Computes the quantile boundaries of a Tensor over the whole dataset.

quantile boundaries are computed using approximate quantiles, and error tolerance is specified using epsilon. The boundaries divide the input tensor into approximately equal num_buckets parts. See go/squawd for details, and how to control the error due to approximation.


  • x: An input Tensor.
  • num_buckets: Values in the x are divided into approximately equal-sized buckets, where the number of buckets is num_buckets. This is a hint. The actual number of buckets computed can be less or more than the requested number. Use the generated metadata to find the computed number of buckets.
  • epsilon: Error tolerance, typically a small fraction close to zero (e.g. 0.01). Higher values of epsilon increase the quantile approximation, and hence result in more unequal buckets, but could improve performance, and resource consumption. Some measured results on memory consumption: For epsilon = 0.001, the amount of memory for each buffer to hold the summary for 1 trillion input values is ~25000 bytes. If epsilon is relaxed to 0.01, the buffer size drops to ~2000 bytes for the same input size. If we use a strict epsilon value of 0, the buffer size is same size as the input, because the intermediate stages have to remember every input and the quantile boundaries can be found only after an equivalent to a full sorting of input. The buffer size also determines the amount of work in the different stages of the beam pipeline, in general, larger epsilon results in fewer and smaller stages, and less time. For more performance trade-offs see also
  • weights: (Optional) Weights tensor for the quantiles. Tensor must have the same shape as x.
  • name: (Optional) A name for this operation.


The bucket boundaries represented as a list, with num_bucket-1 elements See code below for discussion on the type of bucket boundaries.