segmentation and small fixes

feat: layout
WIP
2024-01-23 23:44:10 -06:00 · 2024-01-23 22:45:11 -05:00 · 2024-01-23 22:08:25 -05:00 · 2024-01-23 18:51:02 +00:00 · 2024-01-23 17:09:32 +00:00 · 2024-01-23 10:13:09 -06:00
644 changed files with 23709 additions and 46605 deletions
@@ -8,7 +8,7 @@ machine-learning/
 misc/
 mobile/

-server/node_modules
+server/node_modules/
 server/coverage/
 server/.reverse-geocoding-dump/
 server/upload/
@@ -19,7 +19,10 @@ web/coverage/
 web/.svelte-kit
 web/build/

-cli/node_modules
+cli/node_modules/
 cli/.reverse-geocoding-dump/
 cli/upload/
-cli/dist/
+cli/dist/
+
+open-api/typescript-sdk/node_modules/
+open-api/typescript-sdk/build/
@@ -8,14 +8,9 @@ mobile/openapi/.openapi-generator/FILES linguist-generated=true
 mobile/lib/**/*.g.dart -diff -merge
 mobile/lib/**/*.g.dart linguist-generated=true

-cli/src/api/open-api/**/*.md -diff -merge
-cli/src/api/open-api/**/*.md linguist-generated=true
-cli/src/api/open-api/**/*.ts -diff -merge
-cli/src/api/open-api/**/*.ts linguist-generated=true
-
-web/src/api/open-api/**/*.md -diff -merge
-web/src/api/open-api/**/*.md linguist-generated=true
-web/src/api/open-api/**/*.ts -diff -merge
-web/src/api/open-api/**/*.ts linguist-generated=true
+open-api/typescript-sdk/client/**/*.md -diff -merge
+open-api/typescript-sdk/client/**/*.md linguist-generated=true
+open-api/typescript-sdk/client/**/*.ts -diff -merge
+open-api/typescript-sdk/client/**/*.ts linguist-generated=true

 *.sh text eol=lf
@@ -1,7 +1,5 @@
 name: Report an issue with Immich
 description: Report an issue with Immich
-labels: ["bug", "need triage"]
-title: "[BUG] <title>"
 body:
  - type: markdown
    attributes:
@@ -1,30 +1,42 @@
 changelog:
  categories:
-    - title: Breaking Changes 🛠
+    - title: ⚠️ Breaking Changes
      labels:
        - breaking-change
-    - title: Server
+
+    - title: 🗄️ Server
      labels:
        - 🗄️server
-    - title: Mobile
+
+    - title: 📱 Mobile
      labels:
        - 📱mobile
-    - title: Web
+
+    - title: 🖥️ Web
      labels:
        - 🖥️web
-    - title: Machine Learning
+
+    - title: 🧠 Machine Learning
      labels:
        - 🧠machine-learning
-    - title: CLI
+
+    - title: ⚡ CLI
      labels:
        - cli
-    - title: Documentation
+
+    - title: 📓 Documentation
      labels:
        - documentation
-    - title: Dependency updates
+
+    - title: 🔨 Build
+      labels:
+        - deployment
+
+    - title: 🤖 Dependencies
      labels:
        - dependencies
        - renovate
+
    - title: Other changes
      labels:
        - "*"
@@ -25,15 +25,38 @@ jobs:
      fail-fast: false
      matrix:
        include:
-          - context: "machine-learning"
-            file: "machine-learning/Dockerfile"
-            image: "immich-machine-learning"
-            platforms: "linux/amd64,linux/arm64"
-          - context: "."
-            file: "server/Dockerfile"
-            image: "immich-server"
-            platforms: "linux/arm64,linux/amd64"
+          - image: immich-machine-learning
+            context: machine-learning
+            file: machine-learning/Dockerfile
+            platforms: linux/amd64,linux/arm64
+            device: cpu

+          - image: immich-machine-learning
+            context: machine-learning
+            file: machine-learning/Dockerfile
+            platforms: linux/amd64
+            device: cuda
+            suffix: -cuda
+
+          - image: immich-machine-learning
+            context: machine-learning
+            file: machine-learning/Dockerfile
+            platforms: linux/amd64
+            device: openvino
+            suffix: -openvino
+          
+          - image: immich-machine-learning
+            context: machine-learning
+            file: machine-learning/Dockerfile
+            platforms: linux/arm64
+            device: armnn
+            suffix: -armnn
+
+          - image: immich-server
+            context: .
+            file: server/Dockerfile
+            platforms: linux/amd64,linux/arm64
+            device: cpu
    steps:
      - name: Checkout
        uses: actions/checkout@v4
@@ -79,12 +102,12 @@ jobs:
            name=altran1502/${{matrix.image}},enable=${{ github.event_name == 'release' }}
          tags: |
            # Tag with branch name
-            type=ref,event=branch
+            type=ref,event=branch,suffix=${{ matrix.suffix }}
            # Tag with pr-number
-            type=ref,event=pr
+            type=ref,event=pr,suffix=${{ matrix.suffix }}
            # Tag with git tag on release
-            type=ref,event=tag
-            type=raw,value=release,enable=${{ github.event_name == 'release' }}
+            type=ref,event=tag,suffix=${{ matrix.suffix }}
+            type=raw,value=release,enable=${{ github.event_name == 'release' }},suffix=${{ matrix.suffix }}

      - name: Determine build cache output
        id: cache-target
@@ -106,5 +129,7 @@ jobs:
          push: ${{ !github.event.pull_request.head.repo.fork }}
          cache-from: type=registry,ref=ghcr.io/${{ github.repository_owner }}/immich-build-cache:${{matrix.image}}
          cache-to: ${{ steps.cache-target.outputs.cache-to }}
+          build-args: |
+            DEVICE=${{ matrix.device }}
          tags: ${{ steps.metadata.outputs.tags }}
          labels: ${{ steps.metadata.outputs.labels }}
@@ -83,5 +83,6 @@ jobs:
          files: |
            docker/docker-compose.yml
            docker/example.env
-            docker/hwaccel.yml
+            docker/hwaccel.ml.yml
+            docker/hwaccel.transcoding.yml
            *.apk
@@ -10,9 +10,26 @@ concurrency:
  cancel-in-progress: true

 jobs:
-  e2e-tests:
-    name: Server (e2e)
-    runs-on: ubuntu-latest
+  server-e2e-api:
+    name: Server (e2e-api)
+    runs-on: mich
+    defaults:
+      run:
+        working-directory: ./server
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Run npm install
+        run: npm ci
+
+      - name: Run e2e tests
+        run: npm run e2e:api
+
+  server-e2e-jobs:
+    name: Server (e2e-jobs)
+    runs-on: mich

    steps:
      - name: Checkout code
@@ -21,7 +38,7 @@ jobs:
          submodules: "recursive"

      - name: Run e2e tests
-        run: make test-server-e2e
+        run: make server-e2e-jobs

  doc-tests:
    name: Docs
@@ -41,10 +58,6 @@ jobs:
        run: npm run format
        if: ${{ !cancelled() }}

-      - name: Run tsc
-        run: npm run check
-        if: ${{ !cancelled() }}
-
      - name: Run build
        run: npm run build
        if: ${{ !cancelled() }}
@@ -90,10 +103,14 @@ jobs:
      - name: Checkout code
        uses: actions/checkout@v4

-      - name: Run npm install in cli
+      - name: Run setup typescript-sdk
+        run: npm ci && npm run build
+        working-directory: ./open-api/typescript-sdk
+
+      - name: Run npm install (cli)
        run: npm ci

-      - name: Run npm install in server
+      - name: Run npm install (server)
        run: npm ci
        working-directory: ./server

@@ -126,10 +143,14 @@ jobs:
        with:
          submodules: "recursive"

-      - name: Run npm install in cli
+      - name: Run setup typescript-sdk
+        run: npm ci && npm run build
+        working-directory: ./open-api/typescript-sdk
+
+      - name: Run npm install (cli)
        run: npm ci

-      - name: Run npm install in server
+      - name: Run npm install (server)
        run: npm ci
        working-directory: ./server

@@ -147,6 +168,10 @@ jobs:
      - name: Checkout code
        uses: actions/checkout@v4

+      - name: Run setup typescript-sdk
+        run: npm ci && npm run build
+        working-directory: ./open-api/typescript-sdk
+
      - name: Run npm install
        run: npm ci

@@ -200,7 +225,7 @@ jobs:
          cache: "poetry"
      - name: Install dependencies
        run: |
-          poetry install --with dev
+          poetry install --with dev --with cpu
      - name: Lint with ruff
        run: |
          poetry run ruff check --output-format=github app export
@@ -220,14 +245,14 @@ jobs:
    steps:
      - uses: actions/checkout@v4
      - name: Run API generation
-        run: npm --prefix server run api:generate
+        run: make open-api
      - name: Find file changes
        uses: tj-actions/verify-changed-files@v13.1
        id: verify-changed-files
        with:
          files: |
            mobile/openapi
-            web/src/api/open-api
+            open-api/typescript-sdk
      - name: Verify files have not changed
        if: steps.verify-changed-files.outputs.files_changed == 'true'
        run: |
@@ -1,3 +1,5 @@
+**/node_modules/**
+
 .DS_Store
 .vscode/*
 !.vscode/launch.json
@@ -12,3 +14,7 @@ mobile/gradle.properties
 mobile/openapi/pubspec.lock
 mobile/*.jks
 mobile/libisar.dylib
+
+open-api/typescript-sdk/build
+mobile/android/fastlane/report.xml
+mobile/ios/fastlane/report.xml
@@ -16,8 +16,8 @@ stage:
 pull-stage:
 	docker compose -f ./docker/docker-compose.staging.yml pull

-test-server-e2e:
-	docker compose -f ./server/test/docker-compose.server-e2e.yml up --renew-anon-volumes --abort-on-container-exit --exit-code-from immich-server --remove-orphans --build
+server-e2e-jobs:
+	docker compose -f ./server/e2e/docker-compose.server-e2e.yml up --renew-anon-volumes --abort-on-container-exit --exit-code-from immich-server --remove-orphans --build

 prod:
 	docker compose -f ./docker/docker-compose.prod.yml up --build -V --remove-orphans
@@ -25,8 +25,15 @@ prod:
 prod-scale:
 	docker compose -f ./docker/docker-compose.prod.yml up --build -V --scale immich-server=3 --scale immich-microservices=3 --remove-orphans

-api:
-	npm --prefix server run api:generate
+.PHONY: open-api
+open-api:
+	cd ./open-api && bash ./bin/generate-open-api.sh
+
+open-api-dart:
+	cd ./open-api && bash ./bin/generate-open-api.sh dart
+
+open-api-typescript:
+	cd ./open-api && bash ./bin/generate-open-api.sh typescript

 sql:
 	npm --prefix server run sql:generate
@@ -17,7 +17,7 @@
 </p>
 <br/>
 <a href="https://immich.app">
-<img src="design/immich-screenshots.png" title="Main Screenshot">
+<img src="design/immich-screenshots.png" title="界面截图">
 </a>
 <br/>

@@ -32,6 +32,7 @@
  <a href="README_de_DE.md">Deutsch</a>
  <a href="README_nl_NL.md">Nederlands</a>
  <a href="README_tr_TR.md">Türkçe</a>
+  <a href="README_ru_RU.md">Русский</a>
 </p>

 ## 免责声明
@@ -39,7 +40,7 @@
 - ⚠️ 本项目正在 **非常活跃** 地开发中。
 - ⚠️ 可能存在 bug 或者随时有重大变更。
 - ⚠️ **不要把本软件作为您存储照片或视频的唯一方式。**
- ⚠️ 为了您宝贵的照片与视频，始终遵守 [3-2-1](https://www.backblaze.com/blog/the-3-2-1-backup-strategy/) 备份方案！
+- ⚠️ 为了您宝贵的照片与视频，请始终遵守 [3-2-1](https://www.backblaze.com/blog/the-3-2-1-backup-strategy/) 备份方案！

 ## 目录

@@ -74,40 +75,41 @@

 # 功能特性

-| 功能特性                                     | 移动端  | 网页端 |
-| ------------------------------------------- | ------- | --- |
-| 上传并查看照片和视频                         | 是     | 是 |
-| 软件运行时自动备份                           | 是     | N/A |
-| 选择需要备份的相册          | 是     | N/A |
-| 下载照片和视频到本地        | 是     | 是 |
-| 多用户支持                          | 是     | 是 |
-| 相册                                       | 是     | 是 |
-| 共享相册                               | 是     | 是 |
-| 可拖动的快速导航栏   | 是     | 是 |
-| 支持RAW格式 (HEIC, HEIF, DNG, Apple ProRaw) | 是     | 是 |
-| 元数据视图（EXIF, 地图）                   | 是     | 是 |
-| 通过元数据、对象和标签进行搜索  | 是     | 是  |
-| 管理功能（用户管理）  | 否     | 是 |
-| 后台备份                         | 是 | N/A |
-| 虚拟滚动                             | 是     | 是 |
-| OAuth 支持                               | 是     | 是 |
-| API Keys|N/A|是|
-| 实况照片备份和查看   | 仅 iOS     | 是 |
-|用户自定义存储结构|是|是|
-|公共分享|否|是|
-|归档与收藏功能|是|是|
-|全局地图|否|是|
-|好友分享|是|是|
-|人像识别与分组|是|是|
-|回忆（那年今日）|是|是|
-|离线支持|是|否|
-|只读相册|是|是|
+| 功能特性                                    | 移动端 | 网页端 |
+|---------------------------------------------|--------|--------|
+| 上传并查看照片和视频                        | 是     | 是     |
+| 软件运行时自动备份                          | 是     | N/A    |
+| 选择需要备份的相册                          | 是     | N/A    |
+| 下载照片和视频到本地                        | 是     | 是     |
+| 多用户支持                                  | 是     | 是     |
+| 相册与共享相册                              | 是     | 是     |
+| 可拖动的快速导航栏                          | 是     | 是     |
+| 支持RAW格式                                 | 是     | 是     |
+| 元数据视图（EXIF、地图）                    | 是     | 是     |
+| 通过元数据、对象、人脸和标签进行搜索        | 是     | 是     |
+| 管理功能（用户管理）                        | 否     | 是     |
+| 后台备份                                    | 是     | N/A    |
+| 虚拟滚动                                    | 是     | 是     |
+| OAuth 支持                                  | 是     | 是     |
+| API Keys                                    | N/A    | 是     |
+| 实况照片备份和查看                          | 是     | 是     |
+| 用户自定义存储结构                          | 是     | 是     |
+| 公共分享                                    | 否     | 是     |
+| 归档与收藏功能                              | 是     | 是     |
+| 足迹地图                                    | 是     | 是     |
+| 好友分享                                    | 是     | 是     |
+| 人脸识别与分组                              | 是     | 是     |
+| 回忆（那年今日）                            | 是     | 是     |
+| 离线支持                                    | 是     | 否     |
+| 只读相册                                    | 是     | 是     |
+| 照片堆叠                                    | 是     | 是     |
+

 # 支持本项目

-我已经致力于本项目并且将我会持续更新文档、新增功能和修复问题。但是独木不成林，我需要您给予我坚持下去的动力。
+我已经致力于本项目并且我将会持续更新文档、新增功能和修复问题。但是独木不成林，我需要您给予我坚持下去的动力。

-就像我在 [selfhosted.show - In the episode 'The-organization-must-not-be-name is a Hostile Actor'](https://selfhosted.show/79?t=1418) 节目里说的一样,这是我和团队的一项艰巨任务。并且我希望某一天我能够全职开发本项目，在此我请求您能够助我梦想成真。
+就像我在 [selfhosted.show - In the episode 'The-organization-must-not-be-name is a Hostile Actor'](https://selfhosted.show/79?t=1418) 节目里说的一样，这是我和团队的一项艰巨任务。并且我希望某一天我能够全职开发本项目，在此我请求您能够助我梦想成真。

 如果您使用了本项目一段时间，并且觉得上面的话有道理，那么请您考虑通过下列任一方式支持我吧。

@@ -118,3 +120,9 @@
 - [Librepay](https://liberapay.com/alex.tran1502/)
 - [buymeacoffee](https://www.buymeacoffee.com/altran1502)
 - 比特币: 1FvEp6P6NM8EZEkpGUFAN2LqJ1gxusNxZX
+- ZCash: u1smm4wvqegcp46zss2jf5xptchgeczp4rx7a0wu3mermf2wxahm26yyz5w9mw3f2p4emwlljxjumg774kgs8rntt9yags0whnzane4n67z4c7gppq4yyvcj404ne3r769prwzd9j8ntvqp44fa6d67sf7rmcfjmds3gmeceff4u8e92rh38nd30cr96xw6vfhk6scu4ws90ldzupr3sz
+
+## 贡献者
+<a href="https://github.com/alextran1502/immich/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=immich-app/immich" width="100%"/>
+</a>
@@ -5,7 +5,6 @@ node_modules
 .env
 .env.*
 !.env.example
-src/api/open-api
 *.md
 *.json
 coverage
@@ -1,4 +1,4 @@
-FROM ghcr.io/immich-app/base-server-dev:20231228@sha256:e631113b47c7e16a06ca47d3a99bdf269e831dfa4b94f6f4cc923781fa82c4e3 as test
+FROM ghcr.io/immich-app/base-server-dev:20240118@sha256:92de0ebf1bc2ccc6b7d11023e31b010dc79dd7e16df04146820d2d2cc2e5bf9f as test

 WORKDIR /usr/src/app/server
 COPY server/package.json server/package-lock.json ./
@@ -10,7 +10,7 @@ COPY cli/package.json cli/package-lock.json ./
 RUN npm ci
 COPY ./cli/ .

-FROM ghcr.io/immich-app/base-server-prod:20231228@sha256:e51e418d904124f368eca84b504414e40c5b55f9990be043d1749fdf5d1a045c
+FROM ghcr.io/immich-app/base-server-prod:20240118@sha256:5b78cefcc3c3dda2a48fd22c7b0fd6453cfc5ae61258ff5a082cc9563c4e3c25

