__init__.py 4.0 KB
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#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
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#
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#     http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

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from __future__ import print_function
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# import all class inside framework into fluid module
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import framework
from framework import *
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# import all class inside executor into fluid module
import executor
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from executor import *

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import trainer
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from trainer import Trainer
from trainer import BeginEpochEvent
from trainer import EndEpochEvent
from trainer import BeginStepEvent
from trainer import EndStepEvent
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import inferencer
from inferencer import Inferencer

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import io
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import evaluator
import initializer
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import layers
import nets
import optimizer
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import backward
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import regularizer
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import average
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import metrics
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import transpiler
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from param_attr import ParamAttr, WeightNormParamAttr
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from data_feeder import DataFeeder
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from core import LoDTensor, CPUPlace, CUDAPlace, CUDAPinnedPlace
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from transpiler import DistributeTranspiler, InferenceTranspiler, \
    memory_optimize, release_memory
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from concurrency import (Go, make_channel, channel_send, channel_recv,
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                         channel_close, Select)
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from lod_tensor import create_lod_tensor, create_random_int_lodtensor
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import clip
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import profiler
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import unique_name
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import recordio_writer
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import parallel_executor
from parallel_executor import *
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Tensor = LoDTensor
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__all__ = framework.__all__ + executor.__all__ + concurrency.__all__ + \
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          trainer.__all__ + inferencer.__all__ + transpiler.__all__ + \
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          parallel_executor.__all__ + lod_tensor.__all__ + [
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              'io',
              'initializer',
              'layers',
              'transpiler'
              'nets',
              'optimizer',
              'learning_rate_decay',
              'backward',
              'regularizer',
              'LoDTensor',
              'CPUPlace',
              'CUDAPlace',
              'CUDAPinnedPlace',
              'Tensor',
              'ParamAttr',
              'WeightNormParamAttr',
              'DataFeeder',
              'clip',
              'profiler',
              'unique_name',
              'recordio_writer',
          ]
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def __bootstrap__():
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    """
    Enable reading gflags from environment variables.
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    Returns:
        None
    """
    import sys
    import core
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    import os

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    in_test = 'unittest' in sys.modules

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    try:
        num_threads = int(os.getenv('OMP_NUM_THREADS', '1'))
    except ValueError:
        num_threads = 1

    if num_threads > 1:
        print(
            'WARNING: OMP_NUM_THREADS set to {0}, not 1. The computation '
            'speed will not be optimized if you use data parallel. It will '
            'fail if this PaddlePaddle binary is compiled with OpenBlas since'
            ' OpenBlas does not support multi-threads.'.format(num_threads),
            file=sys.stderr)
        print('PLEASE USE OMP_NUM_THREADS WISELY.', file=sys.stderr)

    os.environ['OMP_NUM_THREADS'] = str(num_threads)

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    read_env_flags = [
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        'use_pinned_memory', 'check_nan_inf', 'benchmark', 'warpctc_dir',
        'eager_delete_scope'
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    ]
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    if core.is_compiled_with_cuda():
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        read_env_flags += [
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            'fraction_of_gpu_memory_to_use', 'cudnn_deterministic'
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        ]
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    core.init_gflags([sys.argv[0]] +
                     ["--tryfromenv=" + ",".join(read_env_flags)])
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    core.init_glog(sys.argv[0])
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    # don't init_p2p when in unittest to save time.
    core.init_devices(not in_test)
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# TODO(panyx0718): Avoid doing complex initialization logic in __init__.py.
# Consider paddle.init(args) or paddle.main(args)
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layers.monkey_patch_variable()
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__bootstrap__()