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https://github.com/vale981/ray
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48 lines
1.4 KiB
Python
48 lines
1.4 KiB
Python
# This workload tests running APEX
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import ray
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from ray.tune import run_experiments
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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**9
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num_nodes = 3
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message = ("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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assert (num_nodes * object_store_memory + num_redis_shards * redis_max_memory <
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ray._private.utils.get_system_memory() / 2), 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(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=20,
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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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# ray.init(address=cluster.address)
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ray.init()
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# Run the workload.
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run_experiments({
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"apex": {
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"run": "APEX",
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"env": "Pong-v0",
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"config": {
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"num_workers": 3,
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"num_gpus": 0,
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"buffer_size": 10000,
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"learning_starts": 0,
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"rollout_fragment_length": 1,
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"train_batch_size": 1,
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"min_iter_time_s": 10,
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"timesteps_per_iteration": 10,
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},
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}
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})
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