update en doc for Distributed Training #9130
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abhinavarora
previously approved these changes
Mar 15, 2018
doc/v2/howto/cluster/index_en.rst
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| - Data shard: training data will be split into multiple partitions, trainers use the partitions of the whole dataset to do the training job. | ||
| - Trainer: each trainer reads the data shard, and train the neural network. Then the trainer will upload calculated "gradients" to parameter servers, and wait for parameters to be optimized on the parameter server side. When that finishes, the trainer download optimized parameters and continues its training. | ||
| - Parameter server: every parameter server stores part of the whole neural network model data. They will do optimization calculations when gradients are uploaded from trainers, and then send updated parameters to trainers. | ||
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| PaddlePaddle can support both synchronize stochastic gradient descent (SGD) and asynchronous SGD. | ||
| The training of synchronous random gradient descent for neural network can be archieved by cooperation of trainers and parameter servers. |
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thanks @abhinavarora, typo fixed. |
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fix: #8911