diff --git a/script/formatting/config/pylintrc b/script/formatting/config/pylintrc
index 02ade7d..4e911c7 100644
--- a/script/formatting/config/pylintrc
+++ b/script/formatting/config/pylintrc
@@ -13,7 +13,7 @@ profile=no
# Add files or directories to the blacklist. They should be base names, not
# paths.
-ignore=CVS,.git,manage.py,0001_initial.py,0002_enable_compression.py,0003_load_initial_data.py,0004_add_lhs.py,0005_add_workload_field.py,0006_session_hyper_parameters.py,credentials.py,create_knob_settings.py
+ignore=CVS,.git,manage.py,0001_initial.py,0002_enable_compression.py,0003_load_initial_data.py,0004_add_lhs.py,0005_add_workload_field.py,0006_session_hyperparameters.py,credentials.py,create_knob_settings.py
# ignore-patterns=**/migrations/*.py
diff --git a/server/website/tests/test_views.py b/server/website/tests/test_views.py
index 80579b9..07928f6 100644
--- a/server/website/tests/test_views.py
+++ b/server/website/tests/test_views.py
@@ -150,7 +150,7 @@ class SessionViewsTests(TestCase):
'storage': '32',
'storage_type': 5,
'dbms': 1,
- 'hyper_parameters': '{}'
+ 'hyperparameters': '{}'
}
def setUp(self):
diff --git a/server/website/website/forms.py b/server/website/website/forms.py
index 7200810..26c8f06 100644
--- a/server/website/website/forms.py
+++ b/server/website/website/forms.py
@@ -134,12 +134,12 @@ class SessionForm(forms.ModelForm):
model = Session
fields = ('name', 'description', 'tuning_session', 'dbms', 'cpu', 'memory', 'storage',
- 'algorithm', 'target_objective', 'hyper_parameters')
+ 'algorithm', 'target_objective', 'hyperparameters')
widgets = {
'name': forms.TextInput(attrs={'required': True}),
'description': forms.Textarea(attrs={'maxlength': 500, 'rows': 5}),
- 'hyper_parameters': forms.Textarea(attrs={'maxlength': 2000, 'rows': 10}),
+ 'hyperparameters': forms.Textarea(attrs={'maxlength': 5000, 'rows': 15}),
}
labels = {
'dbms': 'DBMS',
diff --git a/server/website/website/migrations/0006_session_hyper_parameters.py b/server/website/website/migrations/0006_session_hyper_parameters.py
deleted file mode 100644
index ed15cf7..0000000
--- a/server/website/website/migrations/0006_session_hyper_parameters.py
+++ /dev/null
@@ -1,20 +0,0 @@
-# -*- coding: utf-8 -*-
-# Generated by Django 1.11.23 on 2020-01-12 07:29
-from __future__ import unicode_literals
-
-from django.db import migrations, models
-
-
-class Migration(migrations.Migration):
-
- dependencies = [
- ('website', '0005_add_workload_field'),
- ]
-
- operations = [
- migrations.AddField(
- model_name='session',
- name='hyper_parameters',
- field=models.TextField(default='{}'),
- ),
- ]
diff --git a/server/website/website/migrations/0006_session_hyperparameters.py b/server/website/website/migrations/0006_session_hyperparameters.py
