(self)
return
Setup the default arguments used by neon
Setup the default arguments used by neon
Setup the default arguments used by neon
def setup_default_args(self):
        """
        Setup the default arguments used by neon
        """

        self.add_argument('--version', action='version', version=neon_version)
        self.add_argument('-c', '--config',
                          is_config_file=True,
                          help='Read values for these arguments from the '
                               'configuration file specified here first.')
        self.add_argument('-v', '--verbose', action='count',
                          default=self.defaults.get('verbose', 1),
                          help="verbosity level.  Add multiple v's to "
                               "further increase verbosity")
        # we store the negation of no_progress_bar in args.progress_bar during
        # parsing
        self.add_argument('--no_progress_bar',
                          action="store_true",
                          help="suppress running display of progress bar and "
                               "training loss")

        bm_grp = self.add_argument_group('benchmark')
        bm_grp.add_argument('--profile', action='store_true')
        bm_grp.add_argument('--profiling_method', type=str, default='time')
        bm_grp.add_argument('--profile_inference', action='store_true')
        bm_grp.add_argument('--profile_iterations', type=int, default=50)
        bm_grp.add_argument('--profile_iter_skip', type=int, default=5)

        # runtime specifc options
        rt_grp = self.add_argument_group('runtime')
        rt_grp.add_argument('-w', '--data_dir',
                            default=os.path.join(self.work_dir, 'data'),
                            help='working directory in which to cache '
                                 'downloaded and preprocessed datasets')
        rt_grp.add_argument('-e', '--epochs', type=int,
                            default=self.defaults.get('epochs', 10),
                            help='number of complete passes over the dataset to run')
        rt_grp.add_argument('-s', '--save_path', type=str,
                            default=self.defaults.get('save_path'),
                            help='file path to save model snapshots')
        rt_grp.add_argument('--serialize', nargs='?', type=int,
                            default=self.defaults.get('serialize', 0),
                            const=1, metavar='N',
                            help='serialize model every N epochs')
        rt_grp.add_argument('--model_file', help='load model from pkl file')
        rt_grp.add_argument('-l', '--log', dest='logfile', nargs='?',
                            const=os.path.join(self.work_dir, 'neon_log.txt'),
                            help='log file')
        rt_grp.add_argument('-o', '--output_file',
                            default=self.defaults.get('output_file', None),
                            help='hdf5 data file for metrics computed during '
                                 'the run, optional.  Can be used by nvis for '
                                 'visualization.')
        rt_grp.add_argument('-eval', '--eval_freq', type=int,
                            default=self.defaults.get('eval_freq', None),
                            help='frequency (in epochs) to test the eval set.')
        rt_grp.add_argument('-H', '--history', type=int,
                            default=self.defaults.get('history', 1),
                            help='number of checkpoint files to retain')
        rt_grp.add_argument('--log_token', type=str,
                            default='',
                            help='access token for data logging in real time')
        rt_grp.add_argument('--manifest', action='append', help="manifest files")
        rt_grp.add_argument('--manifest_root', type=str, default=None,
                            help='Common root path for relative path items in the '
                                 'supplied manifest files')
        be_grp = self.add_argument_group('backend')
        be_grp.add_argument('-b', '--backend', choices=Backend.backend_choices(),
                            default='gpu' if get_compute_capability() >= 3.0
                                    else 'mkl' if get_mkl_lib()
                                    else 'cpu',
                            help='backend type. Multi-GPU support is a premium '
                                 'feature available exclusively through the '
                                 'Nervana cloud. Please contact '
                                 'info@nervanasys.com for details.')
        be_grp.add_argument('-i', '--device_id', type=int,
                            default=self.defaults.get('device_id', 0),
                            help='gpu device id (only used with GPU backend)')
        be_grp.add_argument('-m', '--max_devices', type=int,
                            default=self.defaults.get('max_devices', get_device_count()),
                            help='max number of GPUs (only used with mgpu backend')

