mirror of https://github.com/InternLM/InternLM
249 lines
8.6 KiB
Plaintext
249 lines
8.6 KiB
Plaintext
# SOME DESCRIPTIVE TITLE.
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# Copyright (C) 2023, InternLM Team
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# This file is distributed under the same license as the InternLM package.
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# FIRST AUTHOR <EMAIL@ADDRESS>, 2023.
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#
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#, fuzzy
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msgid ""
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msgstr ""
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"Project-Id-Version: InternLM \n"
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"Report-Msgid-Bugs-To: \n"
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"POT-Creation-Date: 2023-09-14 12:23+0800\n"
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"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
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"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
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"Language: zh_CN\n"
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"Language-Team: zh_CN <LL@li.org>\n"
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"Plural-Forms: nplurals=1; plural=0;\n"
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"MIME-Version: 1.0\n"
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"Content-Type: text/plain; charset=utf-8\n"
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"Content-Transfer-Encoding: 8bit\n"
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"Generated-By: Babel 2.12.1\n"
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#: ../../source/initialize.rst:2
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msgid "训练构建"
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msgstr "Training Setup"
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#: ../../source/initialize.rst:4
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msgid "InternLM 的训练流程可以归纳为两个步骤："
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msgstr "The training process of InternLM can be summarized into two steps: "
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#: ../../source/initialize.rst:6
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msgid "初始化"
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msgstr "Initialization"
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#: ../../source/initialize.rst:8
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msgid "初始化模型、优化器、数据加载器、Trainer，生成不同种类的进程组，为混合并行的迭代训练做准备。"
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msgstr ""
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"Initialize model, optimizer, dataloader, trainer, and create different "
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"types of process groups to prepare for iterative steps of hybrid parallel training. "
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#: ../../source/initialize.rst:9
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msgid "初始化Logger、Checkpoint管理器、Monitor管理器、Profiler，对迭代训练的过程观察、预警、记录。"
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msgstr ""
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"Initialize logger, checkpoint manager, monitor manager, and profiler to "
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"watch, alert, and record the iterative training steps. "
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#: ../../source/initialize.rst:11
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msgid "迭代训练"
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msgstr "Iterative training steps"
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#: ../../source/initialize.rst:13
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msgid "根据配置文件定义的张量并行、流水线并行、数据并行的大小，加载训练引擎和调度器进行混合并行训练。"
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msgstr ""
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"Load the training engine and scheduler for hybrid parallel training "
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"according to the configuration such as tensor parallel size, pipeline "
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"parallel size, and data parallel size. "
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#: ../../source/initialize.rst:14
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msgid "在迭代训练中，调用 Trainer API 进行梯度置零，前向传播计算损失并反向传播，参数更新。"
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msgstr ""
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"In iterative training steps, the Trainer API is called to perform zero "
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"gradients, forward-loss-backward, and parameter update."
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#: ../../source/initialize.rst:20
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msgid "InternLM训练流程图"
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msgstr "InternLM training process"
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#: ../../source/initialize.rst:25
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msgid "命令行参数解析"
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msgstr "Argument Parsing"
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#: ../../source/initialize.rst:27
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msgid ""
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"InternLM 使用 `argparse <https://docs.python.org/3/library/argparse.html>`_"
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" 库来向InternLM运行时提供命令行参数配置。"
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msgstr ""
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"InternLM uses the `argparse "
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"<https://docs.python.org/3/library/argparse.html>`_ library to supply "
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"commandline configuration to the InternLM runtime. "
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#: ../../source/initialize.rst:29
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msgid ""
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"用户可使用 ``internlm.initialize.get_default_parser()`` 来获取 InternLM "
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"的默认解析器，其中包含一些内置参数，用户可以向此解析器添加自定义参数。"
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msgstr ""
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"Use ``internlm.initialize.get_default_parser()`` to get InternLM's "
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"default parser with some builtin arguments, users can add custom "
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"parameters to this parser."
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#: internlm.initialize.launch.get_default_parser:1 of
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msgid ""
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"Reads user command line and uses an argument parser to parse the input "
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"arguments. Input arguments include configuration, host, port, world size,"
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" local rank, backend for torch.distributed."
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msgstr ""
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#: internlm.initialize.initialize_trainer.initialize_trainer
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#: internlm.initialize.launch.get_default_parser
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#: internlm.train.training_internlm.get_train_data_loader
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#: internlm.train.training_internlm.initialize_model
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#: internlm.train.training_internlm.initialize_optimizer of
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msgid "返回"
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msgstr ""
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#: internlm.initialize.launch.get_default_parser:4 of
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msgid ""
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"Returns the parser with the default arguments, the user may add "
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"customized arguments into this parser."
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msgstr ""
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#: internlm.initialize.initialize_trainer.initialize_trainer
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#: internlm.initialize.launch.get_default_parser
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#: internlm.train.training_internlm.initialize_model of
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msgid "返回类型"
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msgstr ""
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#: ../../source/initialize.rst:45
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msgid "模型初始化"
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msgstr "Model Initialization"
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#: internlm.train.training_internlm.initialize_model:1 of
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msgid "Initialize model with Automatic Mixed Precision."
