How severe does this issue affect your experience of using Ray?
- Low: It annoys or frustrates me for a moment.
I am running a cluster using microsoft Azure using this yaml file:
# An unique identifier for the head node and workers of this cluster.
cluster_name: default
# The maximum number of workers nodes to launch in addition to the head
# node.
max_workers: 4
# 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 object means disabled.
docker:
image: "rayproject/ray-ml:latest-gpu" # 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"
- --ulimit nofile=65536:65536
# Example of running a GPU head with CPU workers
# head_image: "rayproject/ray-ml:latest-gpu"
# Allow Ray to automatically detect GPUs
# worker_image: "rayproject/ray-ml:latest-cpu"
# worker_run_options: []
# If a node is idle for this many minutes, it will be removed.
idle_timeout_minutes: 5
# Cloud-provider specific configuration.
provider:
type: azure
# https://azure.microsoft.com/en-us/global-infrastructure/locations
location: eastasia
resource_group: ray-cluster
# set subscription id otherwise the default from az cli will be used
# subscription_id: 00000000-0000-0000-0000-000000000000
# How Ray will authenticate with newly launched nodes.
auth:
ssh_user: ubuntu
# you must specify paths to matching private and public key pair files
# use `ssh-keygen -t rsa -b 4096` to generate a new ssh key pair
ssh_private_key: ~/.ssh/id_rsa
# changes to this should match what is specified in file_mounts
ssh_public_key: ~/.ssh/id_rsa.pub
# More specific customization to node configurations can be made using the ARM template azure-vm-template.json file
# See documentation here: https://docs.microsoft.com/en-us/azure/templates/microsoft.compute/2019-03-01/virtualmachines
# Changes to the local file will be used during deployment of the head node, however worker nodes deployment occurs
# on the head node, so changes to the template must be included in the wheel file used in setup_commands section below
# 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:
ray.head.default:
# The resources provided by this node type.
resources: {"CPU": 1}
# Provider-specific config, e.g. instance type.
node_config:
azure_arm_parameters:
vmSize: Standard_D11_v2
# List images https://docs.microsoft.com/en-us/azure/virtual-machines/linux/cli-ps-findimage
imagePublisher: Canonical
imageOffer: 0001-com-ubuntu-server-focal
imageSku: 20_04-lts
imageVersion: latest
#ray.head.default:
## The resources provided by this node type.
#resources: {"CPU": 2}
## Provider-specific config, e.g. instance type.
#node_config:
#azure_arm_parameters:
#vmSize: Standard_D2s_v3
## List images https://docs.microsoft.com/en-us/azure/virtual-machines/linux/cli-ps-findimage
#imagePublisher: Canonical
#imageOffer: 0001-com-ubuntu-server-focal
#imageSku: 20_4-lts-gen2
#imageVersion: latest
ray.worker.default:
# The minimum number of worker nodes of this type to launch.
# This number should be >= 0.
min_workers: 1
# The maximum number of worker nodes of this type to launch.
# This takes precedence over min_workers.
max_workers: 2
# The resources provided by this node type.
resources: {"GPU": 1}
# Provider-specific config, e.g. instance type.
node_config:
azure_arm_parameters:
vmSize: Standard_NC12s_v3
# List images https://docs.microsoft.com/en-us/azure/virtual-machines/linux/cli-ps-findimage
imagePublisher: Canonical
imageOffer: 0001-com-ubuntu-server-focal
imageSku: 20_04-lts-gen2
imageVersion: latest
# optionally set priority to use Spot instances
#priority: Spot
# set a maximum price for spot instances if desired
# billingProfile:
# maxPrice: -1
ray.worker.default1:
