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Version: 1.0

Ray Launcher plugin

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The Ray Launcher plugin provides 2 launchers: ray_aws and ray. ray_aws launches jobs remotely on AWS and is built on top of Ray cluster launcher. ray launches jobs on your local machine or existing ray cluster.

Installation​

$ pip install hydra-ray-launcher --upgrade

Usage​

Once installed, add hydra/launcher=ray_aws or hydra/launcher=ray to your command line. Alternatively, override hydra/launcher in your config:

defaults:
  - hydra/launcher: ray_aws

ray_aws launcher​

important

ray_aws launcher is built on top of ray's cluster launcher cli. To get started, you need to config your AWS credentials. ray cluster launcher expects your AWS credentials have certain permissions for EC2 and IAM. Read this for more information.

ray cluster launcher expects a yaml file to provide configuration for the EC2 cluster; we've schematized the configs in RayClusterConf,

Discover ray_aws launcher's config
$ python my_app.py hydra/launcher=ray_aws --cfg hydra -p hydra.launcher
# @package hydra.launcher
_target_: hydra_plugins.hydra_ray_launcher.ray_aws_launcher.RayAWSLauncher
env_setup:
  pip_packages:
    omegaconf: 2.0.5
    hydra_core: 1.0.4
    ray: 1.0.1.post1
    cloudpickle: 1.6.0
    pickle5: 0.0.11
    hydra_ray_launcher: 0.1.2
  commands:
  - conda create -n hydra_${python_version:micro} python=${python_version:micro} -y
  - echo 'export PATH="$HOME/anaconda3/envs/hydra_${python_version:micro}/bin:$PATH"'
    >> ~/.bashrc
ray:
  init:
    address: null
  remote: {}
  cluster:
    cluster_name: default
    min_workers: 0
    max_workers: 1
    initial_workers: 0
    autoscaling_mode: default
    target_utilization_fraction: 0.8
    idle_timeout_minutes: 5
    docker:
      image: ''
      container_name: ''
      pull_before_run: true
      run_options: []
    provider:
      type: aws
      region: us-west-2
      availability_zone: us-west-2a,us-west-2b
      cache_stopped_nodes: false
      key_pair:
        key_name: hydra
    auth:
      ssh_user: ubuntu
    head_node:
      InstanceType: m5.large
      ImageId: ami-008d8ed4bd7dc2485
    worker_nodes:
      InstanceType: m5.large
      ImageId: ami-008d8ed4bd7dc2485
    file_mounts: {}
    initialization_commands: []
    setup_commands: []
    head_setup_commands: []
    worker_setup_commands: []
    head_start_ray_commands:
    - ray stop
    - ulimit -n 65536; ray start --head --redis-port=6379 --object-manager-port=8076
      --autoscaling-config=~/ray_bootstrap_config.yaml
    worker_start_ray_commands:
    - ray stop
    - ulimit -n 65536; ray start --address=$RAY_HEAD_IP:6379 --object-manager-port=8076
stop_cluster: true
sync_up:
  source_dir: null
  target_dir: null
  include: []
  exclude: []
sync_down:
  source_dir: null
  target_dir: null
  include: []
  exclude: []

Examples​

The following examples can be found here.

Simple app
$ python my_app.py --multirun task=1,2,3
[HYDRA] Ray Launcher is launching 3 jobs, 
[HYDRA]        #0 : task=1
[HYDRA]        #1 : task=2
[HYDRA]        #2 : task=3
[HYDRA] Pickle for jobs: /var/folders/n_/9qzct77j68j6n9lh0lw3vjqcn96zxl/T/tmpqqg4v4i7/job_spec.pkl
[HYDRA] Saving RayClusterConf in a temp yaml file: /var/folders/n_/9qzct77j68j6n9lh0lw3vjqcn96zxl/T/tmpaa07pq3w.yaml.
...
[HYDRA] Output: INFO services.py:1164 -- View the Ray dashboard at http://127.0.0.1:8265
(pid=3374) [__main__][INFO] - Executing task 1
(pid=3374) [__main__][INFO] - Executing task 2
(pid=3374) [__main__][INFO] - Executing task 3
...
[HYDRA] Stopping cluster now. (stop_cluster=true)
[HYDRA] Deleted the cluster (provider.cache_stopped_nodes=false)
[HYDRA] Running command: ['ray', 'down', '-y', '/var/folders/n_/9qzct77j68j6n9lh0lw3vjqcn96zxl/T/tmpaa07pq3w.yaml']

Upload & Download from remote cluster

If your application is dependent on multiple modules, you can configure hydra.launcher.sync_up to upload dependency modules to the remote cluster. You can also configure hydra.launcher.sync_down to download output from remote cluster if needed. This functionality is built on top of rsync, include and exclude is consistent with how it works in rsync.


