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#!/usr/bin/env python3
import argparse
import json
import logging
import sys
import dfatool.cli
import dfatool.utils
from dfatool.behaviour import EventSequenceModel
from dfatool.model import AnalyticModel
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.RawDescriptionHelpFormatter, description=__doc__
)
parser.add_argument("--aggregate", choices=["sum"], default="sum")
parser.add_argument("--aggregate-unit", choices=["s", "B/s"], default="s")
parser.add_argument(
"--aggregate-init",
default=0,
type=float,
)
parser.add_argument(
"--log-level",
metavar="LEVEL",
choices=["debug", "info", "warning", "error"],
default="warning",
help="Set log level",
)
parser.add_argument("--normalize-output", type=str)
parser.add_argument(
"--info",
action="store_true",
help="Show benchmark information (number of measurements, parameter values, ...)",
)
parser.add_argument(
"--models",
nargs="+",
type=str,
help="Path to model file (.json or .json.xz)",
)
parser.add_argument(
"--use-lut",
action="store_true",
help="Use LUT rather than performance model for prediction",
)
parser.add_argument("event", nargs="+", type=str)
args = parser.parse_args()
if args.log_level:
numeric_level = getattr(logging, args.log_level.upper(), None)
if not isinstance(numeric_level, int):
print(f"Invalid log level: {args.log_level}", file=sys.stderr)
sys.exit(1)
logging.basicConfig(
level=numeric_level,
format="{asctime} {levelname}:{name}:{message}",
style="{",
)
models = list()
for model_file in args.models:
with open(model_file, "r") as f:
models.append(AnalyticModel.from_json(json.load(f)))
if args.info:
for i in range(len(models)):
print(f"""{args.models[i]}: {" ".join(models[i].parameters)}""")
_, param_info = models[i].get_fitted()
for name in models[i].names:
for attr in models[i].attributes(name):
print(f" {name}.{attr} {param_info(name, attr)}")
workload = EventSequenceModel(models)
aggregate = workload.eval_strs(
args.event,
aggregate=args.aggregate,
aggregate_init=args.aggregate_init,
use_lut=args.use_lut,
)
if args.normalize_output:
sf = dfatool.cli.parse_shift_function(
"--normalize-output", args.normalize_output
)
print(dfatool.utils.human_readable(sf(aggregate), args.aggregate_unit))
else:
print(dfatool.utils.human_readable(aggregate, args.aggregate_unit))
if __name__ == "__main__":
main()
|