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msgstr ""
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#: internlm.train.training_internlm.initialize_model:3 of
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msgid "The neural network model to be trained or evaluated."
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msgstr ""
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#: ../../source/initialize.rst:49
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msgid "InternLM 在配置文件中使用字段 ``model_type`` 和 ``model`` 来控制模型初始化过程。示例模型初始化配置定义如下："
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msgstr ""
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"InternLM uses the field ``model_type`` and ``model`` in the config file "
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"to control model initialization process. An example model initialization "
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"configuratio"
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#: ../../source/initialize.rst:77
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msgid "字段 ``model_type`` 指明了要初始化的模型类型"
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msgstr ""
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"The field ``model_type`` specifics the model type has been registered and"
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" to be initialized."
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#: ../../source/initialize.rst:78
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msgid "字段 ``model`` 中的参数指定了在模型初始化过程中的参数设置"
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msgstr ""
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"The parameters in field ``model`` specific the configuration settings "
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"during model initialization."
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#: ../../source/initialize.rst:80
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msgid ""
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"值得注意的是，用户可以定义新的模型类型，并使用装饰器 ``@MODEL_INITIALIZER.register_module`` "
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"注册模型的初始化函数，其中 ``MODEL_INITIALIZER`` 是类 "
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"``internlm.util.registry.Registry`` 的一个实例化对象，示例如下所示："
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msgstr ""
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"It is worth noting that, users can define new model type, and register "
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"model's initialization function by decorater "
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"``@MODEL_INITIALIZER.register_module``, which ``MODEL_INITIALIZER`` is an"
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" instantiated object of class ``internlm.util.registry.Registry``, the "
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"example is shown as follows."
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#: ../../source/initialize.rst:92
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msgid "优化器初始化"
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msgstr "Optimizer Initialization"
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#: internlm.train.training_internlm.initialize_optimizer:1 of
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msgid "Initialize optimizer."
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msgstr ""
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#: internlm.initialize.initialize_trainer.initialize_trainer
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#: internlm.train.training_internlm.get_train_data_loader
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#: internlm.train.training_internlm.initialize_optimizer of
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msgid "参数"
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msgstr ""
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#: internlm.train.training_internlm.initialize_optimizer:3 of
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msgid "Your model instance to be trained or evaluated."
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msgstr ""
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#: internlm.train.training_internlm.initialize_optimizer:6 of
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msgid "A tuple of (optimizer, beta2_scheduler, lr_scheduler)."
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msgstr ""
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#: ../../source/initialize.rst:99
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msgid "数据加载器初始化"
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msgstr "Dataloader Initialization"
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#: internlm.train.training_internlm.get_train_data_loader:1 of
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msgid "Generate and return the training data loader."
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msgstr ""
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#: internlm.train.training_internlm.get_train_data_loader:3 of
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msgid "number of subprocesses used for dataloader."
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msgstr ""
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#: internlm.train.training_internlm.get_train_data_loader:5 of
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msgid "generate function for dataset."
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msgstr ""
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#: internlm.train.training_internlm.get_train_data_loader:7 of
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msgid "dataset sampler for training dataloader."
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msgstr ""
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#: internlm.train.training_internlm.get_train_data_loader:9 of
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msgid "collate function for training dataloader."
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msgstr ""
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#: internlm.train.training_internlm.get_train_data_loader:12 of
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msgid "A tuple of (train_dl, dataset_types)."
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msgstr ""
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#: ../../source/initialize.rst:106
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msgid "Trainer 初始化"
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msgstr "Trainer Initialization"
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#: internlm.initialize.initialize_trainer.initialize_trainer:1 of
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msgid ""
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"Core function to wrap the essential training components with our "
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"functionality based on the config which is loaded into gpc.config."
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msgstr ""
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#: internlm.initialize.initialize_trainer.initialize_trainer:4 of
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msgid "Your model instance or a function to build the model."
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msgstr ""
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#: internlm.initialize.initialize_trainer.initialize_trainer:6 of
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msgid "Your optimizer for training."
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msgstr ""
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#: internlm.initialize.initialize_trainer.initialize_trainer:8 of
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msgid "Your criterion instance."
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msgstr ""
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#: internlm.initialize.initialize_trainer.initialize_trainer:10 of
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msgid "Dataloader for training."
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msgstr ""
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#: internlm.initialize.initialize_trainer.initialize_trainer:12 of
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msgid "Dataloader for testing."
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msgstr ""
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#: internlm.initialize.initialize_trainer.initialize_trainer:14 of
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msgid "Your lr scheduler instance, optional."
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msgstr ""
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#: internlm.initialize.initialize_trainer.initialize_trainer:17 of
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msgid ""
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"A tuple of ``(trainer, train_dataloader, test_dataloader, lr_scheduler)``"
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" where only ``trainer`` could not be None."
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msgstr ""
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