mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
Optimized Docker build with support for external working directory (#1544)
* add docker build optimized for size; do not copy models to image useful for cloud deployments. attempts to utilize docker layer caching as effectively as possible. also some quick tools to help with building * add workflow to build cloud img in ci * push cloud image in addition to building * (ci) also tag docker images with git SHA * (docker) rework Makefile for easy cache population and local use * support the new conda-less install; further optimize docker build * (ci) clean up the build-cloud-img action * improve the Makefile for local use * move execution of invoke script from entrypoint to cmd, allows overriding the cmd if needed (e.g. in Runpod * remove unnecessary copyright statements * (docs) add a section on running InvokeAI in the cloud using Docker * (docker) add patchmatch to the cloud image; improve build caching; simplify Makefile * (docker) fix pip requirements path to use binary_installer directory
This commit is contained in:
Eugene Brodsky
GitHub
@ -6,7 +6,7 @@ title: Docker
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!!! warning "For end users"
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We highly recommend to Install InvokeAI locally using [these instructions](index.md)"
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We highly recommend to Install InvokeAI locally using [these instructions](index.md)
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!!! tip "For developers"
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@ -16,6 +16,10 @@ title: Docker
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For general use, install locally to leverage your machine's GPU.
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!!! tip "For running on a cloud instance/service"
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Check out the [Running InvokeAI in the cloud with Docker](#running-invokeai-in-the-cloud-with-docker) section below
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## Why containers?
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They provide a flexible, reliable way to build and deploy InvokeAI. You'll also
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@ -36,7 +40,7 @@ development purposes it's fine. Once you're done with development tasks on your
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laptop you can build for the target platform and architecture and deploy to
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another environment with NVIDIA GPUs on-premises or in the cloud.
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## Installation on a Linux container
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## Installation in a Linux container (desktop)
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### Prerequisites
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@ -117,12 +121,91 @@ also do so.
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./docker-build/run.sh "banana sushi" -Ak_lms -S42 -s10
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```
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This would generate the legendary "banana sushi" with Seed 42, k_lms Sampler and 10 steps.
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This would generate the legendary "banana sushi" with Seed 42, k_lms Sampler and 10 steps.
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Find out more about available CLI-Parameters at [features/CLI.md](../../features/CLI/#arguments)
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---
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## Running InvokeAI in the cloud with Docker
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We offer an optimized Ubuntu-based image that has been well-tested in cloud deployments. Note: it also works well locally on Linux x86_64 systems with an Nvidia GPU. It *may* also work on Windows under WSL2 and on Intel Mac (not tested).
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An advantage of this method is that it does not need any local setup or additional dependencies.
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See the `docker-build/Dockerfile.cloud` file to familizarize yourself with the image's content.
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### Prerequisites
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- a `docker` runtime
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- `make` (optional but helps for convenience)
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- Huggingface token to download models, or an existing InvokeAI runtime directory from a previous installation
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Neither local Python nor any dependencies are required. If you don't have `make` (part of `build-essentials` on Ubuntu), or do not wish to install it, the commands from the `docker-build/Makefile` are readily adaptable to be executed directly.
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### Building and running the image locally
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1. Clone this repo and `cd docker-build`
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1. `make build` - this will build the image. (This does *not* require a GPU-capable system).
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1. _(skip this step if you already have a complete InvokeAI runtime directory)_
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- `make configure` (This does *not* require a GPU-capable system)
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- this will create a local cache of models and configs (a.k.a the _runtime dir_)
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- enter your Huggingface token when prompted
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1. `make web`
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1. Open the `http://localhost:9090` URL in your browser, and enjoy the banana sushi!
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To use InvokeAI on the cli, run `make cli`. To open a Bash shell in the container for arbitraty advanced use, `make shell`.
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#### Building and running without `make`
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(Feel free to adapt paths such as `${HOME}/invokeai` to your liking, and modify the CLI arguments as necessary).
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!!! example "Build the image and configure the runtime directory"
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```Shell
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cd docker-build
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DOCKER_BUILDKIT=1 docker build -t local/invokeai:latest -f Dockerfile.cloud ..
