Ray Core: Worker that was launched by an autoscaling_config loops and crashes

What happened + What you expected to happen

I have the following deploy yaml:

# A unique identifier for the head node and workers of this cluster.
cluster_name: default

# Running Ray in Docker images is optional (this docker section can be commented out).
# This executes all commands on all nodes in the docker container,
# and opens all the necessary ports to support the Ray cluster.
# Empty string means disabled. Assumes Docker is installed.
docker:
    image: "rayproject/ray:latest-py39-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_latest"
    # 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"
        - --gpus 1
        - --ulimit nofile=65536:65536

provider:
    type: local
    head_ip: "129.146.99.48"
    # You may need to supply a public ip for the head node if you need
    # to run `ray up` from outside of the Ray cluster's network
    # (e.g. the cluster is in an AWS VPC and you're starting ray from your laptop)
    # This is useful when debugging the local node provider with cloud VMs.
    # external_head_ip: YOUR_HEAD_PUBLIC_IP
    worker_ips: ["129.146.162.248"]
    # Optional when running automatic cluster management on prem. If you use a coordinator server,
    # then you can launch multiple autoscaling clusters on the same set of machines, and the coordinator
    # will assign individual nodes to clusters as needed.
    #    coordinator_address: "<host>:<port>"

# How Ray will authenticate with newly launched nodes.
auth:
    ssh_user: ubuntu
    # You can comment out `ssh_private_key` if the following machines don't need a private key for SSH access to the Ray
    # cluster:
    #   (1) The machine on which `ray up` is executed.
    #   (2) The head node of the Ray cluster.
    #
    # The machine that runs ray up executes SSH commands to set up the Ray head node. The Ray head node subsequently
    # executes SSH commands to set up the Ray worker nodes. When you run ray up, ssh credentials sitting on the ray up
    # machine are copied to the head node -- internally, the ssh key is added to the list of file mounts to rsync to head node.
    ssh_private_key: ./kaiber_private
# The minimum number of workers nodes to launch in addition to the head
# node. This number should be >= 0.
# Typically, min_workers == max_workers == len(worker_ips).
# This field is optional.
min_workers: 1

# The maximum number of workers nodes to launch in addition to the head node.
# This takes precedence over min_workers.
# Typically, min_workers == max_workers == len(worker_ips).
# This field is optional.
max_workers: 1
# The default behavior for manually managed clusters is
# min_workers == max_workers == len(worker_ips),
# meaning that Ray is started on all available nodes of the cluster.
# For automatically managed clusters, max_workers is required and min_workers defaults to 0.

# 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

idle_timeout_minutes: 5

# Files or directories to copy to the head and worker nodes. The format is a
# dictionary from REMOTE_PATH: LOCAL_PATH. E.g. you could save your conda env to an environment.yaml file, mount
# that directory to all nodes and call `conda -n my_env -f /path1/on/remote/machine/environment.yaml`. In this
# example paths on all nodes must be the same (so that conda can be called always with the same argument)
file_mounts: {
   "~/deforum-stable-diffusion": "~/deforum-stable-diffusion",
   "~/stable-diffusion-webui": "~/stable-diffusion-webui"
}

# 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:
    - sudo usermod -aG docker $USER; sleep 10
    # - wget -nv -O- https://lambdalabs.com/install-lambda-stack.sh | I_AGREE_TO_THE_CUDNN_LICENSE=1 sh -

