def createNewModel():
""" Create a new Delphi model. """
data = json.loads(request.data)
if os.environ.get("CI") == "true":
# When running in a continuous integration run, we set the sampling
# resolution to be small to prevent timeouts.
res = 5
elif os.environ.get("DELPHI_N_SAMPLES") is not None:
# We also enable setting the sampling resolution through the
# environment variable "DELPHI_N_SAMPLES", for development and testing
# purposes.
res = int(os.environ["DELPHI_N_SAMPLES"])
else:
# If neither "CI" or "DELPHI_N_SAMPLES" is set, we default to a
# sampling resolution of 1000.
# TODO - we might want to set the default sampling resolution with some
# kind of heuristic, based on the number of nodes and edges. - Adarsh
res = 1000
G = AnalysisGraph.from_causemos_json_string(request.data, res)
model = DelphiModel(id=data["id"],
model=G.serialize_to_json_string(verbose=False))
db.session.merge(model)
db.session.commit()
response = json.loads(G.generate_create_model_response())
return jsonify(response)
def createNewModel():
""" Create a new Delphi model. """
data = json.loads(request.data)
G = AnalysisGraph.from_causemos_json_string(request.data)
G.id = data["id"]
model = DelphiModel(id=data["id"], model=G.to_json_string())
db.session.merge(model)
db.session.commit()
edge_weights = G.get_edge_weights_for_causemos_viz()
return jsonify({"status": "success", "relations": edge_weights})
def createNewModel():
""" Create a new Delphi model. """
if os.environ.get("CI") == "true":
# When running in a continuous integration run, we set the sampling
# resolution to be small to prevent timeouts.
kde_kernels = 5
sampling_resolution = 5
burn = 5
elif os.environ.get("DELPHI_N_SAMPLES") is not None:
# We also enable setting the sampling resolution through the
# environment variable "DELPHI_N_SAMPLES", for development and testing
# purposes.
# NOTE: When creating, this environment variable is named incorrectly!
kde_kernels = int(os.environ["DELPHI_N_SAMPLES"])
sampling_resolution = 100
burn = 100
else:
# If neither "CI" or "DELPHI_N_SAMPLES" is set, we default to a
# sampling resolution of 1000.
# TODO - we might want to set the default sampling resolution with some
# kind of heuristic, based on the number of nodes and edges. - Adarsh
kde_kernels = 1000
sampling_resolution = 1000
burn = 10000
data = json.loads(request.data)
G = AnalysisGraph.from_causemos_json_string(request.data,
belief_score_cutoff=0,
grounding_score_cutoff=0,
kde_kernels=kde_kernels)
model = DelphiModel(id=data["id"],
model=G.serialize_to_json_string(verbose=False))
db.session.merge(model)
db.session.commit()
response = json.loads(G.generate_create_model_response())
# executor.submit_stored(data["id"], train_model, G, data["id"])
try:
proc = multiprocessing.Process(target=train_model,
args=(G, data["id"],
sampling_resolution, burn,
current_app),
name='training')
proc.start()
except multiprocessing.ProcessError:
print("Error: unable to start training process")
response['status'] = 'server error: training'
return jsonify(response)