🚀 Feature request

Currently we have a mixture of negative and positive formulated arguments, e.g. no_cuda and training here: https://github.com/huggingface/transformers/blob/0054a48cdd64e7309184a64b399ab2c58d75d4e5/src/transformers/benchmark/benchmark_args_utils.py#L61.

We should change all arguments to be positively formulated, *e.g. from no_cuda to cuda. These arguments should

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
    

🐛 Bug

Model summarize is called in two spots, which results in duplication in the logs:

• It's called once in the training loop: https://github.com/PyTorchLightning/pytorch-lightning/blob/master/pytorch_lightning/trainer/training_loop.py#L160

And I'm unsure how it's called again

![119083522_2815247905426328_3914141584028152470_n](https://user-images.githubusercontent.com/2382532/92856

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