new file mode 100644
index 0000000..96b2300
--- /dev/null
+++ b/server/website/website/migrations/0006_session_hyperparameters.py
@@ -0,0 +1,20 @@
+# -*- coding: utf-8 -*-
+# Generated by Django 1.11.23 on 2020-01-18 16:22
+from __future__ import unicode_literals
+
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+
+ dependencies = [
+ ('website', '0005_add_workload_field'),
+ ]
+
+ operations = [
+ migrations.AddField(
+ model_name='session',
+ name='hyperparameters',
+ field=models.TextField(default='{\n "DDPG_ACTOR_HIDDEN_SIZES": [128, 128, 64],\n "DDPG_ACTOR_LEARNING_RATE": 0.02,\n "DDPG_CRITIC_HIDDEN_SIZES": [64, 128, 64],\n "DDPG_CRITIC_LEARNING_RATE": 0.001,\n "DDPG_BATCH_SIZE": 32,\n "DDPG_GAMMA": 0.0,\n "DDPG_SIMPLE_REWARD": true,\n "DDPG_UPDATE_EPOCHS": 30,\n "DDPG_USE_DEFAULT": false,\n "DNN_DEBUG": true,\n "DNN_DEBUG_INTERVAL": 100,\n "DNN_EXPLORE": false,\n "DNN_EXPLORE_ITER": 500,\n "DNN_GD_ITER": 100,\n "DNN_NOISE_SCALE_BEGIN": 0.1,\n "DNN_NOISE_SCALE_END": 0.0,\n "DNN_TRAIN_ITER": 100,\n "FLIP_PROB_DECAY": 0.5,\n "GPR_BATCH_SIZE": 3000,\n "GPR_DEBUG": true,\n "GPR_EPS": 0.001,\n "GPR_EPSILON": 1e-06,\n "GPR_LEARNING_RATE": 0.01,\n "GPR_LENGTH_SCALE": 2.0,\n "GPR_MAGNITUDE": 1.0,\n "GPR_MAX_ITER": 500,\n "GPR_MAX_TRAIN_SIZE": 7000,\n "GPR_MU_MULTIPLIER": 1.0,\n "GPR_MODEL_NAME": "BasicGP",\n "GPR_HP_LEARNING_RATE": 0.001,\n "GPR_HP_MAX_ITER": 5000,\n "GPR_RIDGE": 1.0,\n "GPR_SIGMA_MULTIPLIER": 1.0,\n "GPR_UCB_SCALE": 0.2,\n "GPR_USE_GPFLOW": true,\n "GPR_UCB_BETA": "get_beta_td",\n "IMPORTANT_KNOB_NUMBER": 10000,\n "INIT_FLIP_PROB": 0.3,\n "NUM_SAMPLES": 30,\n "TF_NUM_THREADS": 4,\n "TOP_NUM_CONFIG": 10}'),
+ ),
+ ]
diff --git a/server/website/website/models.py b/server/website/website/models.py
index ee40d33..b3407b6 100644
--- a/server/website/website/models.py
+++ b/server/website/website/models.py
@@ -162,7 +162,48 @@ class Session(BaseModel):
target_objective = models.CharField(
max_length=64, default=target_objectives.default())
- hyper_parameters = models.TextField(default="{}")
+ hyperparameters = models.TextField(default='''{
+ "DDPG_ACTOR_HIDDEN_SIZES": [128, 128, 64],
+ "DDPG_ACTOR_LEARNING_RATE": 0.02,
+ "DDPG_CRITIC_HIDDEN_SIZES": [64, 128, 64],
+ "DDPG_CRITIC_LEARNING_RATE": 0.001,
+ "DDPG_BATCH_SIZE": 32,
+ "DDPG_GAMMA": 0.0,
+ "DDPG_SIMPLE_REWARD": true,
+ "DDPG_UPDATE_EPOCHS": 30,
+ "DDPG_USE_DEFAULT": false,
+ "DNN_DEBUG": true,
+ "DNN_DEBUG_INTERVAL": 100,
+ "DNN_EXPLORE": false,
+ "DNN_EXPLORE_ITER": 500,
+ "DNN_GD_ITER": 100,
+ "DNN_NOISE_SCALE_BEGIN": 0.1,
+ "DNN_NOISE_SCALE_END": 0.0,
+ "DNN_TRAIN_ITER": 100,
+ "FLIP_PROB_DECAY": 0.5,
+ "GPR_BATCH_SIZE": 3000,
+ "GPR_DEBUG": true,
+ "GPR_EPS": 0.001,