# List of shell commands to run to set up each nodes.
setup_commands: 
    - ls
    - conda install -y -c conda-forge ffmpeg
    - cd deforum-stable-diffusion; pip install -r requirements.txt
    - sudo apt-get update
    - sudo apt-get install -y libgl1 libglib2.0-0 libsm6 libxrender1 libxext6
    - cd stable-diffusion-webui/models/Stable-diffusion; wget -nc https://oksami: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@huggingface.co/prompthero/openjourney/resolve/main/mdjrny-v4.ckpt
    - cd stable-diffusion-webui/models/Stable-diffusion; wget -N https://oksami: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@huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.ckpt -O mdjrny-v4.vae.pt
    - cd stable-diffusion-webui; mkdir -p embeddings;
    - cd deforum-stable-diffusion; mkdir -p init_images;
    - cd deforum-stable-diffusion; mkdir -p output;
    - cd deforum-stable-diffusion; pip install torch==1.12.1+cu116 torchvision==0.13.1+cu116 --extra-index-url https://download.pytorch.org/whl/cu116 --force-reinstall
    - cd deforum-stable-diffusion; pip install triton==2.0.0.dev20220701; wget -nc https://github.com/TheLastBen/fast-stable-diffusion/raw/main/precompiled/A100/xformers-0.0.13.dev0-py3-none-any.whl
    - cd deforum-stable-diffusion; pip install ./xformers-0.0.13.dev0-py3-none-any.whl
    # If we have e.g. conda dependencies stored in "/path1/on/local/machine/environment.yaml", we can prepare the
    # work environment on each worker by:
    #   1. making sure each worker has access to this file i.e. see the `file_mounts` section
    #   2. adding a command here that creates a new conda environment on each node or if the environment already exists,
    #     it updates it:
    #      conda env create -q -n my_venv -f /path1/on/local/machine/environment.yaml || conda env update -q -n my_venv -f /path1/on/local/machine/environment.yaml
    #
    # Ray developers:
    # 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-cp37-cp37m-manylinux2014_x86_64.whl"

# Custom commands that will be run on the head node after common setup.
head_setup_commands: []

# Custom commands that will be run on worker nodes after common setup.
worker_setup_commands: [
    'cd stable-diffusion-webui; nohup python launch.py --api &'
]

# Command to start ray on the head node. You don't need to change this.
head_start_ray_commands:
  # If we have e.g. conda dependencies, we could create on each node a conda environment (see `setup_commands` section).
  # In that case we'd have to activate that env on each node before running `ray`:
  # - conda activate my_venv && ray stop
  # - conda activate my_venv && ulimit -c unlimited && ray start --head --port=6379 --autoscaling-config=~/ray_bootstrap_config.yaml
    - ray stop
    - ulimit -c unlimited && ray start --head --port=6379 --autoscaling-config=~/ray_bootstrap_config.yaml --include-dashboard=true
    - cd deforum-stable-diffusion; pkill gunicorn; nohup gunicorn main:app -w 4 -k uvicorn.workers.UvicornWorker --bind 0.0.0.0:8000 & 
    - cd stable-diffusion-webui; nohup python launch.py --api & 

# Command to start ray on worker nodes. You don't need to change this.
worker_start_ray_commands:
  # If we have e.g. conda dependencies, we could create on each node a conda environment (see `setup_commands` section).
  # In that case we'd have to activate that env on each node before running `ray`:
  # - conda activate my_venv && ray stop
  # - ray start --address=$RAY_HEAD_IP:6379
    - ray stop
    - ray start --address=$RAY_HEAD_IP:6379

When I run this, or if I manually start the head with the autoscaling_config, the worker will continue to crash loop with the raylet error:

