mirror of
https://github.com/vale981/ray
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65 lines
1.6 KiB
Python
65 lines
1.6 KiB
Python
# This workload tests running PBT
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import ray
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from ray.tune import run_experiments
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from ray.tune.schedulers import PopulationBasedTraining
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from ray.cluster_utils import Cluster
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from ray.tune.utils.release_test_util import ProgressCallback
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num_redis_shards = 5
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redis_max_memory = 10 ** 8
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object_store_memory = 10 ** 8
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num_nodes = 3
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message = (
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"Make sure there is enough memory on this machine to run this "
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"workload. We divide the system memory by 2 to provide a buffer."
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)
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assert (
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num_nodes * object_store_memory + num_redis_shards * redis_max_memory
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< ray._private.utils.get_system_memory() / 2
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), message
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# Simulate a cluster on one machine.
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cluster = Cluster()
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for i in range(num_nodes):
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cluster.add_node(
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redis_port=6379 if i == 0 else None,
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num_redis_shards=num_redis_shards if i == 0 else None,
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num_cpus=10,
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num_gpus=0,
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resources={str(i): 2},
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object_store_memory=object_store_memory,
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redis_max_memory=redis_max_memory,
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dashboard_host="0.0.0.0",
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)
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ray.init(address=cluster.address)
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# Run the workload.
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pbt = PopulationBasedTraining(
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time_attr="training_iteration",
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metric="episode_reward_mean",
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mode="max",
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perturbation_interval=10,
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hyperparam_mutations={
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"lr": [0.1, 0.01, 0.001, 0.0001],
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},
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)
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run_experiments(
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{
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"pbt_test": {
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"run": "PG",
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"env": "CartPole-v0",
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"num_samples": 8,
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"config": {
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"lr": 0.01,
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},
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}
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},
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callbacks=[ProgressCallback()],
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scheduler=pbt,
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verbose=False,
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)
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