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- # This workload tests running many instances of PPO (many actors)
- # This covers https://github.com/ray-project/ray/pull/12148
- import ray
- from ray.tune import run_experiments
- from ray.tune.utils.release_test_util import ProgressCallback
- from ray._private.test_utils import monitor_memory_usage
- num_redis_shards = 5
- redis_max_memory = 10**8
- object_store_memory = 10**9
- num_nodes = 3
- message = (
- "Make sure there is enough memory on this machine to run this "
- "workload. We divide the system memory by 2 to provide a buffer."
- )
- assert (
- num_nodes * object_store_memory + num_redis_shards * redis_max_memory
- < ray._private.utils.get_system_memory() / 2
- ), message
- # Simulate a cluster on one machine.
- ray.init(address="auto")
- monitor_actor = monitor_memory_usage()
- # Run the workload.
- run_experiments(
- {
- "ppo": {
- "run": "PPO",
- "env": "CartPole-v0",
- "num_samples": 10000,
- "config": {
- "framework": "torch",
- "num_workers": 7,
- "num_gpus": 0,
- "num_sgd_iter": 1,
- },
- "stop": {
- "timesteps_total": 1,
- },
- }
- },
- callbacks=[ProgressCallback()],
- )
- ray.get(monitor_actor.stop_run.remote())
- used_gb, usage = ray.get(monitor_actor.get_peak_memory_info.remote())
- print(f"Peak memory usage: {round(used_gb, 2)}GB")
- print(f"Peak memory usage per processes:\n {usage}")
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