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doc/fluid/design/dist_train/large_model.md

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@@ -11,6 +11,10 @@ the gradient to Parameter Server to execute the optimize program.
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## Design
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**NOTE**: this approach is a feature of Fluid distributed trianing, maybe you want
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to know [Distributed Architecture](./distributed_architecture.md) and
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[Parameter Server](./parameter_server.md) before reading the following content.
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Fluid large model distributed training use
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[Distributed Transpiler](./parameter_server.md#distributed-transpiler) to split
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a large parameter into multiple parameters which stored on Parameter Server, and

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