def test_parallel():
assert not get_parallel_flag()
with parallel():
assert get_parallel_flag()
with disable_parallel():
assert not get_parallel_flag()
assert get_parallel_flag()
assert not get_parallel_flag()
with disable_parallel():
assert not get_parallel_flag()
assert not get_parallel_flag()
def test_device(mock):
device = Device()
copy_device = copy.deepcopy(device)
assert device.get_id() == 0
assert copy_device.get_id() == 0
with parallel():
inc_device = copy.deepcopy(device)
assert device.get_id() == 1
assert inc_device.get_id() == 1
# check circulation
inc2_device = copy.deepcopy(device)
assert device.get_id() == 0
assert inc2_device.get_id() == 0
from d3rlpy.algos import DQN
from d3rlpy.datasets import get_cartpole
from d3rlpy.metrics.scorer import evaluate_on_environment
from d3rlpy.context import parallel
from sklearn.model_selection import GridSearchCV
# obtain dataset
dataset, env = get_cartpole()
# setup algowithm with GPU enabled
dqn = DQN(use_gpu=True)
# grid search with multiple GPUs assigned to individual processs
with parallel():
env_score = evaluate_on_environment(env)
gscv = GridSearchCV(estimator=dqn,
param_grid={
'learning_rate': [1e-3, 3e-4, 1e-4],
'gamma': [0.99, 0.95, 0.9]
},
scoring={'environment': env_score},
refit=False,
n_jobs=3)
gscv.fit(dataset.episodes, n_epochs=1, show_progress=False)
print(gscv.grid_scores_)