ZeroGPU is a shared infrastructure that optimizes GPU usage for AI models and demos on Hugging Face Spaces. It dynamically allocates and releases NVIDIA A100 GPUs as needed, offering:
Unlike traditional single-GPU allocations, ZeroGPU’s efficient system lowers barriers for developers, researchers, and organizations to deploy AI models by maximizing resource utilization and power efficiency.
ZeroGPU Spaces are designed to be compatible with most PyTorch-based GPU Spaces. While compatibility is enhanced for high-level Hugging Face libraries like transformers
and diffusers
, users should be aware that:
To utilize ZeroGPU in your Space, follow these steps:
spaces
module.@spaces.GPU
.This decoration process allows the Space to request a GPU when the function is called and release it upon completion.
import spaces
from diffusers import DiffusionPipeline
pipe = DiffusionPipeline.from_pretrained(...)
pipe.to('cuda')
@spaces.GPU
def generate(prompt):
return pipe(prompt).images
gr.Interface(
fn=generate,
inputs=gr.Text(),
outputs=gr.Gallery(),
).launch()
Note: The @spaces.GPU
decorator is designed to be effect-free in non-ZeroGPU environments, ensuring compatibility across different setups.
For functions expected to exceed the default 60-second of GPU runtime, you can specify a custom duration:
@spaces.GPU(duration=120)
def generate(prompt):
return pipe(prompt).images
This sets the maximum function runtime to 120 seconds. Specifying shorter durations for quicker functions will improve queue priority for Space visitors.
By leveraging ZeroGPU, developers can create more efficient and scalable Spaces, maximizing GPU utilization while minimizing costs.
You can share your feedback on Spaces ZeroGPU directly on the HF Hub: https://huggingface.co/spaces/zero-gpu-explorers/README/discussions
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