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")