{"action":"created","comment":{"url":"https://api.github.com/repos/PaddlePaddle/Paddle/pulls/comments/93731620","pull_request_review_id":14263904,"id":93731620,"diff_hunk":"@@ -0,0 +1,336 @@\n+# 细节描述\n+\n+## 通用\n+\n+* `--job`\n+  - 工作模式，包括: **train, test, checkgrad**，其中checkgrad主要为开发者使用，使用者不需要关心。\n+  - 类型: string (默认: train)\n+\n+* `--config`\n+  - 用于指定网络配置文件。\n+  - 类型: string (默认: null).\n+\n+* `--use_gpu`\n+  - 训练过程是否使用GPU，设置为true使用GPU模式，否则使用CPU模式。\n+  - 类型: bool (默认: 1).\n+\n+* `--local`\n+  - 训练过程是否为本地模式，设置为true使用本地训练或者使用集群上的一个节点，否则使用集群上的多台机器。\n+  - 类型: bool (默认: 1).\n+\n+* `--trainer_count`\n+  - 指定一台机器上使用的线程数。例如，trainer_count = 4, 意思是在GPU模式下使用4个GPU，或者在CPU模式下使用4个线程。每个线程（或GPU）分配到当前数据块样本数的四分之一。也就是说，如果在训练配置中设置batch_size为512，每个线程分配到128个样本用于训练。\n+  - 类型: int32 (默认: 1).\n+\n+* `--num_passes`\n+  - 当模式为`--job=train`时, 该参数的意思是训练num_passes轮。每轮会将数据集中的所有训练样本使用一次。当模式为`--job=test`时，意思是使用第test_pass个模型到第 num_passes-1 个模型测试数据。\n+  - 类型: int32 (默认: 100).\n+\n+* `--config_args`\n+  - 传递给配置文件的参数。格式: key1=value1,key2=value2.\n+  - 类型: string (默认: null).\n+\n+* `--version`\n+  - 是否打印版本信息。\n+  - 类型: bool (默认: 0).\n+\n+* `--show_layer_stat`\n+  - 是否显示**每个批次数据**中每层的数值统计.\n+  - 类型: bool (默认: 0).\n+\n+## 训练\n+\n+* `--log_period`\n+  - 每log_period个批次打印日志进度.\n+  - 类型: int32 (默认: 100).\n+\n+* `--dot_period`\n+  - 每dot_period个批次输出符号'.'.\n+  - 类型: int32 (默认: 1).\n+\n+* `--saving_period`\n+  - 每saving_period轮保存训练参数.\n+  - 类型: int32 (默认: 1).\n+\n+* `--save_dir`\n+  - 保存模型参数的目录，需要明确指定，但不需要提前创建。\n+  - 类型: string (默认: null).\n+\n+* `--start_pass`\n+  - 从start_pass轮开始训练，会加载上一轮的参数。\n+  - 类型: int32 (默认: 0).\n+\n+* `--show_parameter_stats_period`\n+  - 在训练过程中每show_parameter_stats_period个批次输出参数统计。默认不显示。\n+  - 类型: int32 (默认: 0).\n+\n+* `--save_only_one`\n+  - 只保存最后一轮的参数，而之前的参数将会被删除。\n+  - 类型: bool (默认: 0).\n+\n+* `--load_missing_parameter_strategy`\n+  - 当模型参数不存在时，指定加载的方式。目前支持fail/rand/zero三种操作.\n+    - `fail`: 程序直接退出.\n+    - `rand`: 根据网络配置中的**initial\\_strategy**采用均匀分布或者高斯分布初始化。均匀分布的范围是: **[mean - std, mean + std]**, 其中mean和std是训练配置中的参数.\n+    - `zero`: 所有参数置为零.\n+  - 类型: string (默认: fail).\n+\n+* `--init_model_path`\n+   - 初始化模型的路径。如果设置该参数，start\\_pass将不起作用。同样也可以在测试模式中指定模型路径。\n+   - 类型: string (默认: null).\n+\n+* `--saving_period_by_batches`\n+   - 在一轮中每saving_period_by_batches个批次保存一次参数。\n+   - 类型: int32 (默认: 0).\n+\n+* `--log_error_clipping`\n+  - 当在网络层配置中设置**error_clipping_threshold**时，该参数指示是否打印错误截断日志。