 VOLUME /usr/src/app/upload

@@ -9,6 +9,7 @@
      "version": "2.0.6",
      "license": "MIT",
      "dependencies": {
+        "@immich/sdk": "file:../open-api/typescript-sdk",
        "axios": "^1.6.2",
        "byte-size": "^8.1.1",
        "cli-progress": "^3.12.0",
@@ -48,9 +49,21 @@
        "typescript": "^5.0.0"
      }
    },
+    "../open-api/typescript-sdk": {
+      "name": "@immich/sdk",
+      "version": "1.92.1",
+      "license": "MIT",
+      "dependencies": {
+        "axios": "^0.27.2"
+      },
+      "devDependencies": {
+        "@types/node": "^20.11.0",
+        "typescript": "^5.3.3"
+      }
+    },
    "../server": {
      "name": "immich",
-      "version": "1.91.4",
+      "version": "1.93.3",
      "dev": true,
      "license": "UNLICENSED",
      "dependencies": {
@@ -75,10 +88,10 @@
        "class-transformer": "^0.5.1",
        "class-validator": "^0.14.0",
        "cookie-parser": "^1.4.6",
-        "exiftool-vendored": "~23.5.0",
-        "exiftool-vendored.pl": "12.70",
+        "exiftool-vendored": "~24.3.0",
+        "exiftool-vendored.pl": "12.72",
        "fluent-ffmpeg": "^2.1.2",
-        "geo-tz": "^7.0.7",
+        "geo-tz": "^8.0.0",
        "glob": "^10.3.3",
        "handlebars": "^4.7.8",
        "i18n-iso-countries": "^7.6.0",
@@ -944,6 +957,10 @@
      "integrity": "sha512-dvuCeX5fC9dXgJn9t+X5atfmgQAzUOWqS1254Gh0m6i8wKd10ebXkfNKiRK+1GWi/yTvvLDHpoxLr0xxxeslWw==",
      "dev": true
    },
+    "node_modules/@immich/sdk": {
+      "resolved": "../open-api/typescript-sdk",
+      "link": true
+    },
    "node_modules/@isaacs/cliui": {
      "version": "8.0.2",
      "resolved": "https://registry.npmjs.org/@isaacs/cliui/-/cliui-8.0.2.tgz",
@@ -1541,12 +1558,12 @@
      }
    },
    "node_modules/@testcontainers/postgresql": {
-      "version": "10.4.0",
-      "resolved": "https://registry.npmjs.org/@testcontainers/postgresql/-/postgresql-10.4.0.tgz",
-      "integrity": "sha512-d+eJBDQlQE6+PVoWlpX4YpUoDdogoNkW0ag33qt/oOknmqNjQj6c3/mCzThsS59qtS0vPXydwbHiOkpNuCXFFg==",
+      "version": "10.6.0",
+      "resolved": "https://registry.npmjs.org/@testcontainers/postgresql/-/postgresql-10.6.0.tgz",
+      "integrity": "sha512-gHYpsXkVLpCkJ0jg7xE5n8pogTNvw2LyhkXeJm04raH1yz020jcHAB2a1tRW1J2D8mM8WhFH+xH6pzH2ZggFdg==",
      "dev": true,
      "dependencies": {
-        "testcontainers": "^10.4.0"
+        "testcontainers": "^10.6.0"
      }
    },
    "node_modules/@tsconfig/node10": {
@@ -1708,9 +1725,9 @@
      }
    },
    "node_modules/@types/node": {
-      "version": "20.10.6",
-      "resolved": "https://registry.npmjs.org/@types/node/-/node-20.10.6.tgz",
-      "integrity": "sha512-Vac8H+NlRNNlAmDfGUP7b5h/KA+AtWIzuXy0E6OyP8f1tCLYAtPvKRRDJjAPqhpCb0t6U2j7/xqAuLEebW2kiw==",
+      "version": "20.11.5",
+      "resolved": "https://registry.npmjs.org/@types/node/-/node-20.11.5.tgz",
+      "integrity": "sha512-g557vgQjUUfN76MZAN/dt1z3dzcUsimuysco0KeluHgrPdJXkP/XdAURgyO2W9fZWHRtRBiVKzKn8vyOAwlG+w==",
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-          "resolved": "https://registry.npmjs.org/@typescript-eslint/utils/-/utils-6.17.0.tgz",
-          "integrity": "sha512-LofsSPjN/ITNkzV47hxas2JCsNCEnGhVvocfyOcLzT9c/tSZE7SfhS/iWtzP1lKNOEfLhRTZz6xqI8N2RzweSQ==",
+          "version": "6.19.0",
+          "resolved": "https://registry.npmjs.org/@typescript-eslint/utils/-/utils-6.19.0.tgz",
+          "integrity": "sha512-QR41YXySiuN++/dC9UArYOg4X86OAYP83OWTewpVx5ct1IZhjjgTLocj7QNxGhWoTqknsgpl7L+hGygCO+sdYw==",
          "dev": true,
          "requires": {
            "@eslint-community/eslint-utils": "^4.4.0",
            "@types/json-schema": "^7.0.12",
            "@types/semver": "^7.5.0",
-            "@typescript-eslint/scope-manager": "6.17.0",
-            "@typescript-eslint/types": "6.17.0",
-            "@typescript-eslint/typescript-estree": "6.17.0",
+            "@typescript-eslint/scope-manager": "6.19.0",
+            "@typescript-eslint/types": "6.19.0",
+            "@typescript-eslint/typescript-estree": "6.19.0",
            "semver": "^7.5.4"
          }
        },
        "@typescript-eslint/visitor-keys": {
-          "version": "6.17.0",
-          "resolved": "https://registry.npmjs.org/@typescript-eslint/visitor-keys/-/visitor-keys-6.17.0.tgz",
-          "integrity": "sha512-H6VwB/k3IuIeQOyYczyyKN8wH6ed8EwliaYHLxOIhyF0dYEIsN8+Bk3GE19qafeMKyZJJHP8+O1HiFhFLUNKSg==",
+          "version": "6.19.0",
+          "resolved": "https://registry.npmjs.org/@typescript-eslint/visitor-keys/-/visitor-keys-6.19.0.tgz",
+          "integrity": "sha512-hZaUCORLgubBvtGpp1JEFEazcuEdfxta9j4iUwdSAr7mEsYYAp3EAUyCZk3VEEqGj6W+AV4uWyrDGtrlawAsgQ==",
          "dev": true,
          "requires": {
-            "@typescript-eslint/types": "6.17.0",
+            "@typescript-eslint/types": "6.19.0",
            "eslint-visitor-keys": "^3.4.1"
          }
        },
@@ -9300,9 +9325,9 @@
      "integrity": "sha512-Oei9OH4tRh0YqU3GxhX79dM/mwVgvbZJaSNaRk+bshkj0S5cfHcgYakreBjrHwatXKbz+IoIdYLxrKim2MjW0Q=="
    },
    "axios": {
-      "version": "1.6.4",
-      "resolved": "https://registry.npmjs.org/axios/-/axios-1.6.4.tgz",
-      "integrity": "sha512-heJnIs6N4aa1eSthhN9M5ioILu8Wi8vmQW9iHQ9NUvfkJb0lEEDUiIdQNAuBtfUt3FxReaKdpQA5DbmMOqzF/A==",
+      "version": "1.6.5",
+      "resolved": "https://registry.npmjs.org/axios/-/axios-1.6.5.tgz",
+      "integrity": "sha512-Ii012v05KEVuUoFWmMW/UQv9aRIc3ZwkWDcM+h5Il8izZCtRVpDUfwpoFf7eOtajT3QiGR4yDUx7lPqHJULgbg==",
      "requires": {
        "follow-redirects": "^1.15.4",
        "form-data": "^4.0.0",
@@ -10007,18 +10032,18 @@
      "requires": {}
    },
    "eslint-plugin-jest": {
-      "version": "27.6.1",
-      "resolved": "https://registry.npmjs.org/eslint-plugin-jest/-/eslint-plugin-jest-27.6.1.tgz",
-      "integrity": "sha512-WEYkyVXD9NlmFBKvrkmzrC+C9yZoz5pAml2hO19PlS3spJtoiwj4p2u8spd/7zx5IvRsZsCmsoImaAvBB9X93Q==",
+      "version": "27.6.3",
+      "resolved": "https://registry.npmjs.org/eslint-plugin-jest/-/eslint-plugin-jest-27.6.3.tgz",
+      "integrity": "sha512-+YsJFVH6R+tOiO3gCJon5oqn4KWc+mDq2leudk8mrp8RFubLOo9CVyi3cib4L7XMpxExmkmBZQTPDYVBzgpgOA==",
      "dev": true,
      "requires": {
        "@typescript-eslint/utils": "^5.10.0"
      }
    },
    "eslint-plugin-prettier": {
-      "version": "5.1.2",
-      "resolved": "https://registry.npmjs.org/eslint-plugin-prettier/-/eslint-plugin-prettier-5.1.2.tgz",
-      "integrity": "sha512-dhlpWc9vOwohcWmClFcA+HjlvUpuyynYs0Rf+L/P6/0iQE6vlHW9l5bkfzN62/Stm9fbq8ku46qzde76T1xlSg==",
+      "version": "5.1.3",
+      "resolved": "https://registry.npmjs.org/eslint-plugin-prettier/-/eslint-plugin-prettier-5.1.3.tgz",
+      "integrity": "sha512-C9GCVAs4Eq7ZC/XFQHITLiHJxQngdtraXaM+LoUFoFp/lHNl2Zn8f3WQbe9HvTBBQ9YnKFB0/2Ajdqwo5D1EAw==",
      "dev": true,
      "requires": {
        "prettier-linter-helpers": "^1.0.0",
@@ -10545,10 +10570,10 @@
        "eslint": "^8.48.0",
        "eslint-config-prettier": "^9.0.0",
        "eslint-plugin-prettier": "^5.0.0",
-        "exiftool-vendored": "~23.5.0",
-        "exiftool-vendored.pl": "12.70",
+        "exiftool-vendored": "~24.3.0",
+        "exiftool-vendored.pl": "12.72",
        "fluent-ffmpeg": "^2.1.2",
-        "geo-tz": "^7.0.7",
+        "geo-tz": "^8.0.0",
        "glob": "^10.3.3",
        "handlebars": "^4.7.8",
        "i18n-iso-countries": "^7.6.0",
@@ -12547,9 +12572,9 @@
      }
    },
    "testcontainers": {
-      "version": "10.4.0",
-      "resolved": "https://registry.npmjs.org/testcontainers/-/testcontainers-10.4.0.tgz",
-      "integrity": "sha512-kMmJXOAuJeQTRbGSrIEBaAzTzGzmY4+DU5xW5CxgzxgywCoy53ubeiTh3eZ1rzT54YR3zf0nijlb/l7OT4E+/g==",
+      "version": "10.6.0",
+      "resolved": "https://registry.npmjs.org/testcontainers/-/testcontainers-10.6.0.tgz",
+      "integrity": "sha512-FDJ3o3J8IMu1V7Uc6lNZ2MAD8+BV4HdpR/Vf5mHtgYHKdn6k1EbGFwtnvVNOxanJ99FCjf/EU8eA5ZQ4yjlsGA==",
      "dev": true,
      "requires": {
        "@balena/dockerignore": "^1.0.2",
@@ -12,6 +12,7 @@
    "cli"
  ],
  "dependencies": {
+    "@immich/sdk": "file:../open-api/typescript-sdk",
    "axios": "^1.6.2",
    "byte-size": "^8.1.1",
    "cli-progress": "^3.12.0",
@@ -57,7 +58,7 @@
    "format": "prettier --check .",
    "format:fix": "prettier --write .",
    "check": "tsc --noEmit",
-    "test:e2e": "NODE_OPTIONS='--experimental-vm-modules' jest --config test/e2e/jest-e2e.json --runInBand"
+    "test:e2e": "jest --config test/e2e/jest-e2e.json --runInBand"
  },
  "jest": {
    "clearMocks": true,
@@ -9,7 +9,7 @@ import {
  ServerInfoApi,
  SystemConfigApi,
  UserApi,
-} from './open-api';
+} from '@immich/sdk';
 import { ApiConfiguration } from '../cores/api-configuration';
 import FormData from 'form-data';

@@ -1,72 +0,0 @@
-/* tslint:disable */
-/* eslint-disable */
-/**
- * Immich
- * Immich API
- *
- * The version of the OpenAPI document: 1.92.0
- * 
- *
- * NOTE: This class is auto generated by OpenAPI Generator (https://openapi-generator.tech).
- * https://openapi-generator.tech
- * Do not edit the class manually.
- */
-
-
-import type { Configuration } from './configuration';
-// Some imports not used depending on template conditions
-// @ts-ignore
-import type { AxiosPromise, AxiosInstance, AxiosRequestConfig } from 'axios';
-import globalAxios from 'axios';
-
-export const BASE_PATH = "/api".replace(/\/+$/, "");
-
-/**
- *
- * @export
- */
-export const COLLECTION_FORMATS = {
-    csv: ",",
-    ssv: " ",
-    tsv: "\t",
-    pipes: "|",
-};
-
-/**
- *
- * @export
- * @interface RequestArgs
- */
-export interface RequestArgs {
-    url: string;
-    options: AxiosRequestConfig;
-}
-
-/**
- *
- * @export
- * @class BaseAPI
- */
-export class BaseAPI {
-    protected configuration: Configuration | undefined;
-
-    constructor(configuration?: Configuration, protected basePath: string = BASE_PATH, protected axios: AxiosInstance = globalAxios) {
-        if (configuration) {
-            this.configuration = configuration;
-            this.basePath = configuration.basePath || this.basePath;
-        }
-    }
-};
-
-/**
- *
- * @export
- * @class RequiredError
- * @extends {Error}
- */
-export class RequiredError extends Error {
-    constructor(public field: string, msg?: string) {
-        super(msg);
-        this.name = "RequiredError"
-    }
-}
@@ -1,150 +0,0 @@
-/* tslint:disable */
-/* eslint-disable */
-/**
- * Immich
- * Immich API
- *
- * The version of the OpenAPI document: 1.92.0
- * 
- *
- * NOTE: This class is auto generated by OpenAPI Generator (https://openapi-generator.tech).
- * https://openapi-generator.tech
- * Do not edit the class manually.
- */
-
-
-import type { Configuration } from "./configuration";
-import type { RequestArgs } from "./base";
-import type { AxiosInstance, AxiosResponse } from 'axios';
-import { RequiredError } from "./base";
-
-/**
- *
- * @export
- */
-export const DUMMY_BASE_URL = 'https://example.com'
-
-/**
- *
- * @throws {RequiredError}
- * @export
- */
-export const assertParamExists = function (functionName: string, paramName: string, paramValue: unknown) {
-    if (paramValue === null || paramValue === undefined) {
-        throw new RequiredError(paramName, `Required parameter ${paramName} was null or undefined when calling ${functionName}.`);
-    }
-}
-
-/**
- *
- * @export
- */
-export const setApiKeyToObject = async function (object: any, keyParamName: string, configuration?: Configuration) {
-    if (configuration && configuration.apiKey) {
-        const localVarApiKeyValue = typeof configuration.apiKey === 'function'
-            ? await configuration.apiKey(keyParamName)
-            : await configuration.apiKey;
-        object[keyParamName] = localVarApiKeyValue;
-    }
-}
-
-/**
- *
- * @export
- */
-export const setBasicAuthToObject = function (object: any, configuration?: Configuration) {
-    if (configuration && (configuration.username || configuration.password)) {
-        object["auth"] = { username: configuration.username, password: configuration.password };
-    }
-}
-
-/**
- *
- * @export
- */
-export const setBearerAuthToObject = async function (object: any, configuration?: Configuration) {
-    if (configuration && configuration.accessToken) {
-        const accessToken = typeof configuration.accessToken === 'function'
-            ? await configuration.accessToken()
-            : await configuration.accessToken;
-        object["Authorization"] = "Bearer " + accessToken;
-    }
-}
-
-/**
- *
- * @export
- */
-export const setOAuthToObject = async function (object: any, name: string, scopes: string[], configuration?: Configuration) {
-    if (configuration && configuration.accessToken) {
-        const localVarAccessTokenValue = typeof configuration.accessToken === 'function'
-            ? await configuration.accessToken(name, scopes)
-            : await configuration.accessToken;
-        object["Authorization"] = "Bearer " + localVarAccessTokenValue;
-    }
-}
-
-function setFlattenedQueryParams(urlSearchParams: URLSearchParams, parameter: any, key: string = ""): void {
-    if (parameter == null) return;
-    if (typeof parameter === "object") {
-        if (Array.isArray(parameter)) {
-            (parameter as any[]).forEach(item => setFlattenedQueryParams(urlSearchParams, item, key));
-        } 
-        else {
-            Object.keys(parameter).forEach(currentKey => 
-                setFlattenedQueryParams(urlSearchParams, parameter[currentKey], `${key}${key !== '' ? '.' : ''}${currentKey}`)
-            );
-        }
-    } 
-    else {
-        if (urlSearchParams.has(key)) {
-            urlSearchParams.append(key, parameter);
-        } 
-        else {
-            urlSearchParams.set(key, parameter);
-        }
-    }
-}
-
-/**
- *
- * @export
- */
-export const setSearchParams = function (url: URL, ...objects: any[]) {
-    const searchParams = new URLSearchParams(url.search);
-    setFlattenedQueryParams(searchParams, objects);
-    url.search = searchParams.toString();
-}
-
-/**
- *
- * @export
- */
-export const serializeDataIfNeeded = function (value: any, requestOptions: any, configuration?: Configuration) {
-    const nonString = typeof value !== 'string';
-    const needsSerialization = nonString && configuration && configuration.isJsonMime
-        ? configuration.isJsonMime(requestOptions.headers['Content-Type'])
-        : nonString;
-    return needsSerialization
-        ? JSON.stringify(value !== undefined ? value : {})
-        : (value || "");
-}
-
-/**
- *
- * @export
- */
-export const toPathString = function (url: URL) {
-    return url.pathname + url.search + url.hash
-}
-
-/**
- *
- * @export
- */
-export const createRequestFunction = function (axiosArgs: RequestArgs, globalAxios: AxiosInstance, BASE_PATH: string, configuration?: Configuration) {
-    return <T = unknown, R = AxiosResponse<T>>(axios: AxiosInstance = globalAxios, basePath: string = BASE_PATH) => {
-        const axiosRequestArgs = {...axiosArgs.options, url: (configuration?.basePath || basePath) + axiosArgs.url};
-        return axios.request<T, R>(axiosRequestArgs);
-    };
-}
@@ -1,101 +0,0 @@
-/* tslint:disable */
-/* eslint-disable */
-/**
- * Immich
- * Immich API
- *
- * The version of the OpenAPI document: 1.92.0
- * 
- *
- * NOTE: This class is auto generated by OpenAPI Generator (https://openapi-generator.tech).
- * https://openapi-generator.tech
- * Do not edit the class manually.
- */
-
-
-export interface ConfigurationParameters {
-    apiKey?: string | Promise<string> | ((name: string) => string) | ((name: string) => Promise<string>);
-    username?: string;
-    password?: string;
-    accessToken?: string | Promise<string> | ((name?: string, scopes?: string[]) => string) | ((name?: string, scopes?: string[]) => Promise<string>);
-    basePath?: string;
-    baseOptions?: any;
-    formDataCtor?: new () => any;
-}
-
-export class Configuration {
-    /**
-     * parameter for apiKey security
-     * @param name security name
-     * @memberof Configuration
-     */
-    apiKey?: string | Promise<string> | ((name: string) => string) | ((name: string) => Promise<string>);
-    /**
-     * parameter for basic security
-     *
-     * @type {string}
-     * @memberof Configuration
-     */
-    username?: string;
-    /**
-     * parameter for basic security
-     *
-     * @type {string}
-     * @memberof Configuration
-     */
-    password?: string;
-    /**
-     * parameter for oauth2 security
-     * @param name security name
-     * @param scopes oauth2 scope
-     * @memberof Configuration
-     */
-    accessToken?: string | Promise<string> | ((name?: string, scopes?: string[]) => string) | ((name?: string, scopes?: string[]) => Promise<string>);
-    /**
-     * override base path
-     *
-     * @type {string}
-     * @memberof Configuration
-     */
-    basePath?: string;
-    /**
-     * base options for axios calls
-     *
-     * @type {any}
-     * @memberof Configuration
-     */
-    baseOptions?: any;
-    /**
-     * The FormData constructor that will be used to create multipart form data
-     * requests. You can inject this here so that execution environments that
-     * do not support the FormData class can still run the generated client.
-     *
-     * @type {new () => FormData}
-     */
-    formDataCtor?: new () => any;
-
-    constructor(param: ConfigurationParameters = {}) {
-        this.apiKey = param.apiKey;
-        this.username = param.username;
-        this.password = param.password;
-        this.accessToken = param.accessToken;
-        this.basePath = param.basePath;
-        this.baseOptions = param.baseOptions;
-        this.formDataCtor = param.formDataCtor;
-    }
-
-    /**
-     * Check if the given MIME is a JSON MIME.
-     * JSON MIME examples:
-     *   application/json
-     *   application/json; charset=UTF8
-     *   APPLICATION/JSON
-     *   application/vnd.company+json
-     * @param mime - MIME (Multipurpose Internet Mail Extensions)
-     * @return True if the given MIME is JSON, false otherwise.
-     */
-    public isJsonMime(mime: string): boolean {
-        const jsonMime: RegExp = new RegExp('^(application\/json|[^;/ \t]+\/[^;/ \t]+[+]json)[ \t]*(;.*)?$', 'i');
-        return mime !== null && (jsonMime.test(mime) || mime.toLowerCase() === 'application/json-patch+json');
-    }
-}
@@ -2,7 +2,7 @@ import { ImmichApi } from '../api/client';
 import { SessionService } from '../services/session.service';
 import { LoginError } from '../cores/errors/login-error';
 import { exit } from 'node:process';
-import { ServerVersionResponseDto, UserResponseDto } from 'src/api/open-api';
+import { ServerVersionResponseDto, UserResponseDto } from '@immich/sdk';
 import { BaseOptionsDto } from 'src/cores/dto/base-options-dto';

 export abstract class BaseCommand {
@@ -1,6 +1,6 @@
 import { BaseCommand } from '../../cli/base-command';

-export default class LoginKey extends BaseCommand {
+export class LoginKey extends BaseCommand {
  public async run(instanceUrl: string, apiKey: string): Promise<void> {
    console.log('Executing API key auth flow...');

@@ -1,6 +1,6 @@
 import { BaseCommand } from '../cli/base-command';

-export default class Logout extends BaseCommand {
+export class Logout extends BaseCommand {
  public static readonly description = 'Logout and remove persisted credentials';

  public async run(): Promise<void> {
@@ -1,6 +1,6 @@
 import { BaseCommand } from '../cli/base-command';

-export default class ServerInfo extends BaseCommand {
+export class ServerInfo extends BaseCommand {
  public async run() {
    await this.connect();
    const { data: versionInfo } = await this.immichApi.serverInfoApi.getServerVersion();
@@ -6,10 +6,10 @@ import fs from 'node:fs';
 import cliProgress from 'cli-progress';
 import byteSize from 'byte-size';
 import { BaseCommand } from '../cli/base-command';
-import axios, { AxiosRequestConfig } from 'axios';
+import axios, { AxiosRequestConfig, AxiosResponse } from 'axios';
 import FormData from 'form-data';

-export default class Upload extends BaseCommand {
+export class Upload extends BaseCommand {
  uploadLength!: number;

  public async run(paths: string[], options: UploadOptionsDto): Promise<void> {
@@ -107,6 +107,8 @@ export default class Upload extends BaseCommand {
              const formData = asset.getUploadFormData();
              const res = await this.uploadAsset(formData);
              existingAssetId = res.data.id;
+              uploadCounter++;
+              totalSizeUploaded += asset.fileSize;
            }

            if ((options.album || options.albumName) && asset.albumName !== undefined) {
@@ -127,9 +129,6 @@ export default class Upload extends BaseCommand {
              }
            }
          }
-
-          totalSizeUploaded += asset.fileSize;
-          uploadCounter++;
        }

        sizeSoFar += asset.fileSize;
@@ -172,7 +171,7 @@ export default class Upload extends BaseCommand {
    }
  }

-  private async uploadAsset(data: FormData): Promise<axios.AxiosResponse> {
+  private async uploadAsset(data: FormData): Promise<AxiosResponse> {
    const url = this.immichApi.apiConfiguration.instanceUrl + '/asset/upload';

    const config: AxiosRequestConfig = {
@@ -1,10 +1,10 @@
 #! /usr/bin/env node

 import { Option, Command } from 'commander';
-import Upload from './commands/upload';
-import ServerInfo from './commands/server-info';
-import LoginKey from './commands/login/key';
-import Logout from './commands/logout';
+import { Upload } from './commands/upload';
+import { ServerInfo } from './commands/server-info';
+import { LoginKey } from './commands/login/key';
+import { Logout } from './commands/logout';
 import { version } from '../package.json';

 import path from 'node:path';
@@ -16,10 +16,9 @@ import {
 const mockPingServer = jest.fn(() => Promise.resolve({ data: { res: 'pong' } }));
 const mockUserInfo = jest.fn(() => Promise.resolve({ data: { email: 'admin@example.com' } }));

-jest.mock('../api/open-api', () => {
+jest.mock('@immich/sdk', () => {
  return {
-    __esModule: true,
-    ...jest.requireActual('../api/open-api'),
+    ...jest.requireActual('@immich/sdk'),
    UserApi: jest.fn().mockImplementation(() => {
      return { getMyUserInfo: mockUserInfo };
    }),
@@ -7,7 +7,7 @@
  "testRegex": ".e2e-spec.ts$",
  "testTimeout": 6000000,
  "transform": {
-    "^.+\\.(t|j)s$": "ts-jest"
+    "^.+\\.ts$": "ts-jest"
  },
  "collectCoverageFrom": [
    "<rootDir>/src/**/*.(t|j)s",
@@ -1,8 +1,8 @@
-import { api } from '@test/api';
-import { restoreTempFolder, testApp } from 'immich/test/test-utils';
-import { LoginResponseDto } from 'src/api/open-api';
 import { APIKeyCreateResponseDto } from '@app/domain';
-import LoginKey from 'src/commands/login/key';
+import { api } from '@test/../e2e/api/client';
+import { restoreTempFolder, testApp } from '@test/../e2e/jobs/utils';
+import { LoginResponseDto } from '@immich/sdk';
+import { LoginKey } from 'src/commands/login/key';
 import { LoginError } from 'src/cores/errors/login-error';
 import { CLI_BASE_OPTIONS, spyOnConsole } from 'test/cli-test-utils';