*** SIGSEGV received at time=1671481698 on cpu 0 ***
PC: @     0x559d299b6bcf  (unknown)  boost::asio::detail::epoll_reactor::start_op()
    @     0x7fa2ef391420       3184  (unknown)
    @     0x559d299b85b3         64  boost::asio::detail::reactive_socket_service_base::start_accept_op()
    @     0x559d2930ed60        112  plasma::PlasmaStore::DoAccept()
    @     0x559d2930eaca        368  boost::asio::detail::reactive_socket_accept_op<>::do_complete()
    @     0x559d299babcb        128  boost::asio::detail::scheduler::do_run_one()
    @     0x559d299bc391        192  boost::asio::detail::scheduler::run()
    @     0x559d299bc5c0         64  boost::asio::io_context::run()
    @     0x559d2930b218        384  plasma::PlasmaStoreRunner::Start()
    @     0x559d292a6375        208  std::thread::_State_impl<>::_M_run()
    @     0x559d29a0b2d0  (unknown)  execute_native_thread_routine
    @ ... and at least 3 more frames
[2022-12-19 12:28:18,761 E 5877 5901] (raylet) logging.cc:361: *** SIGSEGV received at time=1671481698 on cpu 0 ***
[2022-12-19 12:28:18,761 E 5877 5901] (raylet) logging.cc:361: PC: @     0x559d299b6bcf  (unknown)  boost::asio::detail::epoll_reactor::start_op()
[2022-12-19 12:28:18,761 E 5877 5901] (raylet) logging.cc:361:     @     0x7fa2ef391420       3184  (unknown)
[2022-12-19 12:28:18,761 E 5877 5901] (raylet) logging.cc:361:     @     0x559d299b85b3         64  boost::asio::detail::reactive_socket_service_base::start_accept_op()
[2022-12-19 12:28:18,761 E 5877 5901] (raylet) logging.cc:361:     @     0x559d2930ed60        112  plasma::PlasmaStore::DoAccept()
[2022-12-19 12:28:18,761 E 5877 5901] (raylet) logging.cc:361:     @     0x559d2930eaca        368  boost::asio::detail::reactive_socket_accept_op<>::do_complete()
[2022-12-19 12:28:18,761 E 5877 5901] (raylet) logging.cc:361:     @     0x559d299babcb        128  boost::asio::detail::scheduler::do_run_one()
[2022-12-19 12:28:18,761 E 5877 5901] (raylet) logging.cc:361:     @     0x559d299bc391        192  boost::asio::detail::scheduler::run()
[2022-12-19 12:28:18,761 E 5877 5901] (raylet) logging.cc:361:     @     0x559d299bc5c0         64  boost::asio::io_context::run()
[2022-12-19 12:28:18,761 E 5877 5901] (raylet) logging.cc:361:     @     0x559d2930b218        384  plasma::PlasmaStoreRunner::Start()
[2022-12-19 12:28:18,761 E 5877 5901] (raylet) logging.cc:361:     @     0x559d292a6375        208  std::thread::_State_impl<>::_M_run()
[2022-12-19 12:28:18,761 E 5877 5901] (raylet) logging.cc:361:     @     0x559d29a0b2d0  (unknown)  execute_native_thread_routine
[2022-12-19 12:28:18,761 E 5877 5901] (raylet) logging.cc:361:     @ ... and at least 3 more frames

Everything works if I manually start the servers myself and use ray start without specifying an autoscaling config - however, this is quite tedious if I want to scale up to more servers.

Versions / Dependencies

Ray 2.1.0 Python 3.9.12 Ubuntu 20.04

Reproduction script

ray bootstrap config:

