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update docs with newly added variables

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also remove outdated information
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mauwii 2023-02-04 23:33:41 +01:00 committed by Matthias Wild
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docs/installation/040_INSTALL_DOCKER.md
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@ -16,10 +16,6 @@ title: Installing with Docker
For general use, install locally to leverage your machine's GPU.
!!! tip "For running on a cloud instance/service"
Check out the [Running InvokeAI in the cloud with Docker](#running-invokeai-in-the-cloud-with-docker) section below
## Why containers?
They provide a flexible, reliable way to build and deploy InvokeAI. You'll also
@ -78,16 +74,18 @@ Some Suggestions of variables you may want to change besides the Token:
<figure markdown>
| Environment-Variable | Default value | Description |
| -------------------- | ----------------------------- | -------------------------------------------------------------------------------------------- |
| `HUGGINGFACE_TOKEN` | No default, but **required**! | This is the only **required** variable, without it you can't download the huggingface models |
| `REPOSITORY_NAME` | The Basename of the Repo folder | This name will used as the container repository/image name |
| `VOLUMENAME` | `${REPOSITORY_NAME,,}_data` | Name of the Docker Volume where model files will be stored |
| `ARCH` | arch of the build machine | can be changed if you want to build the image for another arch |
| `INVOKEAI_TAG` | latest | the Container Repository / Tag which will be used |
| `PIP_REQUIREMENTS` | `requirements-lin-cuda.txt` | the requirements file to use (from `environments-and-requirements`) |
| `CONTAINER_FLAVOR` | cuda | the flavor of the image, which can be changed if you build f.e. with amd requirements file. |
| `INVOKE_DOCKERFILE` | `docker-build/Dockerfile` | the Dockerfile which should be built, handy for development |
| Environment-Variable | Default value | Description |
| --------------------- | ------------------------------- | -------------------------------------------------------------------------------------------- |
| `HUGGINGFACE_TOKEN` | No default, but **required**! | This is the only **required** variable, without it you can't download the huggingface models |
| `REPOSITORY_NAME` | The Basename of the Repo folder | This name will used as the container repository/image name |
| `VOLUMENAME` | `${REPOSITORY_NAME,,}_data` | Name of the Docker Volume where model files will be stored |
| `ARCH` | arch of the build machine | can be changed if you want to build the image for another arch |
| `INVOKEAI_TAG` | latest | the Container Repository / Tag which will be used |
| `PIP_REQUIREMENTS` | `requirements-lin-cuda.txt` | the requirements file to use (from `environments-and-requirements`) |
| `CONTAINER_FLAVOR` | cuda | the flavor of the image, which can be changed if you build f.e. with amd requirements file. |
| `INVOKE_DOCKERFILE` | `docker-build/Dockerfile` | the Dockerfile which should be built, handy for development |
| `CONTAINER_FLAVOR` | | the flavor of the image to built, available options are `cuda`, `rocm` and `cpu` |
| `PIP_EXTRA_INDEX_URL` | | if you want to use a custom pip-extra-index-url |
</figure>
@ -130,8 +128,9 @@ also do so.
## Running the container on your GPU
If you have an Nvidia GPU, you can enable InvokeAI to run on the GPU by running the container with an extra
environment variable to enable GPU usage and have the process run much faster:
If you have an Nvidia GPU, you can enable InvokeAI to run on the GPU by running
the container with an extra environment variable to enable GPU usage and have
the process run much faster:
```bash
GPU_FLAGS=all ./docker-build/run.sh
@ -139,7 +138,8 @@ GPU_FLAGS=all ./docker-build/run.sh
This passes the `--gpus all` to docker and uses the GPU.
If you don't have a GPU (or your host is not yet setup to use it) you will see a message like this:
If you don't have a GPU (or your host is not yet setup to use it) you will see a
message like this:
`docker: Error response from daemon: could not select device driver "" with capabilities: [[gpu]].`
@ -147,84 +147,8 @@ You can use the full set of GPU combinations documented here:
https://docs.docker.com/config/containers/resource_constraints/#gpu
For example, use `GPU_FLAGS=device=GPU-3a23c669-1f69-c64e-cf85-44e9b07e7a2a` to choose a specific device identified by a UUID.
## Running InvokeAI in the cloud with Docker
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).
An advantage of this method is that it does not need any local setup or additional dependencies.
See the `docker-build/Dockerfile.cloud` file to familizarize yourself with the image's content.
### Prerequisites
- a `docker` runtime
- `make` (optional but helps for convenience)
- Huggingface token to download models, or an existing InvokeAI runtime directory from a previous installation
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.
### Building and running the image locally
1. Clone this repo and `cd docker-build`
1. `make build` - this will build the image. (This does *not* require a GPU-capable system).
