ImportError: cannot import name 'ParamSpec' from 'typing_extensions' when creating a cluster on Azure

dani · July 18, 2022, 12:55pm

How severe does this issue affect your experience of using Ray?

High: It blocks me to complete my task.

Hello.
I’m having a bit of trouble getting the cluster to work on Azure. The cluster is created successfully and I can attach to it and see the dashboard without any problems, however when I try to check the status or submit some code I always get the following error:

(base) ray@ray-default-head-bd9890040:~$ ray status
No cluster status.
The autoscaler failed with the following error:
Traceback (most recent call last):
  File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/monitor.py", line 483, in run
    self._initialize_autoscaler()
  File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/monitor.py", line 236, in _initialize_autoscaler
    prom_metrics=self.prom_metrics,
  File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py", line 212, in __init__
    self.reset(errors_fatal=True)
  File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py", line 973, in reset
    raise e
  File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py", line 918, in reset
    self.config["provider"], self.config["cluster_name"]
  File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/providers.py", line 241, in _get_node_provider
    provider_cls = _get_node_provider_cls(provider_config)
  File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/providers.py", line 217, in _get_node_provider_cls
    return importer(provider_config)
  File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/providers.py", line 40, in _import_azure
    from ray.autoscaler._private._azure.node_provider import AzureNodeProvider
  File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/_azure/node_provider.py", line 9, in <module>
    from azure.mgmt.network import NetworkManagementClient
  File "/home/ray/anaconda3/lib/python3.7/site-packages/azure/mgmt/network/__init__.py", line 9, in <module>
    from ._network_management_client import NetworkManagementClient
  File "/home/ray/anaconda3/lib/python3.7/site-packages/azure/mgmt/network/_network_management_client.py", line 20, in <module>
    from ._operations_mixin import NetworkManagementClientOperationsMixin
  File "/home/ray/anaconda3/lib/python3.7/site-packages/azure/mgmt/network/_operations_mixin.py", line 19, in <module>
    from azure.core.polling import LROPoller, NoPolling, PollingMethod
  File "/home/ray/anaconda3/lib/python3.7/site-packages/azure/core/polling/__init__.py", line 28, in <module>
    from ._poller import LROPoller, NoPolling, PollingMethod
  File "/home/ray/anaconda3/lib/python3.7/site-packages/azure/core/polling/_poller.py", line 37, in <module>
    from azure.core.tracing.decorator import distributed_trace
  File "/home/ray/anaconda3/lib/python3.7/site-packages/azure/core/tracing/decorator.py", line 31, in <module>
    from typing_extensions import ParamSpec
ImportError: cannot import name 'ParamSpec' from 'typing_extensions' (/home/ray/anaconda3/lib/python3.7/site-packages/typing_extensions.py)

And

(base) ray@ray-default-head-bd9890040:~$ python -c 'import ray; ray.init(address="auto")'
2022-07-18 05:39:39,390 WARNING worker.py:1382 -- The autoscaler failed with the following error:
Traceback (most recent call last):
  File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/monitor.py", line 483, in run
    self._initialize_autoscaler()
  File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/monitor.py", line 236, in _initialize_autoscaler
    prom_metrics=self.prom_metrics,
  File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py", line 212, in __init__
    self.reset(errors_fatal=True)
  File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py", line 973, in reset
    raise e
  File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/autoscaler.py", line 918, in reset
    self.config["provider"], self.config["cluster_name"]
  File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/providers.py", line 241, in _get_node_provider
    provider_cls = _get_node_provider_cls(provider_config)
  File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/providers.py", line 217, in _get_node_provider_cls
    return importer(provider_config)
  File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/providers.py", line 40, in _import_azure
    from ray.autoscaler._private._azure.node_provider import AzureNodeProvider
  File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/_azure/node_provider.py", line 9, in <module>
    from azure.mgmt.network import NetworkManagementClient
  File "/home/ray/anaconda3/lib/python3.7/site-packages/azure/mgmt/network/__init__.py", line 9, in <module>
    from ._network_management_client import NetworkManagementClient
  File "/home/ray/anaconda3/lib/python3.7/site-packages/azure/mgmt/network/_network_management_client.py", line 20, in <module>
    from ._operations_mixin import NetworkManagementClientOperationsMixin
  File "/home/ray/anaconda3/lib/python3.7/site-packages/azure/mgmt/network/_operations_mixin.py", line 19, in <module>
    from azure.core.polling import LROPoller, NoPolling, PollingMethod
  File "/home/ray/anaconda3/lib/python3.7/site-packages/azure/core/polling/__init__.py", line 28, in <module>
    from ._poller import LROPoller, NoPolling, PollingMethod
  File "/home/ray/anaconda3/lib/python3.7/site-packages/azure/core/polling/_poller.py", line 37, in <module>
    from azure.core.tracing.decorator import distributed_trace
  File "/home/ray/anaconda3/lib/python3.7/site-packages/azure/core/tracing/decorator.py", line 31, in <module>
    from typing_extensions import ParamSpec
ImportError: cannot import name 'ParamSpec' from 'typing_extensions' (/home/ray/anaconda3/lib/python3.7/site-packages/typing_extensions.py)

