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Sharding Variables Across Multiple Parameter Servers 327
parameters (such a job is usually named "ps" for parameter server), or performing computations (such a job is usually named "worker").
Sharding Variables Across Multiple Parameter Servers
As we will see shortly, a common pattern when training a neural network on a dis‐ tributed setup is to store the model parameters on a set of parameter servers (i.e.
For large models with millions of parameters, it is useful to shard these parameters across multiple parameter servers, to reduce the risk of saturating a single parameter server's network card.
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