How severe does this issue affect your experience of using Ray?
- High: It blocks me to complete my task.
Hi!
I am having difficulty setting up Ray autoscaler with Azure. The cluster is created and the head node works fine but the worker nodes don’t seem to work. When I try to monitor the cluster with the following command ray monitor example-full.yaml I get the error:
==> /tmp/ray/session_latest/logs/monitor.err <==
==> /tmp/ray/session_latest/logs/monitor.log <==
2022-08-09 09:39:28,677 INFO monitor.py:198 -- Starting autoscaler metrics server on port 44217
2022-08-09 09:39:28,705 INFO monitor.py:215 -- Monitor: Started
2022-08-09 09:39:30,147 INFO environment.py:98 -- No environment configuration found.
2022-08-09 09:39:30,153 INFO managed_identity.py:85 -- ManagedIdentityCredential will use IMDS
2022-08-09 09:39:30,171 INFO autoscaler.py:282 -- StandardAutoscaler: {'cluster_name': 'default', 'max_workers': 2, 'upscaling_speed': 1.0, 'docker': {'image': 'rayproject/ray-ml:latest-gpu', 'container_name': 'ray_container', 'pull_before_run': True, 'run_options': ['--ulimit nofile=65536:65536']}, 'idle_timeout_minutes': 5, 'provider': {'type': 'azure', 'location': 'westeurope', 'resource_group': 'aicluster', 'subscription_id': '00000000-0000-0000-0000-000000000000'}, 'auth': {'ssh_user': 'ubuntu', 'ssh_private_key': '~/ray_bootstrap_key.pem', 'ssh_public_key': '~/.ssh/id_rsa.pub'}, 'available_node_types': {'ray.head.default': {'resources': {'CPU': 2}, 'node_config': {'azure_arm_parameters': {'vmSize': 'Standard_D2s_v3', 'imagePublisher': 'microsoft-dsvm', 'imageOffer': 'ubuntu-1804', 'imageSku': '1804-gen2', 'imageVersion': 'latest', 'adminUsername': 'ubuntu', 'publicKey': ''}}, 'min_workers': 0, 'max_workers': 0}, 'ray.worker.default': {'min_workers': 0, 'max_workers': 2, 'resources': {'CPU': 2}, 'node_config': {'azure_arm_parameters': {'vmSize': 'Standard_D2s_v3', 'imagePublisher': 'microsoft-dsvm', 'imageOffer': 'ubuntu-1804', 'imageSku': '1804-gen2', 'imageVersion': 'latest', 'priority': 'Spot', 'adminUsername': 'ubuntu', 'publicKey': }}}}, 'head_node_type': 'ray.head.default', 'file_mounts': {'~/.ssh/id_rsa.pub': '/home/ray/.ssh/id_rsa.pub'}, 'cluster_synced_files': [], 'file_mounts_sync_continuously': False, 'rsync_exclude': ['**/.git', '**/.git/**'], 'rsync_filter': ['.gitignore'], 'initialization_commands': ['sudo usermod -aG docker $USER || true', 'sleep 10', 'touch ~/.sudo_as_admin_successful'], 'setup_commands': ['pip install -U "ray[default] @ https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-2.0.0.dev0-cp37-cp37m-manylinux2014_x86_64.whl"'], 'head_setup_commands': ['pip install -U "ray[default] @ https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-2.0.0.dev0-cp37-cp37m-manylinux2014_x86_64.whl"', 'pip install -U azure-cli-core==2.22.0 azure-mgmt-compute==14.0.0 azure-mgmt-msi==1.0.0 azure-mgmt-network==10.2.0 azure-mgmt-resource==13.0.0'], 'worker_setup_commands': ['pip install -U "ray[default] @ https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-2.0.0.dev0-cp37-cp37m-manylinux2014_x86_64.whl"'], 'head_start_ray_commands': ['ray stop', 'ray start --head --port=6379 --object-manager-port=8076 --autoscaling-config=~/ray_bootstrap_config.yaml'], 'worker_start_ray_commands': ['ray stop', 'ray start --address=$RAY_HEAD_IP:6379 --object-manager-port=8076'], 'head_node': {}, 'worker_nodes': {}, 'no_restart': False}
2022-08-09 09:39:30,173 INFO monitor.py:354 -- Autoscaler has not yet received load metrics. Waiting.
2022-08-09 09:39:35,472 ERROR autoscaler.py:290 -- StandardAutoscaler: Error during autoscaling.
