How does it assist with memory (OOM) issues?

It suggests concurrency limits in Gradio, batch processing techniques, and explicit memory release patterns to prevent Out-of-Memory errors during heavy workloads.

How does this skill handle ZeroGPU configuration?

It provides implementation patterns for the @spaces.GPU decorator, including dynamic duration calculation based on workload size to optimize queue priority.

Can it help with persistent storage setup?

Yes, it configures the YAML metadata for suggested_storage and provides code patterns for using the /data directory and setting HF_HOME to avoid re-downloading models.

Which deployment files does this skill manage?

It helps configure README.md (YAML header), app.py, requirements.txt, pre-requirements.txt for build tools, and packages.txt for Debian system dependencies.

Hugging Face Spaces Expert

Name: Hugging Face Spaces Expert
Author: prof-ramos

byprof-ramos

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部署与 DevOps

Configures and optimizes Hugging Face Spaces with specialized support for ZeroGPU, persistent storage, and deployment workflows.

关于

The Hugging Face Spaces Expert skill provides specialized guidance for building, deploying, and troubleshooting applications on the Hugging Face platform. It automates the creation of complex README.md YAML configurations, manages multi-layered dependencies (Python and Debian), and implements performance best practices for ZeroGPU workloads. By focusing on memory management, dynamic GPU scheduling, and persistent storage integration, this skill ensures that ML applications are production-ready, stable, and cost-effective within the Hugging Face ecosystem.

主要功能

Automated README.md YAML configuration for SDKs, hardware, and storage
ZeroGPU optimization with dynamic duration decorators and queue management
Advanced troubleshooting for build failures, startup timeouts, and OOM errors
Comprehensive dependency handling via requirements, pre-requirements, and system packages
Persistent storage setup and secrets management for secure, stateful apps
1 GitHub stars

使用场景

Deploying high-performance Gradio or Streamlit apps using ZeroGPU hardware
Migrating local ML models to Hugging Face Spaces with persistent caching
Optimizing heavy PDF or image processing workloads to prevent memory crashes

关于

主要功能

Automated README.md YAML configuration for SDKs, hardware, and storage
ZeroGPU optimization with dynamic duration decorators and queue management
Advanced troubleshooting for build failures, startup timeouts, and OOM errors
Comprehensive dependency handling via requirements, pre-requirements, and system packages
Persistent storage setup and secrets management for secure, stateful apps
1 GitHub stars

使用场景

Deploying high-performance Gradio or Streamlit apps using ZeroGPU hardware
Migrating local ML models to Hugging Face Spaces with persistent caching
Optimizing heavy PDF or image processing workloads to prevent memory crashes