deploy-hf

Stelle eine OpenEnv-Umgebung auf Hugging Face Spaces bereit. Verwende dies, wenn du aufgefordert wirst, etwas bereitzustellen, auf Hugging Face zu pushen oder einen Space zu aktualisieren.

npx skills add https://github.com/huggingface/openenv --skill deploy-hf

Deploy to Hugging Face Spaces

Deploy an OpenEnv environment to Hugging Face Spaces using the OpenEnv CLI.

When to Use This Skill

  • User asks to "deploy to Hugging Face"
  • User says "push to Hugging Face Spaces"
  • User wants to update an existing space
  • After implementing new features that need to be tested in production

Prerequisites

Before deploying, ensure:

  1. The environment has an openenv.yaml file
  2. The environment has a server/Dockerfile
  3. You have Hugging Face credentials configured (automatic via huggingface-cli)

Instructions

1. Identify the Environment

Determine which environment to deploy:

  • If user specifies: use that environment (e.g., "carla_env", "browser_env")
  • If in environment directory: use current directory
  • Otherwise: ask the user

2. Determine the Repository ID

Repository ID format: username/space-name

  • If user provides full ID: use it (e.g., "sergiopaniego/carla-env-real-updated")
  • If user provides only space name: construct ID with their username
  • Check openenv.yaml for default repo-id
  • Otherwise: ask the user

3. Pre-Deployment Setup

IMPORTANT: Always run from the project root directory.

Before deploying, ensure OpenEnv is installed:

cd /path/to/OpenEnv  # Navigate to project root if needed
uv pip install -e .

If this fails with "does not appear to be a Python project", you're not in the project root.

4. Run the Deployment Command

Execute the deployment:

PYTHONPATH=src uv run python -m openenv.cli push <environment-dir> --repo-id <username/space-name>

Parameters:

  • <environment-dir>: Path to environment (e.g., envs/carla_env)
  • --repo-id: Hugging Face Spaces repository ID (e.g., sergiopaniego/carla-env-real-updated)

Optional flags:

  • --private: Deploy as a private space
  • --no-interface: Disable the web interface (deploy API-only)
  • --base-image <image>: Override the base Docker image
  • --hardware <hw> / -H <hw>: Request Hugging Face Space hardware (e.g. t4-medium, a10g-small, cpu-basic)

5. Verify Deployment

After successful deployment:

  1. Note the Space URL returned by the command
  2. Wait for build to complete:
    • CPU environments: ~5 minutes
    • GPU environments (CARLA): ~30-60 minutes
  3. Check the space status at the URL
  4. Test with a simple health check once build completes:
    curl https://<username>-<space-name>.hf.space/health
    

Example Usage

Deploy carla_env to existing space

PYTHONPATH=src uv run python -m openenv.cli push envs/carla_env --repo-id sergiopaniego/carla-env-real-updated

Deploy echo_env as private space

PYTHONPATH=src uv run python -m openenv.cli push envs/echo_env --repo-id username/my-echo-env --private

Deploy with GPU hardware

PYTHONPATH=src uv run python -m openenv.cli push envs/carla_env --repo-id username/carla-env --hardware t4-medium

Deploy with custom base image

PYTHONPATH=src uv run python -m openenv.cli push envs/browser_env --repo-id username/browser-env --base-image nvidia/cuda:11.8.0-runtime-ubuntu22.04

Output Format

Report deployment status:

## Hugging Face Deployment

### Environment
- Environment: <env-name>
- Directory: <path>
- Dockerfile: <path-to-dockerfile>

### Deployment
- Repository ID: <username/space-name>
- Space URL: <https://huggingface.co/spaces/username/space-name>
- Status: ✓ Deployed successfully

### Next Steps
1. Wait for space to build (5 min for CPU, 30-60 min for GPU/CARLA)
2. Visit space URL to check build status
3. Test environment once build completes

### Testing Commands
```bash
# Health check
curl https://<username>-<space-name>.hf.space/health

# Reset environment
curl -X POST https://<username>-<space-name>.hf.space/reset

# Step action
curl -X POST https://<username>-<space-name>.hf.space/step \
  -H "Content-Type: application/json" \
  -d '{"action_type": "observe"}'

## Troubleshooting

### Error: "ModuleNotFoundError: No module named 'openenv'"

**Solution**: Install OpenEnv first (must be run from project root):
```bash
cd /path/to/OpenEnv  # Navigate to project root
uv pip install -e .