Traceback (most recent call last):
File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py", line 287, in update
self._update()
File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py", line 312, in _update
self.non_terminated_nodes = NonTerminatedNodes(self.provider)
File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py", line 101, in __init__
self.all_node_ids = provider.non_terminated_nodes({})
File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/_azure/node_provider.py", line 145, in non_terminated_nodes
nodes = self._get_filtered_nodes(tag_filters=tag_filters)
File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/_azure/node_provider.py", line 38, in wrapper
return f(self, *args, **kwargs)
File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/_azure/node_provider.py", line 83, in _get_filtered_nodes
nodes = [self._extract_metadata(vm) for vm in filter(match_tags, vms)]
File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/_azure/node_provider.py", line 83, in <listcomp>
nodes = [self._extract_metadata(vm) for vm in filter(match_tags, vms)]
File "/home/ray/anaconda3/lib/python3.7/site-packages/msrest/paging.py", line 143, in __next__
self.advance_page()
File "/home/ray/anaconda3/lib/python3.7/site-packages/msrest/paging.py", line 129, in advance_page
self._response = self._get_next(self.next_link)
File "/home/ray/anaconda3/lib/python3.7/site-packages/azure/mgmt/compute/v2020_06_01/operations/_virtual_machines_operations.py", line 917, in internal_paging
response = self._client.send(request, stream=False, **operation_config)
File "/home/ray/anaconda3/lib/python3.7/site-packages/msrest/service_client.py", line 336, in send
pipeline_response = self.config.pipeline.run(request, **kwargs)
File "/home/ray/anaconda3/lib/python3.7/site-packages/msrest/pipeline/__init__.py", line 197, in run
return first_node.send(pipeline_request, **kwargs) # type: ignore
File "/home/ray/anaconda3/lib/python3.7/site-packages/msrest/pipeline/__init__.py", line 150, in send
response = self.next.send(request, **kwargs)
File "/home/ray/anaconda3/lib/python3.7/site-packages/msrest/pipeline/requests.py", line 65, in send
self._creds.signed_session(session)
AttributeError: 'DefaultAzureCredential' object has no attribute 'signed_session'
2022-08-09 09:39:35,481 INFO monitor.py:369 -- :event_summary:Resized to 2 CPUs.
2022-08-09 09:39:40,502 ERROR autoscaler.py:290 -- StandardAutoscaler: Error during autoscaling.
Traceback (most recent call last):
File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py", line 287, in update
self._update()
File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py", line 312, in _update
self.non_terminated_nodes = NonTerminatedNodes(self.provider)
File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py", line 101, in __init__
self.all_node_ids = provider.non_terminated_nodes({})
File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/_azure/node_provider.py", line 145, in non_terminated_nodes
nodes = self._get_filtered_nodes(tag_filters=tag_filters)
File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/_azure/node_provider.py", line 38, in wrapper
return f(self, *args, **kwargs)
File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/_azure/node_provider.py", line 83, in _get_filtered_nodes
nodes = [self._extract_metadata(vm) for vm in filter(match_tags, vms)]
File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/_azure/node_provider.py", line 83, in <listcomp>
nodes = [self._extract_metadata(vm) for vm in filter(match_tags, vms)]
File "/home/ray/anaconda3/lib/python3.7/site-packages/msrest/paging.py", line 143, in __next__
self.advance_page()
File "/home/ray/anaconda3/lib/python3.7/site-packages/msrest/paging.py", line 129, in advance_page
self._response = self._get_next(self.next_link)
File "/home/ray/anaconda3/lib/python3.7/site-packages/azure/mgmt/compute/v2020_06_01/operations/_virtual_machines_operations.py", line 917, in internal_paging
response = self._client.send(request, stream=False, **operation_config)
File "/home/ray/anaconda3/lib/python3.7/site-packages/msrest/service_client.py", line 336, in send
pipeline_response = self.config.pipeline.run(request, **kwargs)
File "/home/ray/anaconda3/lib/python3.7/site-packages/msrest/pipeline/__init__.py", line 197, in run
return first_node.send(pipeline_request, **kwargs) # type: ignore
File "/home/ray/anaconda3/lib/python3.7/site-packages/msrest/pipeline/__init__.py", line 150, in send
response = self.next.send(request, **kwargs)
File "/home/ray/anaconda3/lib/python3.7/site-packages/msrest/pipeline/requests.py", line 65, in send
self._creds.signed_session(session)
AttributeError: 'DefaultAzureCredential' object has no attribute 'signed_session'
2022-08-09 09:39:45,532 ERROR autoscaler.py:290 -- StandardAutoscaler: Error during autoscaling.
