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docs: overhaul Docker documentation, add to main README

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Eugene Brodsky
2024-06-24 16:51:06 -04:00
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docs/installation/040_INSTALL_DOCKER.md
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@ -4,50 +4,37 @@ title: Installing with Docker
# :fontawesome-brands-docker: Docker
!!! warning "macOS and AMD GPU Users"
!!! warning "macOS users"
We highly recommend to Install InvokeAI locally using [these instructions](INSTALLATION.md),
because Docker containers can not access the GPU on macOS.
!!! warning "AMD GPU Users"
Container support for AMD GPUs has been reported to work by the community, but has not received
extensive testing. Please make sure to set the `GPU_DRIVER=rocm` environment variable (see below), and
use the `build.sh` script to build the image for this to take effect at build time.
Docker can not access the GPU on macOS, so your generation speeds will be slow. [Install InvokeAI](INSTALLATION.md) instead.
!!! tip "Linux and Windows Users"
For optimal performance, configure your Docker daemon to access your machine's GPU.
Configure Docker to access your machine's GPU.
Docker Desktop on Windows [includes GPU support](https://www.docker.com/blog/wsl-2-gpu-support-for-docker-desktop-on-nvidia-gpus/).
Linux users should install and configure the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)
## Why containers?
They provide a flexible, reliable way to build and deploy InvokeAI.
See [Processes](https://12factor.net/processes) under the Twelve-Factor App
methodology for details on why running applications in such a stateless fashion is important.
The container is configured for CUDA by default, but can be built to support AMD GPUs
by setting the `GPU_DRIVER=rocm` environment variable at Docker image build time.
Developers on Apple silicon (M1/M2/M3): You
[can't access your GPU cores from Docker containers](https://github.com/pytorch/pytorch/issues/81224)
and performance is reduced compared with running it directly on macOS but for
development purposes it's fine. Once you're done with development tasks on your
laptop you can build for the target platform and architecture and deploy to
another environment with NVIDIA GPUs on-premises or in the cloud.
Linux users should follow the [NVIDIA](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) or [AMD](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/how-to/docker.html) documentation.
## TL;DR
This assumes properly configured Docker on Linux or Windows/WSL2. Read on for detailed customization options.
Ensure your Docker setup is able to use your GPU. Then:
```bash
docker run --runtime=nvidia --gpus=all --publish 9090:9090 ghcr.io/invoke-ai/invokeai
```
Once the container starts up, open http://localhost:9090 in your browser, install some models, and start generating.
## Build-It-Yourself
All the docker materials are located inside the [docker](https://github.com/invoke-ai/InvokeAI/tree/main/docker) directory in the Git repo.
```bash
# docker compose commands should be run from the `docker` directory
cd docker
cp .env.sample .env
docker compose up
```
## Installation in a Linux container (desktop)
We also ship the `run.sh` convenience script. See the `docker/README.md` file for detailed instructions on how to customize the docker setup to your needs.
### Prerequisites
@ -58,18 +45,9 @@ Preferences, Resources, Advanced. Increase the CPUs and Memory to avoid this
[Issue](https://github.com/invoke-ai/InvokeAI/issues/342). You may need to
increase Swap and Disk image size too.
#### Get a Huggingface-Token
Besides the Docker Agent you will need an Account on
[huggingface.co](https://huggingface.co/join).
After you succesfully registered your account, go to
[huggingface.co/settings/tokens](https://huggingface.co/settings/tokens), create
a token and copy it, since you will need in for the next step.
### Setup
Set up your environmnent variables. In the `docker` directory, make a copy of `.env.sample` and name it `.env`. Make changes as necessary.
Set up your environment variables. In the `docker` directory, make a copy of `.env.sample` and name it `.env`. Make changes as necessary.
Any environment variables supported by InvokeAI can be set here - please see the [CONFIGURATION](../features/CONFIGURATION.md) for further detail.
@ -103,10 +81,9 @@ Once the container starts up (and configures the InvokeAI root directory if this
## Troubleshooting / FAQ
- Q: I am running on Windows under WSL2, and am seeing a "no such file or directory" error.
- A: Your `docker-entrypoint.sh` file likely has Windows (CRLF) as opposed to Unix (LF) line endings,
and you may have cloned this repository before the issue was fixed. To solve this, please change
the line endings in the `docker-entrypoint.sh` file to `LF`. You can do this in VSCode
- A: Your `docker-entrypoint.sh` might have has Windows (CRLF) line endings, depending how you cloned the repository.
To solve this, change the line endings in the `docker-entrypoint.sh` file to `LF`. You can do this in VSCode
(`Ctrl+P` and search for "line endings"), or by using the `dos2unix` utility in WSL.
Finally, you may delete `docker-entrypoint.sh` followed by `git pull; git checkout docker/docker-entrypoint.sh`
to reset the file to its most recent version.
For more information on this issue, please see the [Docker Desktop documentation](https://docs.docker.com/desktop/troubleshoot/topics/#avoid-unexpected-syntax-errors-use-unix-style-line-endings-for-files-in-containers)
For more information on this issue, see [Docker Desktop documentation](https://docs.docker.com/desktop/troubleshoot/topics/#avoid-unexpected-syntax-errors-use-unix-style-line-endings-for-files-in-containers)