1. _(skip this step if you already have a complete InvokeAI runtime directory)_
- `make configure` (This does *not* require a GPU-capable system)
- this will create a local cache of models and configs (a.k.a the _runtime dir_)
- enter your Huggingface token when prompted
1. `make web`
1. Open the `http://localhost:9090` URL in your browser, and enjoy the banana sushi!
To use InvokeAI on the cli, run `make cli`. To open a Bash shell in the container for arbitraty advanced use, `make shell`.
#### Building and running without `make`
(Feel free to adapt paths such as `${HOME}/invokeai` to your liking, and modify the CLI arguments as necessary).
!!! example "Build the image and configure the runtime directory"
```Shell
cd docker-build
DOCKER_BUILDKIT=1 docker build -t local/invokeai:latest -f Dockerfile.cloud ..
docker run --rm -it -v ${HOME}/invokeai:/mnt/invokeai local/invokeai:latest -c "python scripts/configure_invokeai.py"
```
!!! example "Run the web server"
```Shell
docker run --runtime=nvidia --gpus=all --rm -it -v ${HOME}/invokeai:/mnt/invokeai -p9090:9090 local/invokeai:latest
```
Access the Web UI at http://localhost:9090
!!! example "Run the InvokeAI interactive CLI"
```
docker run --runtime=nvidia --gpus=all --rm -it -v ${HOME}/invokeai:/mnt/invokeai local/invokeai:latest -c "python scripts/invoke.py"
```
### Running the image in the cloud
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):
1. build this image either in the cloud (you'll need to pull the repo), or locally
1. `docker tag` it as `your-registry/invokeai` and push to your registry (i.e. Dockerhub)
1. `docker pull` it on your cloud instance
1. configure the runtime directory as per above example, using `docker run ... configure_invokeai.py` script
1. use either one of the `docker run` commands above, substituting the image name for your own image.
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.
The template's `README` provides ample detail, but at a high level, the process is as follows:
1. create a pod using this Docker image
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
1. Run the pod with `sleep infinity` as the Docker command
1. Use Runpod basic SSH to connect to the pod, and run `python scripts/configure_invokeai.py` script
1. Stop the pod, and change the Docker command to `python scripts/invoke.py --web --host 0.0.0.0`
1. Run the pod again, connect to your pod on HTTP port 9090, and enjoy the banana sushi!
Running on other cloud providers such as Vast.ai will likely work in a similar fashion.
For example, use `GPU_FLAGS=device=GPU-3a23c669-1f69-c64e-cf85-44e9b07e7a2a` to
choose a specific device identified by a UUID.
---
@ -240,13 +164,12 @@ Running on other cloud providers such as Vast.ai will likely work in a similar f
If you're on a **Linux container** the `invoke` script is **automatically
started** and the output dir set to the Docker volume you created earlier.
If you're **directly on macOS follow these startup instructions**.
With the Conda environment activated (`conda activate ldm`), run the interactive
If you're **directly on macOS follow these startup instructions**. With the
Conda environment activated (`conda activate ldm`), run the interactive
interface that combines the functionality of the original scripts `txt2img` and
`img2img`:
Use the more accurate but VRAM-intensive full precision math because
half-precision requires autocast and won't work.
By default the images are saved in `outputs/img-samples/`.
`img2img`: Use the more accurate but VRAM-intensive full precision math because
half-precision requires autocast and won't work. By default the images are saved
in `outputs/img-samples/`.
```Shell
python3 scripts/invoke.py --full_precision
@ -262,9 +185,9 @@ invoke> q
### Text to Image
For quick (but bad) image results test with 5 steps (default 50) and 1 sample
image. This will let you know that everything is set up correctly.
Then increase steps to 100 or more for good (but slower) results.
The prompt can be in quotes or not.
image. This will let you know that everything is set up correctly. Then increase
steps to 100 or more for good (but slower) results. The prompt can be in quotes
or not.
```Shell
invoke> The hulk fighting with sheldon cooper -s5 -n1
@ -277,10 +200,9 @@ You'll need to experiment to see if face restoration is making it better or
worse for your specific prompt.
If you're on a container the output is set to the Docker volume. You can copy it
wherever you want.
You can download it from the Docker Desktop app, Volumes, my-vol, data.
Or you can copy it from your Mac terminal. Keep in mind `docker cp` can't expand
`*.png` so you'll need to specify the image file name.
wherever you want. You can download it from the Docker Desktop app, Volumes,
my-vol, data. Or you can copy it from your Mac terminal. Keep in mind
`docker cp` can't expand `*.png` so you'll need to specify the image file name.
On your host Mac (you can use the name of any container that mounted the
volume):