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docker run --rm -it -v ${HOME}/invokeai:/mnt/invokeai local/invokeai:latest -c "python scripts/configure_invokeai.py"
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```
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!!! example "Run the web server"
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```Shell
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docker run --runtime=nvidia --gpus=all --rm -it -v ${HOME}/invokeai:/mnt/invokeai -p9090:9090 local/invokeai:latest
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```
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Access the Web UI at http://localhost:9090
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!!! example "Run the InvokeAI interactive CLI"
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```
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docker run --runtime=nvidia --gpus=all --rm -it -v ${HOME}/invokeai:/mnt/invokeai local/invokeai:latest -c "python scripts/invoke.py"
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```
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### Running the image in the cloud
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This image works anywhere you can run a container with a mounted Docker volume. You may either build this image on a cloud instance, or build and push it to your Docker registry. To manually run this on a cloud instance (such as AWS EC2, GCP or Azure VM):
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1. build this image either in the cloud (you'll need to pull the repo), or locally
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1. `docker tag` it as `your-registry/invokeai` and push to your registry (i.e. Dockerhub)
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1. `docker pull` it on your cloud instance
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1. configure the runtime directory as per above example, using `docker run ... configure_invokeai.py` script
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1. use either one of the `docker run` commands above, substituting the image name for your own image.
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To run this on Runpod, please refer to the following Runpod template: https://www.runpod.io/console/gpu-secure-cloud?template=vm19ukkycf (you need a Runpod subscription). When launching the template, feel free to set the image to pull your own build.
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The template's `README` provides ample detail, but at a high level, the process is as follows:
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1. create a pod using this Docker image
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1. ensure the pod has an `INVOKEAI_ROOT=<path_to_your_persistent_volume>` environment variable, and that it corresponds to the path to your pod's persistent volume mount
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1. Run the pod with `sleep infinity` as the Docker command
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1. Use Runpod basic SSH to connect to the pod, and run `python scripts/configure_invokeai.py` script
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1. Stop the pod, and change the Docker command to `python scripts/invoke.py --web --host 0.0.0.0`
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1. Run the pod again, connect to your pod on HTTP port 9090, and enjoy the banana sushi!
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Running on other cloud providers such as Vast.ai will likely work in a similar fashion.
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---
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!!! warning "Deprecated"
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From here on you will find the the previous Docker-Docs, which will still
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@ -135,12 +218,12 @@ also do so.
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If you're on a **Linux container** the `invoke` script is **automatically
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started** and the output dir set to the Docker volume you created earlier.
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If you're **directly on macOS follow these startup instructions**.
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If you're **directly on macOS follow these startup instructions**.
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With the Conda environment activated (`conda activate ldm`), run the interactive
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interface that combines the functionality of the original scripts `txt2img` and
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`img2img`:
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`img2img`:
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Use the more accurate but VRAM-intensive full precision math because
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half-precision requires autocast and won't work.
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half-precision requires autocast and won't work.
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By default the images are saved in `outputs/img-samples/`.
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```Shell
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@ -157,8 +240,8 @@ invoke> q
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### Text to Image
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For quick (but bad) image results test with 5 steps (default 50) and 1 sample
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image. This will let you know that everything is set up correctly.
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Then increase steps to 100 or more for good (but slower) results.
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image. This will let you know that everything is set up correctly.
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Then increase steps to 100 or more for good (but slower) results.
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The prompt can be in quotes or not.
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```Shell
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@ -172,8 +255,8 @@ You'll need to experiment to see if face restoration is making it better or
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worse for your specific prompt.
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If you're on a container the output is set to the Docker volume. You can copy it
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wherever you want.
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You can download it from the Docker Desktop app, Volumes, my-vol, data.
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wherever you want.
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You can download it from the Docker Desktop app, Volumes, my-vol, data.
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Or you can copy it from your Mac terminal. Keep in mind `docker cp` can't expand
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`*.png` so you'll need to specify the image file name.
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