def main():
"""Main runner for shell"""
from ai_benchmark import AIBenchmark
parsed_args = parser.parse_known_args()[0]
benchmark = AIBenchmark(
use_cpu=parsed_args.use_cpu,
verbose_level=parsed_args.verbose,
seed=parsed_args.seed,
)
test_info, results = benchmark.run(
precision=parsed_args.precision,
test_ids=parsed_args.test_ids,
training=parsed_args.run_training,
inference=parsed_args.run_inference,
micro=parsed_args.run_micro,
cpu_cores=parsed_args.cpu_cores,
inter_threads=parsed_args.inter_threads,
intra_threads=parsed_args.intra_threads,
)
if parsed_args.json:
output = vars(results)
output['test_results'] = {
k: vars(v)
for k, v in output['test_results'].items()
}
output['test_info'] = vars(test_info)
output['test_info'].pop('results', None)
print(json.dumps(output, indent=4))
from ai_benchmark import AIBenchmark benchmark = AIBenchmark(use_CPU=True, verbose_level=3) results = benchmark.run(precision="high")
import tensorflow as tf print(tf.__version__) from ai_benchmark import AIBenchmark benchmark = AIBenchmark() results = benchmark.run()
from ai_benchmark import AIBenchmark benchmark = AIBenchmark(use_CPU=True, verbose_level=3) results = benchmark.run()
else:
os.environ["KMP_AFFINITY"] = "verbose,{policy}".format(
policy=thread_mapping_policy)
os.system("export KMP_AFFINITY=verbose,{policy}".format(
policy=thread_mapping_policy))
if __name__ == "__main__":
""" python3 run_experiments.py machine_name start_index n_rounds
"""
dir_path = "/home/users/mwcamargo/td_mapping/src/resultados/"
machine_name = sys.argv[1]
start_index = int(sys.argv[2])
n_rounds = int(sys.argv[3])
benchmark = AIBenchmark(use_CPU=True)
#Generate a doe corresponding to some round and them reads it to pick up the mappings corresponding to the round
for i in range(start_index, start_index + n_rounds):
generateDoeCSV(dir_path + "doe_" + machine_name +
"_{number}.csv".format(number=i))
with open(
dir_path + "doe_" + machine_name +
"_{number}.csv".format(number=i), "r") as doe:
print("------" + i + " round------", flush=True)
experiment_rounds = doe.readlines()
for experiment_round in experiment_rounds:
mappings = experiment_round.split(",")
thread_mapping = mappings[0]
from ai_benchmark import AIBenchmark benchmark = AIBenchmark(use_CPU=False,verbose_level = 1) results = benchmark.run_inference(precision="high")
from ai_benchmark import AIBenchmark teste = AIBenchmark(use_CPU=True) teste.run(precision="high")