{"cluster_name": "default", "auth": {"ssh_user": "ubuntu", "ssh_private_key": "~/ray_bootstrap_key.pem"}, "upscaling_speed": 1.0, "idle_timeout_minutes": 5, "docker": {"image": "rayproject/ray:latest-py39-gpu", "container_name": "ray_latest", "pull_before_run": true, "run_options": ["--gpus 1", "--ulimit nofile=65536:65536"]}, "initialization_commands": ["sudo usermod -aG docker $USER; sleep 10"], "setup_commands": ["ls", "conda install -y -c conda-forge ffmpeg", "cd deforum-stable-diffusion; pip install -r requirements.txt", "sudo apt-get update", "sudo apt-get install -y libgl1 libglib2.0-0 libsm6 libxrender1 libxext6", "cd stable-diffusion-webui/models/Stable-diffusion; wget -nc https://oksami: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@huggingface.co/prompthero/openjourney/resolve/main/mdjrny-v4.ckpt", "cd stable-diffusion-webui/models/Stable-diffusion; wget -N https://oksami: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@huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.ckpt -O mdjrny-v4.vae.pt", "cd stable-diffusion-webui; mkdir -p embeddings;", "cd deforum-stable-diffusion; mkdir -p init_images;", "cd deforum-stable-diffusion; mkdir -p output;", "cd deforum-stable-diffusion; pip install torch==1.12.1+cu116 torchvision==0.13.1+cu116 --extra-index-url https://download.pytorch.org/whl/cu116 --force-reinstall", "cd deforum-stable-diffusion; pip install triton==2.0.0.dev20220701; wget -nc https://github.com/TheLastBen/fast-stable-diffusion/raw/main/precompiled/A100/xformers-0.0.13.dev0-py3-none-any.whl", "cd deforum-stable-diffusion; pip install ./xformers-0.0.13.dev0-py3-none-any.whl"], "head_setup_commands": ["ls", "conda install -y -c conda-forge ffmpeg", "cd deforum-stable-diffusion; pip install -r requirements.txt", "sudo apt-get update", "sudo apt-get install -y libgl1 libglib2.0-0 libsm6 libxrender1 libxext6", "cd stable-diffusion-webui/models/Stable-diffusion; wget -nc https://oksami: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@huggingface.co/prompthero/openjourney/resolve/main/mdjrny-v4.ckpt", "cd stable-diffusion-webui/models/Stable-diffusion; wget -N https://oksami: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@huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.ckpt -O mdjrny-v4.vae.pt", "cd stable-diffusion-webui; mkdir -p embeddings;", "cd deforum-stable-diffusion; mkdir -p init_images;", "cd deforum-stable-diffusion; mkdir -p output;", "cd deforum-stable-diffusion; pip install torch==1.12.1+cu116 torchvision==0.13.1+cu116 --extra-index-url https://download.pytorch.org/whl/cu116 --force-reinstall", "cd deforum-stable-diffusion; pip install triton==2.0.0.dev20220701; wget -nc https://github.com/TheLastBen/fast-stable-diffusion/raw/main/precompiled/A100/xformers-0.0.13.dev0-py3-none-any.whl", "cd deforum-stable-diffusion; pip install ./xformers-0.0.13.dev0-py3-none-any.whl"], "worker_setup_commands": ["ls", "conda install -y -c conda-forge ffmpeg", "cd deforum-stable-diffusion; pip install -r requirements.txt", "sudo apt-get update", "sudo apt-get install -y libgl1 libglib2.0-0 libsm6 libxrender1 libxext6", "cd stable-diffusion-webui/models/Stable-diffusion; wget -nc https://oksami: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@huggingface.co/prompthero/openjourney/resolve/main/mdjrny-v4.ckpt", "cd stable-diffusion-webui/models/Stable-diffusion; wget -N https://oksami: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@huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.ckpt -O mdjrny-v4.vae.pt", "cd stable-diffusion-webui; mkdir -p embeddings;", "cd deforum-stable-diffusion; mkdir -p init_images;", "cd deforum-stable-diffusion; mkdir -p output;", "cd deforum-stable-diffusion; pip install torch==1.12.1+cu116 torchvision==0.13.1+cu116 --extra-index-url https://download.pytorch.org/whl/cu116 --force-reinstall", "cd deforum-stable-diffusion; pip install triton==2.0.0.dev20220701; wget -nc https://github.com/TheLastBen/fast-stable-diffusion/raw/main/precompiled/A100/xformers-0.0.13.dev0-py3-none-any.whl", "cd deforum-stable-diffusion; pip install ./xformers-0.0.13.dev0-py3-none-any.whl", "cd stable-diffusion-webui; nohup python launch.py --api &"], "head_start_ray_commands": ["ray stop", "ulimit -c unlimited && ray start --head --port=6379 --autoscaling-config=~/ray_bootstrap_config.yaml --include-dashboard=true", "cd deforum-stable-diffusion; pkill gunicorn; nohup gunicorn main:app -w 4 -k uvicorn.workers.UvicornWorker --bind 0.0.0.0:8000 &", "cd stable-diffusion-webui; nohup python launch.py --api &"], "worker_start_ray_commands": ["ray stop", "ray start --address=$RAY_HEAD_IP:6379"], "file_mounts": {"~/deforum-stable-diffusion": "~/deforum-stable-diffusion", "~/stable-diffusion-webui": "~/stable-diffusion-webui"}, "cluster_synced_files": [], "file_mounts_sync_continuously": false, "rsync_exclude": ["**/.git", "**/.git/**"], "rsync_filter": [".gitignore"], "provider": {"type": "local", "head_ip": "129.146.99.48", "worker_ips": ["129.146.162.248"]}, "max_workers": 1, "available_node_types": {"local.cluster.node": {"node_config": {}, "resources": {}, "min_workers": 1, "max_workers": 1}}, "head_node_type": "local.cluster.node", "no_restart": false}

Issue Severity

High: It blocks me from completing my task.

ray-project / ray

Ray Core: Worker that was launched by an autoscaling_config loops and crashes #31192

What happened + What you expected to happen

Versions / Dependencies

Reproduction script

Issue Severity