Using zerogpu when runtine is proportional to content length + falling back to CPU

Spaces
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So, I have a space that takes audio (AM and FM radio recordings are the intended use case), and attempts to remove noise and restore lost frequencies. It’s acceptable (50-75% speed) running on the CPU, however a GPU would be faster. All of the 24/7 GPU options are overkill, so I’m looking at the zero gpu option. I have a few questions, though:

  1. It seems like there’s supposed to be an estimate for the amount of time the operation will take, but when I looked it seemed like it was a fixed number, and the inference time is dependent on the length of the audio clip. My local GPU can process audio at a 10-20x+ ratio, so I could probably give a small estimate and be okay, but I’d like to be more precise.
  2. Since CPU inference is slow but acceptable (to me, anyway), I’d like to have a fallback option in case a user is out of GPU time and doesn’t mind waiting, or if there happens to be some kind of issue with the zero gpu service. Is that possible?
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Hi there!
It sounds like an interesting use case! Here are some thoughts and suggestions for your setup:

  1. Inference Time Estimation:
    The time estimate for inference on a zero GPU option can vary depending on the input size, model complexity, and the actual GPU allocated for your task. Since your local GPU processes audio at a 10-20x+ speed ratio, you could benchmark your model on a few test clips of varying lengths locally. This will give you a baseline to estimate how long inference should take on a comparable GPU. Keep in mind that the actual performance may vary slightly depending on the specific hardware or load on the service.

  2. Fallback to CPU:
    Incorporating a CPU fallback is definitely possible. Many frameworks allow you to define a device option (cuda for GPU and cpu for fallback). You could build your application logic to check the availability of GPU resources and switch to CPU if GPU time is unavailable or the user opts for it. For instance, in PyTorch, you can do something like:

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

    This way, you can offer users a choice between faster GPU processing (when available) and slower CPU processing as a fallback.

  3. Zero GPU Concerns:
    For services like zero GPU, there might be a fixed overhead or processing delay, particularly if you’re using shared resources. To mitigate this, ensure you’re submitting small, optimized batches of audio data for processing. Additionally, some services allow for priority or reserved instances for time-sensitive tasks, so that might be worth exploring if you anticipate heavy usage.

  4. Handling User Expectations:
    Given that CPU processing is slower but acceptable, it’s important to communicate this clearly to users. For example, you could display a message like:

    “GPU processing is temporarily unavailable. Falling back to CPU processing—this may take longer but ensures your task is completed.”
    Providing users with this transparency can improve their experience and set realistic expectations.

Hope this help!

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For example, it is easy to prepare a check box for CPU mode or to make it a separate button.
With the exception of global scope, functions without the spaces.GPU decorator cannot see the GPU, so it is OK to prepare such functions and call them as needed.

1 is a little difficult…

Also, although it is inevitable that there will be a quota in the Zero GPU space, there will inevitably be many unique bugs, so it is necessary to use it with a certain degree of detachment. It is actually comfortable to write a dummy spaces decorator and use only the CPU…:sweat_smile: