Spaces:

zero-gpu-explorers

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Hi everyone, I want to share my code to request dynamic GPU duration on ZeroGPU.

I am happy to contribute this code to the spaces package, but I can't find the repo for it. (The link on PyPI is mislinked to the huggingface_hub repo, and I can't find the relevant code in that repo.) Does Hugging Face want to open source the repo for spaces?

from typing import Callable
from functools import partial
import gradio as gr
import spaces
import spaces.config
from spaces.zero.decorator import P, R


def _dynGPU(
    fn: Callable[P, R] | None, duration: Callable[P, int], min=30, max=300, step=10
) -> Callable[P, R]:
    if not spaces.config.Config.zero_gpu:
        return fn

    funcs = [
        (t, spaces.GPU(duration=t)(lambda *args, **kwargs: fn(*args, **kwargs)))
        for t in range(min, max + 1, step)
    ]

    def wrapper(*args, **kwargs):
        requirement = duration(*args, **kwargs)

        # find the function that satisfies the duration requirement
        for t, func in funcs:
            if t >= requirement:
                gr.Info(f"Acquiring ZeroGPU for {t} seconds")
                return func(*args, **kwargs)

        # if no function is found, return the last one
        gr.Info(f"Acquiring ZeroGPU for {funcs[-1][0]} seconds")
        return funcs[-1][1](*args, **kwargs)

    return wrapper


def dynGPU(
    fn: Callable[P, R] | None = None,
    duration: Callable[P, int] = lambda: 60,
    min=30,
    max=300,
    step=10,
) -> Callable[P, R]:
    if fn is None:
        return partial(_dynGPU, duration=duration, min=min, max=max, step=step)
    return _dynGPU(fn, duration, min, max, step)

It's very similar to the @spaces.GPU decorator but accepts duration as a function that shares the same parameters as the decorated one and returns the desired GPU time in seconds.

I have tested it in my space: https://huggingface.co/spaces/JacobLinCool/vocal-separation

The usage in my space requests GPU time based on the audio length:

def measure_duration(audio: str, model: str) -> int:
    y, sr = librosa.load(audio, sr=44100)
    return int(librosa.get_duration(y=y, sr=sr) / 3.0)


@dynGPU(duration=measure_duration)
def separate(audio: str, model: str) -> Tuple[str, str]:
    separator = separators[model]
    outs = separator.separate(audio)
    outs = [os.path.join(tempfile.gettempdir(), out) for out in outs]
    # roformers
    if len(outs) == 2:
        return outs[1], outs[0]
    # demucs
    if len(outs) == 4:
        bgm = merge(outs[:3])
        return outs[3], bgm
    raise gr.Error("Unknown output format")

Which works well for me, and I think others may be interested in this.