mirror of
https://github.com/vale981/ray
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60d4d5e1aa
* Remove all __future__ imports from RLlib. * Remove (object) again from tf_run_builder.py::TFRunBuilder. * Fix 2xLINT warnings. * Fix broken appo_policy import (must be appo_tf_policy) * Remove future imports from all other ray files (not just RLlib). * Remove future imports from all other ray files (not just RLlib). * Remove future import blocks that contain `unicode_literals` as well. Revert appo_tf_policy.py to appo_policy.py (belongs to another PR). * Add two empty lines before Schedule class. * Put back __future__ imports into determine_tests_to_run.py. Fails otherwise on a py2/print related error.
47 lines
1.3 KiB
Python
47 lines
1.3 KiB
Python
# This workload tests running IMPALA with remote envs
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import ray
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from ray.tune import run_experiments
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from ray.cluster_utils import Cluster
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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 = 1
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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.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(
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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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ray.init(address=cluster.address)
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# Run the workload.
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run_experiments({
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"impala": {
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"run": "IMPALA",
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"env": "CartPole-v0",
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"config": {
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"num_workers": 8,
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"num_gpus": 0,
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"num_envs_per_worker": 5,
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"remote_worker_envs": True,
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"remote_env_batch_wait_ms": 99999999,
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"sample_batch_size": 50,
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"train_batch_size": 100,
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},
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},
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})
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