ray/ci/microbenchmark/ray-project/cluster.yaml
2020-05-28 09:37:19 -07:00

56 lines
1.6 KiB
YAML

cluster_name: ray-release-microbenchmark
min_workers: 0
max_workers: 0
target_utilization_fraction: 0.8
idle_timeout_minutes: 5
# Cloud-provider specific configuration.
provider:
type: aws
region: us-west-2
availability_zone: us-west-2a
auth:
ssh_user: ubuntu
head_node:
InstanceType: m4.16xlarge
ImageId: ami-06d51e91cea0dac8d # Ubuntu 18.04
BlockDeviceMappings:
- DeviceName: /dev/sda1
Ebs:
VolumeSize: 150
worker_nodes:
InstanceType: m5.large
ImageId: ami-06d51e91cea0dac8d # Ubuntu 18.04
BlockDeviceMappings:
- DeviceName: /dev/sda1
Ebs:
VolumeSize: 150
# Run workers on spot by default. Comment this out to use on-demand.
InstanceMarketOptions:
MarketType: spot
# List of shell commands to run to set up nodes.
setup_commands:
# Install latest TensorFlow
- echo set-window-option -g mouse on > ~/.tmux.conf
- echo 'termcapinfo xterm* ti@:te@' > ~/.screenrc
# Custom commands that will be run on the head node after common setup.
head_setup_commands:
# Install Anaconda.
- wget --quiet https://repo.continuum.io/archive/Anaconda3-5.0.1-Linux-x86_64.sh || true
- bash Anaconda3-5.0.1-Linux-x86_64.sh -b -p $HOME/anaconda3 || true
- echo 'export PATH="$HOME/anaconda3/bin:$PATH"' >> ~/.bashrc
- pip install -U pip
# 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: []
# Command to start ray on worker nodes. You don't need to change this.
worker_start_ray_commands: []