@@ -1,8 +1,8 @@
-import { api } from '@test/api';
-import { restoreTempFolder, testApp } from 'immich/test/test-utils';
-import { LoginResponseDto } from 'src/api/open-api';
-import ServerInfo from 'src/commands/server-info';
 import { APIKeyCreateResponseDto } from '@app/domain';
+import { api } from '@test/../e2e/api/client';
+import { restoreTempFolder, testApp } from '@test/../e2e/jobs/utils';
+import { LoginResponseDto } from '@immich/sdk';
+import { ServerInfo } from 'src/commands/server-info';
 import { CLI_BASE_OPTIONS, spyOnConsole } from 'test/cli-test-utils';

 describe(`server-info (e2e)`, () => {
@@ -37,7 +37,6 @@ export default async () => {
  }

  process.env.NODE_ENV = 'development';
-  process.env.IMMICH_TEST_ENV = 'true';
-  process.env.IMMICH_CONFIG_FILE = path.normalize(`${__dirname}/../../../server/test/e2e/immich-e2e-config.json`);
+  process.env.IMMICH_CONFIG_FILE = path.normalize(`${__dirname}/../../../server/e2e/jobs/immich-e2e-config.json`);
  process.env.TZ = 'Z';
 };
@@ -1,8 +1,8 @@
-import { api } from '@test/api';
-import { IMMICH_TEST_ASSET_PATH, restoreTempFolder, testApp } from 'immich/test/test-utils';
-import { LoginResponseDto } from 'src/api/open-api';
-import Upload from 'src/commands/upload';
 import { APIKeyCreateResponseDto } from '@app/domain';
+import { api } from '@test/../e2e/api/client';
+import { IMMICH_TEST_ASSET_PATH, restoreTempFolder, testApp } from '@test/../e2e/jobs/utils';
+import { LoginResponseDto } from '@immich/sdk';
+import { Upload } from 'src/commands/upload';
 import { CLI_BASE_OPTIONS, spyOnConsole } from 'test/cli-test-utils';

 describe(`upload (e2e)`, () => {
@@ -1,6 +1,6 @@
 {
  "compilerOptions": {
-    "module": "Node16",
+    "module": "commonjs",
    "strict": true,
    "declaration": true,
    "removeComments": true,
@@ -9,7 +9,6 @@
    "allowSyntheticDefaultImports": true,
    "resolveJsonModule": true,
    "target": "es2021",
-    "moduleResolution": "node16",
    "sourceMap": true,
    "outDir": "./dist",
    "incremental": true,
@@ -15,6 +15,7 @@ x-server-build: &server-common
  restart: always
  volumes:
    - ../server:/usr/src/app
+    - ../open-api:/usr/src/open-api
    - ${UPLOAD_LOCATION}/photos:/usr/src/app/upload
    - ${UPLOAD_LOCATION}/photos/upload:/usr/src/app/upload/upload
    - /usr/src/app/node_modules
@@ -43,8 +44,8 @@ services:
    command: [ "/usr/src/app/bin/immich-dev", "microservices" ]
    <<: *server-common
    # extends:
-    #   file: hwaccel.yml
-    #   service: hwaccel
+    #   file: hwaccel.transcoding.yml
+    #   service: cpu # set to one of [nvenc, quicksync, rkmpp, vaapi, vaapi-wsl] for accelerated transcoding
    ports:
      - 9231:9230
    depends_on:
@@ -53,11 +54,10 @@ services:

  immich-web:
    container_name: immich_web
-    image: immich-web-dev:1.9.0
+    image: immich-web-dev:latest
    build:
      context: ../web
-      dockerfile: Dockerfile
-    command: "node ./node_modules/.bin/vite dev --host 0.0.0.0 --port 3000"
+    command: "/usr/src/app/bin/immich-web"
    env_file:
      - .env
    ports:
@@ -65,6 +65,7 @@ services:
      - 24678:24678
    volumes:
      - ../web:/usr/src/app
+      - ../open-api/:/usr/src/open-api/
      - /usr/src/app/node_modules
    ulimits:
      nofile:
@@ -77,9 +78,14 @@ services:
  immich-machine-learning:
    container_name: immich_machine_learning
    image: immich-machine-learning-dev:latest
+    # extends:
+    #   file: hwaccel.ml.yml
+    #   service: cpu # set to one of [armnn, cuda, openvino, openvino-wsl] for accelerated inference
    build:
      context: ../machine-learning
      dockerfile: Dockerfile
+      args:
+        - DEVICE=cpu # set to one of [armnn, cuda, openvino, openvino-wsl] for accelerated inference
    ports:
      - 3003:3003
    volumes:
@@ -30,8 +30,8 @@ services:
    command: [ "./start-microservices.sh" ]
    <<: *server-common
    # extends:
-    #   file: hwaccel.yml
-    #   service: hwaccel
+    #   file: hwaccel.transcoding.yml
+    #   service: cpu # set to one of [nvenc, quicksync, rkmpp, vaapi, vaapi-wsl] for accelerated transcoding
    depends_on:
      - redis
      - database
@@ -40,9 +40,14 @@ services:
  immich-machine-learning:
    container_name: immich_machine_learning
    image: immich-machine-learning:latest
+    # extends:
+    #   file: hwaccel.ml.yml
+    #   service: cpu # set to one of [armnn, cuda, openvino, openvino-wsl] for accelerated inference
    build:
      context: ../machine-learning
      dockerfile: Dockerfile
+      args:
+        - DEVICE=cpu # set to one of [armnn, cuda, openvino, openvino-wsl] for accelerated inference
    volumes:
      - model-cache:/cache
    env_file:
@@ -30,9 +30,9 @@ services:
  immich-microservices:
    container_name: immich_microservices
    image: ghcr.io/immich-app/immich-server:${IMMICH_VERSION:-release}
-    # extends:
-    #   file: hwaccel.yml
-    #   service: hwaccel
+    # extends: # uncomment this section for hardware acceleration - see https://immich.app/docs/features/hardware-transcoding
+    #   file: hwaccel.transcoding.yml 
+    #   service: cpu # set to one of [nvenc, quicksync, rkmpp, vaapi, vaapi-wsl] for accelerated transcoding
    command: [ "start.sh", "microservices" ]
    volumes:
      - ${UPLOAD_LOCATION}:/usr/src/app/upload
@@ -46,7 +46,12 @@ services:

  immich-machine-learning:
    container_name: immich_machine_learning
+    # For hardware acceleration, add one of -[armnn, cuda, openvino] to the image tag.
+    # Example tag: ${IMMICH_VERSION:-release}-cuda
    image: ghcr.io/immich-app/immich-machine-learning:${IMMICH_VERSION:-release}
+    # extends: # uncomment this section for hardware acceleration - see https://immich.app/docs/features/ml-hardware-acceleration
+    #   file: hwaccel.ml.yml
+    #   service: cpu # set to one of [armnn, cuda, openvino, openvino-wsl] for accelerated inference - use the `-wsl` version for WSL2 where applicable
    volumes:
      - model-cache:/cache
    env_file:
@@ -1,24 +0,0 @@
-version: "3.8"
-
-# Hardware acceleration for transcoding using RKMPP for Rockchip SOCs
-# This is only needed if you want to use hardware acceleration for transcoding.
-# Supported host OS is Ubuntu Jammy 22.04 with custom ffmpeg from ppa:liujianfeng1994/rockchip-multimedia
-
-services:
-  hwaccel:
-    security_opt: # enables full access to /sys and /proc, still far better than privileged: true
-      - systempaths=unconfined
-      - apparmor=unconfined
-    group_add:
-      - video
-    devices:
-      - /dev/rga:/dev/rga
-      - /dev/dri:/dev/dri
-      - /dev/dma_heap:/dev/dma_heap
-      - /dev/mpp_service:/dev/mpp_service
-    volumes:
-      - /usr/bin/ffmpeg:/usr/bin/ffmpeg_mpp:ro
-      - /lib/aarch64-linux-gnu:/lib/ffmpeg-mpp:ro
-      - /lib/aarch64-linux-gnu/libblas.so.3:/lib/ffmpeg-mpp/libblas.so.3:ro # symlink is resolved by mounting
-      - /lib/aarch64-linux-gnu/liblapack.so.3:/lib/ffmpeg-mpp/liblapack.so.3:ro # symlink is resolved by mounting
-      - /lib/aarch64-linux-gnu/pulseaudio/libpulsecommon-15.99.so:/lib/ffmpeg-mpp/libpulsecommon-15.99.so:ro
@@ -0,0 +1,47 @@
+version: "3.8"
+
+# Configurations for hardware-accelerated machine learning
+
+# If using Unraid or another platform that doesn't allow multiple Compose files,
+# you can inline the config for a backend by copying its contents 
+# into the immich-machine-learning service in the docker-compose.yml file.
+
+# See https://immich.app/docs/features/ml-hardware-acceleration for info on usage.
+
+services:
+  armnn:
+    devices:
+      - /dev/mali0:/dev/mali0
+    volumes:
+      - /lib/firmware/mali_csffw.bin:/lib/firmware/mali_csffw.bin:ro # Mali firmware for your chipset (not always required depending on the driver)
+      - /usr/lib/libmali.so:/usr/lib/libmali.so:ro # Mali driver for your chipset (always required)
+
+  cpu:
+
+  cuda:
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: 1
+              capabilities:
+                - gpu
+                - compute
+                - video
+
+  openvino:
+    device_cgroup_rules:
+      - "c 189:* rmw"
+    devices:
+      - /dev/dri:/dev/dri
+    volumes:
+      - /dev/bus/usb:/dev/bus/usb
+
+  openvino-wsl:
+    devices:
+      - /dev/dri:/dev/dri
+      - /dev/dxg:/dev/dxg
+    volumes:
+      - /dev/bus/usb:/dev/bus/usb
+      - /usr/lib/wsl:/usr/lib/wsl
@@ -0,0 +1,59 @@
+version: "3.8"
+
+# Configurations for hardware-accelerated transcoding 
+
+# If using Unraid or another platform that doesn't allow multiple Compose files,
+# you can inline the config for a backend by copying its contents 
+# into the immich-microservices service in the docker-compose.yml file.
+
+# See https://immich.app/docs/features/hardware-transcoding for more info on using hardware transcoding.
+
+services:
+  cpu:
+
+  nvenc:
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: 1
+              capabilities:
+                - gpu
+                - compute
+                - video
+
+  quicksync:
+    devices:
+      - /dev/dri:/dev/dri
+
+  rkmpp:
+    security_opt: # enables full access to /sys and /proc, still far better than privileged: true
+      - systempaths=unconfined
+      - apparmor=unconfined
+    group_add:
+      - video
+    devices:
+      - /dev/rga:/dev/rga
+      - /dev/dri:/dev/dri
+      - /dev/dma_heap:/dev/dma_heap
+      - /dev/mpp_service:/dev/mpp_service
+    volumes:
+      - /usr/bin/ffmpeg:/usr/bin/ffmpeg_mpp:ro
+      - /lib/aarch64-linux-gnu:/lib/ffmpeg-mpp:ro
+      - /lib/aarch64-linux-gnu/libblas.so.3:/lib/ffmpeg-mpp/libblas.so.3:ro # symlink is resolved by mounting
+      - /lib/aarch64-linux-gnu/liblapack.so.3:/lib/ffmpeg-mpp/liblapack.so.3:ro # symlink is resolved by mounting
+      - /lib/aarch64-linux-gnu/pulseaudio/libpulsecommon-15.99.so:/lib/ffmpeg-mpp/libpulsecommon-15.99.so:ro
+
+  vaapi:
+    devices:
+      - /dev/dri:/dev/dri
+
+  vaapi-wsl: # use this for VAAPI if you're running Immich in WSL2
+    devices:
+      - /dev/dri:/dev/dri
+    volumes:
+      - /usr/lib/wsl:/usr/lib/wsl
+    environment:
+      - LD_LIBRARY_PATH=/usr/lib/wsl/lib
+      - LIBVA_DRIVER_NAME=d3d12
@@ -1,22 +0,0 @@
-version: "3.8"
-
-# Hardware acceleration for transcoding - Optional
-# This is only needed if you want to use hardware acceleration for transcoding.
-# Depending on your hardware, you should uncomment the relevant lines below.
-
-services:
-  hwaccel:
-    # devices:
-    #   - /dev/dri:/dev/dri  # If using Intel QuickSync or VAAPI
-    # volumes:
-    #   - /usr/lib/wsl:/usr/lib/wsl # If using VAAPI in WSL2
-    # environment:
-    #   - LD_LIBRARY_PATH=/usr/lib/wsl/lib # If using VAAPI in WSL2
-    #   - LIBVA_DRIVER_NAME=d3d12 # If using VAAPI in WSL2
-    # deploy: # Uncomment this section if using NVIDIA GPU
-    #   resources:
-    #     reservations:
-    #       devices:
-    #         - driver: nvidia
-    #           count: 1
-    #           capabilities: [gpu,video]
@@ -2,6 +2,7 @@
 title: Immich Recap 2023
 authors: [alextran]
 tags: [update, recap-2023]
+date: 2023-12-30T00:00
 ---

