Hydra Ray Launcher

We are happy to announce that we are adding a Ray Launcher to the Hydra Launchers family. Hydra's Launcher plugins enable launching to different environments without changing your existing workflows or application code. The Hydra Ray Launcher can be used to launch your application to a new or existing Ray cluster, locally or on AWS. In this post we demonstrate the major functionalities of the Launcher. For more details on installation and configuration, please check out the Hydra Ray Launcher documentation. As always, please join our community and give us feedback!

Ray is a simple yet powerful Python library for parallel and distributed programming. Among the many features it provides, Ray comes with a cluster launcher that can be used to provision resources and start a Ray cluster on top of them. Hydra Ray Launcher is built on top of the Ray Tasks API and the Ray cluster launcher.

Launching to a new or existing AWS cluster

Hydra Ray Launcher simplifies your experience by allowing the Ray cluster setup to be configured transparently by Hydra (eliminating the need for an external YAML file while maintaining the flexibility). Hydra Ray Launcher comes with reasonable default configurations which can be found here (under the heading, “Discover ray_aws launcher's config”). You can override them in your application config or from the command line to fit your use case. The following Ray Launcher example code (e.g., my_app.py) is runnable and can be found here.

Launch your Hydra application to AWS by simply overriding: hydra/launcher=ray_aws:

$ python my_app.py hydra/launcher=ray_aws task=1,2 --multirun[HYDRA] Ray Launcher is launching 2 jobs, [HYDRA]        #0 : task=1[HYDRA]        #1 : task=2...[HYDRA] Running command: ['ray', 'up', '-y', '/var/folders/n_/9qzct77j68j6n9lh0lw3vjqcn96zxl/T/tmp20qvoy15.yaml'][HYDRA] Output: INFO services.py:1090 -- View the Ray dashboard at http://127.0.0.1:8265(pid=3823)[__main__][INFO] - Executing task 1(pid=3822)[__main__][INFO] - Executing task 2[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/tmpfm2ems9v.yaml']

Launching to a new local Ray Cluster

If you want to do a quick local test, you can spin up a local Ray cluster at application run time by specifying hydra/launcher=ray. In this example, we create a new Ray cluster at application time.

$ python my_app.py  --multirun hydra/launcher=ray [HYDRA] Ray Launcher is launching 1 jobs, sweep output dir: multirun/2020-12-17/16-11-28[HYDRA] Initializing ray with config: {'address': None}2020-12-17 16:11:29,340 INFO services.py:1090 -- View the Ray dashboard at http://127.0.0.1:8265[HYDRA]        #0 : (pid=62642) [__main__][INFO] - Executing task 1

Launching to an existing local Ray Cluster

You can launch the application on an existing local Ray cluster by configuring the cluster address and overriding hydra/launcher=ray. In the following example we configure the Ray cluster address to local ray cluster and Hydra Ray Launcher was able to connect to the existing Ray cluster and execute the application code:

$ 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

Hydra Ray Launcher is built on top of Hydra 1.0 and you have access to all of the benefits Hydra brings:

Parameter sweeps and optimization

Hyperparameter sweeps are common in machine learning research. Hydra has built-in grid search and provides several Sweeper plugins for hyperparameter optimization. Sweepers can be used together with Launchers for sweeping on different computing platforms. Start from our documentation here to find more.

Config type safety

Modern Hydra applications and Hydra Plugins leverage Structured Configs for config validation, and Hydra Ray Launcher is no exception. In the following example, we try to override the Ray cluster’s autoscaling mode with an illegal value:

$ python my_app.py --multirun hydra.launcher.ray.cluster.autoscaling_mode=fooError merging override hydra.launcher.ray.cluster.autoscaling_mode=fooInvalid value 'foo', expected one of [default, aggressive]        full_key: hydra.launcher.ray.cluster.autoscaling_mode        reference_type=RayClusterConf        object_type=RayClusterConf
Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.

That’s it for now! Please try out the new Hydra Ray Launcher and let us know what you think. We are always happy to connect with you via GitHub or the Hydra Chat.