mirror of
https://github.com/vale981/ray
synced 2025-03-06 02:21:39 -05:00
fd0f967d2e
This reverts commit e4ceae19ef.
Reverts #25346
linux://python/ray/tests:test_client_library_integration never fail before this PR.
In the CI of the reverted PR, it also fails (https://buildkite.com/ray-project/ray-builders-pr/builds/34079#01812442-c541-4145-af22-2a012655c128). So high likely it's because of this PR.
And test output failure seems related as well (https://buildkite.com/ray-project/ray-builders-branch/builds/7923#018125c2-4812-4ead-a42f-7fddb344105b)
71 lines
2.2 KiB
Python
71 lines
2.2 KiB
Python
import os
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import sys
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import unittest
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import pytest
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import ray
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from ray import tune
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from ray.rllib.agents import ppo
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from ray.rllib.examples.env.stateless_cartpole import StatelessCartPole
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from ray.util.client.ray_client_helpers import ray_start_client_server
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class TestRayClient(unittest.TestCase):
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def test_connection(self):
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with ray_start_client_server():
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assert ray.util.client.ray.is_connected()
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assert ray.util.client.ray.is_connected() is False
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def test_custom_train_fn(self):
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with ray_start_client_server():
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assert ray.util.client.ray.is_connected()
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config = {
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# Special flag signalling `my_train_fn` how many iters to do.
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"train-iterations": 2,
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"lr": 0.01,
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# Use GPUs iff `RLLIB_NUM_GPUS` env var set to > 0.
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"num_gpus": int(os.environ.get("RLLIB_NUM_GPUS", "0")),
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"num_workers": 0,
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"framework": "tf",
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}
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resources = ppo.PPOTrainer.default_resource_request(config)
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from ray.rllib.examples.custom_train_fn import my_train_fn
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tune.run(my_train_fn, resources_per_trial=resources, config=config)
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def test_cartpole_lstm(self):
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with ray_start_client_server():
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assert ray.util.client.ray.is_connected()
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config = {
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"env": StatelessCartPole,
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}
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stop = {
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"training_iteration": 3,
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}
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tune.run("PPO", config=config, stop=stop, verbose=2)
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def test_custom_experiment(self):
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with ray_start_client_server(ray_init_kwargs={"num_cpus": 3}):
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assert ray.util.client.ray.is_connected()
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config = ppo.DEFAULT_CONFIG.copy()
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# Special flag signalling `experiment` how many iters to do.
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config["train-iterations"] = 2
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config["env"] = "CartPole-v0"
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from ray.rllib.examples.custom_experiment import experiment
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tune.run(
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experiment,
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config=config,
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resources_per_trial=ppo.PPOTrainer.default_resource_request(config),
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)
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if __name__ == "__main__":
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sys.exit(pytest.main(["-v", __file__]))
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