mirror of https://github.com/bmaltais/kohya_ss
68 lines
3.1 KiB
Markdown
68 lines
3.1 KiB
Markdown
### Docker
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#### Get your Docker ready for GPU support
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##### Windows
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Once you have installed [**Docker Desktop**](https://www.docker.com/products/docker-desktop/), [**CUDA Toolkit**](https://developer.nvidia.com/cuda-downloads), [**NVIDIA Windows Driver**](https://www.nvidia.com.tw/Download/index.aspx), and ensured that your Docker is running with [**WSL2**](https://docs.docker.com/desktop/wsl/#turn-on-docker-desktop-wsl-2), you are ready to go.
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Here is the official documentation for further reference.
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<https://docs.nvidia.com/cuda/wsl-user-guide/index.html#nvidia-compute-software-support-on-wsl-2>
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<https://docs.docker.com/desktop/wsl/use-wsl/#gpu-support>
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##### Linux, OSX
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Install an NVIDIA GPU Driver if you do not already have one installed.
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<https://docs.nvidia.com/datacenter/tesla/tesla-installation-notes/index.html>
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Install the NVIDIA Container Toolkit with this guide.
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<https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html>
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#### Design of our Dockerfile
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- It is required that all training data is stored in the `dataset` subdirectory, which is mounted into the container at `/dataset`.
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- Please note that the file picker functionality is not available. Instead, you will need to manually input the folder path and configuration file path.
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- TensorBoard has been separated from the project.
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- TensorBoard is not included in the Docker image.
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- The "Start TensorBoard" button has been hidden.
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- TensorBoard is launched from a distinct container [as shown here](/docker-compose.yaml#L41).
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- The browser won't be launched automatically. You will need to manually open the browser and navigate to [http://localhost:7860/](http://localhost:7860/) and [http://localhost:6006/](http://localhost:6006/)
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- This Dockerfile has been designed to be easily disposable. You can discard the container at any time and restart it with the new code version.
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#### Use the pre-built Docker image
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```bash
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git clone --recursive https://github.com/bmaltais/kohya_ss.git
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cd kohya_ss
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docker compose up -d
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```
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To update the system, do `docker compose down && docker compose up -d --pull always`
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#### Local docker build
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> [!IMPORTANT]
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> Clone the Git repository ***recursively*** to include submodules:
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> `git clone --recursive https://github.com/bmaltais/kohya_ss.git`
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```bash
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git clone --recursive https://github.com/bmaltais/kohya_ss.git
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cd kohya_ss
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docker compose up -d --build
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```
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> [!NOTE]
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> Building the image may take up to 20 minutes to complete.
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To update the system, ***checkout to the new code version*** and rebuild using `docker compose down && docker compose up -d --build --pull always`
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> [!NOTE]
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> If you are running on Linux, an alternative Docker container port with fewer limitations is available [here](https://github.com/P2Enjoy/kohya_ss-docker).
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#### ashleykleynhans runpod docker builds
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You may want to use the following repositories when running on runpod:
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- Standalone Kohya_ss template: <https://github.com/ashleykleynhans/kohya-docker>
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- Auto1111 + Kohya_ss GUI template: <https://github.com/ashleykleynhans/stable-diffusion-docker>
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