This is my config 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: 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: eastus2
    resource_group: RayCluster03
    # 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
                priority: Low

    ray.worker.default:
        # The minimum number of worker nodes of this type to launch.
        # This number should be >= 0.
        min_workers: 2
        # The maximum number of worker nodes of this type to launch.
        # This takes precedence over min_workers.
        max_workers: 4
        # 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: Low
                # 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-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

head_node: {}
worker_nodes: {}

Any ideas on how I can fix this?

Edit: I’m using ray version 1.13.0

cade · July 18, 2022, 5:07pm

Hi @dani. This looks like a version incompatibility with the typing_extensions dependency. I see a similar issue with the Azure SDK and system-installed typing_extensions here.

Could you run pip show typing-extensions and share the output?

cc @gramhagen who knows more about the Azure stack

gramhagen · July 18, 2022, 5:44pm

was this installed from pypi or from local source?

dani · July 18, 2022, 7:29pm

Hi @cade,
This is the output from pip show typing-extensions.

(base) ray@ray-default-head-bd9890040:~$ pip show typing-extensions
Name: typing-extensions
Version: 3.7.4.3
Summary: Backported and Experimental Type Hints for Python 3.5+
Home-page: https://github.com/python/typing/blob/master/typing_extensions/README.rst
Author: Guido van Rossum, Jukka Lehtosalo, Lukasz Langa, Michael Lee
Author-email: levkivskyi@gmail.com
License: PSF
Location: /home/ray/anaconda3/lib/python3.7/site-packages
Requires: 
Required-by: aiohttp, aioitertools, anyio, argon2-cffi, asgiref, async-timeout, azure-core, bokeh, cmd2, GitPython, h11, huggingface-hub, importlib-metadata, jax, jsonschema, kiwisolver, kopf, nevergrad, onnx, pydantic, pytorch-lightning, starlette, tensorflow, torch, torchmetrics, uvicorn, yarl

dani · July 18, 2022, 7:30pm

Hi @gramhagen,
I used pypi to install the Ray package.

Stefan-1313 · July 19, 2022, 1:47pm

More specifically, I know Dani used:

pip install ray==1.13.0 
pip install ray[tune]==1.13.0
pip install ray[rllib]==1.13.0

to install Ray.

gramhagen · July 19, 2022, 1:51pm

hmm, it seems like something could be out of sync with the conda env in the rayproject/ray-ml:latest-gpu image

if I use these values in my yaml file I do not see the errors. does this work for you? I’m not sure if reinstalling the azure packages makes the difference or if it’s ray or has to be both.

# 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"

# 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

cade · July 19, 2022, 5:02pm

@gramhagen what could cause the conda env in the rayprojcet/ray-ml:latest-gpu image to be out of sync?

cc @amogkam

dani · July 20, 2022, 9:39am

Yes the error is gone after adding these lines to the yaml file. Thank you!

gramhagen · July 20, 2022, 1:33pm

it seems like the version of azure packages in the docker image are more recent and could be leading to this issue with the type_extension package.

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ImportError: cannot import name 'ParamSpec' from 'typing_extensions' when creating a cluster on Azure

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