# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2023, InternLM Team
# This file is distributed under the same license as the InternLM package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2023.
#
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#: ../../source/training.rst:2
msgid "训练 API"
msgstr "Training API"

#: ../../source/training.rst:4
msgid ""
"InternLM 的训练 API 由 ``internlm.core.trainer.Trainer`` "
"管理。在定义了训练引擎和调度器之后，我们可以调用 Trainer API 来执行模型训练、评估、梯度清零和参数更新等。"
msgstr ""
"InternLM training API is managed in ``internlm.core.trainer.Trainer``. "
"After defining the training engine and runtime scheduler, we can call "
"training API to perform training, evaluation, zero gradients and "
"parameter update steps."

#: ../../source/training.rst:6
msgid "有关详细用法，请参阅 Trainer API 文档和示例。"
msgstr ""
"For detailed usage, please refer to Trainer API documentation and "
"examples."

#: internlm.core.trainer.Trainer:1 of
msgid ""
"This is a class tending for easy deployments of users' training and "
"evaluation instead of writing their own scripts."
msgstr ""

#: internlm.core.trainer.Trainer internlm.core.trainer.Trainer.execute_schedule
#: of
msgid "参数"
msgstr ""

#: internlm.core.trainer.Trainer:4 of
msgid "Engine responsible for the process function."
msgstr ""

#: internlm.core.trainer.Trainer:6 of
msgid "Runtime schedule. Defaults to None."
msgstr ""

#: internlm.core.trainer.Trainer.engine:1 of
msgid ""
"Returns the engine that responsible for managing the training and "
"evaluation process."
msgstr ""

#: internlm.core.trainer.Trainer.schedule:1 of
msgid "Returns the runtime scheduler."
msgstr ""

#: internlm.core.trainer.Trainer.uses_pipeline:1 of
msgid "Returns whether the pipeline parallel is used or not."
msgstr ""

#: internlm.core.trainer.Trainer.train:1 of
msgid "Sets the model to training mode."
msgstr ""

#: internlm.core.trainer.Trainer.eval:1 of
msgid "Sets the model to evaluation mode."
msgstr ""

#: internlm.core.trainer.Trainer.zero_grad:1 of
msgid "Sets the gradient of all parameters in the model to zero."
msgstr ""

#: internlm.core.trainer.Trainer.step:1 of
msgid "Executes the parameter update step."
msgstr ""

#: internlm.core.trainer.Trainer.execute_schedule:1 of
msgid ""
"Runs the forward, loss computation, and backward for the model. Returns a"
" tuple of (output, label, loss)."
msgstr ""

#: internlm.core.trainer.Trainer.execute_schedule:4 of
msgid "The data iterator."
msgstr ""

#: internlm.core.trainer.Trainer.execute_schedule:6 of
msgid "Additional keyword arguments."
msgstr ""

#: internlm.core.trainer.Trainer.execute_schedule of
msgid "返回"
msgstr ""

#: internlm.core.trainer.Trainer.execute_schedule:8 of
msgid "A tuple of (output, label, loss)."
msgstr ""

#: internlm.core.trainer.Trainer.execute_schedule of
msgid "返回类型"
msgstr ""

#: internlm.core.trainer.Trainer.execute_schedule:9 of
msgid "Tuple[:class:`torch.Tensor`]"
msgstr ""

#~ msgid "InternLM 的训练流程可以归纳为两个步骤："
#~ msgstr "The training process of InternLM can be summarized into two steps: "

#~ msgid "初始化"
#~ msgstr "Initialization"

#~ 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。"
#~ msgstr ""
#~ "Initialize model, optimizer, dataloader, "
#~ "trainer, and create different types of"
#~ " process groups to prepare for "
#~ "iterative steps of hybrid parallel "
#~ "training. "

#~ 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。"
#~ msgstr ""
#~ "Initialize logger, checkpoint manager, monitor"
#~ " manager, and profiler to watch, "
#~ "alert, and record the iterative training"
#~ " steps. "

#~ msgid "迭代训练"
#~ msgstr "Iterative training steps"

#~ 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。"
#~ msgstr ""
#~ "Load the training engine and scheduler"
#~ " for hybrid parallel training according "
#~ "to the configuration such as tensor "
#~ "parallel size, pipeline parallel size, "
#~ "and data parallel size. "

#~ 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。"
#~ msgstr ""
#~ "In iterative training steps, the Trainer"
#~ " API is called to perform zero "
#~ "gradients, forward-loss-backward, and "
#~ "parameter update."

#~ msgid "InternLM训练流程图"
#~ msgstr "InternLM training process"

