How severe does this issue affect your experience of using Ray?
- Medium: It contributes to significant difficulty to complete my task, but I can work around it.
I recently deployed the FastAPI “hello world” example to a minimal GCP cluster.
I have no trouble reaching the endpoint if I attach to the the cluster first with ray attach config.yaml and call the API from there.
But I can’t manage to reach the endpoint from my local machine after replacing localhost with the head node’s external IP, still on port 8000.
Is this simply a firewall issue with the GCP cluster, or am I misunderstanding the purpose of the Ray Serve deployment in that it’s not intended to be reached from a local machine?
The config used for creating the cluster:
# A unique identifier for the head node and workers of this cluster.
cluster_name: minimal
# 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 string means disabled.
docker:
image: "rayproject/ray-ml:nightly-py38-cpu"
# 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: gcp
region: europe-west2
availability_zone: europe-west2-a
project_id: bert-training-test
# How Ray will authenticate with newly launched nodes.
auth:
ssh_user: ubuntu
# By default Ray creates a new private keypair, but you can also use your own.
# If you do so, make sure to also set "KeyName" in the head and worker node
# configurations below. This requires that you have added the key into the
# project wide meta-data.
# ssh_private_key: /path/to/your/key.pem
# 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 for the head node, e.g. instance type. By default
# Ray will auto-configure unspecified fields such as subnets and ssh-keys.
# For more documentation on available fields, see:
# https://cloud.google.com/compute/docs/reference/rest/v1/instances/insert
node_config:
machineType: n1-standard-2
disks:
- boot: true
autoDelete: true
type: PERSISTENT
initializeParams:
diskSizeGb: 50
# See https://cloud.google.com/compute/docs/images for more images
sourceImage: projects/deeplearning-platform-release/global/images/family/common-cpu
# Additional options can be found in in the compute docs at
# https://cloud.google.com/compute/docs/reference/rest/v1/instances/insert
# If the network interface is specified as below in both head and worker
# nodes, the manual network config is used. Otherwise an existing subnet is
# used. To use a shared subnet, ask the subnet owner to grant permission
# for 'compute.subnetworks.use' to the ray autoscaler account...
# networkInterfaces:
# - kind: compute#networkInterface
# subnetwork: path/to/subnet
# aliasIpRanges: []
ray_worker_small:
# 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 for the head node, e.g. instance type. By default
# Ray will auto-configure unspecified fields such as subnets and ssh-keys.
# For more documentation on available fields, see:
# https://cloud.google.com/compute/docs/reference/rest/v1/instances/insert
node_config:
machineType: n1-standard-2
disks:
- boot: true
autoDelete: true
type: PERSISTENT
initializeParams:
diskSizeGb: 50
# See https://cloud.google.com/compute/docs/images for more images
sourceImage: projects/deeplearning-platform-release/global/images/family/common-cpu
# Run workers on preemtible instance by default.
# Comment this out to use on-demand.
scheduling:
- preemptible: true
# Un-Comment this to launch workers with the Service Account of the Head Node
# serviceAccounts:
# - email: ray-autoscaler-sa-v1@<project_id>.iam.gserviceaccount.com
# scopes:
# - https://www.googleapis.com/auth/cloud-platform
# Additional options can be found in in the compute docs at
# https://cloud.google.com/compute/docs/reference/rest/v1/instances/insert
# 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",
}
# 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: []
# List of shell commands to run to set up nodes.
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-cp37-cp37m-manylinux2014_x86_64.whl"
# Custom commands that will be run on the head node after common setup.
head_setup_commands:
- pip install google-api-python-client==1.7.8
# 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
The app:
import ray
from fastapi import FastAPI
from ray import serve
from fastapi.middleware.cors import CORSMiddleware
from starlette.middleware import Middleware
from starlette.middleware.cors import CORSMiddleware as StarletteCORSMiddleware
app = FastAPI()
origins = [
"*",
]
app.add_middleware(
CORSMiddleware,
allow_origins=origins,
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
ray.init(address="auto", namespace="example")
serve.start(detached=True)
@serve.deployment(route_prefix="/hello")
@serve.ingress(app)
class MyFastAPIDeployment:
@app.get("/")
def root(self):
return "Hello, world!"
MyFastAPIDeployment.deploy()
Pinging the API from inside the cluster:
>> ray attach config.yaml
>> python -c 'import requests; requests.get("http://localhost:8000/hello").text'
>> Hello World!
Pinging from local machine:
curl -X GET http://34.105.238.180:8000/hello
# Connection refused