metadata

title: README
emoji: 🌍
colorFrom: indigo
colorTo: indigo
sdk: static
pinned: false

ZeroGPU Spaces

🚨 NEW - Setember 2024 Update: check what's new in this discussion.

ZeroGPU is currently in beta:

It's available for everyone to use for free: Browse dedicated Spaces list.
Hosting ZeroGPU Spaces is available for PRO users or Enterprise organizations.
PRO users also get x5 more daily usage quota and highest priority in GPU queues when using any ZeroGPU Spaces.

Please share your feedback about ZeroGPU in the Community tab.

ZeroGPU works great with Dev Mode. Learn more about Dev Mode.

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: 2.0.1, 2.1.2, 2.2.2 and 2.4.0 (2.3.x is not supported due to a PyToch bug)
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 :

+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()

We first import spaces (importing it first might prevent some issues but is not mandatory)
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:

@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

Getting Started

To explore and use existing ZeroGPU Spaces, browse the following list: https://huggingface.co/spaces/enzostvs/zero-gpu-spaces

To create and host your own ZeroGPU Spaces:

For personal accounts:
- Subscribe to PRO. Once subscribed, ZeroGPU will appear in the hardware list on New Space page when you select the Gradio SDK.
For organizations:
- Subscribe to the Enterprise Hub. This will enable you to host and collaborate on ZeroGPU Spaces with all your organization members.

Limitations

Personal accounts (PRO subscribers) can host up to 10 ZeroGPU Spaces.

Organization accounts subscribed to the Enterprise Hub can host up to 50 ZeroGPU Spaces.