Deployment

📦 Prerequisites

Hardware requirements

Hardware	Minimum	Recommended
CPU	X86_64 or ARM64 CPU, 2 cores or more	4 cores or more
RAM	4GB or more	8GB or more
Storage	10GB or more for libraries, models, and datas	50GB or more, SSD is recommended
GPU	Not required	CUDA supported GPU for acceleration, 4GB of VRAM or more

Software requirements

• For local deployment: Python 3.10 ~ Python 3.12, with uv package manager installed.
• For Docker deployment: Docker and Docker Compose (For CUDA users, nvidia-container-runtime is required) or equivalent container runtime.

🖥️ Local Deployment

Choose a metadata storage method

Qdrant Database (Recommended)

In most cases, we recommend using the Qdrant database to store metadata. The Qdrant database provides efficient retrieval performance, flexible scalability, and better data security.

Please deploy the Qdrant database according to the Qdrant documentation. It is recommended to use Docker for deployment.

If you don't want to deploy Qdrant yourself, you can use the online service provided by Qdrant.

Local File Storage

Local file storage directly stores image metadata (including feature vectors, etc.) in a local SQLite database. It is only recommended for small-scale deployments or development deployments.

Local file storage does not require an additional database deployment process, but has the following disadvantages:

• Local storage does not index and optimize vectors, so the time complexity of all searches is O(n). Therefore, if the data scale is large, the performance of search and indexing will decrease.
• Using local file storage will make NekoImageGallery stateful, so it will lose horizontal scalability.
• When you want to migrate to Qdrant database for storage, the indexed metadata may be difficult to migrate directly.

Deploy NekoImageGallery

NOTE

This tutorial is for NekoImageGallery v1.4.0 and later, in which we switch to uv as package manager. If you are using an earlier version, please refer to the README file in the corresponding version tag.

1. Clone the project directory to your own PC or server, then checkout to a specific version tag (like v1.4.0).

2. Install the required dependencies:

   uv sync --no-dev --extra cpu # For CPU-only deployment
   
   uv sync --no-dev --extra cu124 # For CUDA v12.4 deployment
   
   uv sync --no-dev --extra cu118 # For CUDA v11.8 deployment

NOTE

• It's required to specify the --extra option to install the correct dependencies. If you don't specify the --extra option, PyTorch and its related dependencies will not be installed.
• If you want to use CUDA for accelerated inference, be sure to select the CUDA-enabled extra variant in this step (we recommend cu124 unless your platform does not support cuda12+). After installation, you can use torch.cuda.is_available() to confirm that CUDA is available.
• If you are developing or testing, you can sync without the --no-dev switch to install the dependencies required for development, testing, and code checking.

3. Modify the configuration file in the config directory as needed. You can directly modify default.env, but it is recommended to create a file named local.env to override the configuration in default.env.

4. (Optional) Enable the built-in frontend: NekoImageGallery v1.5.0+ has a built-in frontend application based on NekoImageGallery.App. To enable it, set APP_WITH_FRONTEND=True in your configuration file.

   WARNING

   After enabling the built-in frontend, all APIs will be automatically mounted under the /api sub-path. For example, the original /docs will become /api/docs. This may affect your existing deployment, please proceed with caution.

5. Run the application:

   uv run main.py

   You can specify the ip address to bind to with --host (default is 0.0.0.0) and the port to bind to with --port (default is 8000). You can view all available commands and options with uv run main.py --help.

6. (Optional) Deploy the frontend application: If you do not want to use the built-in frontend, or want to deploy the frontend independently, you can refer to the deployment documentation of NekoImageGallery.App.

🐋 Docker Deployment

About Docker Images

NekoImageGallery's docker image are built and released on Docker Hub, including serval variants:

Tags	Description
edgeneko/neko-image-gallery:<version> edgeneko/neko-image-gallery:<version>-cuda edgeneko/neko-image-gallery:<version>-cuda12.4	Supports GPU inferencing with CUDA12.4
edgeneko/neko-image-gallery:<version>-cuda11.8	Supports GPU inferencing with CUDA11.8
edgeneko/neko-image-gallery:<version>-cpu	Supports CPU inferencing
edgeneko/neko-image-gallery:<version>-cpu-arm	(Alpha) Supports CPU inferencing on ARM64(aarch64) devices

Where <version> is the version number or version alias of NekoImageGallery, as follows:

Version	Description
latest	The latest stable version of NekoImageGallery
v*.*.* / v*.*	The specific version number (correspond to Git tags)
edge	The latest development version of NekoImageGallery, may contain unstable features and breaking changes

In each image, we have bundled the necessary dependencies, openai/clip-vit-large-patch14 model weights, bert-base-chinese model weights and easy-paddle-ocr models to provide a complete and ready-to-use image.

The images uses /opt/NekoImageGallery/static as volume to store image files, mount it to your own volume or directory if local storage is required.

For configuration, we recommend using environment variables to override the default configuration. Secret information (such as API tokens) can be provided through docker secrets.

NOTE

To enable the built-in frontend, please set the environment variable APP_WITH_FRONTEND=True. After enabling, all APIs will be automatically mounted under the /api sub-path, please ensure that your reverse proxy and other configurations are correct.

Prepare nvidia-container-runtime

If you want to support CUDA acceleration during inference, please refer to the Docker GPU related documentation for installation.

Related Document:

1. https://docs.docker.com/config/containers/resource_constraints/#gpu
2. https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker
3. https://nvidia.github.io/nvidia-container-runtime/

Run the server

1. Download the docker-compose.yml file from repository.

   # For cuda deployment (default)
   wget https://raw.githubusercontent.com/hv0905/NekoImageGallery/master/docker-compose.yml
   # For CPU-only deployment
   wget https://raw.githubusercontent.com/hv0905/NekoImageGallery/master/docker-compose-cpu.yml && mv docker-compose-cpu.yml docker-compose.yml

2. Modify the docker-compose.yml file as needed.

3. Run the following command to start the server:

   # start in foreground
   docker compose up
   # start in background(detached mode)
   docker compose up -d

Upload images to NekoImageGallery

There are several ways to upload images to NekoImageGallery:

• Via web interface: You can use the built-in web interface or the standalone NekoImageGallery.App to upload images to the server. Please make sure you have enabled the Admin API and set your Admin Token in the configuration file.

• Via local indexing: This is suitable for local deployment or when the images you want to upload are already on the server. Use the following command to index your local image directory:

  python main.py local-index <path-to-your-image-directory>

  The above command will recursively upload all images in the specified directory and its subdirectories to the server. You can also specify categories/starred for images you upload, see python main.py local-index --help for more information.

• Via API: You can use the upload API provided by NekoImageGallery to upload images. By using this method, the server can prevent saving the image files locally but only store their URLs and metadata. Please make sure you have enabled the Admin API and set your Admin Token in the configuration file. This method is suitable for automated image uploading or synchronizing NekoImageGallery with external systems. For more information, please check the API documentation.