tf.compat.v1.distribute.StrategyExtended

Additional APIs for algorithms that need to be distribution-aware.

Inherits From: StrategyExtended

Some common use cases of functions on this page:

  • Locality

tf.distribute.DistributedValues can have the same locality as a distributed variable, which leads to a mirrored value residing on the same devices as the variable (as opposed to the compute devices). Such values may be passed to a call to tf.distribute.StrategyExtended.update to update the value of a variable. You may use tf.distribute.StrategyExtended.colocate_vars_with to give a variable the same locality as another variable. You may convert a "PerReplica" value to a variable's locality by using tf.distribute.StrategyExtended.reduce_to or tf.distribute.StrategyExtended.batch_reduce_to.

  • How to update a distributed variable

A distributed variable is variables created on multiple devices. As discussed in the glossary, mirrored variable and SyncOnRead variable are two examples. The standard pattern for updating distributed variables is to:

  1. In your function passed to tf.distribute.Strategy.run, compute a list of (update, variable) pairs. For example, the update might be a gradient of the loss with respect to the variable.
  2. Switch to cross-replica mode by calling tf.distribute.get_replica_context().merge_call() with the updates and variables as arguments.
  3. Call tf.distribute.StrategyExtended.reduce_to(VariableAggregation.SUM, t, v) (for one variable) or tf.distribute.StrategyExtended.batch_reduce_to (for a list of variables) to sum the updates.
  4. Call tf.distribute.StrategyExtended.update(v) for each variable to update its value.

Steps 2 through 4 are done automatically by class tf.keras.optimizers.Optimizer if you call its