\"1500m\" - 1.5Gi will be serialized as \"1536Mi\"\n\nNote that the quantity will NEVER be internally represented by a floating point number. That is the whole point of this exercise.\n\nNon-canonical values will still parse as long as they are well formed, but will be re-emitted in their canonical form. (So always use canonical form, or don't diff.)\n\nThis format is intended to make it difficult to use these numbers without writing some sort of special handling code in the hopes that that will cause...

the total number of nodes that should be running the daemon pod (i.e. status.desiredNumberScheduled) can have their a new pod created before the old pod is marked as deleted. The update starts by launching new pods on 30% of nodes. Once an updated pod is available (Ready for at least minReadySeconds) the old DaemonSet pod on that node is marked deleted. If the old pod becomes unavailable for any reason (Ready transitions to false, is evicted, or is drained) an updated pod is immediatedly created on...

*   `tf.distribute`

    *   Opened an experimental API, `tf.distribute.experimental.coordinator.get_current_worker_index`, for retrieving the worker index from within a worker, when using parameter server training with a custom training loop.

*   `tf.experimental.dtensor`

  let arguments = (ins
    TF_IntOrFpTensor:$data,
    Arg<TF_I32OrI64Tensor, [{A 1-D tensor whose size is equal to the size of `data`'s
first dimension.  Values should be sorted and can be repeated.

Caution: The values are always validated to be sorted on CPU, never validated
on GPU.}]>:$segment_ids
  );

  let results = (outs
    Res<TF_IntOrFpTensor, [{Has same shape as data, except for dimension 0 which