docs(ml,server): updated hwaccel docs (#6878)

docs/docs/features/ml-hardware-acceleration.md

@@ -3,7 +3,11 @@
 This feature allows you to use a GPU to accelerate machine learning tasks, such as Smart Search and Facial Recognition, while reducing CPU load.
 As this is a new feature, it is still experimental and may not work on all systems.
 
-## Supported APIs
+:::info
+You do not need to redo any machine learning jobs after enabling hardware acceleration. The acceleration device will be used for any jobs that run after enabling it.
+:::
+
+## Supported Backends
 
 - ARM NN (Mali)
 - CUDA (NVIDIA)
@@ -14,7 +18,8 @@ As this is a new feature, it is still experimental and may not work on all syste
 - The instructions and configurations here are specific to Docker Compose. Other container engines may require different configuration.
 - Only Linux and Windows (through WSL2) servers are supported.
 - ARM NN is only supported on devices with Mali GPUs. Other Arm devices are not supported.
-- The OpenVINO backend has only been tested on an iGPU. ARC GPUs may not work without other changes.
+- There is currently an upstream issue with OpenVINO, so whether it will work is device-dependent.
+- Some models may not be compatible with certain backends. CUDA is the most reliable.
 
 ## Prerequisites
 
@@ -40,10 +45,60 @@ As this is a new feature, it is still experimental and may not work on all syste
 2. In the `docker-compose.yml` under `immich-machine-learning`, uncomment the `extends` section and change `cpu` to the appropriate backend.
 3. Redeploy the `immich-machine-learning` container with these updated settings.
 
+#### Single Compose File
+
+Some platforms, including Unraid and Portainer, do not support multiple Compose files as of writing. As an alternative, you can "inline" the relevant contents of the [`hwaccel.ml.yml`][hw-file] file into the `immich-machine-learning` service directly.
+
+For example, the `cuda` section in this file is:
+
+```yaml
+deploy:
+  resources:
+    reservations:
+      devices:
+        - driver: nvidia
+          count: 1
+          capabilities:
+            - gpu
+            - compute
+            - video
+```
+
+You can add this to the `immich-machine-learning` service instead of extending from `hwaccel.ml.yml`:
+
+```yaml
+immich-machine-learning:
+  container_name: immich_machine_learning
+  image: ghcr.io/immich-app/immich-machine-learning:${IMMICH_VERSION:-release}
+  # Note the lack of an `extends` section
+  deploy:
+    resources:
+      reservations:
+        devices:
+          - driver: nvidia
+            count: 1
+            capabilities:
+              - gpu
+              - compute
+              - video
+  volumes:
+    - model-cache:/cache
+  env_file:
+    - .env
+  restart: always
+```
+
+Once this is done, you can redeploy the `immich-machine-learning` container.
+
+:::info
+You can confirm the device is being recognized and used by checking its utilization (via `nvtop` for CUDA, `intel_gpu_top` for OpenVINO, etc.). You can also enable debug logging by setting `LOG_LEVEL=debug` in the `.env` file and restarting the `immich-machine-learning` container. When a Smart Search or Face Detection job begins, you should see a log for `Available ORT providers` containing the relevant provider. In the case of ARM NN, the absence of a `Could not load ANN shared libraries` log entry means it loaded successfully.
+:::
+
 [hw-file]: https://github.com/immich-app/immich/releases/latest/download/hwaccel.ml.yml
 [nvcr]: https://github.com/NVIDIA/nvidia-container-runtime/
 
 ## Tips
 
+- If you encounter an error when a model is running, try a different model to see if the issue is model-specific.
 - You may want to increase concurrency past the default for higher utilization. However, keep in mind that this will also increase VRAM consumption.
 - Larger models benefit more from hardware acceleration, if you have the VRAM for them.