def main(cmd_args): """Train LM.""" parser = get_parser() args, _ = parser.parse_known_args(cmd_args) if args.backend == "chainer" and args.train_dtype != "float32": raise NotImplementedError( f"chainer backend does not support --train-dtype {args.train_dtype}." "Use --dtype float32.") if args.ngpu == 0 and args.train_dtype in ("O0", "O1", "O2", "O3", "float16"): raise ValueError( f"--train-dtype {args.train_dtype} does not support the CPU backend." ) # parse arguments dynamically model_class = dynamic_import_lm(args.model_module, args.backend) model_class.add_arguments(parser) if args.schedulers is not None: for k, v in args.schedulers: scheduler_class = dynamic_import_scheduler(v) scheduler_class.add_arguments(k, parser) opt_class = dynamic_import_optimizer(args.opt, args.backend) opt_class.add_arguments(parser) args = parser.parse_args(cmd_args) # logging info if args.verbose > 0: logging.basicConfig( level=logging.INFO, format= '%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s') else: logging.basicConfig( level=logging.WARN, format= '%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s') logging.warning('Skip DEBUG/INFO messages') # If --ngpu is not given, # 1. if CUDA_VISIBLE_DEVICES is set, all visible devices # 2. if nvidia-smi exists, use all devices # 3. else ngpu=0 if args.ngpu is None: cvd = os.environ.get("CUDA_VISIBLE_DEVICES") if cvd is not None: ngpu = len(cvd.split(',')) else: logging.warning("CUDA_VISIBLE_DEVICES is not set.") try: p = subprocess.run(['nvidia-smi', '-L'], stdout=subprocess.PIPE, stderr=subprocess.PIPE) except (subprocess.CalledProcessError, FileNotFoundError): ngpu = 0 else: ngpu = len(p.stderr.decode().split('\n')) - 1 args.ngpu = ngpu else: ngpu = args.ngpu logging.info(f"ngpu: {ngpu}") # display PYTHONPATH logging.info('python path = ' + os.environ.get('PYTHONPATH', '(None)')) # seed setting nseed = args.seed random.seed(nseed) np.random.seed(nseed) # load dictionary with open(args.dict, 'rb') as f: dictionary = f.readlines() char_list = [entry.decode('utf-8').split(' ')[0] for entry in dictionary] char_list.insert(0, '<blank>') char_list.append('<eos>') args.char_list_dict = {x: i for i, x in enumerate(char_list)} args.n_vocab = len(char_list) # train logging.info('backend = ' + args.backend) if args.backend == "chainer": from espnet.lm.chainer_backend.lm import train train(args) elif args.backend == "pytorch": from espnet.lm.pytorch_backend.lm import train train(args) else: raise ValueError("Only chainer and pytorch are supported.")
def main(args): parser = argparse.ArgumentParser() # general configuration parser.add_argument('--ngpu', default=0, type=int, help='Number of GPUs') parser.add_argument('--backend', default='chainer', type=str, choices=['chainer', 'pytorch'], help='Backend library') parser.add_argument('--outdir', type=str, required=True, help='Output directory') parser.add_argument('--debugmode', default=1, type=int, help='Debugmode') parser.add_argument('--dict', type=str, required=True, help='Dictionary') parser.add_argument('--seed', default=1, type=int, help='Random seed') parser.add_argument('--resume', '-r', default='', nargs='?', help='Resume the training from snapshot') parser.add_argument('--verbose', '-V', default=0, type=int, help='Verbose option') parser.add_argument('--tensorboard-dir', default=None, type=str, nargs='?', help="Tensorboard log dir path") # task related parser.add_argument('--train-label', type=str, required=True, help='Filename of train label data') parser.add_argument('--valid-label', type=str, required=True, help='Filename of validation label data') parser.add_argument('--test-label', type=str, help='Filename of test label data') # LSTMLM training configuration parser.add_argument('--opt', default='sgd', type=str, choices=['sgd', 'adam'], help='Optimizer') parser.add_argument('--sortagrad', default=0, type=int, nargs='?', help="How many epochs to use sortagrad for. 0 = deactivated, -1 = all epochs") parser.add_argument('--batchsize', '-b', type=int, default=300, help='Number of examples in each mini-batch') parser.add_argument('--epoch', '-e', type=int, default=20, help='Number of sweeps over the dataset to train') parser.add_argument('--early-stop-criterion', default='validation/main/loss', type=str, nargs='?', help="Value to monitor to trigger an early stopping of the training") parser.add_argument('--patience', default=3, type=int, nargs='?', help="Number of epochs to wait without improvement before stopping the training") parser.add_argument('--gradclip', '-c', type=float, default=5, help='Gradient norm threshold to clip') parser.add_argument('--type', type=str, default="lstm", nargs='?', choices=['lstm', 'gru'], help="Which type of RNN to use") parser.add_argument('--layer', '-l', type=int, default=2, help='Number of hidden layers') parser.add_argument('--unit', '-u', type=int, default=650, help='Number of hidden units') parser.add_argument('--maxlen', type=int, default=40, help='Batch size is reduced if the input sequence > ML') args = parser.parse_args(args) # logging info if args.verbose > 0: logging.basicConfig( level=logging.INFO, format='%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s') else: logging.basicConfig( level=logging.WARN, format='%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s') logging.warning('Skip DEBUG/INFO messages') # check CUDA_VISIBLE_DEVICES if args.ngpu > 0: # python 2 case if platform.python_version_tuple()[0] == '2': if "clsp.jhu.edu" in subprocess.check_output(["hostname", "-f"]): cvd = subprocess.check_output(["/usr/local/bin/free-gpu", "-n", str(args.ngpu)]).strip() logging.info('CLSP: use gpu' + cvd) os.environ['CUDA_VISIBLE_DEVICES'] = cvd # python 3 case else: if "clsp.jhu.edu" in subprocess.check_output(["hostname", "-f"]).decode(): cvd = subprocess.check_output(["/usr/local/bin/free-gpu", "-n", str(args.ngpu)]).decode().strip() logging.info('CLSP: use gpu' + cvd) os.environ['CUDA_VISIBLE_DEVICES'] = cvd cvd = os.environ.get("CUDA_VISIBLE_DEVICES") if cvd is None: logging.warning("CUDA_VISIBLE_DEVICES is not set.") elif args.ngpu != len(cvd.split(",")): logging.error("#gpus is not matched with CUDA_VISIBLE_DEVICES.") sys.exit(1) # display PYTHONPATH logging.info('python path = ' + os.environ.get('PYTHONPATH', '(None)')) # seed setting nseed = args.seed random.seed(nseed) np.random.seed(nseed) # load dictionary with open(args.dict, 'rb') as f: dictionary = f.readlines() char_list = [entry.decode('utf-8').split(' ')[0] for entry in dictionary] char_list.insert(0, '<blank>') char_list.append('<eos>') args.char_list_dict = {x: i for i, x in enumerate(char_list)} args.n_vocab = len(char_list) # train logging.info('backend = ' + args.backend) if args.backend == "chainer": from espnet.lm.chainer_backend.lm import train train(args) elif args.backend == "pytorch": from espnet.lm.pytorch_backend.lm import train train(args) else: raise ValueError("Only chainer and pytorch are supported.")