如果为true，**每批次**的反向传播将会打印日志信息。该截断会影响**输出的梯度**.\n+  - 类型: bool (默认: 0).\n+\n+* `--log_clipping`\n+  - 当在训练配置中设置**gradient_clipping_threshold**时，该参数指示是否打印日志截断信息。该截断会影响**权重更新的梯度**.\n+  - 类型: bool (默认: 0).\n+\n+* `--use_old_updater`\n+  - 是否使用旧的RemoteParameterUpdater。 默认使用ConcurrentRemoteParameterUpdater，主要为开发者使用，使用者通常无需关心.\n+  - 类型: bool (默认: 0).\n+\n+* `--enable_grad_share`\n+  - 启用梯度参数的阈值，在多CPU训练时共享该参数.\n+  - 类型: int32 (默认: 100 \\* 1024 \\* 1024).\n+\n+* `--grad_share_block_num`\n+  - 梯度参数的分块数目，在多CPU训练时共享该参数.\n+  - 类型: int32 (默认: 64).\n+\n+## 测试\n+\n+* `--test_pass`\n+  - 加载test_pass轮的模型用于测试.\n+  - 类型: int32 (默认: -1).\n+\n+* `--test_period`\n+   - 如果为0，每轮结束时对所有测试数据进行测试；如果不为0，每test_period个批次对所有测试数据进行测试.\n+  - 类型: int32 (默认: 0).\n+\n+* `--test_wait`\n+  - 指示当指定轮的测试模型不存在时，是否需要等待该轮模型参数。如果在集群的提交环境中设置了test_data_path，将会启动一个进程执行测试，因此我们需要设置test_wait=1。需要注意的是，在集群提交环境中，该参数已经默认设置为True.\n+  - 类型: bool (默认: 0).\n+\n+* `--model_list`\n+  - 测试时指定的存储模型列表的文件。在集群提交环境中，在指定model_path之后，该参数会自动设置.\n+  - 类型: string (默认: \"\", null).\n+\n+* `--predict_output_dir`\n+  - 保存网络层输出结果的目录。该参数在网络配置的Outputs()中指定，默认为null，意思是不保存结果。在测试阶段，如果你想要保存某些层的特征图，请指定该目录。需要注意的是，网络层的输出是经过激活函数之后的值.\n+  - 类型: string (默认: \"\", null).\n+\n+* `--average_test_period`\n+  - 使用`average_test_period`个批次的参数平均值进行测试。该参数必须能被FLAGS_log_period整除，默认为0，意思是不使用平均参数执行测试.\n+  - 类型: int32 (默认: 0).\n+\n+* `--distribute_test`\n+  - 在分布式环境中测试，将多台机器的测试结果合并.\n+  - 类型: bool (默认: 0).\n+\n+* `--predict_file`\n+  - 保存预测结果的文件名。该参数默认为null，意思是不保存结果。目前该参数仅用于AucValidationLayer和PnpairValidationLayer层，每轮都会保存预测结果.\n+  - 类型: string (默认: \"\", null).\n+\n+## GPU\n+\n+* `--gpu_id`\n+  - 指示使用哪个GPU核.\n+  - 类型: int32 (默认: 0).\n+\n+* `--allow_only_one_model_on_one_gpu`\n+  - 如果为true，一个GPU设备上不允许配置多个模型.\n+  - 类型: bool (默认: 1).\n+\n+* `--parallel_nn`\n+  - 指示是否使用多线程来计算一个神经网络。如果为false，设置gpu_id指定使用哪个GPU核（训练配置中的设备属性将会无效）。如果为true，GPU核在训练配置中指定（gpu_id无效）.\n+  - 类型: bool (默认: 0).\n+\n+* `--cudnn_dir`\n+  - 选择路径来动态加载NVIDIA CuDNN库，例如，/usr/local/cuda/lib64. [默认]: LD_LIBRARY_PATH\n+  - 类型: string (默认: \"\", null)\n+\n+* `--cuda_dir`\n+  - 选择路径来动态加载NVIDIA CUDA库，例如，/usr/local/cuda/lib64. [默认]: LD_LIBRARY_PATH\n+  - 类型: string (默认: \"\", null)\n+\n+* `--cudnn_conv_workspace_limit_in_mb`\n+  - 指定cuDNN的最大工作空间容限，单位是MB，默认为4096MB=4GB. \n+  - 类型: int32 (默认: 4096MB=4GB)\n+\n+## 自然语言处理(NLP): RNN/LSTM/GRU\n+* `--rnn_use_batch`\n+  - 指示在简单的RecurrentLayer层的计算中是否使用批处理方法.\n+  - 类型: bool (默认: 0).