 Hi everyone,
@@ -1,122 +0,0 @@
---
-sidebar_position: 7
---
-
-# FAQ
-
-### What is the difference between the cloud icon on the mobile app?
-
-| Icon                               | Description                                                                                                                                     |
-| ---------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------- |
-| ![cloud](/img/cloud.svg)           | Asset is only available in the cloud and was uploaded from some other device (like the web client) or was deleted from this device after upload |
-| ![cloud-cross](/img/cloud-off.svg) | Asset is only available locally and has not yet been backed up                                                                                  |
-| ![cloud-done](/img/cloud-done.svg) | Asset was uploaded from this device and is now backed up in the cloud/server and still available in original on the device                      |
-
-### Can I add my existing photo library?
-
-Yes, with an [external library](/docs/features/libraries.md).
-
-### Why are only photos and not videos being uploaded to Immich?
-
-This often happens when using a reverse proxy or cloudflare tunnel in front of Immich. Make sure to set your reverse proxy to allow large POST requests. In `nginx`, set `client_max_body_size 50000M;` or similar. Cloudflare tunnels are limited to 100 mb file sizes. Also check the disk space of your reverse proxy, in some cases proxies caches requests to disk before passing them on, and if disk space runs out the request fails.
-
-### Why is Immich slow on low-memory systems like the Raspberry Pi?
-
-Immich optionally uses machine learning for several features. However, it can be too heavy to run on a Raspberry Pi. You can [mitigate](/docs/FAQ#how-can-i-lower-immichs-cpu-usage) this or [disable](/docs/FAQ.md#how-can-i-disable-machine-learning) machine learning entirely.
-
-### How can I lower Immich's CPU usage?
-
-The initial backup is the most intensive due to the number of jobs running. The most CPU-intensive ones are transcoding and machine learning jobs (Tag Images, Smart Search, Recognize Faces), and to a lesser extent thumbnail generation. Here are some ways to lower their CPU usage:
-
- Lower the job concurrency for these jobs to 1.
- Under Settings > Transcoding Settings > Threads, set the number of threads to a low number like 1 or 2.
- Under Settings > Machine Learning Settings > Facial Recognition > Model Name, you can change the facial recognition model to `buffalo_s` instead of `buffalo_l`. The former is a smaller and faster model, albeit not as good.
-  - You _must_ re-run the Recognize Faces job for all images after this for facial recognition on new images to work properly.
- If these changes are not enough, see [below](/docs/FAQ.md#how-can-i-disable-machine-learning) for how you can disable machine learning.
-
-### How can I disable machine learning?
-
-:::info
-Disabling machine learning will result in a poor experience for searching and the 'Explore' page, as these are reliant on it to work as intended.
-:::
-
-Machine learning can be disabled under Settings > Machine Learning Settings, either entirely or by model type. For instance, you can choose to disable smart search with CLIP, but keep facial recognition enabled. This means that the machine learning service will only process the enabled jobs.
-
-However, disabling all jobs will not disable the machine learning service itself. To prevent it from starting up at all in this case, you can comment out the `immich-machine-learning` section of the docker-compose.yml.
-
-### I'm getting errors about models being corrupt or failing to download. What do I do?
-
-You can delete the model cache volume, which is where models are downloaded. This will give the service a clean environment to download the model again.
-
-### What happens to existing files after I choose a new [Storage Template](/docs/administration/storage-template.mdx)?
-
-Template changes will only apply to new assets. To retroactively apply the template to previously uploaded assets, run the Storage Migration Job, available on the [Jobs](/docs/administration/jobs.md) page.
-
-### In the uploads folder, why are photos stored in the wrong date?
-
-This is fixed by running the storage migration job.
-
-### Why are there so many thumbnail generation jobs?
-
-Immich generates three thumbnails for each asset (blurred, small, and large), as well as a thumbnail for each recognized face.
-
-### How can I see Immich logs?
-
-Most Immich components are typically deployed using docker. To see logs for deployed docker containers, you can use the [Docker CLI](https://docs.docker.com/engine/reference/commandline/cli/), specifically the `docker logs` command. For examples, see [Docker Help](/docs/guides/docker-help.md)
-
-### How can I run Immich as a non-root user?
-
-1. Set the `PUID`/`PGID` environment variables (in `.env`).
-2. Set the corresponding `user` argument in `docker-compose` for each service.
-3. Add an additional volume to `immich-microservices` that mounts internally to `/usr/src/app/.reverse-geocoding-dump`.
-
-The non-root user/group needs read/write access to the volume mounts, including `UPLOAD_LOCATION`.
-
-### How can I reset the admin password?
-
-The admin password can be reset by running the [reset-admin-password](/docs/administration/server-commands.md) command on the immich-server.
-
-### How can I backup data from Immich?
-
-See [backup and restore](/docs/administration/backup-and-restore.md).
-
-### How can I **purge** data from Immich?
-
-Data for Immich comes in two forms:
-
-1. **Metadata** stored in a postgres database, persisted via the `pg_data` volume
-2. **Files** (originals, thumbs, profile, etc.), stored in the `UPLOAD_LOCATION` folder.
-
-To remove the **Metadata** you can stop Immich and delete the volume.
-
-```bash title="Remove Immich (containers and volumes)"
-docker-compose down -v
-```
-
-After removing the containers and volumes, the **Files** can be cleaned up (if necessary) from the `UPLOAD_LOCATION` by simply deleting an unwanted files or folders.
-
-### How can I move all data (photos, persons, albums) from one user to another?
-
-This requires some database queries. You can do this on the command line (in the PostgreSQL container using the psql command), or you can add for example an [Adminer](https://www.adminer.org/) container to the `docker-compose.yml` file, so that you can use a web-interface.
-
-:::warning
-This is an advanced operation. If you can't to do it with the steps described here, this is not for you.
-:::
-
-1. **MAKE A BACKUP** - See [backup and restore](/docs/administration/backup-and-restore.md).
-2. Find the id of both the 'source' and the 'destination' user (it's the id column in the users table)
-3. Three tables need to be updated:
-
-   ```sql
-   // reassign albums
-   update albums set "ownerId" = '<destinationId>' where "ownerId" = '<sourceId>';
-
-   // reassign people
-   update person set "ownerId" = '<destinationId>' where "ownerId" = '<sourceId>';
-
-   // reassign assets
-   update assets set "ownerId" = '<destinationId>' where "ownerId" = '<sourceId>'
-    and checksum not in (select checksum from assets where "ownerId" = '<destinationId>');
-   ```
-
-4. There might be left-over assets in the 'source' user's library if they are skipped by the last query because of duplicate checksums. These are probably duplicates anyway, and can probably be removed.
@@ -0,0 +1,311 @@
+# FAQ
+
+## User
+
+### How can I reset the admin password?
+
+The admin password can be reset by running the [reset-admin-password](/docs/administration/server-commands.md) command on the immich-server.
+
+### How can I see list of all users in Immich?
+
+You can see the list of all users by running [list-users](/docs/administration/server-commands.md) Command on the Immich-server.
+
+---
+
+## Mobile App
+
+### What is the difference between the cloud icons on the mobile app?
+
+| Icon                               | Description                                                                                                                                     |
+| ---------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------- |
+| ![cloud](/img/cloud.svg)           | Asset is only available in the cloud and was uploaded from some other device (like the web client) or was deleted from this device after upload |
+| ![cloud-cross](/img/cloud-off.svg) | Asset is only available locally and has not yet been backed up                                                                                  |
+| ![cloud-done](/img/cloud-done.svg) | Asset was uploaded from this device and is now backed up to the server; the original file is still on the device                                |
+
+### I cannot log into the application after an update. What can I do?
+
+First, verify that the mobile app and server are both running the same version (major and minor).
+
+:::note
+App store updates sometimes take longer because the stores (play store; Google and app store; Apple)
+need to approve the update first which may take some time.
+:::
+
+If you still cannot login to the app, try the following:
+
+- Check the mobile logs
+- Make sure login credentials are correct by logging in on the web app
+
+---
+
+## Assets
+
+### Can I add my existing photo library?
+
+Yes, with an [External Library](/docs/features/libraries.md).
+
+### What happens to existing files after I choose a new [Storage Template](/docs/administration/storage-template.mdx)?
+
+Template changes will only apply to _new_ assets. To retroactively apply the template to previously uploaded assets, run the Storage Migration Job, available on the [Jobs](/docs/administration/jobs.md) page.
+
+### Why are only photos and not videos being uploaded to Immich?
+
+This often happens when using a reverse proxy (such as nginx or Cloudflare tunnel) in front of Immich. Make sure to set your reverse proxy to allow large `POST` requests. In `nginx`, set `client_max_body_size 50000M;` or similar. Also check the disk space of your reverse proxy, in some cases proxies cache requests to disk before passing them on, and if disk space runs out the request fails.
+
+### Why are some photos stored in the file system with the wrong date?
+
+There are a few different scenarios that can lead to this situation. The solution is to simply run the storage migration job again. The job is only _automatically_ run once per asset, after upload. If metadata extraction originally failed, the jobs were cleared/cancelled, etc. the job may not have run automatically the first time.
+
+### How can I hide photos from the timeline?
+
+You can _archive_ them.
+
+### How can I backup data from Immich?
+
+See [Backup and Restore](/docs/administration/backup-and-restore.md).
+
+### Does Immich support reading existing face tag metadata?
+
+No, it currently does not.
+
+### Does Immich support filtering of NSFW images?
+
+No, it currently does not, but there is an [open discussion about it On Github](https://github.com/immich-app/immich/discussions/2451). You can submit a pull request or vote for the discussion.
+
+### Why are there so many thumbnail generation jobs?
+
+There are three thubmanil jobs for each asset:
+
+- Blurred (thumbhash)
+- Small (webp)
+- Large (jpeg)
+
+Also, there are additional jobs for person (face) thumbnails.
+
+### What happens if an asset exists in more than one account?
+
+There are no requirements for assets to be unique across users. If multiple users upload the same image they are processed as if they were distinct assets and jobs run and thumbnails are generated accordingly.
+
+### How can I delete transcoded videos without deleting the original?
+
+The transcode of an asset can be deleted by setting a transcode policy that makes it unnecessary, then running a transcoding job for that asset. This can be done on a per-asset basis by starting a transcoding job for an asset (with the _Refresh encoded videos_ button in the asset viewer options. Or, for all assets by running transcoding jobs for all assets.
+
+To update the transcode policy, navigate to Administration > Video Transcoding Settings > Transcoding Policy and select a policy from the drop-down. This policy will determine whether an existing transcode will be deleted or overwritten in the transcoding job. If a video should be transcoded according to this policy, an existing transcode is overwritten. If not, then it is deleted.
+
+:::note
+For example, say you have existing transcodes with the policy "Videos higher than normal resolution or not in the desired format" and switch to a narrower policy: "Videos not in the desired format". If an asset was only transcoded due to its resolution, then running a transcoding job for it will now delete the existing transcode. This is because resolution is no longer part of the transcode policy and the transcode is unnecessary as a result. Likewise, if you set the policy to "Don't transcode any videos" and run transcoding jobs for all assets, this will delete all existing transcodes as they are all unnecessary.
+:::
+
+### Is it possible to compress images during backup?
+
+No. Our golden rule is that the original assets should always be untouched, so we don't think this feature is a good fit for Immich.
+
+### How can I move all data (photos, persons, albums) from one user to another?
+
+This is not officially supported, but can be accomplished with some database updates. You can do this on the command line (in the PostgreSQL container using the psql command), or you can add for example an [Adminer](https://www.adminer.org/) container to the `docker-compose.yml` file, so that you can use a web-interface.
+
+:::warning
+This is an advanced operation. If you can't do it with the steps described here, this is not for you.
+:::
+
+<details>
+<summary>Steps</summary>
+
+1. **MAKE A BACKUP** - See [backup and restore](/docs/administration/backup-and-restore.md).
+
+2. Find the id of both the 'source' and the 'destination' user (it's the id column in the users table)
+
+3. Three tables need to be updated:
+
+```sql
+// reassign albums
+UPDATE albums SET "ownerId" = '<destinationId>' WHERE "ownerId" = '<sourceId>';
+
+// reassign people
+UPDATE person SET "ownerId" = '<destinationId>' WHERE "ownerId" = '<sourceId>';
+
+// reassign assets
+UPDATE assets SET "ownerId" = '<destinationId>' WHERE "ownerId" = '<sourceId>'
+ AND CHECKSUM NOT IN (SELECT CHECKSUM FROM assets WHERE "ownerId" = '<destinationId>');
+```
+
+4. There might be left-over assets in the 'source' user's library if they are skipped by the last query because of duplicate checksums. These are probably duplicates anyway, and can probably be removed.
+
+</details>
+
+---
+
+## Albums
+
+### Can I keep my existing album structure while importing assets into Immich?
+
+Yes. You can by use [Immich CLI](/docs/features/command-line-interface) along with the `--album` flag.
+
+### Is there a way to reorder photos within an album?
+
+No, not yet. For updates on this planned feature, follow the [GitHub discussion](https://github.com/immich-app/immich/discussions/1689),
+
+---
+
+## External Library
+
+### Can I add an external library while keeping the existing albums structure?
+
+We haven't put in an official mechanism to create albums from external libraries at the moment., but there are some [workarounds from the community](https://github.com/immich-app/immich/discussions/4279) which you can find here to help you achieve that.
+
+### What happens to duplicates in external libraries?
+
+Duplicate checking only exists for upload libraries, using the file hash. Furthermore, duplicate checking is not global, but _per library_. Therefore, a situation where the same file appears twice in the timeline is possible, especially for external libraries.
+
+---
+
+## Machine Learning
+
+### How does smart search work?
+
+Immich uses CLIP models, for more information about CLIP and its capabilities read about it [here](https://openai.com/research/clip).
+
+### How does facial recognition work?
+
+For face detection and recognition, Immich uses [InsightFace models](https://github.com/deepinsight/insightface/tree/master/model_zoo).
+
+### How can I disable machine learning?
+
+:::info
+Disabling machine learning will result in a poor experience for searching and the 'Explore' page, as these are reliant on it to work as intended.
+:::
+
+Machine learning can be disabled under Administration > Settings > Machine Learning Settings, either entirely or by model type. For instance, you can choose to disable smart search with CLIP, but keep facial recognition enabled. This means that the machine learning service will only process the enabled jobs.
+
+However, disabling all jobs will not disable the machine learning service itself. To prevent it from starting up at all in this case, you can comment out the `immich-machine-learning` section of the docker-compose.yml.
+
+### I'm getting errors about models being corrupt or failing to download. What do I do?
+
+You can delete the model cache volume, which is where models are downloaded to. This will give the service a clean environment to download the model again.
+
+### Why did Immich decide to remove object detection?
+
+The feature added keywords to images for metadata search, but wasn't used for smart search. Smart search made it unnecessary as it isn't limited to exact keywords. Combined with it causing crashes on some devices, using many dependencies and causing user confusion as to how search worked, it was better to remove the job altogether.
+For more info see [here](https://github.com/immich-app/immich/pull/5903)
+
+### Can I use a custom CLIP model?
+
+No, this is not supported. Only models listed in the [Huggingface](https://huggingface.co/immich-app) are compatible. Feel free to make a feature request if there's a model not listed here that you think should be added.
+
+### I want to be able to search in other languages besides English. How can I do that?
+
+You can change to a multilingual model listed [here](https://huggingface.co/collections/immich-app/multilingual-clip-654eb08c2382f591eeb8c2a7) by going to Administration > Machine Learning Settings > Smart Search and replacing the name of the model. Be sure to re-run Smart Search on all assets after this change. You can then search in over 100 languages.
+
+:::note
+Feel free to make a feature request if there's a model you want to use that isn't in [Immich Huggingface list](https://huggingface.co/immich-app).
+:::
+
+### Does Immich support Facial Recognition for videos ?
+
+This is not currently implemented, but may be in the future.
+
+On the other hand, Immich does scan video thumbnails for faces, so it can perform recognition if the face is clear in the video thumbnail.
+
+### Does Immich have animal recognition?
+
+No.
+
+### The immich_model-cache volume takes up a lot of space, what could be the problem?
+
+If you installed several models and chose not to use some of them, it might be worth deleting the old models that are in immich_model-cache.
+
+To do this you can run:
+
+- `docker run -it --rm -v immich_model-cache:/mnt ubuntu bash`
+- `cd mnt`
+- `ls`
+- and delete unused models with `rm -r <model_name>`.
+
+---
+
+## Performance
+
+### Why is Immich slow on low-memory systems like the Raspberry Pi?
+
+Immich optionally uses machine learning for several features. However, it can be too heavy to run on a Raspberry Pi. You can [mitigate](/docs/FAQ#can-i-lower-cpu-and-ram-usage) this or transfer to host Immich's machine-learning container on a [more powerful system](/docs/guides/remote-machine-learning) ,or [disable](/docs/FAQ#how-can-i-disable-machine-learning) machine learning entirely.
+
+### Can I lower CPU and RAM usage?
+
+The initial backup is the most intensive due to the number of jobs running. The most CPU-intensive ones are transcoding and machine learning jobs (Smart Search, Face Detection), and to a lesser extent thumbnail generation. Here are some ways to lower their CPU usage:
+
+- Lower the job concurrency for these jobs to 1.
+- Under Settings > Transcoding Settings > Threads, set the number of threads to a low number like 1 or 2.
+- Under Settings > Machine Learning Settings > Facial Recognition > Model Name, you can change the facial recognition model to `buffalo_s` instead of `buffalo_l`. The former is a smaller and faster model, albeit not as good.
+  - You _must_ re-run the Face Detection job for all images after this for facial recognition on new images to work properly.
+- If these changes are not enough, see [below](/docs/FAQ#how-can-i-disable-machine-learning) for how you can disable machine learning.
+
+### Can I limit the amount of CPU and RAM usage?
+
+By default, a container has no resource constraints and can use as much of a given resource as the host's kernel scheduler allows.
+You can look at the [original docker docs](https://docs.docker.com/config/containers/resource_constraints/) or use this [guide](https://www.baeldung.com/ops/docker-memory-limit) to learn how to do this.
+
+### How an I boost machine learning speed?
+
+:::note
+This advice improves throughput, not latency. This is to say that it will make Smart Search jobs process more quickly, but it won't make searching faster.
+:::
+
+You can increase throughput by increasing the job concurrency for machine learning jobs (Smart Search, Face Detection). With higher concurrency, the host will work on more assets in parallel. You can do this by navigating to Administration > Settings > Job Settings and increasing concurrency as needed.
+
+:::danger
+On a normal machine, 2 or 3 concurrent jobs can probably max the CPU, so if you're not hitting those maximums with, say, 30 jobs.
+Note that storage speed and latency may quickly become the limiting factor; particularly when using HDDs.
+
+Do not exaggerate with the amount of jobs because you're probably thoroughly overloading the server.
+
+more info [here](https://discord.com/channels/979116623879368755/994044917355663450/1174711719994605708)
+:::
+
+### Why is Immich using so much of my CPU?
+
+When a large amount of assets are uploaded to Immich it makes sense that the CPU and RAM will be heavily used due to machine learning work and creating image thumbnails after that, the percentage of CPU usage will drop to around 3-5% usage
+
+---
+
+## Docker
+
+### How can I see Immich logs?
+
+Most Immich components are typically deployed using docker. To see logs for deployed docker containers, you can use the [Docker CLI](https://docs.docker.com/engine/reference/commandline/cli/), specifically the `docker logs` command. For examples, see [Docker Help](/docs/guides/docker-help.md).
+
+### How can I run Immich as a non-root user?
+
+1. Set the `PUID`/`PGID` environment variables (in `.env`).
+2. Set the corresponding `user` argument in `docker-compose` for each service.
+3. Add an additional volume to `immich-microservices` that mounts internally to `/usr/src/app/.reverse-geocoding-dump`.
+
+The non-root user/group needs read/write access to the volume mounts, including `UPLOAD_LOCATION`.
+
+### How can I **purge** data from Immich?
+
+Data for Immich comes in two forms:
+
+1. **Metadata** stored in a postgres database, persisted via the `pg_data` volume
+2. **Files** (originals, thumbs, profile, etc.), stored in the `UPLOAD_LOCATION` folder.
+
+To remove the **Metadata** you can stop Immich and delete the volume.
+
+```bash title="Remove Immich (containers and volumes)"
+docker compose down -v
+```
+
+After removing the containers and volumes, the **Files** can be cleaned up (if necessary) from the `UPLOAD_LOCATION` by simply deleting any unwanted files or folders.
+
+### Why does the machine learning service report workers crashing?
+
+:::note
+If the error says the worker is exiting, then this is normal. This is a feature intended to reduce RAM consumption when the service isn't being used.
+:::
+
+There are a few reasons why this can happen.
+
+If the error mentions SIGKILL or error code 137, it most likely means the service is running out of memory. Consider either increasing the server's RAM or moving the service to a server with more RAM.
+
+If it mentions SIGILL (note the lack of a K) or error code 132, it most likely means your server's CPU is incompatible. This is unlikely to occur on version 1.92.0 or later. Consider upgrading if your version of Immich is below that.
+
+If your version of Immich is below 1.92.0 and the crash occurs after logs about tracing or exporting a model, consider either upgrading or disabling the Tag Objects job.
@@ -1,6 +1,6 @@
 # Backup and Restore

-A [3-2-1 backup strategy](https://www.backblaze.com/blog/the-3-2-1-backup-strategy/) is recommended to protect your data. You should keep copies of your uploaded photos/videos as well as the Immich database for a comprehensive backup solution. This page provides an overview on how to backup the database and the location of user-uploaded pictures and videos. A template bash script that can be run as a cron job is provided [here](../guides/template-backup-script)
+A [3-2-1 backup strategy](https://www.backblaze.com/blog/the-3-2-1-backup-strategy/) is recommended to protect your data. You should keep copies of your uploaded photos/videos as well as the Immich database for a comprehensive backup solution. This page provides an overview on how to backup the database and the location of user-uploaded pictures and videos. A template bash script that can be run as a cron job is provided [here](/docs/guides/template-backup-script.md)

 ## Database

@@ -66,3 +66,34 @@ Immich stores two types of content in the filesystem: (1) original, unmodified c
 1. `UPLOAD_LOCATION/library`
 1. `UPLOAD_LOCATION/upload`
 1. `UPLOAD_LOCATION/profile`
+
+**1. User-Specific Folders:**
+
+- Each user has a unique string representing them.
+  - The main user is "Admin" (but only for `\library\library\`)
+  - Other users have different string identifiers.
+- You can find your user ID in Account Account Settings > Account > User ID.
+
+**2. Asset Types and Storage Locations:**
+
+- **Source Assets:**
+  - Original assets uploaded through the browser interface&mobile&CLI.
+  - Stored in `\library\library\<userID>`.
+- **Avatar Images:**
+  - User profile images.
+  - Stored in `\library\profile\<userID>`.
+- **Thumbs Images:**
+  - Preview images (blurred, small, large) for each asset and thumbnails for recognized faces.
+  - Stored in `\library\thumbs\<userID>`.
+- **Encoded Assets:**
+  - By default, unless otherwise specified re-encoded video assets for wider compatibility .
+  - Stored in `\library\encoded-video\<userID>`.
+- **Files in Upload Queue (Mobile):**
+  - Files uploaded through mobile apps.
+  - Temporarily located in `\library\upload\<userID>`.
+  - Transferred to `\library\library\<userID>` upon successful upload.
+
+:::danger
+Do not touch the files inside these folders under any circumstances except taking a backup, changing or removing an asset can cause untracked and missing files.
+You can think of it as App-Which-Must-Not-Be-Named, the only access to viewing, changing and deleting assets is only through the mobile or browser interface.
+:::
@@ -1,4 +1,4 @@
-import StorageTemplate from '../partials/_storage-template.md';
+import StorageTemplate from '/docs/partials/_storage-template.md';

 # Storage Template

@@ -1,5 +1,5 @@
-import RegisterAdminUser from '../partials/_register-admin.md';
-import UserCreate from '../partials/_user-create.md';
+import RegisterAdminUser from '/docs/partials/_register-admin.md';
+import UserCreate from '/docs/partials/_user-create.md';

 # User Management

@@ -107,4 +107,4 @@ See [Database Migrations](./database-migrations.md) for more information about h

 ### Redis

-Immich uses [Redis](https://redis.com/) via [BullMQ](https://docs.bullmq.io/) to manage job queues. Some jobs trigger subsequent jobs. For example, object detection relies on thumbnail generation and automatically run after one is generated.
+Immich uses [Redis](https://redis.com/) via [BullMQ](https://docs.bullmq.io/) to manage job queues. Some jobs trigger subsequent jobs. For example, Smart Search and Facial Recognition relies on thumbnail generation and automatically run after one is generated.
@@ -7,11 +7,7 @@ Immich uses the [OpenAPI](https://swagger.io/specification/) standard to generat
 OpenAPI is used to generate the client (Typescript, Dart) SDK. `openapi-generator-cli` can be installed [here](https://openapi-generator.tech/docs/installation/). The generated SDK is based on the `immich-openapi-specs.json` file, which is autogenerated by the server **when running in development mode**. The `immich-openapi-specs.json` file can be modified with `@nestjs/swagger` decorators used or referenced by controller endpoints. See the [NestJS OpenAPI docs](https://docs.nestjs.com/openapi/types-and-parameters) for more info. When you add a new endpoint or modify an existing one, you must run the server in development mode and run the command below to update the client SDK.

 ```bash
-npm run api:generate # Run from the `server/` directory
+make open-api
 ```

-You can find the generated client SDK in the `web/src/api` for Typescript SDK and `mobile/openapi` for Dart SDK.
-
-:::tip
-This can also be run via `make api` from the project root directory (not in the `server` folder)
-:::
+You can find the generated client SDK in the `open-api/typescript-sdk/client` for Typescript SDK and `mobile/openapi` for Dart SDK.
@@ -6,7 +6,7 @@ When contributing code through a pull request, please check the following:

 - [ ] `npm run lint` (linting via ESLint)
 - [ ] `npm run format` (formatting via Prettier)
- [ ] `npm run check` (Type checking via SvelteKit)
+- [ ] `npm run check:svelte` (Type checking via SvelteKit)
 - [ ] `npm test` (Tests via Jest)

 :::tip
@@ -4,6 +4,16 @@ sidebar_position: 2

 # Setup

+:::note
+If there's a feature you're planning to work on, just give us a heads up in [Discord](https://discord.com/channels/979116623879368755/1071165397228855327) so we can:
+
+1. Let you know if it's something we would accept into Immich
+2. Provide any guidance on how something like that would ideally be implemented
+3. Ensure nobody is already working on that issue/feature so we don't duplicate effort
+
+Thanks for being interested in contributing 😊
+:::
+
 ## Environment

 ### Server and web app
@@ -16,7 +26,6 @@ This environment includes the following services:
 - Web app - `/web`
 - Redis
 - PostgreSQL development database with exposed port `5432` so you can use any database client to acess it
- NGINX Proxy - `nginx/nginx.conf`

 All the services are packaged to run as with single Docker Compose command.

@@ -37,9 +46,11 @@ All the services will be started with hot-reloading enabled for a quick feedback

 You can access the web from `http://your-machine-ip:2283` or `http://localhost:2283` and access the server from the mobile app at `http://your-machine-ip:2283/api`

+**Note:** the "web" development container runs with uid 1000. If that uid does not have read/write permissions on the mounted volumes, you may encounter errors
+
 ### Mobile app

-The mobile app `(/mobile)` will required Flutter toolchain to be installed on your system.
+The mobile app `(/mobile)` will required Flutter toolchain 3.13.x to be installed on your system.

 Please refer to the [Flutter's official documentation](https://flutter.dev/docs/get-started/install) for more information on setting up the toolchain on your machine.

@@ -10,8 +10,8 @@ Unit are run by calling `npm run test` from the `server` directory.

 The backend has an end-to-end test suite that can be called with `npm run test:e2e` from the `server` directory. This will set up a dummy database inside a temporary container and run the tests against it. Setup and teardown is automatically taken care of. That test, however, can not set up all prerequisites to parse file formats, as that is very complex and error-prone. As such, this test excludes some test cases like HEIC file imports. The test suite will also print a friendly warning to remind you that not all tests are being run.

-Note that there is a bug in nodejs <20.8 that causes segmentation faults when running these tests. If you run into segfaults, ensure you are using at least version 20.8.
+Note that there is a bug in nodejs \<20.8 that causes segmentation faults when running these tests. If you run into segfaults, ensure you are using at least version 20.8.

 To perform a full e2e test, you need to run e2e tests inside docker. The easiest way to do that is to run `make test-e2e` in the root directory. This will build and start a docker-compose consisting of the server, microservices, and a postgres database. It will then perform the tests and exit.

-If you manually install the dependencies (see the DOCKERFILE) on your development machine, you can also run the full e2e tests manually by setting the `IMMICH_RUN_ALL_TESTS` environment value to true, i.e. `IMMICH_RUN_ALL_TESTS=true npm run test:e2e`.
+If you manually install the dependencies (see the DOCKERFILE) on your development machine, you can also run the full e2e tests manually by setting the `IMMICH_RUN_ALL_TESTS` environment value to true, i.e. `IMMICH_RUN_ALL_TESTS=true npm run e2e:jobs`.
@@ -10,7 +10,7 @@ Immich has a CLI that allows you to perform certain actions from the command lin
 More features are planned for the future.

 :::tip Google Photos Takeout
-If you are looking to import your Google Photos takeout, we recommed this community maintained tool [immich-go](https://github.com/simulot/immich-go)
+If you are looking to import your Google Photos takeout, we recommend this community maintained tool [immich-go](https://github.com/simulot/immich-go)
 :::

 ## Requirements
@@ -34,9 +34,11 @@ If you add assets from an external library to an album and then move the asset t

 ### Deleted External Assets

+Note: Either a manual or scheduled library scan must have been performed to identify offline assets before this process will work.
+
 In all above scan methods, Immich will check if any files are missing. This can happen if files are deleted, or if they are on a storage location that is currently unavailable, like a network drive that is not mounted, or a USB drive that has been unplugged. In order to prevent accidental deletion of assets, Immich will not immediately delete an asset from the library if the file is missing. Instead, the asset will be internally marked as offline and will still be visible in the main timeline. If the file is moved back to its original location and the library is scanned again, the asset will be restored.

-Finally, files can be deleted from Immich via the `Remove Offline Files` job. Any assets marked as offline will then be removed from Immich. Run this job whenever files have been deleted from the file system and you want to remove them from Immich. Note that a library scan must be performed first to mark the assets as offline.
+Finally, files can be deleted from Immich via the `Remove Offline Files` job. This job can be found by the three dots menu for the associated external storage that was configured under user account settings > libraries (the same location described at [create external libraries](#create-external-libraries)). When this job is run, any assets marked as offline will then be removed from Immich. Run this job whenever files have been deleted from the file system and you want to remove them from Immich.

 ### Import Paths

@@ -51,6 +53,7 @@ Sometimes, an external library will not scan correctly. This can happen if the i
 - Are the volumes identical between the `server` and `microservices` container?
 - Are the import paths set correctly, and do they match the path set in docker-compose file?
 - Are the permissions set correctly?
+- Are you using forward slashes everywhere? (`/`)

 If all else fails, you can always start a shell inside the container and check if the path is accessible. For example, `docker exec -it immich_microservices /bin/bash` will start a bash shell. If your import path, for instance, is `/data/import/photos`, you can check if the files are accessible by running `ls /data/import/photos`. Also check the `immich_server` container in the same way.