+ "GPR_EPSILON": 1e-06,
+ "GPR_LEARNING_RATE": 0.01,
+ "GPR_LENGTH_SCALE": 2.0,
+ "GPR_MAGNITUDE": 1.0,
+ "GPR_MAX_ITER": 500,
+ "GPR_MAX_TRAIN_SIZE": 7000,
+ "GPR_MU_MULTIPLIER": 1.0,
+ "GPR_MODEL_NAME": "BasicGP",
+ "GPR_HP_LEARNING_RATE": 0.001,
+ "GPR_HP_MAX_ITER": 5000,
+ "GPR_RIDGE": 1.0,
+ "GPR_SIGMA_MULTIPLIER": 1.0,
+ "GPR_UCB_SCALE": 0.2,
+ "GPR_USE_GPFLOW": true,
+ "GPR_UCB_BETA": "get_beta_td",
+ "IMPORTANT_KNOB_NUMBER": 10000,
+ "INIT_FLIP_PROB": 0.3,
+ "NUM_SAMPLES": 30,
+ "TF_NUM_THREADS": 4,
+ "TOP_NUM_CONFIG": 10}''')
def clean(self):
if self.target_objective is None:
diff --git a/server/website/website/settings/constants.py b/server/website/website/settings/constants.py
index 51da41d..717c7b6 100644
--- a/server/website/website/settings/constants.py
+++ b/server/website/website/settings/constants.py
@@ -4,8 +4,6 @@
# Copyright (c) 2017-18, Carnegie Mellon University Database Group
#
-
-# ---------------------------------------------
# These parameters are not specified for any session, so they can only be set here
# address categorical knobs (enum, boolean)
@@ -14,133 +12,3 @@ ENABLE_DUMMY_ENCODER = False
# ---PIPELINE CONSTANTS---
# how often to run the background tests, in seconds
RUN_EVERY = 300
-
-
-# ---------------------------------------------
-# The following parameters can be viewed and modified on the session page on the website
-
-# ---SAMPLING CONSTANTS---
-# the number of samples (staring points) in gradient descent
-NUM_SAMPLES = 30
-
-# the number of selected tuning knobs
-# set it to a large value if you want to disable the knob identification
-# phase (i.e. tune all session knobs)
-IMPORTANT_KNOB_NUMBER = 10000
-
-# top K config with best performance put into prediction
-TOP_NUM_CONFIG = 10
-
-# ---CONSTRAINTS CONSTANTS---
-# Initial probability to flip categorical feature in apply_constraints
-# server/analysis/constraints.py
-INIT_FLIP_PROB = 0.3
-
-# The probability that we flip the i_th categorical feature is
-# FLIP_PROB_DECAY * (probability we flip (i-1)_th categorical feature)
-FLIP_PROB_DECAY = 0.5
-
-# ---GPR CONSTANTS---
-USE_GPFLOW = True
-
-GPR_DEBUG = True
-
-DEFAULT_LENGTH_SCALE = 2.0
-
-DEFAULT_MAGNITUDE = 1.0
-
-# Max training size in GPR model
-MAX_TRAIN_SIZE = 7000
-
-# Batch size in GPR model
-BATCH_SIZE = 3000
-
-# Threads for TensorFlow config
-NUM_THREADS = 4
-
-# Value of beta for UCB
-UCB_BETA = 'get_beta_td'
-
-# Name of the GPR model to use (GPFLOW only)
-GPR_MODEL_NAME = 'BasicGP'
-
-# ---GRADIENT DESCENT CONSTANTS---
-# the maximum iterations of gradient descent
-MAX_ITER = 500
-
-DEFAULT_LEARNING_RATE = 0.01
-
-# ---GRADIENT DESCENT FOR GPR---
-# a small bias when using training data points as starting points.
