🚀 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?

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

🚀 Feature

Enable training purely based on number of iterations instead of epochs

Motivation

This can be useful for certain training runs. Without this feature, the user must set an unreachably high value for max_epochs and set max_steps to the desired iteration count. With this setup, the trainer will break from the training loop based on max_steps since we'd never reach `max_e

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

Please can you train ghostnet.
(i don't have the imagenet dataset)