Kepler Model Server API

Getting Powers from Estimator

module: estimator (src/estimate/estimator.py)

/tmp/estimator.socket

Parameters of PowerRequest

key	value	description
metrics	list of string	list of available input features (measured metrics)
output_type	either of the following values: AbsPower (for node-level power model), DynPower (for container-level power model)	the requested model type
trainer_name (optional)	string	filter model with trainer name.
filter (optional)	string	expression in the form attribute1:threshold1; attribute2:threshold2.

module: server (src/server/model_server.py)

:8100/model
POST

Parameters of ModelRequest

key	value	description
metrics	list of string	list of available input features (measured metrics)
output_type	either of the following values: AbsPower (for node-level power model), DynPower (for container-level power model)	the requested model type
weight	boolean	return model weights in json format if true. Otherwise, return model in zip file format.
trainer_name (optional)	string	filter model with trainer name.
node_type (optional)	string	filter model with node type.
filter (optional)	string	expression in the form attribute1:threshold1; attribute2:threshold2.

module: offline trainer (src/train/offline_trainer.py)

:8102/train
POST

Parameters of TrainRequest

key	value	description
name	string	pipeline/model name
energy_source	valid key in PowerSourceMap	target energy source to train for
trainer	TrainAttribute	attributes for training
prome_response	json	prom response with workload for power model training

TrainAttribute

key	value	description
abs_trainers	list of available trainer class names	trainer classes in the pipeline to train for absolute power
dyn_trainers	list of available trainer class names	trainer classes in the pipeline to train for dynamic power
isolator	valid isolator class name	isolator class of the pipeline to isolate the target data to train for dynamic power
isolator_args	dict	mapping between isolator-specific argument name and value

module: server (src/server/model_server.py)

/metrics
GET

module: online trainer (src/train/online_trainer.py) running as a sidecar to server

periodically query prometheus metric server on SAMPLING INTERVAL

module: profiler (src/profile/profiler.py)