-GPR_EPS = 0.001
-
-DEFAULT_RIDGE = 1.00
-
-DEFAULT_EPSILON = 1e-6
-
-DEFAULT_SIGMA_MULTIPLIER = 1.0
-
-DEFAULT_MU_MULTIPLIER = 1.0
-
-DEFAULT_UCB_SCALE = 0.2
-
-# ---HYPERPARAMETER TUNING FOR GPR---
-HP_MAX_ITER = 5000
-
-HP_LEARNING_RATE = 0.001
-
-# ---GRADIENT DESCENT FOR DNN---
-DNN_TRAIN_ITER = 100
-
-# Gradient Descent iteration for recommendation
-DNN_GD_ITER = 100
-
-DNN_EXPLORE = False
-
-DNN_EXPLORE_ITER = 500
-
-# noise scale for paramater space exploration
-DNN_NOISE_SCALE_BEGIN = 0.1
-
-DNN_NOISE_SCALE_END = 0.0
-
-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
-
-# Learning rate of actor network
-ACTOR_LEARNING_RATE = 0.02
-
-# Learning rate of critic network
-CRITIC_LEARNING_RATE = 0.001
-
-# Number of update epochs per iteration
-UPDATE_EPOCHS = 30
-
-# The number of hidden units in each layer of the actor MLP
-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 = False
-# 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
diff --git a/server/website/website/tasks/async_tasks.py b/server/website/website/tasks/async_tasks.py
index fc4c2da..19caece 100644
--- a/server/website/website/tasks/async_tasks.py
+++ b/server/website/website/tasks/async_tasks.py
@@ -322,7 +322,7 @@ def train_ddpg(result_id):
LOG.info('Add training data to ddpg and train ddpg')
result = Result.objects.get(pk=result_id)
session = Result.objects.get(pk=result_id).session
- params = JSONUtil.loads(session.hyper_parameters)
+ params = JSONUtil.loads(session.hyperparameters)
session_results = Result.objects.filter(session=session,
creation_time__lt=result.creation_time)
result_info = {}
@@ -402,16 +402,18 @@ def train_ddpg(result_id):
LOG.info('reward: %f', reward)
# Update ddpg
- ddpg = DDPG(n_actions=knob_num, n_states=metric_num, alr=params['ACTOR_LEARNING_RATE'],
- clr=params['CRITIC_LEARNING_RATE'], gamma=params['DDPG_GAMMA'],
- batch_size=params['DDPG_BATCH_SIZE'], a_hidden_sizes=params['ACTOR_HIDDEN_SIZES'],
- c_hidden_sizes=params['CRITIC_HIDDEN_SIZES'], use_default=params['USE_DEFAULT'])
+ ddpg = DDPG(n_actions=knob_num, n_states=metric_num, alr=params['DDPG_ACTOR_LEARNING_RATE'],
+ clr=params['DDPG_CRITIC_LEARNING_RATE'], gamma=params['DDPG_GAMMA'],
+ batch_size=params['DDPG_BATCH_SIZE'],
+ a_hidden_sizes=params['DDPG_ACTOR_HIDDEN_SIZES'],
+ c_hidden_sizes=params['DDPG_CRITIC_HIDDEN_SIZES'],
+ use_default=params['DDPG_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:
ddpg.replay_memory.set(session.ddpg_reply_memory)
ddpg.add_sample(normalized_metric_data, knob_data, reward, normalized_metric_data)
- for _ in range(params['UPDATE_EPOCHS']):
+ for _ in range(params['DDPG_UPDATE_EPOCHS']):
ddpg.update()
session.ddpg_actor_model, session.ddpg_critic_model = ddpg.get_model()
session.ddpg_reply_memory = ddpg.replay_memory.get()
@@ -443,7 +445,7 @@ def configuration_recommendation_ddpg(result_info): # pylint: disable=invalid-n
result_list = Result.objects.filter(pk=result_id)
result = result_list.first()
session = result.session
- params = JSONUtil.loads(session.hyper_parameters)
+ params = JSONUtil.loads(session.hyperparameters)
agg_data = DataUtil.aggregate_data(result_list)
metric_data, _ = clean_metric_data(agg_data['y_matrix'], agg_data['y_columnlabels'], session)
metric_data = metric_data.flatten()
@@ -455,8 +457,9 @@ 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=params['ACTOR_HIDDEN_SIZES'],
- c_hidden_sizes=params['CRITIC_HIDDEN_SIZES'], use_default=params['USE_DEFAULT'])
+ a_hidden_sizes=params['DDPG_ACTOR_HIDDEN_SIZES'],
+ c_hidden_sizes=params['DDPG_CRITIC_HIDDEN_SIZES'],
