🚀 Feature request

In pipelines tests and documentation they are recurringly named nlp, the goal is to rename them to something more appropriate.

Motivation

nlp = pipeline(task='conversational', model='XXX')

This is a bit pre

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.

🚀 Feature

See title

https://github.com/PyTorchLightning/pytorch-lightning/blob/9137b16068fe03e6db8df548235363e5f5476aac/pytorch_lightning/trainer/connectors/logger_connector/epoch_result_store.py#L25-L42

Motivation

No longer used after #5579

Pitch

Proper deprecation process

Add a new API for converting a model to external data. Today the conversion happens in 2 steps
external_data_helper.convert_model_to_external_data(<model>, <all_tensors_to_one_file>, <size_threshold>) save_model(model, output_path)
We want to add another api which combines the 2 steps
`
save_model_to_external_data(, <output_

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

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

Current pytorch implementation ignores the argument split_f in the function train_batch_ch13 as shown below.

def train_batch_ch13(net, X, y, loss, trainer, devices):
    if isinstance(X, list):
        # Required for BERT Fine-tuning (to be covered later)
        X = [x.to(devices[0]) for x in X]
    else:
        X = X.to(devices[0])
...

Todo: Define the argument `

Is it possible to run this on a (recent) Mac, which does not support CUDA? I would have guessed setting --GPU 0 would not attempt to call CUDA, but it fails.

File "/Users/../Desktop/bopbtl/venv/lib/python3.7/site-packages/torch/cuda/__init__.py", line 61, in _check_driver
    raise AssertionError("Torch not compiled with CUDA enabled")
AssertionError: Torch not compiled with CUDA enable

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