|---| | [ View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.15.1/tensorflow/dtensor/python/mesh_util.py#L243-L310) | Runs a barrier on the mesh. ```python tf.experimental.dtensor.barrier( mesh: `tf.experimental.dtensor.Mesh`, barrier_name: Optional[str] = None, timeout_in_ms: Optional[int] = None ) ``` Upon returning from the barrier, all operations run before the barrier would have completed across all clients. Currently we allocate a fully sharded tensor with mesh shape and run an all_reduce on it. #### Example: A barrier can be used before application exit to ensure completion of pending ops. ```python x = [1, 2, 3] x = dtensor.relayout(x, dtensor.Layout.batch_sharded(mesh, 'batch', 1)) dtensor.barrier(mesh) # At this point all devices on all clients in the mesh have completed # operations before the barrier. Therefore it is OK to tear down the clients. sys.exit() ```


| ## Args || |---|---| | `mesh` | The mesh to run the barrier on. | | `barrier_name` | The name of the barrier. Mainly used for logging purpose. | | `timeout_in_ms` | The timeout of the barrier in ms. If omitted, blocks indefinitely till the barrier is reached from all clients. |