Version: 0.11

Ray example

Ray is a framework for building and running distributed applications. Hydra can be used with Ray to configure Ray itself as well as complex remote calls through the compose API. A future plugin will enable launching to Ray clusters directly from the command line.

import hydra
from hydra.experimental import compose
import ray
import time
@ray.remote
def train(overrides, cfg):
print(cfg.pretty())
time.sleep(5)
return overrides, 0.9
@hydra.main(config_path="conf/config.yaml")
def main(cfg):
ray.init(**cfg.ray.init)
results = []
for model in ["alexnet", "resnet"]:
for dataset in ["cifar10", "imagenet"]:
overrides = [f"dataset={dataset}", f"model={model}"]
run_cfg = compose(overrides=overrides)
ret = train.remote(overrides, run_cfg)
results.append(ret)
for overrides, score in ray.get(results):
print(f"Result from {overrides} : {score}")
if __name__ == "__main__":
main()

Output:

(pid=11571) dataset:
(pid=11571) name: cifar10
(pid=11571) path: /datasets/cifar10
(pid=11571) model:
(pid=11571) num_layers: 7
(pid=11571) type: alexnet
(pid=11571)
(pid=11572) dataset:
(pid=11572) name: imagenet
(pid=11572) path: /datasets/imagenet
(pid=11572) model:
(pid=11572) num_layers: 7
(pid=11572) type: alexnet
(pid=11572)
(pid=11573) dataset:
(pid=11573) name: cifar10
(pid=11573) path: /datasets/cifar10
(pid=11573) model:
(pid=11573) num_layers: 50
(pid=11573) type: resnet
(pid=11573) width: 10
(pid=11573)
(pid=11574) dataset:
(pid=11574) name: imagenet
(pid=11574) path: /datasets/imagenet
(pid=11574) model:
(pid=11574) num_layers: 50
(pid=11574) type: resnet
(pid=11574) width: 10
(pid=11574)
Result from ['dataset=cifar10', 'model=alexnet'] : 0.9
Result from ['dataset=imagenet', 'model=alexnet'] : 0.9
Result from ['dataset=cifar10', 'model=resnet'] : 0.9
Result from ['dataset=imagenet', 'model=resnet'] : 0.9
Last updated on by Omry Yadan