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2026-07-13 13:17:40 +08:00

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YAML

# Example command to start a cluster with this config:
#
# ray start --autoscaling-config=default.yaml --head --block
#
cluster_name: spark
max_workers: 8
provider:
type: spark
# This must be true since the nodes share the same ip!
use_node_id_as_ip: True
disable_node_updaters: True
disable_launch_config_check: True
available_node_types:
ray.head.default:
# You must set this manually to your "head" node resources!! The head
# node is launched via `ray start` and hence the autoscaler cannot
# configure its resources. The resources specified for its node type
# must line up with what Ray detects/is configured with on start.
resources:
CPU: 8 # <-- set this to num CPUs used/detected in `ray start`
GPU: 0 # <-- set this to num GPUs used/detected in `ray start`
node_config: {}
max_workers: 0
ray.worker:
resources:
CPU: 1
object_store_memory: 1000000000
node_config: {}
min_workers: 0
max_workers: 4
head_node_type: ray.head.default
upscaling_speed: 1.0
idle_timeout_minutes: 1.0
#
# !!! Configurations below are not supported in spark cluster mode
#
auth: {}
docker: {}
initialization_commands: []
setup_commands: []
head_setup_commands: []
worker_setup_commands: []
head_start_ray_commands: []
worker_start_ray_commands: []
file_mounts: {}
cluster_synced_files: []
file_mounts_sync_continuously: false
rsync_exclude: []
rsync_filter: []