A very good first issue IMO!

See huggingface/transformers#4829 (comment)

we should update the eval dataset to pick one start_position (or the most frequent one)

Optionally, use the huggingface/nlp library to get the eval dataset, and hook it into the Trainer.

Also referenced in huggingface/transformers#6997 (comment)

some recordings have low volume so the output can be sometimes really quiet. how about we add a volume bar so we can make the output louder/quieter?

We keep this issue open to collect feature requests from users and hear your voice. Our monthly release plan is also available here.

You can either:

1. Suggest a new feature by leaving a comment.
2. Vote for a feature request with 👍 or be against with 👎. (Remember that developers are busy and cannot respond to all feature requests, so vote for your most favorable one!)
3. Tell us that

Change tensor.data to tensor.detach() due to
pytorch/pytorch#6990 (comment)
tensor.detach() is more robust than tensor.data.

more details at: allenai/allennlp#2264 (comment)

Bug Report

These tests were run on s390x. s390x is big-endian architecture.

Failure log for helper_test.py

________________________________________________ TestHelperTensorFunctions.test_make_tensor ________________________________________________

self = <helper_test.TestHelperTensorFunctions testMethod=test_make_tensor>

    def test_make_tensor(self):  # type: () -> None
    

🚀 Feature

Motivation

Some users wish to launch their training program as a module with python -m some.module.py

Pitch

We should evalute whether this is possible for ddp and support this option when possible.
We need to strip the -m argument and append it to the command with which we launch the child process

What would you like to be added: As title

Why is this needed: All pruning schedule except AGPPruner only support level, L1, L2. While there are FPGM, APoZ, MeanActivation and Taylor, it would be much better if we can choose any pruner with any pruning schedule.

**Without this feature, how does current nni

To begin I tried logging in with GitHub and also creating an account on the pyro forums, but neither of those is working.

Problem

I need to fit a batch of four independent Gaussian Processes and I don't want to have to use for loops for fitting each one. The current GP's are able to broadcast properly to my outputs, but I can't batch them so that the inputs are independent.

My input d