parallel

Problem

When checkpointing a Torch model's state_dict, there may be some inconsistencies when saving/loading depending on whether the model is wrapped in DDP.

Proposal

Provide a utility method that always fetches the non-DDP version of the state_dict.

Without DDP:

model.state_dict()

With DDP:

model.module.state_dict()

Also see

[torch.nn.modu

Summary

mypy shows some issues in LightGBM's Python package.

mypy \
    --exclude='python-package/compile/|python-package/build' \
    --ignore-missing-imports \
    python-package/

python-package/lightgbm/compat.py:12: error: Name 'Series' already defined (possibly by an import)
python-package

I see comments suggesting adding this to understand how loops are being handled by numba, and in the their own FAQ (https://numba.pydata.org/numba-doc/latest/user/faq.html)

from llvmlite import binding as llvm
llvm.set_option('','--debug-only=loop-vectorize')

You would then create your njit function and run it, and I believe the idea is that it prints debug information about whether

Motivation

Since lightgbm v3.3.0, train/cv's arguments have been deprecated as described in optuna/optuna#3013. It would be great to replace such deprecated arguments with recommended way by lightgbm.

Description

Replace the deprecated arguments with callback-ish way. The ta

Version of Singularity:

3.0.3

Expected behavior

Some progress or update information during the creation of the SIF file.

Actual behavior

During "Creating SIF file..." step, the console does not produce any visible output for the user - but does create the resulting file eventually.

Steps to reproduce behavior

I was executing a few “larger” builds through the bui

• I have searched issues for similar feature requests

What is this feature about (1 sentence)

Base path should be runtime configurable via the env variable.

Why is it needed? What is the value? For whom do we build it?

If one wants to expose the dashboard not on the domain root but let's say at my-domain.com/sorry-cypress/dashboard the dashboard will try to load the r

1. The rf204_extrangefit.py should be translated also to C++
2. The rf204b_extendedLikelihood_rangedFit.C tutorial should also be translated to C++
3. The [rf204b_extendedLikelihood_rangedFit.C](https://root.cern.ch/doc/master/rf204b__extendedLikelihood_