+ use_default=params['DDPG_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:
@@ -492,7 +495,7 @@ def combine_workload(target_data):
newest_result = Result.objects.get(pk=target_data['newest_result_id'])
session = newest_result.session
- params = JSONUtil.loads(session.hyper_parameters)
+ params = JSONUtil.loads(session.hyperparameters)
cleaned_workload_knob_data = clean_knob_data(workload_knob_data["data"],
workload_knob_data["columnlabels"],
newest_result.session)
@@ -661,7 +664,7 @@ def configuration_recommendation(recommendation_input):
LOG.info('configuration_recommendation called')
newest_result = Result.objects.get(pk=target_data['newest_result_id'])
session = newest_result.session
- params = JSONUtil.loads(session.hyper_parameters)
+ params = JSONUtil.loads(session.hyperparameters)
if target_data['bad'] is True:
target_data_res = create_and_save_recommendation(
@@ -726,17 +729,17 @@ def configuration_recommendation(recommendation_input):
elif algorithm == AlgorithmType.GPR:
# default gpr model
- if params['USE_GPFLOW']:
+ if params['GPR_USE_GPFLOW']:
model_kwargs = {}
- model_kwargs['model_learning_rate'] = params['HP_LEARNING_RATE']
- model_kwargs['model_maxiter'] = params['HP_MAX_ITER']
+ model_kwargs['model_learning_rate'] = params['GPR_HP_LEARNING_RATE']
+ model_kwargs['model_maxiter'] = params['GPR_HP_MAX_ITER']
opt_kwargs = {}
- opt_kwargs['learning_rate'] = params['DEFAULT_LEARNING_RATE']
- opt_kwargs['maxiter'] = params['MAX_ITER']
+ opt_kwargs['learning_rate'] = params['GPR_LEARNING_RATE']
+ opt_kwargs['maxiter'] = params['GPR_MAX_ITER']
opt_kwargs['bounds'] = [X_min, X_max]
opt_kwargs['debug'] = params['GPR_DEBUG']
- opt_kwargs['ucb_beta'] = ucb.get_ucb_beta(params['UCB_BETA'],
- scale=params['DEFAULT_UCB_SCALE'],
+ opt_kwargs['ucb_beta'] = ucb.get_ucb_beta(params['GPR_UCB_BETA'],
+ scale=params['GPR_UCB_SCALE'],
t=i + 1., ndim=X_scaled.shape[1])
tf.reset_default_graph()
graph = tf.get_default_graph()
@@ -745,17 +748,17 @@ def configuration_recommendation(recommendation_input):
**model_kwargs)
res = tf_optimize(m.model, X_samples, **opt_kwargs)
else:
- model = GPRGD(length_scale=params['DEFAULT_LENGTH_SCALE'],
- magnitude=params['DEFAULT_MAGNITUDE'],
- max_train_size=params['MAX_TRAIN_SIZE'],
- batch_size=params['BATCH_SIZE'],
- num_threads=params['NUM_THREADS'],
- learning_rate=params['DEFAULT_LEARNING_RATE'],
- epsilon=params['DEFAULT_EPSILON'],
- max_iter=params['MAX_ITER'],
- sigma_multiplier=params['DEFAULT_SIGMA_MULTIPLIER'],
- mu_multiplier=params['DEFAULT_MU_MULTIPLIER'],
- ridge=params['DEFAULT_RIDGE'])
+ model = GPRGD(length_scale=params['GPR_LENGTH_SCALE'],
+ magnitude=params['GPR_MAGNITUDE'],
+ max_train_size=params['GPR_MAX_TRAIN_SIZE'],
+ batch_size=params['GPR_BATCH_SIZE'],
+ num_threads=params['TF_NUM_THREADS'],
+ learning_rate=params['GPR_LEARNING_RATE'],
+ epsilon=params['GPR_EPSILON'],
+ max_iter=params['GPR_MAX_ITER'],
+ sigma_multiplier=params['GPR_SIGMA_MULTIPLIER'],
+ mu_multiplier=params['GPR_MU_MULTIPLIER'],
+ ridge=params['GPR_RIDGE'])
model.fit(X_scaled, y_scaled, X_min, X_max)
res = model.predict(X_samples, constraint_helper=constraint_helper)
@@ -814,7 +817,7 @@ def map_workload(map_workload_input):
newest_result = Result.objects.get(pk=target_data['newest_result_id'])
session = newest_result.session
- params = JSONUtil.loads(session.hyper_parameters)
+ params = JSONUtil.loads(session.hyperparameters)
target_workload = newest_result.workload
X_columnlabels = np.array(target_data['X_columnlabels'])
y_columnlabels = np.array(target_data['y_columnlabels'])
@@ -929,11 +932,11 @@ def map_workload(map_workload_input):
