Machine learning

Current implementation of Go binding can not specify options.

GPUOptions struct is in internal package. And go generate doesn't work for protobuf directory. So we can't specify GPUOptions for NewSession.

Hello,

The code says that it will add compatibility for Postponed Evaluation of Annotations (PEP 563) when Python 3.9 is released (which already happened on 2020.10.5). Is there any plan to complete this?

https://github.com/huggingface/transformers/blob/2c2a31ffbcfe03339b1721348781aac4fc05bc5e/src/transformers/hf_argparser.py#L85-L90

🐛 Describe the bug

Floating point exception in mkldnn_convolution when stride contains 0s.

Example to reproduce

import torch

self = torch.full((1, 1, 1, 24000,), 1, dtype=torch.float32, requires_grad=False)
weight = torch.full((1, 1, 1, 24000,), 1, dtype=torch.float32, requires_grad=False)
bias = torch.full((1, 1, 1, 24000,), 1, dtype=torch.float32, requires_grad

See in #22547

MatplotlibDeprecationWarning: Axes3D(fig) adding itself to the figure is deprecated since 3.4. Pass the keyword argument auto_add_to_figure=False and use fig.add_axes(ax) to suppress this warning. The default value of auto_add_to_figure will change to False in mpl3.5 and True values will no longer work in 3.6.  This is consistent with other Axes classes

We need to rep

Currently, we mostly map julia function arguments directly to LLVM arguments regardless of how many there are. That probably causes us to hit some pathological cases, since LLVM is not likely to care about or optimize handling 1000 arguments, since most front-ends do not generate that.

For example:

julia> fields = [:($(Symbol("var$i"))::Int) for i in 1:1000];

julia> @eval struct x
   

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.