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System information

• TensorFlow version: 1.13.1
• Doc Link: https://www.tensorflow.org/api_docs/python/tf/cus

(e.g. for links and images), because some of these examples are now being rendered in the docs.

Added by @fchollet in requests for contributions.

🚀 Feature

Add support for torch.max with:

• CUDA bfloat16
• CPU float16 and bfloat16

Motivation

Currently, torch.max has support for CUDA float16:

>>> torch.rand(10, dtype=torch.float16, device='cuda').max()
tensor(0.8530, device='cuda:0', dtype=torch.float16)

But all three other combinations of CPU/CUDA and float16/bfloat16 are not supported:

>>> torch.ra

Most functions in scipy.linalg functions (e.g. svd, qr, eig, eigh, pinv, pinv2 ...) have a default kwarg check_finite=True that we typically leave to the default value in scikit-learn.

As we already validate the input data for most estimators in scikit-learn, this check is redundant and can cause significant overhead, especially at predict / transform time. We should probably a

One of the main use cases of lpad and rpad is, at least for me personally, to align things in the terminal. However, lpad and rpad are defined (and documented) to work in terms of code units (i.e. they call length on the input string) which means that when length and textwidth disagree, you get unaligned output:

julia> s1 = "⟨k|H₁|k̃⟩"
"⟨k|H₁|k̃⟩"
julia> s2 = "⟨k|H₁|k⟩"
"⟨

Currently many more Python projects like dask and optuna are using Python type hints. With the Python package of xgboost gaining more and more features, we should also adopt mypy as a safe guard against some type errors and for better code documentation.