# and then predict the performance of each metric for each of
# the knob configurations attempted so far by the target.
y_col = y_col.reshape(-1, 1)
- model = GPRNP(length_scale=params['DEFAULT_LENGTH_SCALE'],
- magnitude=params['DEFAULT_MAGNITUDE'],
- max_train_size=params['MAX_TRAIN_SIZE'],
- batch_size=params['BATCH_SIZE'])
- model.fit(X_scaled, y_col, ridge=params['DEFAULT_RIDGE'])
+ model = GPRNP(length_scale=params['GPR_LENGTH_SCALE'],
+ magnitude=params['GPR_MAGNITUDE'],
+ max_train_size=params['GPR_MAX_TRAIN_SIZE'],
+ batch_size=params['GPR_BATCH_SIZE'])
+ model.fit(X_scaled, y_col, ridge=params['GPR_RIDGE'])
predictions[:, j] = model.predict(X_target).ypreds.ravel()
# Bin each of the predicted metric columns by deciles and then
# compute the score (i.e., distance) between the target workload
diff --git a/server/website/website/templates/edit_session.html b/server/website/website/templates/edit_session.html
index f93ca66..9af3c6a 100644
--- a/server/website/website/templates/edit_session.html
+++ b/server/website/website/templates/edit_session.html
@@ -49,9 +49,9 @@
| {{ form.target_objective.label_tag }} |
{{ form.target_objective }} |
-
- | {{ form.hyper_parameters.label_tag }} |
- {{ form.hyper_parameters }} |
+
+ | {{ form.hyperparameters.label_tag }} |
+ {{ form.hyperparameters }} |
| {{ form.gen_upload_code.label_tag }} |
diff --git a/server/website/website/views.py b/server/website/website/views.py
index d9e608b..46bf4e8 100644
--- a/server/website/website/views.py
+++ b/server/website/website/views.py
@@ -330,14 +330,12 @@ def create_or_edit_session(request, project_id, session_id=''):
else:
# Return a new form with defaults for creating a new session
session = None
- hyper_parameters = JSONUtil.dumps(utils.get_constants())
form_kwargs.update(
initial={
'dbms': DBMSCatalog.objects.get(
type=DBMSType.POSTGRES, version='9.6'),
'algorithm': AlgorithmType.GPR,
- 'target_objective': target_objectives.default(),
- 'hyper_parameters': hyper_parameters
+ 'target_objective': target_objectives.default()
})
form = SessionForm(**form_kwargs)
context = {
@@ -1457,36 +1455,31 @@ def create_test_website(request): # pylint: disable=unused-argument
password='ottertune_test_user')
test_project = Project.objects.create(user=test_user, name='ottertune_test_project',
creation_time=now(), last_update=now())
- hyper_parameters = JSONUtil.dumps(utils.get_constants())
# create no tuning session
s1 = Session.objects.create(name='test_session_no_tuning', tuning_session='no_tuning_session',
dbms_id=1, hardware=test_hardware, project=test_project,
creation_time=now(), last_update=now(), user=test_user,
- upload_code='ottertuneTestNoTuning',
- hyper_parameters=hyper_parameters)
+ upload_code='ottertuneTestNoTuning')
set_default_knobs(s1)
# create gpr session
s2 = Session.objects.create(name='test_session_gpr', tuning_session='tuning_session',
dbms_id=1, hardware=test_hardware, project=test_project,
creation_time=now(), last_update=now(), algorithm=AlgorithmType.GPR,
- upload_code='ottertuneTestTuningGPR', user=test_user,
- hyper_parameters=hyper_parameters)
+ upload_code='ottertuneTestTuningGPR', user=test_user)
set_default_knobs(s2)
# create dnn session
s3 = Session.objects.create(name='test_session_dnn', tuning_session='tuning_session',
dbms_id=1, hardware=test_hardware, project=test_project,
creation_time=now(), last_update=now(), algorithm=AlgorithmType.DNN,
- upload_code='ottertuneTestTuningDNN', user=test_user,
- hyper_parameters=hyper_parameters)
+ upload_code='ottertuneTestTuningDNN', user=test_user)
set_default_knobs(s3)
# create ddpg session
s4 = Session.objects.create(name='test_session_ddpg', tuning_session='tuning_session',
dbms_id=1, hardware=test_hardware, project=test_project,
creation_time=now(), last_update=now(), user=test_user,
upload_code='ottertuneTestTuningDDPG',
- algorithm=AlgorithmType.DDPG,
- hyper_parameters=hyper_parameters)
+ algorithm=AlgorithmType.DDPG)
set_default_knobs(s4)
response = HttpResponse("Success: create test website successfully")
return response