-
Notifications
You must be signed in to change notification settings - Fork 136
Ray scheduler driver and job api #329
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Closed
Changes from all commits
Commits
File filter
Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
There are no files selected for viewing
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,8 @@ | ||
Ray | ||
================= | ||
|
||
.. automodule:: torchx.schedulers.ray_scheduler | ||
.. currentmodule:: torchx.schedulers.ray_scheduler | ||
|
||
.. autoclass:: RayScheduler | ||
:members: |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,142 @@ | ||
# An unique identifier for the head node and workers of this cluster. | ||
cluster_name: gpu-docker | ||
|
||
min_workers: 1 | ||
max_workers: 4 | ||
|
||
# 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-gpu" | ||
# image: rayproject/ray:latest-gpu # use this one if you don't need ML dependencies, it's faster to pull | ||
container_name: "ray_nvidia_docker" # e.g. ray_docker | ||
|
||
|
||
# If a node is idle for this many minutes, it will be removed. | ||
idle_timeout_minutes: 5 | ||
|
||
# Cloud-provider specific configuration. | ||
provider: | ||
type: aws | ||
region: us-west-2 | ||
# Availability zone(s), comma-separated, that nodes may be launched in. | ||
# Nodes are currently spread between zones by a round-robin approach, | ||
# however this implementation detail should not be relied upon. | ||
availability_zone: us-west-2a,us-west-2b | ||
security_group: | ||
GroupName: dashboard_group | ||
IpPermissions: | ||
- FromPort: 20002 | ||
ToPort: 20002 | ||
IpProtocol: TCP | ||
IpRanges: | ||
- CidrIp: 0.0.0.0/0 | ||
|
||
|
||
# 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. | ||
# 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: | ||
# GPU head node. | ||
ray.head.gpu: | ||
# worker_image: rayproject/ray:latest-gpu # use this one if you don't need ML dependencies, it's faster to pull | ||
# The node type's CPU and GPU resources are auto-detected based on AWS instance type. | ||
# If desired, you can override the autodetected CPU and GPU resources advertised to the autoscaler. | ||
# You can also set custom resources. | ||
# For example, to mark a node type as having 1 CPU, 1 GPU, and 5 units of a resource called "custom", set | ||
# resources: {"CPU": 1, "GPU": 1, "custom": 5} | ||
resources: {} | ||
# Provider-specific config for this node type, e.g. instance type. By default | ||
# Ray will auto-configure unspecified fields such as SubnetId and KeyName. | ||
# For more documentation on available fields, see: | ||
# http://boto3.readthedocs.io/en/latest/reference/services/ec2.html#EC2.ServiceResource.create_instances | ||
node_config: | ||
InstanceType: p2.xlarge | ||
ImageId: ami-0a2363a9cff180a64 # Deep Learning AMI (Ubuntu) Version 30 | ||
# You can provision additional disk space with a conf as follows | ||
BlockDeviceMappings: | ||
- DeviceName: /dev/sda1 | ||
Ebs: | ||
VolumeSize: 100 | ||
# Additional options in the boto docs. | ||
# CPU workers. | ||
ray.worker.default: | ||
# Override global docker setting. | ||
# This node type will run a CPU image, | ||
# rather than the GPU image specified in the global docker settings. | ||
docker: | ||
worker_image: "rayproject/ray-ml:latest-cpu" | ||
# The minimum number of nodes of this type to launch. | ||
# This number should be >= 0. | ||
min_workers: 1 | ||
# The maximum number of workers nodes of this type to launch. | ||
# This takes precedence over min_workers. | ||
max_workers: 2 | ||
# The node type's CPU and GPU resources are auto-detected based on AWS instance type. | ||
# If desired, you can override the autodetected CPU and GPU resources advertised to the autoscaler. | ||
# You can also set custom resources. | ||
# For example, to mark a node type as having 1 CPU, 1 GPU, and 5 units of a resource called "custom", set | ||
# resources: {"CPU": 1, "GPU": 1, "custom": 5} | ||
resources: {} | ||
# Provider-specific config for this node type, e.g. instance type. By default | ||
# Ray will auto-configure unspecified fields such as SubnetId and KeyName. | ||
# For more documentation on available fields, see: | ||
# http://boto3.readthedocs.io/en/latest/reference/services/ec2.html#EC2.ServiceResource.create_instances | ||
node_config: | ||
InstanceType: m5.large | ||
ImageId: ami-0a2363a9cff180a64 # Deep Learning AMI (Ubuntu) Version 30 | ||
# Run workers on spot by default. Comment this out to use on-demand. | ||
InstanceMarketOptions: | ||
MarketType: spot | ||
# Additional options can be found in the boto docs, e.g. | ||
# SpotOptions: | ||
# MaxPrice: MAX_HOURLY_PRICE | ||
# Additional options in the boto docs. | ||
|
||
# Specify the node type of the head node (as configured above). | ||
head_node_type: ray.head.gpu | ||
|
||
# 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", | ||
} | ||
|
||
# List of shell commands to run to set up nodes. | ||
# NOTE: rayproject/ray:latest has ray latest bundled | ||
setup_commands: [] | ||
# - pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-2.0.0.dev0-cp36-cp36m-manylinux2014_x86_64.whl | ||
# - 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 boto3==1.4.8 # 1.4.8 adds InstanceMarketOptions | ||
|
||
# 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 | ||
- ulimit -n 65536; ray start --dashboard-port 20002 --dashboard-host=0.0.0.0 --include-dashboard True --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 | ||
- ulimit -n 65536; ray start --address=$RAY_HEAD_IP:6379 --object-manager-port=8076 |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,6 @@ | ||
#!/usr/bin/env python3 | ||
# Copyright (c) Meta Platforms, Inc. and affiliates. | ||
# All rights reserved. | ||
# | ||
# This source code is licensed under the BSD-style license found in the | ||
# LICENSE file in the root directory of this source tree. |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,36 @@ | ||
# Copyright (c) Meta Platforms, Inc. and affiliates. | ||
# All rights reserved. | ||
# | ||
# This source code is licensed under the BSD-style license found in the | ||
# LICENSE file in the root directory of this source tree. | ||
|
||
from dataclasses import dataclass, field | ||
from typing import Dict | ||
|
||
|
||
@dataclass | ||
class RayActor: | ||
"""Describes an actor (a.k.a. role in TorchX terms). | ||
|
||
Attributes: | ||
name: | ||
The name of the actor. | ||
command: | ||
The command that the actor should run as a subprocess. | ||
env: | ||
The environment variables to set before executing the command. | ||
num_replicas: | ||
The number of replicas (i.e. Ray actors) to run. | ||
num_cpus: | ||
The number of CPUs to allocate. | ||
num_gpus: | ||
The number of GPUs to allocate. | ||
""" | ||
|
||
name: str | ||
command: str | ||
env: Dict[str, str] = field(default_factory=dict) | ||
num_replicas: int = 1 | ||
num_cpus: int = 1 | ||
num_gpus: int = 0 | ||
# TODO: memory_size, max_retries, retry_policy |
Oops, something went wrong.
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Uh oh!
There was an error while loading. Please reload this page.