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.

As reported by some people (see NielsRogge/Transformers-Tutorials#53 and on the forum), the generate() method currently does not take into account config.decoder.eos_token_id, only config.eos_token_id to properly stop generation.

Hence, models that are made using EncoderDecoderModel/`VisionEnco

🐛 Describe the bug

Usage of RRefContext::handleException in torch/csrc/distributed/rpc/rref_context.cpp is wrong when the future has an error.

RRefContext::handleException uses TORCH_CHECK which throws.

Callers of RRefContext::handleException don't expect that and run code after it without any guarding.

Versions

master

cc @pietern @mrshenli @pritamdamania87

These examples take quite a long time to run, and they make our documentation CI fail quite frequently due to timeout. It'd be nice to speed the up a little bit.

To contributors: if you want to work on an example, first have a look at the example, and if you think you're comfortable working on it and have found a potential way to speed-up execution time while preserving the educational message

After a discussion in IRC today I realised that there were a few things about parametric types that aren't easily understood. It seems that the manual could help a bit more in introducing parametric types. I'm opening this issue to outline some of the things I think could be documented better, and also to gather ideas or examples that might help to write a more hands-on parametric types section in

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.