Docker Installation

VideoLingo provides a Dockerfile that you can use to build the current VideoLingo package. Here are detailed instructions for building and running:

System Requirements

CUDA version > 12.4
NVIDIA Driver version > 550

Building and Running the Docker Image or Pulling from DockerHub

# Build the Docker image
docker build -t videolingo .
 
# Run the Docker container
docker run -d -p 8501:8501 --gpus all videolingo

Pulling from DockerHub

You can directly pull the pre-built VideoLingo image from DockerHub:

docker pull rqlove/videolingo:latest

After pulling, use the following command to run the container:

docker run -d -p 8501:8501 --gpus all rqlove/videolingo:latest

Note:

The -d parameter runs the container in the background
-p 8501:8501 maps port 8501 of the container to port 8501 of the host
--gpus all enables support for all available GPUs
Make sure to use the full image name rqlove/videolingo:latest

Models

The Whisper model is not included in the image and will be automatically downloaded when the container is first run. If you want to skip the automatic download process, you can download the model weights from here (opens in a new tab) or Baidu Netdisk (opens in a new tab) (Passcode: 2kgs).

After downloading, use the following command to run the container, mounting the model file into the container:

docker run -d -p 8501:8501 --gpus all -v /path/to/your/model:/app/_model_cache rqlove/videolingo:latest

Please replace /path/to/your/model with the actual local path where you downloaded the model file.

Additional Information

Base image: nvidia/cuda:12.4.1-devel-ubuntu20.04
Python version: 3.10
Pre-installed software: git, curl, sudo, ffmpeg, fonts-noto, etc.
PyTorch version: 2.0.0 (CUDA 11.8)
Exposed port: 8501 (Streamlit application)

For more detailed information, please refer to the Dockerfile.

Future Plans

Continue to improve the Dockerfile to reduce image size
Push the Docker image to Docker Hub
Support mounting required models to the host machine using the -v parameter

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