# The minimum number of worker nodes of this type to launch.
# This number should be >= 0.
min_workers: 1
# The maximum number of worker nodes of this type to launch.
# This takes precedence over min_workers.
max_workers: 2
# The resources provided by this node type.
resources: {"GPU": 1}
# Provider-specific config, e.g. instance type.
node_config:
azure_arm_parameters:
vmSize: Standard_NC6s_v3
# List images https://docs.microsoft.com/en-us/azure/virtual-machines/linux/cli-ps-findimage
imagePublisher: Canonical
imageOffer: 0001-com-ubuntu-server-focal
imageSku: 20_4-lts-gen2
imageVersion: latest
# optionally set priority to use Spot instances
#priority: Spot
# set a maximum price for spot instances if desired
# billingProfile:
# maxPrice: -1
# Specify the node type of the head node (as configured above).
head_node_type: ray.head.default
# 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",
"~/.ssh/id_rsa.pub": "~/.ssh/id_rsa.pub"
}
# 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:
- curl -fsSL https://get.docker.com -o get-docker.sh
- sudo sh get-docker.sh
- sudo usermod -aG docker $USER
- sudo systemctl restart docker -f
# enable docker setup
- sudo usermod -aG docker $USER || true
- sleep 10 # delay to avoid docker permission denied errors
# get rid of annoying Ubuntu message
- touch ~/.sudo_as_admin_successful
#- sudo apt update
#- sudo apt upgrade -y
#- sudo apt -y install python3-pip
#- pip install protobuf==3.20.*
#- pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113
#- pip install pandas
#- pip install wandb
#- pip install hyperopt
#- sleep 10
# List of shell commands to run to set up nodes.
# NOTE: rayproject/ray-ml:latest has ray latest bundled
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).
# To run the nightly version of ray (as opposed to the latest), either use a rayproject docker image
# that has the "nightly" (e.g. "rayproject/ray-ml:nightly-gpu") or uncomment the following line:
# - pip install -U "ray[default] @ https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-3.0.0.dev0-cp38-cp38-manylinux2014_x86_64.whl"
sudo apt update,
sudo apt upgrade -y,
sudo apt -y install python3-pip,
pip install protobuf==3.20.*,
pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113,
pip install pandas,
pip install wandb,
pip install hyperopt,
sleep 10]
# Custom commands that will be run on the head node after common setup.
# NOTE: rayproject/ray-ml:latest has azure packages bundled
head_setup_commands: [conda install -c conda-forge typing_extensions -y]
# - pip install -U azure-cli-core==2.22.0 azure-mgmt-compute==14.0.0 azure-mgmt-msi==1.0.0 azure-mgmt-network==10.2.0 azure-mgmt-resource==13.0.0
# 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
- 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
- ray start --address=$RAY_HEAD_IP:6379 --object-manager-port=8076
head_node: {}
worker_nodes: {}
The cluster however only consists of the head node and no worker nodes. Running ray status gives me this
(base) ray@ray-default-head-3237b1190:~$ ray status --address 10.221.0.6:62345
======== Autoscaler status: 2022-06-28 01:33:00.726622 ========
Node status
---------------------------------------------------------------
Healthy:
1 node_91a518bfa2356bc6fd9082ced8b2b19e8828f1b4ab3c0311ec979554
Pending:
(no pending nodes)
Recent failures:
(no failures)
Resources
---------------------------------------------------------------
Usage:
2.0/2.0 CPU (2.0 used of 2.0 reserved in placement groups)
0.00/7.289 GiB memory
0.00/3.644 GiB object_store_memory
Demands:
{'CPU': 1.0} * 1 (PACK): 1+ pending placement groups
(base) ray@ray-default-head-3237b1190:~$ ray status --address 10.221.0.6:6379
======== Autoscaler status: 2022-06-28 01:39:02.525790 ========
Node status
---------------------------------------------------------------
Healthy:
1 ray.head.default
Pending:
ray.worker.default, 1 launching
ray.worker.default1, 1 launching
Recent failures:
(no failures)
Resources
---------------------------------------------------------------
Usage:
0.0/1.0 CPU
0.00/7.776 GiB memory
0.00/3.888 GiB object_store_memory
Demands:
(no resource demands)
As you can see, no errors so I’m unsure of what to do now.
