Instructions to use MAS-AI-0000/GameNet-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use MAS-AI-0000/GameNet-1 with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://MAS-AI-0000/GameNet-1") - Notebooks
- Google Colab
- Kaggle
Download Dockerfile from MAS-AI-0000/GameNet-1: direct link, hf CLI and curl.
- Browser
- Download file 453 Bytes
-
https://huggingface.co/MAS-AI-0000/GameNet-1/resolve/main/Dockerfile
- Command line
-
hf download hf://MAS-AI-0000/GameNet-1/Dockerfile
-
curl -L -o Dockerfile https://huggingface.co/MAS-AI-0000/GameNet-1/resolve/main/Dockerfile
453 Bytes
| # Use a minimal Python 3.12 base image | |
| FROM python:3.12-slim | |
| # Set working directory inside the container | |
| WORKDIR /code | |
| # Copy requirements | |
| COPY requirements.txt . | |
| # Install dependencies | |
| RUN pip install --no-cache-dir -r requirements.txt | |
| # Copy your full project code | |
| COPY . . | |
| # Run FastAPI using uvicorn | |
| CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"] | |