@@ -102,6 +105,7 @@ First, we need to plan how we want to organize the libraries. The christmas trip
 +     - /mnt/nas/christmas-trip:/mnt/media/christmas-trip:ro
 +     - /home/user/old-pics:/mnt/media/old-pics:ro
 +     - /mnt/media/videos:/mnt/media/videos:ro
+     - "C:/Users/user_name/Desktop/my media:/mnt/media/my-media:ro" # import path in Windows system.


  immich-microservices:
@@ -110,6 +114,7 @@ First, we need to plan how we want to organize the libraries. The christmas trip
 +     - /mnt/nas/christmas-trip:/mnt/media/christmas-trip:ro
 +     - /home/user/old-pics:/mnt/media/old-pics:ro
 +     - /mnt/media/videos:/mnt/media/videos:ro
+     - "C:/Users/user_name/Desktop/my media:/mnt/media/my-media:ro" # import path in Windows system.
 ```

 :::tip
@@ -125,6 +130,14 @@ Only an admin can do this.
 - Navigate to `Administration > Users` page on the web.
 - Click on the user edit button.
 - Set `/mnt/media` to be the external path. This folder will only contain the three folders that we want to import, so nothing else can be accessed.
+  :::note
+  Spaces in the internal path aren't currently supported.
+
+  You must import it as:
+  `..:/mnt/media/my-media:ro`
+  instead of
+  `..:/mnt/media/my media:ro`
+  :::

 ### Create External Libraries

@@ -1,6 +1,6 @@
-import MobileAppDownload from '../partials/_mobile-app-download.md';
-import MobileAppLogin from '../partials/_mobile-app-login.md';
-import MobileAppBackup from '../partials/_mobile-app-backup.md';
+import MobileAppDownload from '/docs/partials/_mobile-app-download.md';
+import MobileAppLogin from '/docs/partials/_mobile-app-login.md';
+import MobileAppBackup from '/docs/partials/_mobile-app-backup.md';

 # Mobile App

@@ -6,6 +6,8 @@ Smart search is powered by the [pgvecto.rs](https://github.com/tensorchord/pgvec

 Metadata search (prefixed with `m:`) can search specifically by text without the use of a model.

+Archived photos are not included in search results by default. To include them, add the query parameter `withArchived=true` to the url.
+
 Some search examples:
 <img src={require('./img/search-ex-2.webp').default} title='Search Example 1' />

@@ -0,0 +1,40 @@
+# Database GUI
+
+A short guide on connecting [pgAdmin](https://www.pgadmin.org/) to Immich.
+
+:::note
+
+- In order to connect to the database the immich_postgres container **must be running**.
+- The passwords and usernames used below match the ones specified in the example `.env` file. If changed, please use actual values instead.
+
+:::
+
+## 1. Install pgAdmin
+
+Download and install [pgAdmin](https://www.pgadmin.org/download/) following the official documentation.
+
+## 2. Add a Server
+
+Open pgAdmin and click "Add New Server".
+
+<img src={require('./img/add-new-server-option.png').default} width="50%" title="new server option" />
+
+## 3. Enter Connection Details
+
+| Name                 | Value       |
+| -------------------- | ----------- |
+| Host name/address    | `localhost` |
+| Port                 | `5432`      |
+| Maintenance database | `immich`    |
+| Username             | `postgres`  |
+| Password             | `postgres`  |
+
+<img src={require('./img/Connection-Pgadmin.png').default} width="75%" title="Connection" />
+
+## 4. Save Connection
+
+Click on "Save" to connect to the Immich database.
+
+:::tip
+View [Database Queries](https://immich.app/docs/guides/database-queries/) for common database queries.
+:::
@@ -1,6 +1,6 @@
 # External Library

-This guide walks you through adding an [External Library](../features/libraries#external-libraries).
+This guide walks you through adding an [External Library](/docs/features/libraries#external-libraries).
 This guide assumes you are running Immich in Docker and that the files you wish to access are stored
 in a directory on the same machine.

@@ -78,7 +78,7 @@ In the Immich web UI:
 - Click \*_Add path_
  <img src={require('./img/add-path-button.png').default} width="50%" title="Add Path button" />

- Enter **.** as the path and click Add
+- Enter **/usr/src/app/external** as the path and click Add
  <img src={require('./img/add-path-field.png').default} width="50%" title="Add Path field" />

 - Save the new path
@@ -1,6 +1,7 @@
 # Remote Access

 This page gives a few pointers on how to access your Immich instance from outside your LAN.
+You can read the [full discussion in Discord](https://discord.com/channels/979116623879368755/1122615710846308484)

 :::danger
 Never forward port 2283 directly to the internet without additional configuration. This will expose the web interface via http to the internet, making you succeptible to [man in the middle](https://en.wikipedia.org/wiki/Man-in-the-middle_attack) attacks.
@@ -1,6 +1,6 @@
 # Remote Machine Learning

-To alleviate [performance issues on low-memory systems](/docs/FAQ.md#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):
+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):

 - Set the URL in Machine Learning Settings on the Admin Settings page to point to the designated ML system, e.g. `http://workstation:3003`.
 - Copy the following `docker-compose.yml` to your ML system.
@@ -0,0 +1,38 @@
+# Remove Offline Files
+
+:::note
+**Before running the script**, please make sure you have a [backup](/docs/administration/backup-and-restore) of your assets and database
+:::
+
+This page is a guide to get rid of offline files from the repair page.
+
+This way works by downloading a JSON file that contains a list of all the files that are defined as offline files, running a script that uses the [Immich API](/docs/api/delete-assets) in order to remove the offline files.
+
+1. Create an API key under Admin User -> Account Settings -> API Keys -> New API Key -> Copy to clipboard.
+2. Download the JSON file under Administration -> repair -> Export.
+3. Replace `YOUR_IP_HERE` and `YOUR_API_KEY_HERE` with your actual IP address and API key in the script.
+4. Run the script in the same folder where the JSON file is located.
+
+## Script for Linux based systems:
+
+```bash
+awk -F\" '/entityId/ {print $4}' orphans.json | while read line; do curl --location --request DELETE 'http://YOUR_IP_HERE:2283/api/asset' --header 'Content- Type: application/json' --header 'x-api-key: YOUR_API_KEY_HERE' --data '{ "force": true, "ids": ["'"$line"'"]}';done
+```
+
+## Script for the Windows system (run through PowerShell):
+
+```powershell
+Get-Content orphans.json | Select-String -Pattern 'entityId' | ForEach-Object {
+  $line = $_ -split '"' | Select-Object -Index 3
+  $body = [pscustomobject]@{
+    'ids' = @($line)
+    'force' = (' true ' | ConvertFrom-Json)
+  } | ConvertTo-Json -Depth 3
+  Invoke-RestMethod -Uri 'http://YOUR_IP_HERE:2283/api/asset' -Method Delete -Headers @{
+    'Content-Type' = 'application/json'
+    'x-api-key' = 'YOUR_API_KEY_HERE'
+  } -Body $body
+}
+```
+
+Thanks to [DooMRunneR](https://discord.com/channels/979116623879368755/1179655214870040596/1194308198413373482) for writing this script.
@@ -79,7 +79,7 @@ The default configuration looks like this:
      "modelName": "buffalo_l",
      "minScore": 0.7,
      "maxDistance": 0.6,
-      "minFaces": 1
+      "minFaces": 3
    }
  },
  "map": {
@@ -121,16 +121,16 @@ Redis (Sentinel) URL example JSON before encoding:

 ## Machine Learning

-| Variable                                         | Description                                                       |       Default       | Services         |
-| :----------------------------------------------- | :---------------------------------------------------------------- | :-----------------: | :--------------- |
-| `MACHINE_LEARNING_MODEL_TTL`                     | Inactivity time (s) before a model is unloaded (disabled if <= 0) |        `300`        | machine learning |
-| `MACHINE_LEARNING_MODEL_TTL_POLL_S`              | Interval (s) between checks for the model TTL (disabled if <= 0)  |        `10`         | machine learning |
-| `MACHINE_LEARNING_CACHE_FOLDER`                  | Directory where models are downloaded                             |      `/cache`       | machine learning |
-| `MACHINE_LEARNING_REQUEST_THREADS`<sup>\*1</sup> | Thread count of the request thread pool (disabled if <= 0)        | number of CPU cores | machine learning |
-| `MACHINE_LEARNING_MODEL_INTER_OP_THREADS`        | Number of parallel model operations                               |         `1`         | machine learning |
-| `MACHINE_LEARNING_MODEL_INTRA_OP_THREADS`        | Number of threads for each model operation                        |         `2`         | machine learning |
-| `MACHINE_LEARNING_WORKERS`<sup>\*2</sup>         | Number of worker processes to spawn                               |         `1`         | machine learning |
-| `MACHINE_LEARNING_WORKER_TIMEOUT`                | Maximum time (s) of unresponsiveness before a worker is killed    |        `120`        | machine learning |
+| Variable                                         | Description                                                        |       Default       | Services         |
+| :----------------------------------------------- | :----------------------------------------------------------------- | :-----------------: | :--------------- |
+| `MACHINE_LEARNING_MODEL_TTL`                     | Inactivity time (s) before a model is unloaded (disabled if \<= 0) |        `300`        | machine learning |
+| `MACHINE_LEARNING_MODEL_TTL_POLL_S`              | Interval (s) between checks for the model TTL (disabled if \<= 0)  |        `10`         | machine learning |
+| `MACHINE_LEARNING_CACHE_FOLDER`                  | Directory where models are downloaded                              |      `/cache`       | machine learning |
+| `MACHINE_LEARNING_REQUEST_THREADS`<sup>\*1</sup> | Thread count of the request thread pool (disabled if \<= 0)        | number of CPU cores | machine learning |
+| `MACHINE_LEARNING_MODEL_INTER_OP_THREADS`        | Number of parallel model operations                                |         `1`         | machine learning |
+| `MACHINE_LEARNING_MODEL_INTRA_OP_THREADS`        | Number of threads for each model operation                         |         `2`         | machine learning |
+| `MACHINE_LEARNING_WORKERS`<sup>\*2</sup>         | Number of worker processes to spawn                                |         `1`         | machine learning |
+| `MACHINE_LEARNING_WORKER_TIMEOUT`                | Maximum time (s) of unresponsiveness before a worker is killed     |        `120`        | machine learning |

 \*1: It is recommended to begin with this parameter when changing the concurrency levels of the machine learning service and then tune the other ones.

@@ -2,12 +2,12 @@
 sidebar_position: 90
 ---

-import RegisterAdminUser from '../partials/_register-admin.md';
-import UserCreate from '../partials/_user-create.md';
-import StorageTemplate from '../partials/_storage-template.md';
-import MobileAppDownload from '../partials/_mobile-app-download.md';
-import MobileAppLogin from '../partials/_mobile-app-login.md';
-import MobileAppBackup from '../partials/_mobile-app-backup.md';
+import RegisterAdminUser from '/docs/partials/_register-admin.md';
+import UserCreate from '/docs/partials/_user-create.md';
+import StorageTemplate from '/docs/partials/_storage-template.md';
+import MobileAppDownload from '/docs/partials/_mobile-app-download.md';
+import MobileAppLogin from '/docs/partials/_mobile-app-login.md';
+import MobileAppBackup from '/docs/partials/_mobile-app-backup.md';

 # Post Install Steps

@@ -0,0 +1,12 @@
+---
+sidebar_position: 2
+---
+
+# Comparison
+
+If you're new here and came from other photo self-hosting alternatives you might want to look at a comparison between Immich and your current self-hosting.
+Here you can see a [comparison between the various OpenSource Photo Libraries](https://meichthys.github.io/foss_photo_libraries/) including Immich.
+
+:::note
+It is important to remember, Immich is under very active development. Expect bugs and changes. Do not use it as the only way to store your photos and videos!
+:::
@@ -1,12 +1,12 @@
 ---
-sidebar_position: 5
+sidebar_position: 6
 ---

 # Help Me!

 Running into an issue or have a question? Try the following:

-1. Check the [FAQs](/docs/FAQ.md).
+1. Check the [FAQs](/docs/FAQ.mdx).
 2. Read through the [Release Notes][github-releases].
 3. Search through existing [GitHub Issues][github-issues].
 4. Open a help ticket on [Discord][discord-link].
@@ -1,5 +1,5 @@
 ---
-sidebar_position: 3
+sidebar_position: 4
 ---

 # Logo
@@ -1,5 +1,5 @@
 ---
-sidebar_position: 2
+sidebar_position: 3
 ---

 # Quick Start
@@ -10,11 +10,11 @@ to install and use it.

 ## Requirements

-Check the [requirements page](../install/requirements) to get started.
+Check the [requirements page](/docs/install/requirements) to get started.

 ## Install and launch via Docker Compose

-Follow the [Docker Compose (Recommended)](../install/docker-compose) instructions
+Follow the [Docker Compose (Recommended)](/docs/install/docker-compose) instructions
 to install the server.

 - Where random passwords are required, `pwgen` is a handy utility.
@@ -24,7 +24,7 @@ to install the server.

 ## Try the Web UI

-import RegisterAdminUser from '../partials/_register-admin.md';
+import RegisterAdminUser from '/docs/partials/_register-admin.md';

 <RegisterAdminUser />

@@ -36,13 +36,13 @@ Try uploading a picture from your browser.

 ### Download the Mobile App

-import MobileAppDownload from '../partials/_mobile-app-download.md';
+import MobileAppDownload from '/docs/partials/_mobile-app-download.md';

 <MobileAppDownload />

 ### Login to the Mobile App

-import MobileAppLogin from '../partials/_mobile-app-login.md';
+import MobileAppLogin from '/docs/partials/_mobile-app-login.md';

 <MobileAppLogin />

@@ -50,7 +50,7 @@ In the mobile app, you should see the photo you uploaded from the web UI.

 ### Transfer Photos from your Mobile Device

-import MobileAppBackup from '../partials/_mobile-app-backup.md';
+import MobileAppBackup from '/docs/partials/_mobile-app-backup.md';

 <MobileAppBackup />

@@ -59,13 +59,13 @@ take quite a while.

 You can select the Jobs tab to see Immich processing your photos.

-<img src={require('../guides/img/jobs-tab.png').default} title="Jobs tab" />
+<img src={require('/docs/guides/img/jobs-tab.png').default} title="Jobs tab" />

 ## Set up your backups

 You may want to back up the content of your Immich instance
 along with other parts of your server; be sure to read about
-[database backup](../administration/backup-and-restore).
+[database backup](/docs/administration/backup-and-restore).

 ## Where to go from here?

@@ -77,11 +77,11 @@ even those not on your mobile device, via Google Takeout.
 You can use [immich-go](https://github.com/simulot/immich-go) for this.

 You may want to
-[upload photos from your own archive](../features/command-line-interface).
+[upload photos from your own archive](/docs/features/command-line-interface).

 You may want to incorporate an immutable archive of photos from an
-[External Library](../features/libraries#external-libraries);
-there's a [Guide](../guides/external-library) for that.
+[External Library](/docs/features/libraries#external-libraries);
+there's a [Guide](/docs/guides/external-library) for that.

 You may want your mobile device to
-[back photos up to your server automatically](../features/automatic-backup).
+[back photos up to your server automatically](/docs/features/automatic-backup).
@@ -1,5 +1,5 @@
 ---
-sidebar_position: 4
+sidebar_position: 5
 ---

 # Support The Project
@@ -1,8 +1,7 @@
 // @ts-check
 // Note: type annotations allow type checking and IDEs autocompletion

-const lightCodeTheme = require('prism-react-renderer/themes/github');
-const darkCodeTheme = require('prism-react-renderer/themes/dracula');
+const prism = require('prism-react-renderer');

 /** @type {import('@docusaurus/types').Config} */
 const config = {
@@ -56,7 +55,7 @@ const config = {
          editUrl: 'https://github.com/immich-app/immich/tree/main/docs/',
        },
        api: {
-          path: '../server/immich-openapi-specs.json',
+          path: '../open-api/immich-openapi-specs.json',
          routeBasePath: '/docs/api',
        },
        // blog: {
@@ -165,9 +164,9 @@ const config = {
        copyright: `Immich is available as open source under the terms of the MIT License.`,
      },
      prism: {
-        theme: lightCodeTheme,
-        darkTheme: darkCodeTheme,
-        additionalLanguages: ['sql'],
+        theme: prism.themes.github,
+        darkTheme: prism.themes.dracula,
+        additionalLanguages: ['sql', 'diff', 'bash', 'powershell', 'nginx'],
      },
      image: 'overview/img/feature-panel.png',
    }),
@@ -13,32 +13,31 @@
    "clear": "docusaurus clear",
    "serve": "docusaurus serve",
    "write-translations": "docusaurus write-translations",
-    "write-heading-ids": "docusaurus write-heading-ids",
-    "check": "tsc"
+    "write-heading-ids": "docusaurus write-heading-ids"
  },
  "dependencies": {
-    "@docusaurus/core": "^2.4.3",
-    "@docusaurus/preset-classic": "^2.4.3",
+    "@docusaurus/core": "^3.1.0",
+    "@docusaurus/preset-classic": "^3.1.0",
    "@mdi/js": "^7.3.67",
    "@mdi/react": "^1.6.1",
-    "@mdx-js/react": "^1.6.22",
-    "autoprefixer": "^10.4.13",
+    "@mdx-js/react": "^3.0.0",
+    "autoprefixer": "^10.4.17",
    "classnames": "^2.3.2",
    "clsx": "^2.0.0",
-    "docusaurus-lunr-search": "^2.3.2",
-    "docusaurus-preset-openapi": "^0.6.3",
+    "docusaurus-lunr-search": "^3.3.2",
+    "docusaurus-preset-openapi": "^0.7.3",
    "postcss": "^8.4.25",
-    "prism-react-renderer": "^1.3.5",
+    "prism-react-renderer": "^2.3.1",
    "raw-loader": "^4.0.2",
-    "react": "^17.0.2",
-    "react-dom": "^17.0.2",
+    "react": "^18.0.0",
+    "react-dom": "^18.0.0",
    "tailwindcss": "^3.2.4",
    "url": "^0.11.0"
  },
  "devDependencies": {
-    "@docusaurus/module-type-aliases": "^2.4.1",
-    "@tsconfig/docusaurus": "^1.0.5",
-    "prettier": "^3.0.0",
+    "@docusaurus/module-type-aliases": "^3.1.0",
+    "@tsconfig/docusaurus": "^2.0.2",
+    "prettier": "^3.2.4",
    "typescript": "^5.1.6"
  },
  "browserslist": {
@@ -1,70 +0,0 @@
-import React from 'react';
-import clsx from 'clsx';
-import styles from './styles.module.css';
-
-type FeatureItem = {
-  title: string;
-  Svg: React.ComponentType<React.ComponentProps<'svg'>>;
-  description: JSX.Element;
-};
-
-const FeatureList: FeatureItem[] = [
-  {
-    title: 'Easy to Use',
-    Svg: require('@site/static/img/undraw_docusaurus_mountain.svg').default,
-    description: (
-      <>
-        Docusaurus was designed from the ground up to be easily installed and used to get your website up and running
-        quickly.
-      </>
-    ),
-  },
-  {
-    title: 'Focus on What Matters',
-    Svg: require('@site/static/img/undraw_docusaurus_tree.svg').default,
-    description: (
-      <>
-        Docusaurus lets you focus on your docs, and we&apos;ll do the chores. Go ahead and move your docs into the{' '}
-        <code>docs</code> directory.
-      </>
-    ),
-  },
-  {
-    title: 'Powered by React',
-    Svg: require('@site/static/img/undraw_docusaurus_react.svg').default,
-    description: (
-      <>
-        Extend or customize your website layout by reusing React. Docusaurus can be extended while reusing the same
-        header and footer.
-      </>
-    ),
-  },
-];
-
-function Feature({ title, Svg, description }: FeatureItem) {
-  return (
-    <div className={clsx('col col--4')}>
-      <div className="text--center">
-        <Svg className={styles.featureSvg} role="img" />
-      </div>
-      <div className="text--center padding-horiz--md">
-        <h3>{title}</h3>
-        <p>{description}</p>
-      </div>
-    </div>
-  );
-}
-
-export default function HomepageFeatures(): JSX.Element {
-  return (
-    <section className={styles.features}>
-      <div className="container">
-        <div className="row">
-          {FeatureList.map((props, idx) => (
-            <Feature key={idx} {...props} />
-          ))}
-        </div>
-      </div>
-    </section>
-  );
-}
@@ -1,11 +0,0 @@
-.features {
-  display: flex;
-  align-items: center;
-  padding: 2rem 0;
-  width: 100%;
-}
-
-.featureSvg {
-  height: 200px;
-  width: 200px;
-}
@@ -1,171 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" width="1088" height="687.962" viewBox="0 0 1088 687.962">
-  <title>Easy to Use</title>
