_HEAD_IP:6379