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* Documentation updates * PR feedback * PR feedback * Originally implemented using #11880 * add to FAQ * Remove mTLS --------- Co-authored-by: Jason Rasmussen <jason@rasm.me>
44 lines
2 KiB
Markdown
44 lines
2 KiB
Markdown
# Remote Machine Learning
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To alleviate [performance issues on low-memory systems](/docs/FAQ.mdx#why-is-immich-slow-on-low-memory-systems-like-the-raspberry-pi) like the Raspberry Pi, you may also host Immich's machine-learning container on a more powerful system (e.g. your laptop or desktop computer):
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- Set the URL in Machine Learning Settings on the Admin Settings page to point to the designated ML system, e.g. `http://workstation:3003`.
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- Copy the following `docker-compose.yml` to your ML system.
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- If using [hardware acceleration](/docs/features/ml-hardware-acceleration), the [hwaccel.ml.yml](https://github.com/immich-app/immich/releases/latest/download/hwaccel.ml.yml) file also needs to be added
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- Start the container by running `docker compose up -d`.
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:::info
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Smart Search and Face Detection will use this feature, but Facial Recognition is handled in the server.
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:::
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:::danger
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When using remote machine learning, the thumbnails are sent to the remote machine learning container. Use this option carefully when running this on a public computer or a paid processing cloud.
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:::
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```yaml
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name: immich_remote_ml
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services:
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immich-machine-learning:
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container_name: immich_machine_learning
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# For hardware acceleration, add one of -[armnn, cuda, openvino] to the image tag.
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# Example tag: ${IMMICH_VERSION:-release}-cuda
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image: ghcr.io/immich-app/immich-machine-learning:${IMMICH_VERSION:-release}
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# extends:
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# file: hwaccel.ml.yml
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# service: # set to one of [armnn, cuda, openvino, openvino-wsl] for accelerated inference - use the `-wsl` version for WSL2 where applicable
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volumes:
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- model-cache:/cache
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restart: always
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ports:
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- 3003:3003
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volumes:
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model-cache:
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```
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Please note that version mismatches between both hosts may cause instabilities and bugs, so make sure to always perform updates together.
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:::caution
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As an internal service, the machine learning container has no security measures whatsoever. Please be mindful of where it's deployed and who can access it.
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:::
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