$  python train.py --multirun random_seed=1,2,3
[HYDRA] Ray Launcher is launching 3 jobs, 
[HYDRA]        #0 : random_seed=1
[HYDRA]        #1 : random_seed=2
[HYDRA]        #2 : random_seed=3
[HYDRA] Pickle for jobs: /var/folders/n_/9qzct77j68j6n9lh0lw3vjqcn96zxl/T/tmptdkye9of/job_spec.pkl
[HYDRA] Saving RayClusterConf in a temp yaml file: /var/folders/n_/9qzct77j68j6n9lh0lw3vjqcn96zxl/T/tmp2reaoixs.yaml.
[HYDRA] Running command: ['ray', 'up', '-y', '/var/folders/n_/9qzct77j68j6n9lh0lw3vjqcn96zxl/T/tmp2reaoixs.yaml']
...
[HYDRA] Output: INFO services.py:1164 -- View the Ray dashboard at http://127.0.0.1:8265
(pid=1772) [__main__][INFO] - Start training...
(pid=1772) [INFO] - Init my model
(pid=1772) [INFO] - Created dir for checkpoints. dir=checkpoint
(pid=1772) [__main__][INFO] - Start training...
(pid=1772) [INFO] - Init my model
(pid=1772) [INFO] - Created dir for checkpoints. dir=checkpoint
(pid=1772) [__main__][INFO] - Start training...
(pid=1772) [INFO] - Init my model
(pid=1772) [INFO] - Created dir for checkpoints. dir=checkpoint
Loaded cached provider configuration
...
[HYDRA] Output: receiving file list ... done
16-32-25/
16-32-25/0/
16-32-25/0/checkpoint/
16-32-25/0/checkpoint/checkpoint_1.pt
16-32-25/1/
16-32-25/1/checkpoint/
16-32-25/1/checkpoint/checkpoint_2.pt
16-32-25/2/
16-32-25/2/checkpoint/
16-32-25/2/checkpoint/checkpoint_3.pt
...
[HYDRA] Stopping cluster now. (stop_cluster=true)
[HYDRA] NOT deleting the cluster (provider.cache_stopped_nodes=true)
[HYDRA] Running command: ['ray', 'down', '-y', '/var/folders/n_/9qzct77j68j6n9lh0lw3vjqcn96zxl/T/tmpy430k4xr.yaml']
Manage Cluster LifeCycle​

You can manage the Ray EC2 cluster lifecycle by configuring the two flags provided by the plugin:

  • Default setting (no need to specify on commandline): Delete cluster after job finishes remotely:
hydra.launcher.stop_cluster=true
hydra.launcher.ray.cluster.provider.cache_stopped_nodes=False
  • Keep cluster running after jobs finishes remotely
hydra.launcher.stop_cluster=False
  • Power off EC2 instances without deletion
hydra.launcher.ray.cluster.provider.cache_stopped_nodes=true

ray launcher​

ray launcher lets you launch application on your ray cluster or local machine. You can easily config how your jobs are executed by changing ray launcher's configuration here ~/hydra/plugins/hydra_ray_launcher/hydra_plugins/hydra_ray_launcher/conf/hydra/launcher/ray.yaml

The example application starts a new ray cluster. 

$ python my_app.py  --multirun hydra/launcher=ray
[HYDRA] Ray Launcher is launching 1 jobs, sweep output dir: multirun/2020-11-10/15-16-28
[HYDRA] Initializing ray with config: {}
INFO services.py:1164 -- View the Ray dashboard at http://127.0.0.1:8266
[HYDRA]        #0 : 
(pid=97801) [__main__][INFO] - Executing task 1

You can run the example application on your existing ray cluster as well by overriding hydra.launcher.ray.init.address:

$ python my_app.py  --multirun hydra/launcher=ray hydra.launcher.ray.init.address=localhost:6379'
[HYDRA] Ray Launcher is launching 1 jobs, sweep output dir: multirun/2020-11-10/15-13-32
[HYDRA] Initializing ray with config: {'num_cpus': None, 'num_gpus': None, 'address': 'localhost:6379'}
INFO worker.py:633 -- Connecting to existing Ray cluster at address: 10.30.99.17:6379
[HYDRA]        #0 : 
(pid=93358) [__main__][INFO] - Executing task 1

Configure ray.init() and ray.remote()​

Ray launcher is built on top of ray.init() and ray.remote(). You can configure ray by overriding hydra.launcher.ray.init and hydra.launcher.ray.remote. Check out an example config.