116 lines
3.2 KiB
116 lines
3.2 KiB
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# 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
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import optimizer
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import layer
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import activation
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import parameters
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import trainer
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import event
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import data_type
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import topology
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import networks
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import evaluator
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from . import dataset
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from . import reader
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from . import plot
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import attr
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import op
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import pooling
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import inference
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import networks
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import minibatch
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import plot
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import image
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import paddle.trainer.config_parser as cp
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__all__ = [
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'default_startup_program',
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'default_main_program',
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'optimizer',
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'layer',
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'activation',
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'parameters',
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'init',
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'trainer',
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'event',
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'data_type',
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'attr',
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'pooling',
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'dataset',
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'reader',
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'topology',
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'networks',
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'infer',
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'plot',
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'evaluator',
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'image',
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'master',
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]
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cp.begin_parse()
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def init(**kwargs):
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import py_paddle.swig_paddle as api
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args = []
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args_dict = {}
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# NOTE: append arguments if they are in ENV
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for ek, ev in os.environ.iteritems():
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if ek.startswith("PADDLE_INIT_"):
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args_dict[ek.replace("PADDLE_INIT_", "").lower()] = str(ev)
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args_dict.update(kwargs)
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# NOTE: overwrite arguments from ENV if it is in kwargs
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for key in args_dict.keys():
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args.append('--%s=%s' % (key, str(args_dict[key])))
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# auto set cpu environment
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def set_env(key, value):
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'''If the key has not been set in the environment, set it with value.'''
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assert isinstance(key, str)
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assert isinstance(value, str)
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envset = os.environ.get(key)
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if envset is None:
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os.environ[key] = value
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ht = os.popen("lscpu |grep \"per core\"|awk -F':' '{print $2}'|xargs")
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ht = int(ht.read())
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if ht == 1: # ht is off
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set_env("OMP_DYNAMIC", "false")
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set_env("KMP_AFFINITY", "granularity=fine,compact,0,0")
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else:
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set_env("OMP_DYNAMIC", "true")
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set_env("KMP_AFFINITY", "granularity=fine,compact,1,0")
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processors = os.popen("grep \"processor\" /proc/cpuinfo|sort -u|wc -l")
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processors = int(processors.read())
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trainers = kwargs.get('trainer_count', 1)
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threads = processors / trainers
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threads = '1' if threads < 1 else str(threads)
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set_env("OMP_NUM_THREADS", threads)
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set_env("MKL_NUM_THREADS", threads)
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if 'use_gpu' in kwargs:
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cp.g_command_config_args['use_gpu'] = kwargs['use_gpu']
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if 'use_mkldnn' in kwargs:
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cp.g_command_config_args['use_mkldnn'] = kwargs['use_mkldnn']
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assert 'parallel_nn' not in kwargs, ("currently 'parallel_nn' is not "
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"supported in v2 APIs.")
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api.initPaddle(*args)
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infer = inference.infer
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batch = minibatch.batch
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