ZeroGPU

An innovative hardware integration platform introduced by Hugging Face

Key features

ZeroGPU is an innovative hardware integration platform introduced by Hugging Face, designed to provide free GPU resources to support the development of AI and machine learning applications. ZeroGPU aims to democratize access to high-performance computing by offering the following key features:

1. Free GPU Access:

• ZeroGPU utilizes powerful Nvidia A100 GPUs, each offering 40GB of VRAM, suitable for large-scale parallel processing and high-performance computing tasks
• It employs a dynamic system that efficiently holds and releases GPUs as needed, optimizing resource usage oai_citation:3,zero-gpu-explorers (ZeroGPU Explorers).

1. Compatibility:

• ZeroGPU is compatible with most PyTorch-based GPU Spaces, especially those using Hugging Face libraries like transformers and diffusers. However, due to its new architecture, users might encounter some compatibility issues when migrating old code or experimenting with new models

1. Usage:

• To use GPU in a Space, users need to decorate the relevant Python functions with @spaces.GPU. This ensures that a GPU is allocated during the function’s execution and released afterward

1. User Experience:

• ZeroGPU is available to all users, with PRO users having the ability to host their own ZeroGPU Spaces. Users can create a Space on the Hugging Face Spaces page, select the Gradio SDK, and choose Zero NVIDIA A100 as the hardware option

Getting Started with ZeroGPU

To start using ZeroGPU, follow these steps:

1. Register for an account on Hugging Face.
2. Create a Space and select the Gradio SDK.
3. Choose Zero NVIDIA A100 as the hardware option.
4. Use the @spaces.GPU decorator for Python functions that require GPU access oai_citation:10,zero-gpu-explorers (ZeroGPU Explorers) oai_citation:11,Spaces Launch – Hugging Face.

FAQs

Q: What is ZeroGPU?
A: ZeroGPU is a hardware integration by Hugging Face, providing free access to Nvidia A100 GPUs to support AI and machine learning applications

Q: How do I use ZeroGPU in my Space?
A: Decorate the Python functions requiring GPU with @spaces.GPU to allocate a GPU during the function’s execution. 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()

This ensures efficient GPU usage and resource allocation oai_citation:14,zero-gpu-explorers (ZeroGPU Explorers).

Q: Is ZeroGPU compatible with all GPU Spaces?
A: ZeroGPU is compatible with most PyTorch-based GPU Spaces, particularly those using Hugging Face libraries like transformers and diffusers. However, there might be some compatibility issues due to its new architecture oai_citation:15,zero-gpu-explorers (ZeroGPU Explorers)

Q: Can I host my own ZeroGPU Spaces?
A: Yes, PRO users can host their own ZeroGPU Spaces. To do this, create a Space on the Hugging Face Spaces page, select the Gradio SDK, and choose Zero NVIDIA A100 as the hardware option

Q: What if my GPU function takes more than the default duration?
A: You can specify a duration parameter in the @spaces.GPU decorator to extend the GPU allocation time. For example:

@spaces.GPU(duration=120)
def generate(prompt):
    return pipe(prompt).images

This sets the maximum duration of the function call to 120 seconds oai_citation:19,zero-gpu-explorers (ZeroGPU Explorers).

For more detailed information and tutorials, you can visit the Hugging Face official documentation Spaces Launch – Hugging Face.