I was trying earlier to setup a cluster manually for horovod but it won’t work properly. I am now trying to set it up with cluster launcher on GCP. I created a IAM with owner priviledges and added it to the path. I am using this yaml:
# 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 string means disabled.
docker:
# image: "rayproject/ray-ml:latest-cpu" # 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:latest-cpu"
container_name: "ray_head"
# Allow Ray to automatically detect GPUs
worker_image: "rayproject/ray-ml:latest-cpu"
container_name: "ray_worker"
# worker_run_options: []
# If a node is idle for this many minutes, it will be removed.
# Cloud-provider specific configuration.
provider:
type: gcp
region: us-west4
availability_zone: us-west4-a
project_id: learning-orchestra-347022 # Globally unique project id
# 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 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: 0
# The resources provided by this node type.
resources: {"CPU": 0}
# 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: 2
# 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": 1}
# 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
worker_default_node_type: ray_worker_small
# 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-2.0.0.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
- sudo apt-get update
- sudo apt-get -y install build-essential
- sudo apt-get -y install cmake gcc g++
- HOROVOD_WITH_GLOO=1 HOROVOD_WITH_TENSORFLOW=1 pip install 'horovod[ray,tensorflow,keras]'
# Custom commands that will be run on worker nodes after common setup.
worker_setup_commands:
- sudo apt-get update
- sudo apt-get -y install build-essential
- sudo apt-get -y install cmake gcc g++
- HOROVOD_WITH_GLOO=1 HOROVOD_WITH_TENSORFLOW=1 pip install 'horovod[ray,tensorflow,keras]'
# 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: {}
am i missing anything?