wehub/ray-project--ray
1
0
Fork 0
Code Issues Pull Requests Actions Deployments Models Agent Notes Packages Projects Releases Wiki Activity

chore: import upstream snapshot with attribution

Browse Source
This commit is contained in:
wehub-resource-sync
2026-07-13 13:17:40 +08:00
commit f1825c8ceb
10096 changed files with 2364182 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files
python/ray/tests/additional_property.yaml
+29
View File
@@ -0,0 +1,29 @@
# An unique identifier for the head node and workers of this cluster.
cluster_name: minimal
hi: 1
# The maximum number of workers nodes to launch in addition to the head
# node. This takes precedence over min_workers. min_workers default to 0.
min_workers: 0
max_workers: 0
provider:
type: aws
region: us-west-2
availability_zone: us-west-2b
# How Ray will authenticate with newly launched nodes.
auth:
ssh_user: ubuntu
head_node:
InstanceType: m4.10xlarge
ImageId: ami-3b6bce43 # Amazon Deep Learning AMI (Ubuntu)
setup_commands:
- error me
# - echo 'export PATH="$HOME/anaconda3/envs/tensorflow_p36/bin:$PATH"' >> ~/.bashrc
# # Command to start ray on the head node. You don't need to change this.
head_start_ray_commands:
- ray stop
- ray start --head --port=6379 --object-manager-port=8076 --autoscaling-config=~/ray_bootstrap_config.yaml