        be_grp.add_argument('-r', '--rng_seed', type=int,
                            default=self.defaults.get('rng_seed', None),
                            metavar='SEED',
                            help='random number generator seed')
        be_grp.add_argument('-u', '--rounding',
                            const=True,
                            type=int,
                            nargs='?',
                            metavar='BITS',
                            default=self.defaults.get('rounding', False),
                            help='use stochastic rounding [will round to BITS number '
                                 'of bits if specified]')
        be_grp.add_argument('-d', '--datatype', choices=['f16', 'f32', 'f64'],
                            default=self.defaults.get('datatype', 'f32'),
                            metavar='default datatype',
                            help='default floating point '
                            'precision for backend [f64 for cpu only]')
        be_grp.add_argument('-z', '--batch_size', type=int,
                            default=self.defaults.get('batch_size', 128),
                            help='batch size')
        be_grp.add_argument('--caffe', action='store_true',
                            help='match caffe when computing conv and pool layer output '
                                 'sizes and dropout implementation')
        be_grp.add_argument('--deterministic', action='store_true',
                            help='Use deterministic kernels where applicable')
        return
def setup_default_args ( self ) : self . add_argument ( '--version' , action = 'version' , version = neon_version ) self . add_argument ( '-c' , '--config' , is_config_file = True , help = 'Read values for these arguments from the ' 'configuration file specified here first.' ) self . add_argument ( '-v' , '--verbose' , action = 'count' , default = self . defaults . get ( 'verbose' , 1 ) , help = "verbosity level.  Add multiple v's to " "further increase verbosity" ) # we store the negation of no_progress_bar in args.progress_bar during # parsing self . add_argument ( '--no_progress_bar' , action = "store_true" , help = "suppress running display of progress bar and " "training loss" ) bm_grp = self . add_argument_group ( 'benchmark' ) bm_grp . add_argument ( '--profile' , action = 'store_true' ) bm_grp . add_argument ( '--profiling_method' , type = str , default = 'time' ) bm_grp . add_argument ( '--profile_inference' , action = 'store_true' ) bm_grp . add_argument ( '--profile_iterations' , type = int , default = 50 ) bm_grp . add_argument ( '--profile_iter_skip' , type = int , default = 5 ) # runtime specifc options rt_grp = self . add_argument_group ( 'runtime' ) rt_grp . add_argument ( '-w' , '--data_dir' , default = os . path . join ( self . work_dir , 'data' ) , help = 'working directory in which to cache ' 'downloaded and preprocessed datasets' ) rt_grp . add_argument ( '-e' , '--epochs' , type = int , default = self . defaults . get ( 'epochs' , 10 ) , help = 'number of complete passes over the dataset to run' ) rt_grp . add_argument ( '-s' , '--save_path' , type = str , default = self . defaults . get ( 'save_path' ) , help = 'file path to save model snapshots' ) rt_grp . add_argument ( '--serialize' , nargs = '?' , type = int , default = self . defaults . get ( 'serialize' , 0 ) , const = 1 , metavar = 'N' , help = 'serialize model every N epochs' ) rt_grp . add_argument ( '--model_file' , help = 'load model from pkl file' ) rt_grp . add_argument ( '-l' , '--log' , dest = 'logfile' , nargs = '?' , const = os . path . join ( self . work_dir , 'neon_log.txt' ) , help = 'log file' ) rt_grp . add_argument ( '-o' , '--output_file' , default = self . defaults . get ( 'output_file' , None ) , help = 'hdf5 data file for metrics computed during ' 'the run, optional.  Can be used by nvis for ' 'visualization.' ) rt_grp . add_argument ( '-eval' , '--eval_freq' , type = int , default = self . defaults . get ( 'eval_freq' , None ) , help = 'frequency (in epochs) to test the eval set.' ) rt_grp . add_argument ( '-H' , '--history' , type = int , default = self . defaults . get ( 'history' , 1 ) , help = 'number of checkpoint files to retain' ) rt_grp . add_argument ( '--log_token' , type = str , default = '' , help = 'access token for data logging in real time' ) rt_grp . add_argument ( '--manifest' , action = 'append' , help = "manifest files" ) rt_grp . add_argument ( '--manifest_root' , type = str , default = None , help = 'Common root path for relative path items in the ' 'supplied manifest files' ) be_grp = self . add_argument_group ( 'backend' ) be_grp . add_argument ( '-b' , '--backend' , choices = Backend . backend_choices ( ) , default = 'gpu' if get_compute_capability ( ) >= 3.0 else 'mkl' if get_mkl_lib ( ) else 'cpu' , help = 'backend type. Multi-GPU support is a premium ' 'feature available exclusively through the ' 'Nervana cloud. Please contact ' 'info@nervanasys.com for details.' ) be_grp . add_argument ( '-i' , '--device_id' , type = int , default = self . defaults . get ( 'device_id' , 0 ) , help = 'gpu device id (only used with GPU backend)' ) be_grp . add_argument ( '-m' , '--max_devices' , type = int , default = self . defaults . get ( 'max_devices' , get_device_count ( ) ) , help = 'max number of GPUs (only used with mgpu backend' ) be_grp . add_argument ( '-r' , '--rng_seed' , type = int , default = self . defaults . get ( 'rng_seed' , None ) , metavar = 'SEED' , help = 'random number generator seed' ) be_grp . add_argument ( '-u' , '--rounding' , const = True , type = int , nargs = '?' , metavar = 'BITS' , default = self . defaults . get ( 'rounding' , False ) , help = 'use stochastic rounding [will round to BITS number ' 'of bits if specified]' ) be_grp . add_argument ( '-d' , '--datatype' , choices = [ 'f16' , 'f32' , 'f64' ] , default = self . defaults . get ( 'datatype' , 'f32' ) , metavar = 'default datatype' , help = 'default floating point ' 'precision for backend [f64 for cpu only]' ) be_grp . add_argument ( '-z' , '--batch_size' , type = int , default = self . defaults . get ( 'batch_size' , 128 ) , help = 'batch size' ) be_grp . add_argument ( '--caffe' , action = 'store_true' , help = 'match caffe when computing conv and pool layer output ' 'sizes and dropout implementation' ) be_grp . add_argument ( '--deterministic' , action = 'store_true' , help = 'Use deterministic kernels where applicable' ) return