Traceback (most recent call last):
File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py", line 287, in update
self._update()
File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py", line 312, in _update
self.non_terminated_nodes = NonTerminatedNodes(self.provider)
File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py", line 101, in __init__
self.all_node_ids = provider.non_terminated_nodes({})
File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/_azure/node_provider.py", line 145, in non_terminated_nodes
nodes = self._get_filtered_nodes(tag_filters=tag_filters)
File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/_azure/node_provider.py", line 38, in wrapper
return f(self, *args, **kwargs)
File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/_azure/node_provider.py", line 83, in _get_filtered_nodes
nodes = [self._extract_metadata(vm) for vm in filter(match_tags, vms)]
File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/_azure/node_provider.py", line 83, in <listcomp>
nodes = [self._extract_metadata(vm) for vm in filter(match_tags, vms)]
File "/home/ray/anaconda3/lib/python3.7/site-packages/msrest/paging.py", line 143, in __next__
self.advance_page()
File "/home/ray/anaconda3/lib/python3.7/site-packages/msrest/paging.py", line 129, in advance_page
self._response = self._get_next(self.next_link)
File "/home/ray/anaconda3/lib/python3.7/site-packages/azure/mgmt/compute/v2020_06_01/operations/_virtual_machines_operations.py", line 917, in internal_paging
response = self._client.send(request, stream=False, **operation_config)
File "/home/ray/anaconda3/lib/python3.7/site-packages/msrest/service_client.py", line 336, in send
pipeline_response = self.config.pipeline.run(request, **kwargs)
File "/home/ray/anaconda3/lib/python3.7/site-packages/msrest/pipeline/__init__.py", line 197, in run
return first_node.send(pipeline_request, **kwargs) # type: ignore
File "/home/ray/anaconda3/lib/python3.7/site-packages/msrest/pipeline/__init__.py", line 150, in send
response = self.next.send(request, **kwargs)
File "/home/ray/anaconda3/lib/python3.7/site-packages/msrest/pipeline/requests.py", line 65, in send
self._creds.signed_session(session)
AttributeError: 'DefaultAzureCredential' object has no attribute 'signed_session'
The config file that I am using is as follows:
# 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: 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 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: westeurope
resource_group: aicluster
# 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": 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: microsoft-dsvm
imageOffer: ubuntu-1804
imageSku: 1804-gen2
imageVersion: latest
ray.worker.default:
# The minimum number of worker nodes of this type to launch.
# This number should be >= 0.
min_workers: 0
# 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: {"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: microsoft-dsvm
imageOffer: ubuntu-1804
imageSku: 1804-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:
# enable docker setup
- sudo usermod -aG docker $USER || true
- sleep 10 # delay to avoid docker permission denied errors
# get rid of annoying Ubuntu message
- touch ~/.sudo_as_admin_successful
# List of shell commands to run to set up nodes.
# NOTE: rayproject/ray-ml:latest has ray latest bundled
setup_commands:
# Note: if you're developing Ray, you probably want to create a Docker image that
# has your Ray repo pre-cloned. Then, you can replace the pip installs
# below with a git checkout <your_sha> (and possibly a recompile).
# To run the nightly version of ray (as opposed to the latest), either use a rayproject docker image
# that has the "nightly" (e.g. "rayproject/ray-ml:nightly-gpu") or uncomment the following line:
- pip install -U "ray[default] @ https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-2.0.0.dev0-cp37-cp37m-manylinux2014_x86_64.whl"
# - pip install -U "ray[default] @ https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-3.0.0.dev0-cp38-cp38-manylinux2014_x86_64.whl"
# Custom commands that will be run on the head node after common setup.
# NOTE: rayproject/ray-ml:latest has azure packages bundled
head_setup_commands:
- pip install -U azure-cli-core==2.22.0 azure-mgmt-compute==14.0.0 azure-mgmt-msi==1.0.0 azure-mgmt-network==10.2.0 azure-mgmt-resource==13.0.0
# Custom commands that will be run on worker nodes after common setup.
worker_setup_commands: []
# Command to start ray on the head node. You don't need to change this.
head_start_ray_commands:
- ray stop
- ray start --head --port=6379 --object-manager-port=8076 --autoscaling-config=~/ray_bootstrap_config.yaml
# Command to start ray on worker nodes. You don't need to change this.
worker_start_ray_commands:
- ray stop
- ray start --address=$RAY_HEAD_IP:6379 --object-manager-port=8076
Does anyone know what can be wrong or fixed?
Thank you