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use GPFlow in workload mapping

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This commit is contained in:
BohanZhang
2020-01-20 03:09:01 -05:00
committed by Dana Van Aken
parent 389174302f
commit 25d1950e67
4 changed files with 73 additions and 10 deletions
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18
server/website/website/tasks/async_tasks.py
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@@ -22,7 +22,7 @@ from analysis.gp_tf import GPRGD
from analysis.nn_tf import NeuralNet
from analysis.gpr import gpr_models
from analysis.gpr import ucb
from analysis.gpr.optimize import tf_optimize
from analysis.gpr.optimize import tf_optimize, gpflow_predict
from analysis.preprocessing import Bin, DummyEncoder
from analysis.constraints import ParamConstraintHelper
from website.models import PipelineData, PipelineRun, Result, Workload, SessionKnob, MetricCatalog
@@ -932,12 +932,16 @@ 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['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()
model_kwargs = {'lengthscales': params['GPR_LENGTH_SCALE'],
'variance': params['GPR_MAGNITUDE'],
'noise_variance': params['GPR_RIDGE']}
tf.reset_default_graph()
graph = tf.get_default_graph()
gpflow.reset_default_session(graph=graph)
m = gpr_models.create_model(params['GPR_MODEL_NAME'], X=X_scaled, y=y_col,
**model_kwargs)
gpr_result = gpflow_predict(m.model, X_target)
predictions[:, j] = gpr_result.ypreds.ravel()
# Bin each of the predicted metric columns by deciles and then
# compute the score (i.e., distance) between the target workload
# and each of the known workloads