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restore CDBTune

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This commit is contained in:
yangdsh
2019-12-05 06:13:26 +00:00
committed by Dana Van Aken
parent 1e17c78956
commit 5579d8d94f
3 changed files with 124 additions and 50 deletions
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22
server/website/website/settings/constants.py
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@@ -52,11 +52,11 @@ DEFAULT_LEARNING_RATE = 0.01
# a small bias when using training data points as starting points.
GPR_EPS = 0.001
DEFAULT_RIDGE = 1.0
DEFAULT_RIDGE = 0.01
DEFAULT_EPSILON = 1e-6
DEFAULT_SIGMA_MULTIPLIER = 1.0
DEFAULT_SIGMA_MULTIPLIER = 3.0
DEFAULT_MU_MULTIPLIER = 1.0
@@ -84,6 +84,13 @@ DNN_DEBUG = True
DNN_DEBUG_INTERVAL = 100
# ---DDPG CONSTRAINTS CONSTANTS---
# Use a simple reward
DDPG_SIMPLE_REWARD = True
# The weight of future rewards in Q value
DDPG_GAMMA = 0.0
# Batch size in DDPG model
DDPG_BATCH_SIZE = 32
@@ -101,3 +108,14 @@ ACTOR_HIDDEN_SIZES = [128, 128, 64]
# The number of hidden units in each layer of the critic MLP
CRITIC_HIDDEN_SIZES = [64, 128, 64]
# Use the same setting from the CDBTune paper
USE_DEFAULT = True
# Overwrite the DDPG settings if using CDBTune
if USE_DEFAULT:
DDPG_SIMPLE_REWARD = False
DDPG_GAMMA = 0.99
DDPG_BATCH_SIZE = 32
ACTOR_LEARNING_RATE = 0.001
CRITIC_LEARNING_RATE = 0.001
UPDATE_EPOCHS = 1

49
server/website/website/tasks/async_tasks.py
View File

@@ -36,6 +36,7 @@ from website.settings import (USE_GPFLOW, DEFAULT_LENGTH_SCALE, DEFAULT_MAGNITUD
DEFAULT_EPSILON, MAX_ITER, GPR_EPS,
DEFAULT_SIGMA_MULTIPLIER, DEFAULT_MU_MULTIPLIER,
DEFAULT_UCB_SCALE, HP_LEARNING_RATE, HP_MAX_ITER,
DDPG_SIMPLE_REWARD, DDPG_GAMMA, USE_DEFAULT,
DDPG_BATCH_SIZE, ACTOR_LEARNING_RATE,
CRITIC_LEARNING_RATE, UPDATE_EPOCHS,
ACTOR_HIDDEN_SIZES, CRITIC_HIDDEN_SIZES,
@@ -285,18 +286,25 @@ def train_ddpg(result_id):
result = Result.objects.get(pk=result_id)
session = Result.objects.get(pk=result_id).session
session_results = Result.objects.filter(session=session,
creation_time__lte=result.creation_time)
creation_time__lt=result.creation_time)
result_info = {}
result_info['newest_result_id'] = result_id
# Extract data from result
# Extract data from result and previous results
result = Result.objects.filter(pk=result_id)
base_result_id = session_results[0].pk
if len(session_results) == 0:
base_result_id = result_id
prev_result_id = result_id
else:
base_result_id = session_results[0].pk
prev_result_id = session_results[len(session_results)-1].pk
base_result = Result.objects.filter(pk=base_result_id)
prev_result = Result.objects.filter(pk=prev_result_id)
agg_data = DataUtil.aggregate_data(result)
metric_data = agg_data['y_matrix'].flatten()
base_metric_data = (DataUtil.aggregate_data(base_result))['y_matrix'].flatten()
prev_metric_data = (DataUtil.aggregate_data(prev_result))['y_matrix'].flatten()
metric_scalar = MinMaxScaler().fit(metric_data.reshape(1, -1))
normalized_metric_data = metric_scalar.transform(metric_data.reshape(1, -1))[0]
@@ -323,21 +331,37 @@ def train_ddpg(result_id):
target_objective))
objective = metric_data[target_obj_idx]
base_objective = base_metric_data[target_obj_idx]
prev_objective = prev_metric_data[target_obj_idx]
metric_meta = db.target_objectives.get_metric_metadata(
result.session.dbms.pk, result.session.target_objective)
# Calculate the reward
objective = objective / base_objective
if metric_meta[target_objective].improvement == '(less is better)':
reward = -objective
if DDPG_SIMPLE_REWARD:
objective = objective / base_objective
if metric_meta[target_objective].improvement == '(less is better)':
reward = -objective
else:
reward = objective
else:
reward = objective
if metric_meta[target_objective].improvement == '(less is better)':
if objective - base_objective <= 0: # positive reward
reward = (np.square((2 * base_objective - objective) / base_objective) - 1)\
* abs(2 * prev_objective - objective) / prev_objective
else: # negative reward
reward = -(np.square(objective / base_objective) - 1) * objective / prev_objective
else:
if objective - base_objective > 0: # positive reward
reward = (np.square(objective / base_objective) - 1) * objective / prev_objective
else: # negative reward
reward = -(np.square((2 * base_objective - objective) / base_objective) - 1)\
* abs(2 * prev_objective - objective) / prev_objective
LOG.info('reward: %f', reward)
# Update ddpg
ddpg = DDPG(n_actions=knob_num, n_states=metric_num, alr=ACTOR_LEARNING_RATE,
clr=CRITIC_LEARNING_RATE, gamma=0, batch_size=DDPG_BATCH_SIZE,
a_hidden_sizes=ACTOR_HIDDEN_SIZES, c_hidden_sizes=CRITIC_HIDDEN_SIZES)
clr=CRITIC_LEARNING_RATE, gamma=DDPG_GAMMA, batch_size=DDPG_BATCH_SIZE,
a_hidden_sizes=ACTOR_HIDDEN_SIZES, c_hidden_sizes=CRITIC_HIDDEN_SIZES,
use_default=USE_DEFAULT)
if session.ddpg_actor_model and session.ddpg_critic_model:
ddpg.set_model(session.ddpg_actor_model, session.ddpg_critic_model)
if session.ddpg_reply_memory:
@@ -368,7 +392,7 @@ def configuration_recommendation_ddpg(result_info): # pylint: disable=invalid-n
metric_num = len(metric_data)
ddpg = DDPG(n_actions=knob_num, n_states=metric_num, a_hidden_sizes=ACTOR_HIDDEN_SIZES,
c_hidden_sizes=CRITIC_HIDDEN_SIZES)
c_hidden_sizes=CRITIC_HIDDEN_SIZES, use_default=USE_DEFAULT)
if session.ddpg_actor_model is not None and session.ddpg_critic_model is not None:
ddpg.set_model(session.ddpg_actor_model, session.ddpg_critic_model)
if session.ddpg_reply_memory is not None:
@@ -646,9 +670,8 @@ def configuration_recommendation(recommendation_input):
epsilon=DEFAULT_EPSILON,
max_iter=MAX_ITER,
sigma_multiplier=DEFAULT_SIGMA_MULTIPLIER,
mu_multiplier=DEFAULT_MU_MULTIPLIER,
ridge=DEFAULT_RIDGE)
model.fit(X_scaled, y_scaled, X_min, X_max)
mu_multiplier=DEFAULT_MU_MULTIPLIER)
model.fit(X_scaled, y_scaled, X_min, X_max, ridge=DEFAULT_RIDGE)
res = model.predict(X_samples, constraint_helper=constraint_helper)
best_config_idx = np.argmin(res.minl.ravel())