# tf.compat.v2.VariableAggregation

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/ops/variables.py#L93-L124) |

Indicates how a distributed variable will be aggregated.

[`tf.distribute.Strategy`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/distribute/Strategy) distributes a model by making multiple copies
(called "replicas") acting data-parallel on different elements of the input
batch. When performing some variable-update operation, say
`var.assign_add(x)`, in a model, we need to resolve how to combine the
different values for `x` computed in the different replicas.

- `NONE`: This is the default, giving an error if you use a variable-update operation with multiple replicas.
- `SUM`: Add the updates across replicas.
- `MEAN`: Take the arithmetic mean ("average") of the updates across replicas.
- `ONLY_FIRST_REPLICA`: This is for when every replica is performing the same update, but we only want to perform the update once. Used, e.g., for the global step counter.

## Class Variables

- `MEAN`
- `NONE`
- `ONLY_FIRST_REPLICA`
- `SUM`