ray/rllib/tests/test_export.py

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#!/usr/bin/env python
import os
import shutil
import unittest
import ray
from ray.rllib.agents.registry import get_trainer_class
from ray.rllib.utils.framework import try_import_tf
from ray.tune.trial import ExportFormat
tf1, tf, tfv = try_import_tf()
CONFIGS = {
"A3C": {
"explore": False,
"num_workers": 1,
},
"APEX_DDPG": {
"explore": False,
"observation_filter": "MeanStdFilter",
"num_workers": 2,
"min_time_s_per_reporting": 1,
"optimizer": {
"num_replay_buffer_shards": 1,
},
},
"ARS": {
"explore": False,
"num_rollouts": 10,
"num_workers": 2,
"noise_size": 2500000,
"observation_filter": "MeanStdFilter",
},
"DDPG": {
"explore": False,
"timesteps_per_iteration": 100,
},
"DQN": {
"explore": False,
},
"ES": {
"explore": False,
"episodes_per_batch": 10,
"train_batch_size": 100,
"num_workers": 2,
"noise_size": 2500000,
"observation_filter": "MeanStdFilter",
},
"PPO": {
"explore": False,
"num_sgd_iter": 5,
"train_batch_size": 1000,
"num_workers": 2,
},
"SAC": {
"explore": False,
},
}
def export_test(alg_name, failures, framework="tf"):
def valid_tf_model(model_dir):
return os.path.exists(os.path.join(model_dir, "saved_model.pb")) and os.listdir(
os.path.join(model_dir, "variables")
)
def valid_tf_checkpoint(checkpoint_dir):
return (
os.path.exists(os.path.join(checkpoint_dir, "model.meta"))
and os.path.exists(os.path.join(checkpoint_dir, "model.index"))
and os.path.exists(os.path.join(checkpoint_dir, "checkpoint"))
)
cls = get_trainer_class(alg_name)
config = CONFIGS[alg_name].copy()
config["framework"] = framework
if "DDPG" in alg_name or "SAC" in alg_name:
[RLlib] Upgrade gym version to 0.21 and deprecate pendulum-v0. (#19535) * Fix QMix, SAC, and MADDPA too. * Unpin gym and deprecate pendulum v0 Many tests in rllib depended on pendulum v0, however in gym 0.21, pendulum v0 was deprecated in favor of pendulum v1. This may change reward thresholds, so will have to potentially rerun all of the pendulum v1 benchmarks, or use another environment in favor. The same applies to frozen lake v0 and frozen lake v1 Lastly, all of the RLlib tests and have been moved to python 3.7 * Add gym installation based on python version. Pin python<= 3.6 to gym 0.19 due to install issues with atari roms in gym 0.20 * Reformatting * Fixing tests * Move atari-py install conditional to req.txt * migrate to new ale install method * Fix QMix, SAC, and MADDPA too. * Unpin gym and deprecate pendulum v0 Many tests in rllib depended on pendulum v0, however in gym 0.21, pendulum v0 was deprecated in favor of pendulum v1. This may change reward thresholds, so will have to potentially rerun all of the pendulum v1 benchmarks, or use another environment in favor. The same applies to frozen lake v0 and frozen lake v1 Lastly, all of the RLlib tests and have been moved to python 3.7 * Add gym installation based on python version. Pin python<= 3.6 to gym 0.19 due to install issues with atari roms in gym 0.20 Move atari-py install conditional to req.txt migrate to new ale install method Make parametric_actions_cartpole return float32 actions/obs Adding type conversions if obs/actions don't match space Add utils to make elements match gym space dtypes Co-authored-by: Jun Gong <jungong@anyscale.com> Co-authored-by: sven1977 <svenmika1977@gmail.com>
2021-11-03 08:24:00 -07:00
algo = cls(config=config, env="Pendulum-v1")
else:
algo = cls(config=config, env="CartPole-v0")
for _ in range(1):
res = algo.train()
print("current status: " + str(res))
export_dir = os.path.join(
ray._private.utils.get_user_temp_dir(), "export_dir_%s" % alg_name
)
print("Exporting model ", alg_name, export_dir)
algo.export_policy_model(export_dir)
if framework == "tf" and not valid_tf_model(export_dir):
failures.append(alg_name)
shutil.rmtree(export_dir)
if framework == "tf":
print("Exporting checkpoint", alg_name, export_dir)
algo.export_policy_checkpoint(export_dir)
if framework == "tf" and not valid_tf_checkpoint(export_dir):
failures.append(alg_name)
shutil.rmtree(export_dir)
print("Exporting default policy", alg_name, export_dir)
algo.export_model([ExportFormat.CHECKPOINT, ExportFormat.MODEL], export_dir)
if not valid_tf_model(
os.path.join(export_dir, ExportFormat.MODEL)
) or not valid_tf_checkpoint(os.path.join(export_dir, ExportFormat.CHECKPOINT)):
failures.append(alg_name)
# Test loading the exported model.
model = tf.saved_model.load(os.path.join(export_dir, ExportFormat.MODEL))
assert model
shutil.rmtree(export_dir)
algo.stop()
class TestExport(unittest.TestCase):
@classmethod
def setUpClass(cls) -> None:
ray.init(num_cpus=4)
@classmethod
def tearDownClass(cls) -> None:
ray.shutdown()
def test_export_a3c(self):
failures = []
export_test("A3C", failures, "tf")
assert not failures, failures
def test_export_ddpg(self):
failures = []
export_test("DDPG", failures, "tf")
assert not failures, failures
def test_export_dqn(self):
failures = []
export_test("DQN", failures, "tf")
assert not failures, failures
def test_export_ppo(self):
failures = []
export_test("PPO", failures, "torch")
export_test("PPO", failures, "tf")
assert not failures, failures
def test_export_sac(self):
failures = []
export_test("SAC", failures, "tf")
assert not failures, failures
print("All export tests passed!")
if __name__ == "__main__":
import pytest
import sys
sys.exit(pytest.main(["-v", __file__]))