(backend='mkl' if get_mkl_lib() else 'cpu',
                rng_seed=None, datatype=np.float32,
                batch_size=0, stochastic_round=False, device_id=0,
                max_devices=get_device_count(), compat_mode=None,
                deterministic_update=None, deterministic=None)
return be
Construct and return a backend instance of the appropriate type based on
    the arguments given. With no parameters, a single CPU core, float32
    backend is returned.

    Arguments:
        backend (string, optional): 'cpu', 'mkl' or 'gpu'.
        rng_seed (numeric, optional): Set this to a numeric value which can be used to seed the
                                      random number generator of the instantiated backend.
                                      Defaults to None, which doesn't explicitly seed (so each run
                                      will be different)
        datatype (dtype): Default tensor data type. CPU backend supports np.float64, np.float32,
                          and np.float16; GPU backend supports np.float32 and np.float16.
        batch_size (int): Set the size the data batches.
        stochastic_round (int/bool, optional): Set this to True or an integer to implent
                                               stochastic rounding. If this is False rounding will
                                               be to nearest. If True will perform stochastic
                                               rounding using default bit width. If set to an
                                               integer will round to that number of bits.
                                               Only affects the gpu backend.
        device_id (numeric, optional): Set this to a numeric value which can be used to select
                                       device on which to run the process
        max_devices (int, optional): For use with multi-GPU backend only.
                                      Controls the maximum number of GPUs to run
                                      on.
        compat_mode (str, optional): if this is set to 'caffe' then the conv and pooling
                                     layer output sizes will match that of caffe as will
                                     the dropout layer implementation
        deterministic (bool, optional): if set to true, all operations will be done deterministically.

    Returns:
        Backend: newly constructed backend instance of the specifed type.

    Notes:
        * Attempts to construct a GPU instance without a CUDA capable card or without nervanagpu
          package installed will cause the program to display an error message and exit.
Construct and return a backend instance of the appropriate type based on
    the arguments given. With no parameters, a single CPU core, float32
    backend is returned.
Construct and return a backend instance of the appropriate type based on the arguments given . With no parameters a single CPU core float32 backend is returned .
def gen_backend(backend='mkl' if get_mkl_lib() else 'cpu',
                rng_seed=None, datatype=np.float32,
                batch_size=0, stochastic_round=False, device_id=0,
                max_devices=get_device_count(), compat_mode=None,
                deterministic_update=None, deterministic=None):
    """
    Construct and return a backend instance of the appropriate type based on
    the arguments given. With no parameters, a single CPU core, float32
    backend is returned.

    Arguments:
        backend (string, optional): 'cpu', 'mkl' or 'gpu'.
        rng_seed (numeric, optional): Set this to a numeric value which can be used to seed the
                                      random number generator of the instantiated backend.
                                      Defaults to None, which doesn't explicitly seed (so each run
                                      will be different)
        datatype (dtype): Default tensor data type. CPU backend supports np.float64, np.float32,
                          and np.float16; GPU backend supports np.float32 and np.float16.
        batch_size (int): Set the size the data batches.
        stochastic_round (int/bool, optional): Set this to True or an integer to implent
                                               stochastic rounding. If this is False rounding will
                                               be to nearest. If True will perform stochastic
                                               rounding using default bit width. If set to an
                                               integer will round to that number of bits.
                                               Only affects the gpu backend.
        device_id (numeric, optional): Set this to a numeric value which can be used to select
                                       device on which to run the process
        max_devices (int, optional): For use with multi-GPU backend only.
                                      Controls the maximum number of GPUs to run
                                      on.
        compat_mode (str, optional): if this is set to 'caffe' then the conv and pooling
                                     layer output sizes will match that of caffe as will
                                     the dropout layer implementation
        deterministic (bool, optional): if set to true, all operations will be done deterministically.

    Returns:
        Backend: newly constructed backend instance of the specifed type.

    Notes:
        * Attempts to construct a GPU instance without a CUDA capable card or without nervanagpu
          package installed will cause the program to display an error message and exit.
    """
    logger = logging.getLogger(__name__)

    if NervanaObject.be is not None:
        # backend was already generated clean it up first
        cleanup_backend()
    else:
        # at exit from python force cleanup of backend only register this function once, will use
        # NervanaObject.be instead of a global
        atexit.register(cleanup_backend)

    if deterministic_update is not None or deterministic is not None:
       logger.warning('deterministic_update and deterministic args are deprecated in favor of '
                      'specifying random seed')
       deterministic = None

    from neon.backends.backend import Backend
    be = Backend.allocate_backend(backend,
                                    rng_seed=rng_seed,
                                    default_dtype=datatype,
                                    stochastic_round=stochastic_round,
                                    device_id=device_id,
                                    num_devices=max_devices,
                                    compat_mode=compat_mode,
                                    deterministic=deterministic)

    logger.info("Backend: {}, RNG seed: {}".format(backend, rng_seed))

    NervanaObject.be = be
    be.bsz = batch_size
    return be
def gen_backend ( backend = 'mkl' if get_mkl_lib ( ) else 'cpu' , rng_seed = None , datatype = np . float32 , batch_size = 0 , stochastic_round = False , device_id = 0 , max_devices = get_device_count ( ) , compat_mode = None , deterministic_update = None , deterministic = None ) : logger = logging . getLogger ( __name__ ) if NervanaObject . be is not None : # backend was already generated clean it up first cleanup_backend ( ) else : # at exit from python force cleanup of backend only register this function once, will use # NervanaObject.be instead of a global atexit . register ( cleanup_backend ) if deterministic_update is not None or deterministic is not None : logger . warning ( 'deterministic_update and deterministic args are deprecated in favor of ' 'specifying random seed' ) deterministic = None from neon . backends . backend import Backend be = Backend . allocate_backend ( backend , rng_seed = rng_seed , default_dtype = datatype , stochastic_round = stochastic_round , device_id = device_id , num_devices = max_devices , compat_mode = compat_mode , deterministic = deterministic ) logger . info ( "Backend: {}, RNG seed: {}" . format ( backend , rng_seed ) ) NervanaObject . be = be be . bsz = batch_size return be