-  <g id="Group_12" data-name="Group 12" transform="translate(-57 -56)">
-    <g id="Group_11" data-name="Group 11" transform="translate(57 56)">
-      <path id="Path_83" data-name="Path 83" d="M1017.81,560.461c-5.27,45.15-16.22,81.4-31.25,110.31-20,38.52-54.21,54.04-84.77,70.28a193.275,193.275,0,0,1-27.46,11.94c-55.61,19.3-117.85,14.18-166.74,3.99a657.282,657.282,0,0,0-104.09-13.16q-14.97-.675-29.97-.67c-15.42.02-293.07,5.29-360.67-131.57-16.69-33.76-28.13-75-32.24-125.27-11.63-142.12,52.29-235.46,134.74-296.47,155.97-115.41,369.76-110.57,523.43,7.88C941.15,276.621,1036.99,396.031,1017.81,560.461Z" transform="translate(-56 -106.019)" fill="#3f3d56"/>
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-</svg>
@@ -4,6 +4,6 @@

  "compilerOptions": {
    "baseUrl": ".",
-    "module": "Node16"
-  }
+    "module": "Node16",
+  },
 }
@@ -1,21 +1,74 @@
-FROM python:3.11-bookworm@sha256:291405e32318285d8913b7b03293777c255fb1e89305c82aa495ac747b0049fe as builder
+ARG DEVICE=cpu

+FROM python:3.11-bookworm@sha256:8d773321add21be41f1f891b43177a41aaae0d1b83f3638872ec84636179594a as builder-cpu
+
+FROM openvino/ubuntu22_runtime:2023.1.0@sha256:002842a9005ba01543b7169ff6f14ecbec82287f09c4d1dd37717f0a8e8754a7 as builder-openvino
+USER root
+RUN apt-get update && apt-get install -y --no-install-recommends python3-dev
+
+FROM builder-cpu as builder-cuda
+
+FROM builder-cpu as builder-armnn
+
+ENV ARMNN_PATH=/opt/armnn
+COPY ann /opt/ann
+RUN mkdir /opt/armnn && \
+    curl -SL "https://github.com/ARM-software/armnn/releases/download/v23.11/ArmNN-linux-aarch64.tar.gz" | tar -zx -C /opt/armnn && \
+    cd /opt/ann && \
+    sh build.sh
+
+FROM builder-${DEVICE} as builder
+
+ARG DEVICE
 ENV PYTHONDONTWRITEBYTECODE=1 \
  PYTHONUNBUFFERED=1 \
-  PIP_NO_CACHE_DIR=true
+  PIP_NO_CACHE_DIR=true \
+  VIRTUAL_ENV="/opt/venv" \
+  PATH="/opt/venv/bin:${PATH}"

 RUN pip install --upgrade pip && pip install poetry
 RUN poetry config installer.max-workers 10 && \
-  poetry config virtualenvs.create false
-RUN python -m venv /opt/venv
-ENV VIRTUAL_ENV="/opt/venv" PATH="/opt/venv/bin:${PATH}"
+    poetry config virtualenvs.create false
+RUN python3 -m venv /opt/venv

 COPY poetry.lock pyproject.toml ./
-RUN poetry install --sync --no-interaction --no-ansi --no-root --only main
+RUN poetry install --sync --no-interaction --no-ansi --no-root --with ${DEVICE} --without dev

-FROM python:3.11-slim-bookworm@sha256:8f64a67710f3d981cf3008d6f9f1dbe61accd7927f165f4e37ea3f8b883ccc3f
+FROM python:3.11-slim-bookworm@sha256:d11b9bd5e49ea7401753d78f4d3b56f3aec952b85b49bcae88981f0452818e0b as prod-cpu

-RUN apt-get update && apt-get install -y --no-install-recommends tini libmimalloc2.0 && rm -rf /var/lib/apt/lists/*
+FROM openvino/ubuntu22_runtime:2023.1.0@sha256:002842a9005ba01543b7169ff6f14ecbec82287f09c4d1dd37717f0a8e8754a7 as prod-openvino
+USER root
+
+FROM nvidia/cuda:11.8.0-cudnn8-runtime-ubuntu22.04@sha256:85fb7ac694079fff1061a0140fd5b5a641997880e12112d92589c3bbb1e8b7ca as prod-cuda
+
+COPY --from=builder-cuda /usr/local/bin/python3 /usr/local/bin/python3
+COPY --from=builder-cuda /usr/local/lib/python3.11 /usr/local/lib/python3.11
+COPY --from=builder-cuda /usr/local/lib/libpython3.11.so /usr/local/lib/libpython3.11.so
+
+FROM prod-cpu as prod-armnn
+
+ENV LD_LIBRARY_PATH=/opt/armnn
+
+RUN apt-get update && apt-get install -y --no-install-recommends ocl-icd-libopencl1 mesa-opencl-icd && \
+    rm -rf /var/lib/apt/lists/* && \
+    mkdir --parents /etc/OpenCL/vendors && \
+    echo "/usr/lib/libmali.so" > /etc/OpenCL/vendors/mali.icd
+
+COPY --from=builder-armnn \
+      /opt/armnn/libarmnn.so.?? \
+      /opt/armnn/libarmnnOnnxParser.so.?? \
+      /opt/armnn/libarmnnDeserializer.so.?? \
+      /opt/armnn/libarmnnTfLiteParser.so.?? \
+      /opt/armnn/libprotobuf.so.?.??.?.? \
+      /opt/ann/libann.s[o] \
+      /opt/ann/build.sh \
+      /opt/armnn/
+
+FROM prod-${DEVICE} as prod
+
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends tini libmimalloc2.0 && \
+    rm -rf /var/lib/apt/lists/*

 WORKDIR /usr/src/app
 ENV NODE_ENV=production \
@@ -31,6 +84,7 @@ RUN echo "hard core 0" >> /etc/security/limits.conf && \
    echo 'ulimit -S -c 0 > /dev/null 2>&1' >> /etc/profile

 COPY --from=builder /opt/venv /opt/venv
+COPY ann/ann.py /usr/src/ann/ann.py
 COPY start.sh log_conf.json ./
 COPY app .
 ENTRYPOINT ["tini", "--"]
@@ -6,10 +6,11 @@
 # Setup

 This project uses [Poetry](https://python-poetry.org/docs/#installation), so be sure to install it first.
-Running `poetry install --no-root --with dev` will install everything you need in an isolated virtual environment.
+Running `poetry install --no-root --with dev --with cpu` will install everything you need in an isolated virtual environment.
+CUDA and OpenVINO are supported as acceleration APIs. To use them, you can replace `--with cpu` with either of `--with cuda` or `--with openvino`.

 To add or remove dependencies, you can use the commands `poetry add $PACKAGE_NAME` and `poetry remove $PACKAGE_NAME`, respectively.
-Be sure to commit the `poetry.lock` and `pyproject.toml` files to reflect any changes in dependencies.
+Be sure to commit the `poetry.lock` and `pyproject.toml` files with `poetry lock --no-update` to reflect any changes in dependencies.


 # Load Testing
@@ -20,4 +21,4 @@ You can change the models or adjust options like score thresholds through the Lo

 To get started, you can simply run `locust --web-host 127.0.0.1` and open `localhost:8089` in a browser to access the UI. See the [Locust documentation](https://docs.locust.io/en/stable/index.html) for more info on running Locust. 

-Note that in Locust's jargon, concurrency is measured in `users`, and each user runs one task at a time. To achieve a particular per-endpoint concurrency, multiply that number by the number of endpoints to be queried. For example, if there are 3 endpoints and you want each of them to receive 8 requests at a time, you should set the number of users to 24.
+Note that in Locust's jargon, concurrency is measured in `users`, and each user runs one task at a time. To achieve a particular per-endpoint concurrency, multiply that number by the number of endpoints to be queried. For example, if there are 3 endpoints and you want each of them to receive 8 requests at a time, you should set the number of users to 24.
@@ -0,0 +1 @@
+from .ann import Ann, is_available
@@ -0,0 +1,281 @@
+#include <fstream>
+#include <mutex>
+#include <atomic>
+
+#include "armnn/IRuntime.hpp"
+#include "armnn/INetwork.hpp"
+#include "armnn/Types.hpp"
+#include "armnnDeserializer/IDeserializer.hpp"
+#include "armnnTfLiteParser/ITfLiteParser.hpp"
+#include "armnnOnnxParser/IOnnxParser.hpp"
+
+using namespace armnn;
+
+struct IOInfos
+{
+    std::vector<BindingPointInfo> inputInfos;
+    std::vector<BindingPointInfo> outputInfos;
+};
+
+// from https://rigtorp.se/spinlock/
+struct SpinLock
+{
+    std::atomic<bool> lock_ = {false};
+
+    void lock()
+    {
+        for (;;)
+        {
+            if (!lock_.exchange(true, std::memory_order_acquire))
+            {
+                break;
+            }
+            while (lock_.load(std::memory_order_relaxed))
+                ;
+        }
+    }
+
+    void unlock() { lock_.store(false, std::memory_order_release); }
+};
+
+class Ann
+{
+
+public:
+    int load(const char *modelPath,
+             bool fastMath,
+             bool fp16,
+             bool saveCachedNetwork,
+             const char *cachedNetworkPath)
+    {
+        INetworkPtr network = loadModel(modelPath);
+        IOptimizedNetworkPtr optNet = OptimizeNetwork(network.get(), fastMath, fp16, saveCachedNetwork, cachedNetworkPath);
+        const IOInfos infos = getIOInfos(optNet.get());
+        NetworkId netId;
+        mutex.lock();
+        Status status = runtime->LoadNetwork(netId, std::move(optNet));
+        mutex.unlock();
+        if (status != Status::Success)
+        {
+            return -1;
+        }
+        spinLock.lock();
+        ioInfos[netId] = infos;
+        mutexes.emplace(netId, std::make_unique<std::mutex>());
+        spinLock.unlock();
+        return netId;
+    }
+
+    void execute(NetworkId netId, const void **inputData, void **outputData)
+    {
+        spinLock.lock();
+        const IOInfos *infos = &ioInfos[netId];
+        auto m = mutexes[netId].get();
+        spinLock.unlock();
+        InputTensors inputTensors;
+        inputTensors.reserve(infos->inputInfos.size());
+        size_t i = 0;
+        for (const BindingPointInfo &info : infos->inputInfos)
+            inputTensors.emplace_back(info.first, ConstTensor(info.second, inputData[i++]));
+        OutputTensors outputTensors;
+        outputTensors.reserve(infos->outputInfos.size());
+        i = 0;
+        for (const BindingPointInfo &info : infos->outputInfos)
+            outputTensors.emplace_back(info.first, Tensor(info.second, outputData[i++]));
+        m->lock();
+        runtime->EnqueueWorkload(netId, inputTensors, outputTensors);
+        m->unlock();
+    }
+
+    void unload(NetworkId netId)
+    {
+        mutex.lock();
+        runtime->UnloadNetwork(netId);
+        mutex.unlock();
+    }
+
+    int tensors(NetworkId netId, bool isInput = false)
+    {
+        spinLock.lock();
+        const IOInfos *infos = &ioInfos[netId];
+        spinLock.unlock();
+        return (int)(isInput ? infos->inputInfos.size() : infos->outputInfos.size());
+    }
+
+    unsigned long shape(NetworkId netId, bool isInput = false, int index = 0)
+    {
+        spinLock.lock();
+        const IOInfos *infos = &ioInfos[netId];
+        spinLock.unlock();
+        const TensorShape shape = (isInput ? infos->inputInfos : infos->outputInfos)[index].second.GetShape();
+        unsigned long s = 0;
+        for (unsigned int d = 0; d < shape.GetNumDimensions(); d++)
+            s |= ((unsigned long)shape[d]) << (d * 16); // stores up to 4 16-bit values in a 64-bit value
+        return s;
+    }
+
+    Ann(int tuningLevel, const char *tuningFile)
+    {
+        IRuntime::CreationOptions runtimeOptions;
+        BackendOptions backendOptions{"GpuAcc",
+                                      {
+                                          {"TuningLevel", tuningLevel},
+                                          {"MemoryOptimizerStrategy", "ConstantMemoryStrategy"}, // SingleAxisPriorityList or ConstantMemoryStrategy
+                                      }};
+        if (tuningFile)
+            backendOptions.AddOption({"TuningFile", tuningFile});
+        runtimeOptions.m_BackendOptions.emplace_back(backendOptions);
+        runtime = IRuntime::CreateRaw(runtimeOptions);
+    };
+    ~Ann()
+    {
+        IRuntime::Destroy(runtime);
+    };
+
+private:
+    INetworkPtr loadModel(const char *modelPath)
+    {
+        const auto path = std::string(modelPath);
+        if (path.rfind(".tflite") == path.length() - 7) // endsWith()
+        {
+            auto parser = armnnTfLiteParser::ITfLiteParser::CreateRaw();
+            return parser->CreateNetworkFromBinaryFile(modelPath);
+        }
+        else if (path.rfind(".onnx") == path.length() - 5) // endsWith()
+        {
+            auto parser = armnnOnnxParser::IOnnxParser::CreateRaw();
+            return parser->CreateNetworkFromBinaryFile(modelPath);
+        }
+        else
+        {
+            std::ifstream ifs(path, std::ifstream::in | std::ifstream::binary);
+            auto parser = armnnDeserializer::IDeserializer::CreateRaw();
+            return parser->CreateNetworkFromBinary(ifs);
+        }
+    }
+
+    static BindingPointInfo getInputTensorInfo(LayerBindingId inputBindingId, TensorInfo info)
+    {
+        const auto newInfo = TensorInfo{info.GetShape(), info.GetDataType(),
+                                        info.GetQuantizationScale(),
+                                        info.GetQuantizationOffset(),
+                                        true};
+        return {inputBindingId, newInfo};
+    }
+
+    IOptimizedNetworkPtr OptimizeNetwork(INetwork *network, bool fastMath, bool fp16, bool saveCachedNetwork, const char *cachedNetworkPath)
+    {
+        const bool allowExpandedDims = false;
+        const ShapeInferenceMethod shapeInferenceMethod = ShapeInferenceMethod::ValidateOnly;
+
+        OptimizerOptionsOpaque options;
+        options.SetReduceFp32ToFp16(fp16);
+        options.SetShapeInferenceMethod(shapeInferenceMethod);
+        options.SetAllowExpandedDims(allowExpandedDims);
+
+        BackendOptions gpuAcc("GpuAcc", {{"FastMathEnabled", fastMath}});
+        if (cachedNetworkPath)
+        {
+            gpuAcc.AddOption({"SaveCachedNetwork", saveCachedNetwork});
+            gpuAcc.AddOption({"CachedNetworkFilePath", cachedNetworkPath});
+        }
+        options.AddModelOption(gpuAcc);
+
+        // No point in using ARMNN for CPU, use ONNX (quantized) instead.
+        // BackendOptions cpuAcc("CpuAcc",
+        //                       {
+        //                           {"FastMathEnabled", fastMath},
+        //                           {"NumberOfThreads", 0},
+        //                       });
+        // options.AddModelOption(cpuAcc);
+
+        BackendOptions allowExDimOpt("AllowExpandedDims",
+                                     {{"AllowExpandedDims", allowExpandedDims}});
+        options.AddModelOption(allowExDimOpt);
+        BackendOptions shapeInferOpt("ShapeInferenceMethod",
+                                     {{"InferAndValidate", shapeInferenceMethod == ShapeInferenceMethod::InferAndValidate}});
+        options.AddModelOption(shapeInferOpt);
+
+        std::vector<BackendId> backends = {
+            BackendId("GpuAcc"),
+            // BackendId("CpuAcc"),
+            // BackendId("CpuRef"),
+        };
+        return Optimize(*network, backends, runtime->GetDeviceSpec(), options);
+    }
+
+    IOInfos getIOInfos(IOptimizedNetwork *optNet)
+    {
+        struct InfoStrategy : IStrategy
+        {
+            void ExecuteStrategy(const IConnectableLayer *layer,
+                                 const BaseDescriptor &descriptor,
+                                 const std::vector<ConstTensor> &constants,
+                                 const char *name,
+                                 const LayerBindingId id = 0) override
+            {
+                IgnoreUnused(descriptor, constants, id);
+                const LayerType lt = layer->GetType();
+                if (lt == LayerType::Input)
+                    ioInfos.inputInfos.push_back(getInputTensorInfo(id, layer->GetOutputSlot(0).GetTensorInfo()));
+                else if (lt == LayerType::Output)
+                    ioInfos.outputInfos.push_back({id, layer->GetInputSlot(0).GetTensorInfo()});
+            }
+            IOInfos ioInfos;
+        };
+
+        InfoStrategy infoStrategy;
+        optNet->ExecuteStrategy(infoStrategy);
+        return infoStrategy.ioInfos;
+    }
+
+    IRuntime *runtime;
+    std::map<NetworkId, IOInfos> ioInfos;
+    std::map<NetworkId, std::unique_ptr<std::mutex>> mutexes; // mutex per network to not execute the same the same network concurrently
+    std::mutex mutex; // global mutex for load/unload calls to the runtime
+    SpinLock spinLock; // fast spin lock to guard access to the ioInfos and mutexes maps
+};
+
+extern "C" void *init(int logLevel, int tuningLevel, const char *tuningFile)
+{
+    LogSeverity level = static_cast<LogSeverity>(logLevel);
+    ConfigureLogging(true, true, level);
+
+    Ann *ann = new Ann(tuningLevel, tuningFile);
+    return ann;
+}
+
+extern "C" void destroy(void *ann)
+{
+    delete ((Ann *)ann);
+}
+
+extern "C" int load(void *ann,
+                    const char *path,
+                    bool fastMath,
+                    bool fp16,
+                    bool saveCachedNetwork,
+                    const char *cachedNetworkPath)
+{
+    return ((Ann *)ann)->load(path, fastMath, fp16, saveCachedNetwork, cachedNetworkPath);
+}
+
+extern "C" void unload(void *ann, NetworkId netId)
+{
+    ((Ann *)ann)->unload(netId);
+}
+
+extern "C" void execute(void *ann, NetworkId netId, const void **inputData, void **outputData)
+{
+    ((Ann *)ann)->execute(netId, inputData, outputData);
+}
+
+extern "C" unsigned long shape(void *ann, NetworkId netId, bool isInput, int index)
+{
+    return ((Ann *)ann)->shape(netId, isInput, index);
+}
+
+extern "C" int tensors(void *ann, NetworkId netId, bool isInput)
+{
+    return ((Ann *)ann)->tensors(netId, isInput);
+}
@@ -0,0 +1,162 @@
+from __future__ import annotations
+
+from ctypes import CDLL, Array, c_bool, c_char_p, c_int, c_ulong, c_void_p
+from os.path import exists
+from typing import Any, Protocol, TypeVar
+
+import numpy as np
+from numpy.typing import NDArray
+
+from app.config import log
+
+try:
+    CDLL("libmali.so")  # fail if libmali.so is not mounted into container
+    libann = CDLL("libann.so")
+    libann.init.argtypes = c_int, c_int, c_char_p
+    libann.init.restype = c_void_p
+    libann.load.argtypes = c_void_p, c_char_p, c_bool, c_bool, c_bool, c_char_p
+    libann.load.restype = c_int
+    libann.execute.argtypes = c_void_p, c_int, Array[c_void_p], Array[c_void_p]
+    libann.unload.argtypes = c_void_p, c_int
+    libann.destroy.argtypes = (c_void_p,)
+    libann.shape.argtypes = c_void_p, c_int, c_bool, c_int
+    libann.shape.restype = c_ulong
+    libann.tensors.argtypes = c_void_p, c_int, c_bool
+    libann.tensors.restype = c_int
+    is_available = True
+except OSError as e:
+    log.debug("Could not load ANN shared libraries, using ONNX: %s", e)
+    is_available = False
+
+T = TypeVar("T", covariant=True)
+
+
+class Newable(Protocol[T]):
+    def new(self) -> None:
+        ...
+
+
+class _Singleton(type, Newable[T]):
+    _instances: dict[_Singleton[T], Newable[T]] = {}
+
+    def __call__(cls, *args: Any, **kwargs: Any) -> Newable[T]:
+        if cls not in cls._instances:
+            obj: Newable[T] = super(_Singleton, cls).__call__(*args, **kwargs)
+            cls._instances[cls] = obj
+        else:
+            obj = cls._instances[cls]
+            obj.new()
+        return obj
+
+
+class Ann(metaclass=_Singleton):
+    def __init__(self, log_level: int = 3, tuning_level: int = 1, tuning_file: str | None = None) -> None:
+        if not is_available:
+            raise RuntimeError("libann is not available!")
+        if tuning_file and not exists(tuning_file):
+            raise ValueError("tuning_file must point to an existing (possibly empty) file!")
+        if tuning_level == 0 and tuning_file is None:
+            raise ValueError("tuning_level == 0 reads existing tuning information and requires a tuning_file")
+        if tuning_level < 0 or tuning_level > 3:
+            raise ValueError("tuning_level must be 0 (load from tuning_file), 1, 2 or 3.")
+        if log_level < 0 or log_level > 5:
+            raise ValueError("log_level must be 0 (trace), 1 (debug), 2 (info), 3 (warning), 4 (error) or 5 (fatal)")
+        self.log_level = log_level
+        self.tuning_level = tuning_level
+        self.tuning_file = tuning_file
+        self.output_shapes: dict[int, tuple[tuple[int], ...]] = {}
+        self.input_shapes: dict[int, tuple[tuple[int], ...]] = {}
+        self.ann: int | None = None
+        self.new()
+
+    def new(self) -> None:
+        if self.ann is None:
+            self.ann = libann.init(
+                self.log_level,
+                self.tuning_level,
+                self.tuning_file.encode() if self.tuning_file is not None else None,
+            )
+            self.ref_count = 0
+
+        self.ref_count += 1
+
+    def destroy(self) -> None:
+        self.ref_count -= 1
+        if self.ref_count <= 0 and self.ann is not None:
+            libann.destroy(self.ann)
+            self.ann = None
+
+    def __del__(self) -> None:
+        if self.ann is not None:
+            libann.destroy(self.ann)
+            self.ann = None
+
+    def load(
+        self,
+        model_path: str,
+        fast_math: bool = True,
+        fp16: bool = False,
+        save_cached_network: bool = False,
+        cached_network_path: str | None = None,
+    ) -> int:
+        if not model_path.endswith((".armnn", ".tflite", ".onnx")):
+            raise ValueError("model_path must be a file with extension .armnn, .tflite or .onnx")
+        if not exists(model_path):
+            raise ValueError("model_path must point to an existing file!")