Error: "does not appear to be a Python project"

Cause: You're not in the project root directory.

Solution: Navigate to the OpenEnv project root where pyproject.toml exists:

cd /Users/sergiopaniegoblanco/Documents/Projects/OpenEnv  # Adjust path
uv pip install -e .

Error: "Directory does not exist"

Solution: Ensure you're passing the correct environment directory path:

# Correct
PYTHONPATH=src uv run python -m openenv.cli push envs/carla_env --repo-id ...

# Incorrect
PYTHONPATH=src uv run python -m openenv.cli push carla_env --repo-id ...

Error: "Authentication required"

Solution: Login to Hugging Face CLI first:

huggingface-cli login

Space build fails

Solutions:

  1. Check Dockerfile syntax and dependencies
  2. Verify hardware requirements (GPU spaces need --hardware setting on HF)
  3. Check space logs on Hugging Face for detailed errors
  4. Ensure openenv.yaml is valid

Common Environments

EnvironmentPathTypical Repo IDHardware
carla_env (standalone)envs/carla_envusername/carla-env-realGPU (T4/A10G)
carla_env (mock)envs/carla_envusername/carla-env-mockCPU
echo_envenvs/echo_envusername/echo-envCPU
browser_envenvs/browser_envusername/browser-envCPU
tbench2_envenvs/tbench2_envusername/tbench2-envCPU

Notes

  • Deployment requires Hugging Face authentication (automatic if huggingface-cli is logged in)
  • By default, spaces are public (use --private for private spaces)
  • By default, web interface is enabled (use --no-interface for API-only)
  • GPU spaces can request hardware via --hardware (e.g. --hardware t4-medium)
  • Build times vary: CPU (~5 min), GPU with CARLA (~30-60 min)
  • The CLI automatically moves Dockerfile to repository root for Hugging Face compatibility

Related Documentation

Mehr Skills von huggingface

sync-models
huggingface
Synchronisiere die Modellkonfiguration von chat-ui mit dem HuggingFace-Router — füge Beschreibungen für neue Modelle hinzu, kennzeichne reasoning-fähige Modelle, aktiviere Artefakte für Modelle mit 32B+…
custom-blocks
huggingface
Use when the user has written (or wants to write) a `ModularPipelineBlocks` subclass in a local Python file and needs to package it into a Hub-uploadable…
self-review
huggingface
Use before opening a PR, or whenever asked to self-review a diffusers contribution. Applies the same rubric as the `@claude` CI (checks the diff against…
hf-cloud-sagemaker-production-defaults
huggingface
Erstellen Sie einen SageMaker-Endpunkt (Echtzeit oder asynchron) mit automatischer Skalierung, CloudWatch-Alarmen und standardmäßig aktiviertem Tagging. Verwenden Sie diese Fähigkeit, wenn Sie kurz davor sind, einen … zu erstellen.
hf-cloud-serving-image-selection
huggingface
Wählen Sie den richtigen Serving-Container für eine SageMaker-Modellbereitstellung aus und finden Sie dessen aktuelles Image-URI. Verwenden Sie diese Fähigkeit, wenn Sie ein Modell auf einer… bereitstellen möchten.
Hugging Face Cli
huggingface
Execute Hugging Face Hub operations using the `hf` CLI. Use when the user needs to download models/datasets/spaces, upload files to Hub repositories, create repos, manage local cache, or run compute jobs on HF infrastructure. Covers authentication, file transfers, repository creation, cache operations, and cloud compute.
Hugging Face Datasets
huggingface
Erstellen und verwalten Sie Datensätze auf dem Hugging Face Hub. Unterstützt das Initialisieren von Repos, das Definieren von Konfigurationen/System-Prompts, das Streamen von Zeilenaktualisierungen und SQL-basierte Abfragen/Transformationen von Datensätzen. Entwickelt, um zusammen mit dem HF MCP-Server für umfassende Dataset-Workflows zu arbeiten.
Hugging Face Evaluation
huggingface
Evaluierungsergebnisse in Hugging Face-Modellkarten hinzufügen und verwalten. Unterstützt das Extrahieren von Evaluierungstabellen aus README-Inhalten, das Importieren von Bewertungen aus der Artificial Analysis API und das Durchführen benutzerdefinierter Modellevaluierungen mit vLLM/lighteval. Funktioniert mit dem model-index-Metadatenformat.