From eb7df1c8158af419656e21cb9aeb86b5e9089068 Mon Sep 17 00:00:00 2001
From: mapingshuo <mps2012@yeah.net>
Date: Fri, 18 Sep 2020 15:33:27 +0800
Subject: [PATCH] add Recompute and Gradient Merge API Document.  (#2652)

* test=develop
---
 .../fleet/DistributedStrategy_cn.rst          | 48 +++++++++++++++++++
 1 file changed, 48 insertions(+)

diff --git a/doc/paddle/api/paddle/distributed/fleet/DistributedStrategy_cn.rst b/doc/paddle/api/paddle/distributed/fleet/DistributedStrategy_cn.rst
index 1797fbe93..7d9008351 100644
--- a/doc/paddle/api/paddle/distributed/fleet/DistributedStrategy_cn.rst
+++ b/doc/paddle/api/paddle/distributed/fleet/DistributedStrategy_cn.rst
@@ -6,5 +6,53 @@ DistributedStrategy
 .. py:class:: paddle.distributed.fleet.DistributedStrategy
 
 
+属性
+::::::::::::
 
+.. py:attribute:: recompute
 
+是否启用Recompute来优化内存空间，默认值：False
+
+**示例代码**
+
+.. code-block:: python
+
+  import paddle.distributed.fleet as fleet
+  strategy = fleet.DistributedStrategy()
+  strategy.recompute = True
+  # suppose x and y are names of checkpoint tensors for recomputation
+  strategy.recompute_configs = {"checkpoints": ["x", "y"]}
+
+
+.. py:attribute:: recompute_configs
+
+设置Recompute策略的配置。目前来讲，用户使用Recompute策略时，必须配置 checkpoints 参数。
+
+**checkpoints(int):** Recompute策略的检查点，默认为空列表，也即不启用Recompute。
+
+.. py:attribute:: gradient_merge
+
+梯度累加，是一种大Batch训练的策略。添加这一策略后，模型的参数每过 **k_steps** 步更新一次，
+**k_steps** 是用户定义的步数。在不更新参数的步数里，Paddle只进行前向、反向网络的计算；
+在更新参数的步数里，Paddle执行优化网络，通过特定的优化器（比如SGD、Adam），
+将累加的梯度应用到模型参数上。
+
+**示例代码**
+
+.. code-block:: python
+
+  import paddle.distributed.fleet as fleet
+  strategy = fleet.DistributedStrategy()
+  strategy.gradient_merge = True
+  strategy.gradient_merge_configs = {"k_steps": 4, "avg": True}  
+
+.. py:attribute:: gradient_merge_configs
+
+设置 **distribute_strategy** 策略的配置。
+
+**k_steps(int):** 参数更新的周期，默认为1
+
+**avg(bool):** 梯度的融合方式，有两种选择：
+
+- **sum**: 梯度求和
+- **avg**: 梯度求平均
\ No newline at end of file
-- 
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