+        if cached_network_path is not None and not exists(cached_network_path):
+            raise ValueError("cached_network_path must point to an existing (possibly empty) file!")
+        if save_cached_network and cached_network_path is None:
+            raise ValueError("save_cached_network is True, cached_network_path must be specified!")
+        net_id: int = libann.load(
+            self.ann,
+            model_path.encode(),
+            fast_math,
+            fp16,
+            save_cached_network,
+            cached_network_path.encode() if cached_network_path is not None else None,
+        )
+
+        self.input_shapes[net_id] = tuple(
+            self.shape(net_id, input=True, index=i) for i in range(self.tensors(net_id, input=True))
+        )
+        self.output_shapes[net_id] = tuple(
+            self.shape(net_id, input=False, index=i) for i in range(self.tensors(net_id, input=False))
+        )
+        return net_id
+
+    def unload(self, network_id: int) -> None:
+        libann.unload(self.ann, network_id)
+        del self.output_shapes[network_id]
+
+    def execute(self, network_id: int, input_tensors: list[NDArray[np.float32]]) -> list[NDArray[np.float32]]:
+        if not isinstance(input_tensors, list):
+            raise ValueError("input_tensors needs to be a list!")
+        net_input_shapes = self.input_shapes[network_id]
+        if len(input_tensors) != len(net_input_shapes):
+            raise ValueError(f"input_tensors lengths {len(input_tensors)} != network inputs {len(net_input_shapes)}")
+        for net_input_shape, input_tensor in zip(net_input_shapes, input_tensors):
+            if net_input_shape != input_tensor.shape:
+                raise ValueError(f"input_tensor shape {input_tensor.shape} != network input shape {net_input_shape}")
+            if not input_tensor.flags.c_contiguous:
+                raise ValueError("input_tensors must be c_contiguous numpy ndarrays")
+        output_tensors: list[NDArray[np.float32]] = [
+            np.ndarray(s, dtype=np.float32) for s in self.output_shapes[network_id]
+        ]
+        input_type = c_void_p * len(input_tensors)
+        inputs = input_type(*[t.ctypes.data_as(c_void_p) for t in input_tensors])
+        output_type = c_void_p * len(output_tensors)
+        outputs = output_type(*[t.ctypes.data_as(c_void_p) for t in output_tensors])
+        libann.execute(self.ann, network_id, inputs, outputs)
+        return output_tensors
+
+    def shape(self, network_id: int, input: bool = False, index: int = 0) -> tuple[int]:
+        s = libann.shape(self.ann, network_id, input, index)
+        a = []
+        while s != 0:
+            a.append(s & 0xFFFF)
+            s >>= 16
+        return tuple(a)
+
+    def tensors(self, network_id: int, input: bool = False) -> int:
+        tensors: int = libann.tensors(self.ann, network_id, input)
+        return tensors
@@ -0,0 +1 @@
+g++ -shared -O3 -o libann.so -fuse-ld=gold -std=c++17 -I$ARMNN_PATH/include -larmnn -larmnnDeserializer -larmnnTfLiteParser -larmnnOnnxParser -L$ARMNN_PATH ann.cpp
@@ -0,0 +1,2 @@
+armnn*
+output/
@@ -0,0 +1,4 @@
+#!/bin/sh
+
+cd armnn-23.11/
+g++ -o ../armnnconverter -O1 -DARMNN_ONNX_PARSER -DARMNN_SERIALIZER -DARMNN_TF_LITE_PARSER -fuse-ld=gold -std=c++17 -Iinclude -Isrc/armnnUtils -Ithird-party -larmnn -larmnnDeserializer -larmnnTfLiteParser -larmnnOnnxParser -larmnnSerializer -L../armnn src/armnnConverter/ArmnnConverter.cpp
@@ -0,0 +1,8 @@
+#!/bin/sh
+
+# binaries
+mkdir armnn
+curl -SL "https://github.com/ARM-software/armnn/releases/download/v23.11/ArmNN-linux-x86_64.tar.gz" | tar -zx -C armnn
+
+# source to build ArmnnConverter
+curl -SL "https://github.com/ARM-software/armnn/archive/refs/tags/v23.11.tar.gz" | tar -zx
@@ -0,0 +1,201 @@
+name: annexport
+channels:
+  - pytorch
+  - nvidia
+  - conda-forge
+dependencies:
+  - _libgcc_mutex=0.1=conda_forge
+  - _openmp_mutex=4.5=2_kmp_llvm
+  - aiohttp=3.9.1=py310h2372a71_0
+  - aiosignal=1.3.1=pyhd8ed1ab_0
+  - arpack=3.8.0=nompi_h0baa96a_101
+  - async-timeout=4.0.3=pyhd8ed1ab_0
+  - attrs=23.1.0=pyh71513ae_1
+  - aws-c-auth=0.7.3=h28f7589_1
+  - aws-c-cal=0.6.1=hc309b26_1
+  - aws-c-common=0.9.0=hd590300_0
+  - aws-c-compression=0.2.17=h4d4d85c_2
+  - aws-c-event-stream=0.3.1=h2e3709c_4
+  - aws-c-http=0.7.11=h00aa349_4
+  - aws-c-io=0.13.32=he9a53bd_1
+  - aws-c-mqtt=0.9.3=hb447be9_1
+  - aws-c-s3=0.3.14=hf3aad02_1
+  - aws-c-sdkutils=0.1.12=h4d4d85c_1
+  - aws-checksums=0.1.17=h4d4d85c_1
+  - aws-crt-cpp=0.21.0=hb942446_5
+  - aws-sdk-cpp=1.10.57=h85b1a90_19
+  - blas=2.120=openblas
+  - blas-devel=3.9.0=20_linux64_openblas
+  - brotli-python=1.0.9=py310hd8f1fbe_9
+  - bzip2=1.0.8=hd590300_5
+  - c-ares=1.23.0=hd590300_0
+  - ca-certificates=2023.11.17=hbcca054_0
+  - certifi=2023.11.17=pyhd8ed1ab_0
+  - charset-normalizer=3.3.2=pyhd8ed1ab_0
+  - click=8.1.7=unix_pyh707e725_0
+  - colorama=0.4.6=pyhd8ed1ab_0
+  - coloredlogs=15.0.1=pyhd8ed1ab_3
+  - cuda-cudart=11.7.99=0
+  - cuda-cupti=11.7.101=0
+  - cuda-libraries=11.7.1=0
+  - cuda-nvrtc=11.7.99=0
+  - cuda-nvtx=11.7.91=0
+  - cuda-runtime=11.7.1=0
+  - dataclasses=0.8=pyhc8e2a94_3
+  - datasets=2.14.7=pyhd8ed1ab_0
+  - dill=0.3.7=pyhd8ed1ab_0
+  - filelock=3.13.1=pyhd8ed1ab_0
+  - flatbuffers=23.5.26=h59595ed_1
+  - freetype=2.12.1=h267a509_2
+  - frozenlist=1.4.0=py310h2372a71_1
+  - fsspec=2023.10.0=pyhca7485f_0
+  - ftfy=6.1.3=pyhd8ed1ab_0
+  - gflags=2.2.2=he1b5a44_1004
+  - glog=0.6.0=h6f12383_0
+  - glpk=5.0=h445213a_0
+  - gmp=6.3.0=h59595ed_0
+  - gmpy2=2.1.2=py310h3ec546c_1
+  - huggingface_hub=0.17.3=pyhd8ed1ab_0
+  - humanfriendly=10.0=pyhd8ed1ab_6
+  - icu=73.2=h59595ed_0
+  - idna=3.6=pyhd8ed1ab_0
+  - importlib-metadata=7.0.0=pyha770c72_0
+  - importlib_metadata=7.0.0=hd8ed1ab_0
+  - joblib=1.3.2=pyhd8ed1ab_0
+  - keyutils=1.6.1=h166bdaf_0
+  - krb5=1.21.2=h659d440_0
+  - lcms2=2.15=h7f713cb_2
+  - ld_impl_linux-64=2.40=h41732ed_0
+  - lerc=4.0.0=h27087fc_0
+  - libabseil=20230125.3=cxx17_h59595ed_0
+  - libarrow=12.0.1=hb87d912_8_cpu
+  - libblas=3.9.0=20_linux64_openblas
+  - libbrotlicommon=1.0.9=h166bdaf_9
+  - libbrotlidec=1.0.9=h166bdaf_9
+  - libbrotlienc=1.0.9=h166bdaf_9
+  - libcblas=3.9.0=20_linux64_openblas
+  - libcrc32c=1.1.2=h9c3ff4c_0
+  - libcublas=11.10.3.66=0
+  - libcufft=10.7.2.124=h4fbf590_0
+  - libcufile=1.8.1.2=0
+  - libcurand=10.3.4.101=0
+  - libcurl=8.5.0=hca28451_0
+  - libcusolver=11.4.0.1=0
+  - libcusparse=11.7.4.91=0
+  - libdeflate=1.19=hd590300_0
+  - libedit=3.1.20191231=he28a2e2_2
+  - libev=4.33=hd590300_2
+  - libevent=2.1.12=hf998b51_1
+  - libffi=3.4.2=h7f98852_5
+  - libgcc-ng=13.2.0=h807b86a_3
+  - libgfortran-ng=13.2.0=h69a702a_3
+  - libgfortran5=13.2.0=ha4646dd_3
+  - libgoogle-cloud=2.12.0=hac9eb74_1
+  - libgrpc=1.54.3=hb20ce57_0
+  - libhwloc=2.9.3=default_h554bfaf_1009
+  - libiconv=1.17=hd590300_1
+  - libjpeg-turbo=2.1.5.1=hd590300_1
+  - liblapack=3.9.0=20_linux64_openblas
+  - liblapacke=3.9.0=20_linux64_openblas
+  - libnghttp2=1.58.0=h47da74e_1
+  - libnpp=11.7.4.75=0
+  - libnsl=2.0.1=hd590300_0
+  - libnuma=2.0.16=h0b41bf4_1
+  - libnvjpeg=11.8.0.2=0
+  - libopenblas=0.3.25=pthreads_h413a1c8_0
+  - libpng=1.6.39=h753d276_0
+  - libprotobuf=3.21.12=hfc55251_2
+  - libsentencepiece=0.1.99=h180e1df_0
+  - libsqlite=3.44.2=h2797004_0
+  - libssh2=1.11.0=h0841786_0
+  - libstdcxx-ng=13.2.0=h7e041cc_3
+  - libthrift=0.18.1=h8fd135c_2
+  - libtiff=4.6.0=h29866fb_1
+  - libutf8proc=2.8.0=h166bdaf_0
+  - libuuid=2.38.1=h0b41bf4_0
+  - libwebp-base=1.3.2=hd590300_0
+  - libxcb=1.15=h0b41bf4_0
+  - libxml2=2.11.6=h232c23b_0
+  - libzlib=1.2.13=hd590300_5
+  - llvm-openmp=17.0.6=h4dfa4b3_0
+  - lz4-c=1.9.4=hcb278e6_0
+  - mkl=2022.2.1=h84fe81f_16997
+  - mkl-devel=2022.2.1=ha770c72_16998
+  - mkl-include=2022.2.1=h84fe81f_16997
+  - mpc=1.3.1=hfe3b2da_0
+  - mpfr=4.2.1=h9458935_0
+  - mpmath=1.3.0=pyhd8ed1ab_0
+  - multidict=6.0.4=py310h2372a71_1
+  - multiprocess=0.70.15=py310h2372a71_1
+  - ncurses=6.4=h59595ed_2
+  - numpy=1.26.2=py310hb13e2d6_0
+  - onnx=1.14.0=py310ha3deec4_1
+  - onnx2torch=1.5.13=pyhd8ed1ab_0
+  - onnxruntime=1.16.3=py310hd4b7fbc_1_cpu
+  - open-clip-torch=2.23.0=pyhd8ed1ab_1
+  - openblas=0.3.25=pthreads_h7a3da1a_0
+  - openjpeg=2.5.0=h488ebb8_3
+  - openssl=3.2.0=hd590300_1
+  - orc=1.9.0=h2f23424_1
+  - packaging=23.2=pyhd8ed1ab_0
+  - pandas=2.1.4=py310hcc13569_0
+  - pillow=10.0.1=py310h29da1c1_1
+  - pip=23.3.1=pyhd8ed1ab_0
+  - protobuf=4.21.12=py310heca2aa9_0
+  - pthread-stubs=0.4=h36c2ea0_1001
+  - pyarrow=12.0.1=py310h0576679_8_cpu
+  - pyarrow-hotfix=0.6=pyhd8ed1ab_0
+  - pysocks=1.7.1=pyha2e5f31_6
+  - python=3.10.13=hd12c33a_0_cpython
+  - python-dateutil=2.8.2=pyhd8ed1ab_0
+  - python-flatbuffers=23.5.26=pyhd8ed1ab_0
+  - python-tzdata=2023.3=pyhd8ed1ab_0
+  - python-xxhash=3.4.1=py310h2372a71_0
+  - python_abi=3.10=4_cp310
+  - pytorch=1.13.1=cpu_py310hd11e9c7_1
+  - pytorch-cuda=11.7=h778d358_5
+  - pytorch-mutex=1.0=cuda
+  - pytz=2023.3.post1=pyhd8ed1ab_0
+  - pyyaml=6.0.1=py310h2372a71_1
+  - rdma-core=28.9=h59595ed_1
+  - re2=2023.03.02=h8c504da_0
+  - readline=8.2=h8228510_1
+  - regex=2023.10.3=py310h2372a71_0
+  - requests=2.31.0=pyhd8ed1ab_0
+  - s2n=1.3.49=h06160fa_0
+  - sacremoses=0.0.53=pyhd8ed1ab_0
+  - safetensors=0.3.3=py310hcb5633a_1
+  - sentencepiece=0.1.99=hff52083_0
+  - sentencepiece-python=0.1.99=py310hebdb9f0_0
+  - sentencepiece-spm=0.1.99=h180e1df_0
+  - setuptools=68.2.2=pyhd8ed1ab_0
+  - six=1.16.0=pyh6c4a22f_0
+  - sleef=3.5.1=h9b69904_2
+  - snappy=1.1.10=h9fff704_0
+  - sympy=1.12=pypyh9d50eac_103
+  - tbb=2021.11.0=h00ab1b0_0
+  - texttable=1.7.0=pyhd8ed1ab_0
+  - timm=0.9.12=pyhd8ed1ab_0
+  - tk=8.6.13=noxft_h4845f30_101
+  - tokenizers=0.14.1=py310h320607d_2
+  - torchvision=0.14.1=cpu_py310hd3d2ac3_1
+  - tqdm=4.66.1=pyhd8ed1ab_0
+  - transformers=4.35.2=pyhd8ed1ab_0
+  - typing-extensions=4.9.0=hd8ed1ab_0
+  - typing_extensions=4.9.0=pyha770c72_0
+  - tzdata=2023c=h71feb2d_0
+  - ucx=1.14.1=h64cca9d_5
+  - urllib3=2.1.0=pyhd8ed1ab_0
+  - wcwidth=0.2.12=pyhd8ed1ab_0
+  - wheel=0.42.0=pyhd8ed1ab_0
+  - xorg-libxau=1.0.11=hd590300_0
+  - xorg-libxdmcp=1.1.3=h7f98852_0
+  - xxhash=0.8.2=hd590300_0
+  - xz=5.2.6=h166bdaf_0
+  - yaml=0.2.5=h7f98852_2
+  - yarl=1.9.3=py310h2372a71_0
+  - zipp=3.17.0=pyhd8ed1ab_0
+  - zlib=1.2.13=hd590300_5
+  - zstd=1.5.5=hfc55251_0
+  - pip:
+      - git+https://github.com/fyfrey/TinyNeuralNetwork.git
@@ -0,0 +1,157 @@
+import logging
+import os
+import platform
+import subprocess
+from abc import abstractmethod
+
+import onnx
+import open_clip
+import torch
+from onnx2torch import convert
+from onnxruntime.tools.onnx_model_utils import fix_output_shapes, make_input_shape_fixed
+from tinynn.converter import TFLiteConverter
+
+
+class ExportBase(torch.nn.Module):
+    input_shape: tuple[int, ...]
+
+    def __init__(self, device: torch.device, name: str):
+        super().__init__()
+        self.device = device
+        self.name = name
+        self.optimize = 5
+        self.nchw_transpose = False
+
+    @abstractmethod
+    def forward(self, input_tensor: torch.Tensor) -> torch.Tensor | tuple[torch.Tensor]:
+        pass
+
+    def dummy_input(self) -> torch.FloatTensor:
+        return torch.rand((1, 3, 224, 224), device=self.device)
+
+
+class ArcFace(ExportBase):
+    input_shape = (1, 3, 112, 112)
+
+    def __init__(self, onnx_model_path: str, device: torch.device):
+        name, _ = os.path.splitext(os.path.basename(onnx_model_path))
+        super().__init__(device, name)
+        onnx_model = onnx.load_model(onnx_model_path)
+        make_input_shape_fixed(onnx_model.graph, onnx_model.graph.input[0].name, self.input_shape)
+        fix_output_shapes(onnx_model)
+        self.model = convert(onnx_model).to(device)
+        if self.device.type == "cuda":
+            self.model = self.model.half()
+
+    def forward(self, input_tensor: torch.Tensor) -> torch.FloatTensor:
+        embedding: torch.FloatTensor = self.model(
+            input_tensor.half() if self.device.type == "cuda" else input_tensor
+        ).float()
+        assert isinstance(embedding, torch.FloatTensor)
+        return embedding
+
+    def dummy_input(self) -> torch.FloatTensor:
+        return torch.rand(self.input_shape, device=self.device)
+
+
+class RetinaFace(ExportBase):
+    input_shape = (1, 3, 640, 640)
+
+    def __init__(self, onnx_model_path: str, device: torch.device):
+        name, _ = os.path.splitext(os.path.basename(onnx_model_path))
+        super().__init__(device, name)
+        self.optimize = 3
+        self.model = convert(onnx_model_path).eval().to(device)
+        if self.device.type == "cuda":
+            self.model = self.model.half()
+
+    def forward(self, input_tensor: torch.Tensor) -> tuple[torch.FloatTensor]:
+        out: torch.Tensor = self.model(input_tensor.half() if self.device.type == "cuda" else input_tensor)
+        return tuple(o.float() for o in out)
+
+    def dummy_input(self) -> torch.FloatTensor:
+        return torch.rand(self.input_shape, device=self.device)
+
+
+class ClipVision(ExportBase):
+    input_shape = (1, 3, 224, 224)
+
+    def __init__(self, model_name: str, weights: str, device: torch.device):
+        super().__init__(device, model_name + "__" + weights)
+        self.model = open_clip.create_model(
+            model_name,
+            weights,
+            precision="fp16" if device.type == "cuda" else "fp32",
+            jit=False,
+            require_pretrained=True,
+            device=device,
+        )
+
+    def forward(self, input_tensor: torch.Tensor) -> torch.FloatTensor:
+        embedding: torch.Tensor = self.model.encode_image(
+            input_tensor.half() if self.device.type == "cuda" else input_tensor,
+            normalize=True,
+        ).float()
+        return embedding
+
+
+def export(model: ExportBase) -> None:
+    model.eval()
+    for param in model.parameters():
+        param.requires_grad = False
+    dummy_input = model.dummy_input()
+    model(dummy_input)
+    jit = torch.jit.trace(model, dummy_input)  # type: ignore[no-untyped-call,attr-defined]
+    tflite_model_path = f"output/{model.name}.tflite"
+    os.makedirs("output", exist_ok=True)
+
+    converter = TFLiteConverter(
+        jit,
+        dummy_input,
+        tflite_model_path,
+        optimize=model.optimize,
+        nchw_transpose=model.nchw_transpose,
+    )
+    # segfaults on ARM, must run on x86_64 / AMD64
+    converter.convert()
+
+    armnn_model_path = f"output/{model.name}.armnn"
+    os.environ["LD_LIBRARY_PATH"] = "armnn"
+    subprocess.run(
+        [
+            "./armnnconverter",
+            "-f",
+            "tflite-binary",
+            "-m",
+            tflite_model_path,
+            "-i",
+            "input_tensor",
+            "-o",
+            "output_tensor",
+            "-p",
+            armnn_model_path,
+        ]
+    )
+
+
+def main() -> None:
+    if platform.machine() not in ("x86_64", "AMD64"):
+        raise RuntimeError(f"Can only run on x86_64 / AMD64, not {platform.machine()}")
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    if device.type != "cuda":
+        logging.warning(
+            "No CUDA available, cannot create fp16 model! proceeding to create a fp32 model (use only for testing)"
+        )
+    models = [
+        ClipVision("ViT-B-32", "openai", device),
+        ArcFace("buffalo_l_rec.onnx", device),
+        RetinaFace("buffalo_l_det.onnx", device),
+    ]
+    for model in models:
+        export(model)
+
+
+if __name__ == "__main__":
+    with torch.no_grad():
+        main()
@@ -24,8 +24,9 @@ class Settings(BaseSettings):
    workers: int = 1
    test_full: bool = False
    request_threads: int = os.cpu_count() or 4
-    model_inter_op_threads: int = 1
-    model_intra_op_threads: int = 2
+    model_inter_op_threads: int = 0
+    model_intra_op_threads: int = 0
+    ann: bool = True

    class Config:
        env_prefix = "MACHINE_LEARNING_"
@@ -5,10 +5,10 @@ from unittest import mock
 import numpy as np
 import pytest
 from fastapi.testclient import TestClient
+from numpy.typing import NDArray
 from PIL import Image

 from .main import app
-from .schemas import ndarray_f32


@pytest.fixture
@@ -17,7 +17,7 @@ def pil_image() -> Image.Image:


@pytest.fixture
-def cv_image(pil_image: Image.Image) -> ndarray_f32:
+def cv_image(pil_image: Image.Image) -> NDArray[np.float32]:
    return np.asarray(pil_image)[:, :, ::-1]  # PIL uses RGB while cv2 uses BGR


@@ -93,3 +93,15 @@ def clip_tokenizer_cfg() -> dict[str, Any]:
        "tokenizer_class": "CLIPTokenizer",
        "unk_token": "<|endoftext|>",
    }
+
+
+@pytest.fixture(scope="function")
+def providers(request: pytest.FixtureRequest) -> Iterator[dict[str, Any]]:
+    marker = request.node.get_closest_marker("providers")
+    if marker is None:
+        raise ValueError("Missing marker 'providers'")
+
+    providers = marker.args[0]
+    with mock.patch("app.models.base.ort.get_available_providers") as mocked:
+        mocked.return_value = providers
+        yield providers
@@ -2,11 +2,11 @@ import asyncio
 import gc
 import os
 import signal
-import sys
 import threading
 import time
 from concurrent.futures import ThreadPoolExecutor
-from typing import Any, Iterator
+from contextlib import asynccontextmanager
+from typing import Any, AsyncGenerator, Callable, Iterator
 from zipfile import BadZipFile

 import orjson
@@ -26,7 +26,6 @@ from .schemas import (
 )

 MultiPartParser.max_file_size = 2**26  # spools to disk if payload is 64 MiB or larger
-app = FastAPI()

 model_cache = ModelCache(ttl=settings.model_ttl, revalidate=settings.model_ttl > 0)
 thread_pool: ThreadPoolExecutor | None = None
@@ -35,8 +34,8 @@ active_requests = 0
 last_called: float | None = None


-@app.on_event("startup")
-def startup() -> None:
+@asynccontextmanager
+async def lifespan(_: FastAPI) -> AsyncGenerator[None, None]:
    global thread_pool
    log.info(
        (
@@ -44,21 +43,22 @@ def startup() -> None:
            f"{f'after {settings.model_ttl}s of inactivity' if settings.model_ttl > 0 else 'disabled'}."
        )
    )
-    # asyncio is a huge bottleneck for performance, so we use a thread pool to run blocking code
-    thread_pool = ThreadPoolExecutor(settings.request_threads) if settings.request_threads > 0 else None
-    if settings.model_ttl > 0 and settings.model_ttl_poll_s > 0:
-        asyncio.ensure_future(idle_shutdown_task())
-    log.info(f"Initialized request thread pool with {settings.request_threads} threads.")

-
-@app.on_event("shutdown")
-def shutdown() -> None:
-    log.handlers.clear()
-    for model in model_cache.cache._cache.values():
-        del model
-    if thread_pool is not None:
-        thread_pool.shutdown()
-    gc.collect()
+    try:
+        if settings.request_threads > 0:
+            # asyncio is a huge bottleneck for performance, so we use a thread pool to run blocking code
+            thread_pool = ThreadPoolExecutor(settings.request_threads) if settings.request_threads > 0 else None
+            log.info(f"Initialized request thread pool with {settings.request_threads} threads.")
+        if settings.model_ttl > 0 and settings.model_ttl_poll_s > 0:
+            asyncio.ensure_future(idle_shutdown_task())
+        yield
+    finally:
+        log.handlers.clear()
+        for model in model_cache.cache._cache.values():
+            del model
+        if thread_pool is not None:
+            thread_pool.shutdown()
+        gc.collect()


 def update_state() -> Iterator[None]:
@@ -71,6 +71,9 @@ def update_state() -> Iterator[None]:
        active_requests -= 1


+app = FastAPI(lifespan=lifespan)
+
+
@app.get("/", response_model=MessageResponse)
 async def root() -> dict[str, str]:
    return {"message": "Immich ML"}
@@ -102,14 +105,14 @@ async def predict(

    model = await load(await model_cache.get(model_name, model_type, **kwargs))
    model.configure(**kwargs)
-    outputs = await run(model, inputs)
+    outputs = await run(model.predict, inputs)
    return ORJSONResponse(outputs)


-async def run(model: InferenceModel, inputs: Any) -> Any:
+async def run(func: Callable[..., Any], inputs: Any) -> Any:
    if thread_pool is None:
-        return model.predict(inputs)
-    return await asyncio.get_running_loop().run_in_executor(thread_pool, model.predict, inputs)
+        return func(inputs)
+    return await asyncio.get_running_loop().run_in_executor(thread_pool, func, inputs)


 async def load(model: InferenceModel) -> InferenceModel:
@@ -128,7 +131,7 @@ async def load(model: InferenceModel) -> InferenceModel:
            await loop.run_in_executor(thread_pool, _load)
        return model
    except (OSError, InvalidProtobuf, BadZipFile, NoSuchFile):
-        log.warn(
+        log.warning(
            (
                f"Failed to load {model.model_type.replace('_', ' ')} model '{model.model_name}'."
                "Clearing cache and retrying."
@@ -0,0 +1,68 @@
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any, NamedTuple
+
+import numpy as np
+from numpy.typing import NDArray
+
+from ann.ann import Ann
+
+from ..config import log, settings
+
+
+class AnnSession:
+    """
+    Wrapper for ANN to be drop-in replacement for ONNX session.
+    """
+
+    def __init__(self, model_path: Path):
+        tuning_file = Path(settings.cache_folder) / "gpu-tuning.ann"
+        with tuning_file.open(mode="a"):
+            # make sure tuning file exists (without clearing contents)
+            # once filled, the tuning file reduces the cost/time of the first
+            # inference after model load by 10s of seconds
+            pass
+        self.ann = Ann(tuning_level=3, tuning_file=tuning_file.as_posix())
+        log.info("Loading ANN model %s ...", model_path)
+        cache_file = model_path.with_suffix(".anncache")
+        save = False
+        if not cache_file.is_file():
+            save = True
+            with cache_file.open(mode="a"):
+                # create empty model cache file
+                pass
+
+        self.model = self.ann.load(
+            model_path.as_posix(),
+            save_cached_network=save,
+            cached_network_path=cache_file.as_posix(),
+        )
+        log.info("Loaded ANN model with ID %d", self.model)
+
+    def __del__(self) -> None:
+        self.ann.unload(self.model)
+        log.info("Unloaded ANN model %d", self.model)
+        self.ann.destroy()
+
+    def get_inputs(self) -> list[AnnNode]:
+        shapes = self.ann.input_shapes[self.model]
+        return [AnnNode(None, s) for s in shapes]
+
+    def get_outputs(self) -> list[AnnNode]:
+        shapes = self.ann.output_shapes[self.model]
+        return [AnnNode(None, s) for s in shapes]
+
+    def run(
+        self,
+        output_names: list[str] | None,
+        input_feed: dict[str, NDArray[np.float32]] | dict[str, NDArray[np.int32]],
+        run_options: Any = None,
+    ) -> list[NDArray[np.float32]]:
+        inputs: list[NDArray[np.float32]] = [np.ascontiguousarray(v) for v in input_feed.values()]
+        return self.ann.execute(self.model, inputs)
+
+
+class AnnNode(NamedTuple):
+    name: str | None
+    shape: tuple[int, ...]
@@ -10,8 +10,12 @@ import onnxruntime as ort
 from huggingface_hub import snapshot_download
 from typing_extensions import Buffer

+import ann.ann
+from app.models.constants import SUPPORTED_PROVIDERS
+
 from ..config import get_cache_dir, get_hf_model_name, log, settings
 from ..schemas import ModelType
+from .ann import AnnSession


 class InferenceModel(ABC):
@@ -21,36 +25,17 @@ class InferenceModel(ABC):
        self,
        model_name: str,
        cache_dir: Path | str | None = None,
-        inter_op_num_threads: int = settings.model_inter_op_threads,
-        intra_op_num_threads: int = settings.model_intra_op_threads,
+        providers: list[str] | None = None,
+        provider_options: list[dict[str, Any]] | None = None,
+        sess_options: ort.SessionOptions | None = None,
        **model_kwargs: Any,
    ) -> None:
-        self.model_name = model_name
        self.loaded = False
-        self._cache_dir = Path(cache_dir) if cache_dir is not None else None
-        self.providers = model_kwargs.pop("providers", ["CPUExecutionProvider"])
-        #  don't pre-allocate more memory than needed
-        self.provider_options = model_kwargs.pop(
-            "provider_options", [{"arena_extend_strategy": "kSameAsRequested"}] * len(self.providers)
-        )
-        log.debug(
-            (
-                f"Setting '{self.model_name}' execution providers to {self.providers} "
-                "in descending order of preference"
-            ),
-        )
-        log.debug(f"Setting execution provider options to {self.provider_options}")
-        self.sess_options = PicklableSessionOptions()
-        # avoid thread contention between models
-        if inter_op_num_threads > 1:
-            self.sess_options.execution_mode = ort.ExecutionMode.ORT_PARALLEL
-
-        log.debug(f"Setting execution_mode to {self.sess_options.execution_mode.name}")
-        log.debug(f"Setting inter_op_num_threads to {inter_op_num_threads}")
-        log.debug(f"Setting intra_op_num_threads to {intra_op_num_threads}")
-        self.sess_options.inter_op_num_threads = inter_op_num_threads
-        self.sess_options.intra_op_num_threads = intra_op_num_threads
-        self.sess_options.enable_cpu_mem_arena = False
+        self.model_name = model_name
+        self.cache_dir = Path(cache_dir) if cache_dir is not None else self.cache_dir_default
+        self.providers = providers if providers is not None else self.providers_default
+        self.provider_options = provider_options if provider_options is not None else self.provider_options_default
+        self.sess_options = sess_options if sess_options is not None else self.sess_options_default

    def download(self) -> None:
        if not self.cached:
@@ -92,33 +77,9 @@ class InferenceModel(ABC):
    def _load(self) -> None:
        ...

-    @property
-    def model_type(self) -> ModelType:
-        return self._model_type
-
-    @property
-    def cache_dir(self) -> Path:
-        return self._cache_dir if self._cache_dir is not None else get_cache_dir(self.model_name, self.model_type)
-
-    @cache_dir.setter
-    def cache_dir(self, cache_dir: Path) -> None:
-        self._cache_dir = cache_dir
-
-    @property
-    def cached(self) -> bool:
-        return self.cache_dir.exists() and any(self.cache_dir.iterdir())
-
-    @classmethod
-    def from_model_type(cls, model_type: ModelType, model_name: str, **model_kwargs: Any) -> InferenceModel:
-        subclasses = {subclass._model_type: subclass for subclass in cls.__subclasses__()}
-        if model_type not in subclasses:
-            raise ValueError(f"Unsupported model type: {model_type}")
-
-        return subclasses[model_type](model_name, **model_kwargs)
-
    def clear_cache(self) -> None:
        if not self.cache_dir.exists():
-            log.warn(
+            log.warning(
                f"Attempted to clear cache for model '{self.model_name}', but cache directory does not exist",
            )
            return
@@ -129,7 +90,7 @@ class InferenceModel(ABC):
            log.info(f"Cleared cache directory for model '{self.model_name}'.")
            rmtree(self.cache_dir)
        else:
-            log.warn(
+            log.warning(
                (
                    f"Encountered file instead of directory at cache path "
                    f"for '{self.model_name}'. Removing file and replacing with a directory."
@@ -138,6 +99,122 @@ class InferenceModel(ABC):
            self.cache_dir.unlink()
        self.cache_dir.mkdir(parents=True, exist_ok=True)

+    def _make_session(self, model_path: Path) -> AnnSession | ort.InferenceSession:
+        armnn_path = model_path.with_suffix(".armnn")
+        if settings.ann and ann.ann.is_available and armnn_path.is_file():
+            session = AnnSession(armnn_path)
+        elif model_path.is_file():
+            session = ort.InferenceSession(
+                model_path.as_posix(),
+                sess_options=self.sess_options,
+                providers=self.providers,
+                provider_options=self.provider_options,
+            )
+        else:
+            raise ValueError(f"the file model_path='{model_path}' does not exist")
+        return session
+
+    @property
+    def model_type(self) -> ModelType:
+        return self._model_type
+
+    @property
+    def cache_dir(self) -> Path:
+        return self._cache_dir
+
+    @cache_dir.setter
+    def cache_dir(self, cache_dir: Path) -> None:
+        self._cache_dir = cache_dir
+
+    @property
+    def cache_dir_default(self) -> Path:
+        return get_cache_dir(self.model_name, self.model_type)
+
+    @property
+    def cached(self) -> bool:
+        return self.cache_dir.exists() and any(self.cache_dir.iterdir())
+
+    @property
+    def providers(self) -> list[str]:
+        return self._providers
+
+    @providers.setter
+    def providers(self, providers: list[str]) -> None:
+        log.debug(
+            (f"Setting '{self.model_name}' execution providers to {providers}, " "in descending order of preference"),
+        )
+        self._providers = providers
+
+    @property
+    def providers_default(self) -> list[str]:
+        available_providers = set(ort.get_available_providers())
+        log.debug(f"Available ORT providers: {available_providers}")
+        return [provider for provider in SUPPORTED_PROVIDERS if provider in available_providers]
+
+    @property
+    def provider_options(self) -> list[dict[str, Any]]:
+        return self._provider_options
+
+    @provider_options.setter
+    def provider_options(self, provider_options: list[dict[str, Any]]) -> None:
+        log.debug(f"Setting execution provider options to {provider_options}")
+        self._provider_options = provider_options
+
+    @property
+    def provider_options_default(self) -> list[dict[str, Any]]:
+        options = []
+        for provider in self.providers:
+            match provider:
+                case "CPUExecutionProvider" | "CUDAExecutionProvider":
+                    option = {"arena_extend_strategy": "kSameAsRequested"}
+                case "OpenVINOExecutionProvider":
+                    try:
+                        device_ids: list[str] = ort.capi._pybind_state.get_available_openvino_device_ids()
+                        log.debug(f"Available OpenVINO devices: {device_ids}")
+                        gpu_devices = [device_id for device_id in device_ids if device_id.startswith("GPU")]
+                        option = {"device_id": gpu_devices[0]} if gpu_devices else {}
+                    except AttributeError as e:
+                        log.warning("Failed to get OpenVINO device IDs. Using default options.")
+                        log.error(e)
+                        option = {}
+                case _:
+                    option = {}
+            options.append(option)
+        return options
+
+    @property
+    def sess_options(self) -> ort.SessionOptions:
+        return self._sess_options
+
+    @sess_options.setter
+    def sess_options(self, sess_options: ort.SessionOptions) -> None:
+        log.debug(f"Setting execution_mode to {sess_options.execution_mode.name}")
+        log.debug(f"Setting inter_op_num_threads to {sess_options.inter_op_num_threads}")
+        log.debug(f"Setting intra_op_num_threads to {sess_options.intra_op_num_threads}")
+        self._sess_options = sess_options
+
+    @property
+    def sess_options_default(self) -> ort.SessionOptions:
+        sess_options = PicklableSessionOptions()
+        sess_options.enable_cpu_mem_arena = False
+
+        # avoid thread contention between models
+        if settings.model_inter_op_threads > 0:
+            sess_options.inter_op_num_threads = settings.model_inter_op_threads
+        # these defaults work well for CPU, but bottleneck GPU
+        elif settings.model_inter_op_threads == 0 and self.providers == ["CPUExecutionProvider"]:
+            sess_options.inter_op_num_threads = 1
+
+        if settings.model_intra_op_threads > 0:
+            sess_options.intra_op_num_threads = settings.model_intra_op_threads
+        elif settings.model_intra_op_threads == 0 and self.providers == ["CPUExecutionProvider"]:
+            sess_options.intra_op_num_threads = 2
+
+        if sess_options.inter_op_num_threads > 1:
+            sess_options.execution_mode = ort.ExecutionMode.ORT_PARALLEL
+
+        return sess_options
+

 # HF deep copies configs, so we need to make session options picklable
 class PicklableSessionOptions(ort.SessionOptions):  # type: ignore[misc]
@@ -6,13 +6,13 @@ from pathlib import Path
 from typing import Any, Literal

 import numpy as np
-import onnxruntime as ort
+from numpy.typing import NDArray
 from PIL import Image
 from tokenizers import Encoding, Tokenizer

 from app.config import clean_name, log
 from app.models.transforms import crop, get_pil_resampling, normalize, resize, to_numpy
-from app.schemas import ModelType, ndarray_f32, ndarray_i32
+from app.schemas import ModelType

 from .base import InferenceModel

@@ -23,7 +23,7 @@ class BaseCLIPEncoder(InferenceModel):
    def __init__(
        self,
        model_name: str,
-        cache_dir: str | None = None,
+        cache_dir: Path | str | None = None,
        mode: Literal["text", "vision"] | None = None,
        **model_kwargs: Any,
    ) -> None:
@@ -33,27 +33,15 @@ class BaseCLIPEncoder(InferenceModel):
    def _load(self) -> None:
        if self.mode == "text" or self.mode is None:
            log.debug(f"Loading clip text model '{self.model_name}'")
-
-            self.text_model = ort.InferenceSession(
-                self.textual_path.as_posix(),
-                sess_options=self.sess_options,
-                providers=self.providers,
-                provider_options=self.provider_options,
-            )
+            self.text_model = self._make_session(self.textual_path)
            log.debug(f"Loaded clip text model '{self.model_name}'")

        if self.mode == "vision" or self.mode is None:
            log.debug(f"Loading clip vision model '{self.model_name}'")
-
-            self.vision_model = ort.InferenceSession(
-                self.visual_path.as_posix(),
-                sess_options=self.sess_options,
-                providers=self.providers,
-                provider_options=self.provider_options,
-            )
+            self.vision_model = self._make_session(self.visual_path)
            log.debug(f"Loaded clip vision model '{self.model_name}'")

-    def _predict(self, image_or_text: Image.Image | str) -> ndarray_f32:
+    def _predict(self, image_or_text: Image.Image | str) -> NDArray[np.float32]:
        if isinstance(image_or_text, bytes):
            image_or_text = Image.open(BytesIO(image_or_text))

@@ -61,12 +49,10 @@ class BaseCLIPEncoder(InferenceModel):
            case Image.Image():
                if self.mode == "text":
                    raise TypeError("Cannot encode image as text-only model")
-
-                outputs: ndarray_f32 = self.vision_model.run(None, self.transform(image_or_text))[0][0]
+                outputs: NDArray[np.float32] = self.vision_model.run(None, self.transform(image_or_text))[0][0]
            case str():
                if self.mode == "vision":
                    raise TypeError("Cannot encode text as vision-only model")
-
                outputs = self.text_model.run(None, self.tokenize(image_or_text))[0][0]
            case _:
                raise TypeError(f"Expected Image or str, but got: {type(image_or_text)}")
@@ -74,11 +60,11 @@ class BaseCLIPEncoder(InferenceModel):
        return outputs

    @abstractmethod
-    def tokenize(self, text: str) -> dict[str, ndarray_i32]:
+    def tokenize(self, text: str) -> dict[str, NDArray[np.int32]]:
        pass

    @abstractmethod
-    def transform(self, image: Image.Image) -> dict[str, ndarray_f32]:
+    def transform(self, image: Image.Image) -> dict[str, NDArray[np.float32]]:
        pass

    @property
@@ -150,7 +136,7 @@ class OpenCLIPEncoder(BaseCLIPEncoder):
    def __init__(
        self,
        model_name: str,
-        cache_dir: str | None = None,
+        cache_dir: Path | str | None = None,
        mode: Literal["text", "vision"] | None = None,
        **model_kwargs: Any,
    ) -> None:
@@ -176,11 +162,11 @@ class OpenCLIPEncoder(BaseCLIPEncoder):
        self.tokenizer.enable_truncation(max_length=context_length)
        log.debug(f"Loaded tokenizer for CLIP model '{self.model_name}'")

-    def tokenize(self, text: str) -> dict[str, ndarray_i32]:
+    def tokenize(self, text: str) -> dict[str, NDArray[np.int32]]:
        tokens: Encoding = self.tokenizer.encode(text)
        return {"text": np.array([tokens.ids], dtype=np.int32)}

-    def transform(self, image: Image.Image) -> dict[str, ndarray_f32]:
+    def transform(self, image: Image.Image) -> dict[str, NDArray[np.float32]]:
        image = resize(image, self.size)
        image = crop(image, self.size)
        image_np = to_numpy(image)
@@ -189,7 +175,7 @@ class OpenCLIPEncoder(BaseCLIPEncoder):


 class MCLIPEncoder(OpenCLIPEncoder):
-    def tokenize(self, text: str) -> dict[str, ndarray_i32]:
+    def tokenize(self, text: str) -> dict[str, NDArray[np.int32]]:
        tokens: Encoding = self.tokenizer.encode(text)
        return {
            "input_ids": np.array([tokens.ids], dtype=np.int32),
@@ -51,6 +51,13 @@ _INSIGHTFACE_MODELS = {
 }


+SUPPORTED_PROVIDERS = [
+    "CUDAExecutionProvider",
+    "OpenVINOExecutionProvider",
+    "CPUExecutionProvider",
+]
+
+
 def is_openclip(model_name: str) -> bool:
    return clean_name(model_name) in _OPENCLIP_MODELS

@@ -3,12 +3,12 @@ from typing import Any

 import cv2
 import numpy as np
-import onnxruntime as ort
 from insightface.model_zoo import ArcFaceONNX, RetinaFace
 from insightface.utils.face_align import norm_crop
+from numpy.typing import NDArray

 from app.config import clean_name
-from app.schemas import BoundingBox, Face, ModelType, ndarray_f32
+from app.schemas import Face, ModelType, is_ndarray

 from .base import InferenceModel

@@ -27,23 +27,8 @@ class FaceRecognizer(InferenceModel):
        super().__init__(clean_name(model_name), cache_dir, **model_kwargs)

    def _load(self) -> None:
-        self.det_model = RetinaFace(
-            session=ort.InferenceSession(
-                self.det_file.as_posix(),
-                sess_options=self.sess_options,
-                providers=self.providers,
-                provider_options=self.provider_options,
-            ),
-        )
-        self.rec_model = ArcFaceONNX(
-            self.rec_file.as_posix(),
-            session=ort.InferenceSession(
-                self.rec_file.as_posix(),
-                sess_options=self.sess_options,
-                providers=self.providers,
-                provider_options=self.provider_options,
-            ),
-        )
+        self.det_model = RetinaFace(session=self._make_session(self.det_file))
+        self.rec_model = ArcFaceONNX(self.rec_file.as_posix(), session=self._make_session(self.rec_file))

        self.det_model.prepare(
            ctx_id=0,
@@ -52,22 +37,25 @@ class FaceRecognizer(InferenceModel):
        )
        self.rec_model.prepare(ctx_id=0)

-    def _predict(self, image: ndarray_f32 | bytes) -> list[Face]:
+    def _predict(self, image: NDArray[np.uint8] | bytes) -> list[Face]:
        if isinstance(image, bytes):
-            image = cv2.imdecode(np.frombuffer(image, np.uint8), cv2.IMREAD_COLOR)
-        bboxes, kpss = self.det_model.detect(image)
+            decoded_image = cv2.imdecode(np.frombuffer(image, np.uint8), cv2.IMREAD_COLOR)
+        else:
+            decoded_image = image
+        assert is_ndarray(decoded_image, np.uint8)
+        bboxes, kpss = self.det_model.detect(decoded_image)
        if bboxes.size == 0:
            return []
-        assert isinstance(image, np.ndarray) and isinstance(kpss, np.ndarray)
+        assert is_ndarray(kpss, np.float32)

        scores = bboxes[:, 4].tolist()
        bboxes = bboxes[:, :4].round().tolist()

        results = []
-        height, width, _ = image.shape
+        height, width, _ = decoded_image.shape
        for (x1, y1, x2, y2), score, kps in zip(bboxes, scores, kpss):
-            cropped_img = norm_crop(image, kps)
-            embedding: ndarray_f32 = self.rec_model.get_feat(cropped_img)[0]
+            cropped_img = norm_crop(decoded_image, kps)
+            embedding: NDArray[np.float32] = self.rec_model.get_feat(cropped_img)[0]
            face: Face = {
                "imageWidth": width,
                "imageHeight": height,
--- a/Show More
+++ b/Show More
				`@@ -0,0 +1 @@`
				`g++ -shared -O3 -o libann.so -fuse-ld=gold -std=c++17 -I$ARMNN_PATH/include -larmnn -larmnnDeserializer -larmnnTfLiteParser -larmnnOnnxParser -L$ARMNN_PATH ann.cpp`