--- title: README emoji: 🌍 colorFrom: indigo colorTo: indigo sdk: static pinned: false --- # ZeroGPU Spaces

ZeroGPU is currently in beta. It's available to use for everyone and PRO users can host their own ZeroGPU Spaces.

Please share your feedback about ZeroGPU in the Community tab.

Browse ZeroGPU Spaces here: Dedicated list.

*ZeroGPU* is a new kind of hardware for Spaces. It has two goals : - Provide **free GPU access** for Spaces - Allow Spaces to run on **multiple GPUs** This is achieved by making Spaces efficiently hold and release GPUs as needed (as opposed to a classical GPU Space that holds exactly one GPU at any point in time) ZeroGPU uses _Nvidia A100_ GPU devices under the hood (40GB of vRAM are available for each workloads)

# Compatibility *ZeroGPU* Spaces should mostly be compatible with any PyTorch-based GPU Space.
Compatibility with high level HF libraries like `transformers` or `diffusers` is slightly more guaranteed
That said, ZeroGPU Spaces are not as broadly compatible as classical GPU Spaces and you might still encounter unexpected bugs Also, for now, ZeroGPU Spaces only works with the **Gradio SDK** Supported versions: - Gradio: 4+ - PyTorch: All versions from `2.0.0` to `2.2.0` - Python: `3.10.13` # Usage In order to make your Space work with ZeroGPU you need to **decorate** the Python functions that actually require a GPU with `@spaces.GPU`
During the time when a decorated function is invoked, the Space will be attributed a GPU, and it will release it upon completion of the function.
Here is a practical example : ```diff +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() ``` 1. We first `import spaces` (importing it first might prevent some issues but is not mandatory) 2. Then we decorate the `generate` function by adding a `@spaces.GPU` line before its definition Note that `@spaces.GPU` is effect-free and can be safely used on non-ZeroGPU environments ## Duration If you expect your GPU function to take more than __60s__ then you need to specify a `duration` param in the decorator like: ```python @spaces.GPU(duration=120) def generate(prompt): return pipe(prompt).images ``` It will set the maximum duration of your function call to 120s. You can also specify a duration if you know that your function will take far less than the 60s default. The lower the duration, the higher priority your Space visitors will have in the queue