\n+\n+* `--prev_batch_state`\n+  - 指示批次之间是否是连续的.\n+  - 类型: bool (默认: 0).\n+\n+* `--beam_size`\n+  - 集束搜索使用广度优先搜索的方式构建查找树。在树的每一层上，都会产生当前层状态的所有继承结果，按启发式损失的大小递增排序。然而，每层上只能保存固定数目个最好的状态，该数目是提前定义好的，称之为集束大小.\n+  - 类型: int32 (默认: 1).\n+\n+* `--diy_beam_search_prob_so`\n+  - 指定共享的动态库，用户可以定义Paddle之外的文件.\n+  - 类型: string (默认: \"\", null).\n+\n+## 度量学习(Metric Learning)\n+* `--external`\n+   - 指示是否使用外部机器进行度量学习.\n+   - 类型: bool (默认: 0).\n+\n+* `--data_server_port`\n+  - 数据服务器(data server)的监听端口，主要用在度量学习中.\n+  - 类型: int32 (默认: 21134).\n+\n+## 数据支持(DataProvider)\n+\n+* `--memory_threshold_on_load_data`\n+  - 内存容限阈值，当超过该阈值时，停止加载数据.\n+  - 类型: double (默认: 1.0).\n+\n+## Unit Test\n+\n+* `--checkgrad_eps`\n+  - 使用checkgrad模式时的参数变化大小.\n+  - 类型: double (默认: 1e-05).\n+\n+## 参数服务器(Parameter Server)和分布式通信(Distributed Communication)\n+\n+* `--start_pserver`\n+  - 指示是否开启参数服务器(parameter server).\n+  - 类型: bool (默认: 0).\n+\n+* `--pservers`\n+  - 参数服务器的IP地址，以逗号间隔，在集群提交环境中自动设置.\n+  - 类型: string (默认: \"127.0.0.1\").\n+\n+* `--port`\n+  - 参数服务器的监听端口.\n+  - 类型: int32 (默认: 20134).\n+\n+* `--ports_num`\n+  - 发送参数的端口号，根据默认端口号递增.\n+  - 类型: int32 (默认: 1).\n+\n+* `--trainer_id`\n+  - 在分布式训练中，每个训练器必须指定一个唯一的id号，从0到num_trainers-1。0号训练器是主训练器。使用者无需关心这个参数.","path":"doc/howto/usage/cmd_parameter/detail_introduction_cn.md","position":223,"original_position":223,"commit_id":"e9954cf395749b090be1fa9ca824ca2bf77068ee","original_commit_id":"e9954cf395749b090be1fa9ca824ca2bf77068ee","user":{"login":"qingqing01","id":7845005,"avatar_url":"https://avatars.githubusercontent.com/u/7845005?v=3","gravatar_id":"","url":"https://api.github.com/users/qingqing01","html_url":"https://github.com/qingqing01","followers_url":"https://api.github.com/users/qingqing01/followers","following_url":"https://api.github.com/users/qingqing01/following{/other_user}","gists_url":"https://api.github.com/users/qingqing01/gists{/gist_id}","starred_url":"https://api.github.com/users/qingqing01/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/qingqing01/subscriptions","organizations_url":"https://api.github.com/users/qingqing01/orgs","repos_url":"https://api.github.com/users/qingqing01/repos","events_url":"https://api.github.com/users/qingqing01/events{/privacy}","received_events_url":"https://api.github.com/users/qingqing01/received_events","type":"User","site_admin":false},"body":"训练器 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