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85 lines
3.8 KiB
85 lines
3.8 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#include "Flags.h"
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#ifndef PADDLE_WITH_CUDA
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DEFINE_bool(use_gpu, false, "Only support CPU training");
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#else
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DEFINE_bool(use_gpu, true, "Whether to use GPU for training");
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#endif
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#ifdef PADDLE_USE_MKLDNN
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// TODO(TJ): change to true when MKLDNN layers support multi-inputs
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DEFINE_bool(use_mkldnn, false, "Default still keep use CPU training");
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#else
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DEFINE_bool(use_mkldnn, false, "Only support CPU training");
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#endif
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DEFINE_bool(parallel_nn,
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false,
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"Whether to use multi-threads to calculate one neural network."
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"If it was set false, use gpu_id specify which gpu core to use"
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"(the device property in the trainer config file will be ingored)."
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"If it was set true, the gpu core is specified by the trainer"
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" config file(gpu_id will be ignored).");
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DEFINE_int32(trainer_count, 1, "Defined how many trainers to train");
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DEFINE_int32(gpu_id, 0, "Which gpu core to use");
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DEFINE_int32(port, 20134, "Listening port for pserver");
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DEFINE_int32(ports_num,
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1,
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"Number of ports for sending dense parameter,"
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" following ports on parameter server will be visited"
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" for sending dense parameter: [port, port+ports_num-1]");
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DEFINE_int32(ports_num_for_sparse,
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0,
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"Number of ports for sending sparse parameter,"
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" following ports on parameter server will be visited"
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" for sending sparse parameter:"
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" [port+ports_num, port+ports_num+ports_num_for_sparse-1]");
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DEFINE_string(nics, "xgbe0,xgbe1", "network device name for pservers");
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DEFINE_string(rdma_tcp, "tcp", "use rdma or tcp rdma transport protocol");
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DEFINE_int32(trainer_id,
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0,
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"For distributed training, each trainer must be given an unique id"
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" ranging from 0 to num_trainers-1. Trainer 0 is the master"
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" trainer");
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DEFINE_int32(num_gradient_servers, 1, "number of gradient servers");
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DEFINE_string(comment, "", "A string for commenting this training task");
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DEFINE_string(load_missing_parameter_strategy,
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"fail",
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"which operation to take on load model fails. support "
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"fail/rand/zero only.");
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DEFINE_int32(log_period, 100, "Log progress every so many batches");
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DEFINE_int32(log_period_server,
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500,
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"Log progress every so many batches at pserver end");
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DEFINE_double(checkgrad_eps, 1e-5, "parameter change size for checkgrad");
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DEFINE_int32(enable_parallel_vector, 0, "threshold for enable parallel vector");
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DEFINE_bool(loadsave_parameters_in_pserver,
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false,
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"load and save parameters in pserver. "
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"only work while parameter set sparse_remote_update.");
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DEFINE_int32(beam_size,
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1,
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"Beam size used in generating most probable output sequences.");
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DEFINE_bool(show_layer_stat, false, "show the statistics of each layer");
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DEFINE_string(predict_file, "", "File name for saving predict result");
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DEFINE_bool(prev_batch_state, false, "batch is continue with next batch");
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DEFINE_string(init_model_path,
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"",
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"Path of the initial model parameters."
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"If it was set, start_pass will be ignored.");
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