def main(args): parser = get_parser() args = parser.parse_args(args) # logging info if args.verbose > 0: logging.basicConfig( level=logging.INFO, format= '%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s') else: logging.basicConfig( level=logging.WARN, format= '%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s') logging.warning('Skip DEBUG/INFO messages') # check CUDA_VISIBLE_DEVICES if args.ngpu > 0: # python 2 case if platform.python_version_tuple()[0] == '2': if "clsp.jhu.edu" in subprocess.check_output(["hostname", "-f"]): cvd = subprocess.check_output( ["/usr/local/bin/free-gpu", "-n", str(args.ngpu)]).strip() logging.info('CLSP: use gpu' + cvd) os.environ['CUDA_VISIBLE_DEVICES'] = cvd # python 3 case else: if "clsp.jhu.edu" in subprocess.check_output(["hostname", "-f"]).decode(): cvd = subprocess.check_output( ["/usr/local/bin/free-gpu", "-n", str(args.ngpu)]).decode().strip() logging.info('CLSP: use gpu' + cvd) os.environ['CUDA_VISIBLE_DEVICES'] = cvd cvd = os.environ.get("CUDA_VISIBLE_DEVICES") if cvd is None: logging.warning("CUDA_VISIBLE_DEVICES is not set.") elif args.ngpu != len(cvd.split(",")): logging.error("#gpus is not matched with CUDA_VISIBLE_DEVICES.") sys.exit(1) # display PYTHONPATH logging.info('python path = ' + os.environ.get('PYTHONPATH', '(None)')) # seed setting nseed = args.seed random.seed(nseed) np.random.seed(nseed) # load dictionary with open(args.dict, 'rb') as f: dictionary = f.readlines() char_list = [entry.decode('utf-8').split(' ')[0] for entry in dictionary] char_list.insert(0, '<blank>') char_list.append('<eos>') args.char_list_dict = {x: i for i, x in enumerate(char_list)} args.n_vocab = len(char_list) # train logging.info('backend = ' + args.backend) if args.backend == "chainer": from espnet.lm.chainer_backend.lm import train train(args) elif args.backend == "pytorch": from espnet.lm.pytorch_backend.lm import train train(args) else: raise ValueError("Only chainer and pytorch are supported.")
def main(args): parser = get_parser() args = parser.parse_args(args) # logging info if args.verbose > 0: logging.basicConfig( level=logging.INFO, format= '%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s') else: logging.basicConfig( level=logging.WARN, format= '%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s') logging.warning('Skip DEBUG/INFO messages') # If --ngpu is not given, # 1. if CUDA_VISIBLE_DEVICES is set, all visible devices # 2. if nvidia-smi exists, use all devices # 3. else ngpu=0 if args.ngpu is None: cvd = os.environ.get("CUDA_VISIBLE_DEVICES") if cvd is not None: ngpu = len(cvd.split(',')) else: logging.warning("CUDA_VISIBLE_DEVICES is not set.") try: p = subprocess.run(['nvidia-smi', '-L'], stdout=subprocess.PIPE, stderr=subprocess.PIPE) except (subprocess.CalledProcessError, FileNotFoundError): ngpu = 0 else: ngpu = len(p.stderr.decode().split('\n')) - 1 else: ngpu = args.ngpu logging.info(f"ngpu: {ngpu}") # display PYTHONPATH logging.info('python path = ' + os.environ.get('PYTHONPATH', '(None)')) # seed setting nseed = args.seed random.seed(nseed) np.random.seed(nseed) # load dictionary with open(args.dict, 'rb') as f: dictionary = f.readlines() char_list = [entry.decode('utf-8').split(' ')[0] for entry in dictionary] char_list.insert(0, '<blank>') char_list.append('<eos>') args.char_list_dict = {x: i for i, x in enumerate(char_list)} args.n_vocab = len(char_list) # train logging.info('backend = ' + args.backend) if args.backend == "chainer": from espnet.lm.chainer_backend.lm import train train(args) elif args.backend == "pytorch": from espnet.lm.pytorch_backend.lm import train train(args) else: raise ValueError("Only chainer and pytorch are supported.")