mirror of
https://github.com/immich-app/immich
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84 lines
3.8 KiB
Markdown
84 lines
3.8 KiB
Markdown
# Hardware Transcoding [Experimental]
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This feature allows you to use a GPU to accelerate transcoding and reduce CPU load.
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Note that hardware transcoding is much less efficient for file sizes.
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As this is a new feature, it is still experimental and may not work on all systems.
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## Supported APIs
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- NVENC (NVIDIA)
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- Quick Sync (Intel)
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- RKMPP (Rockchip)
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- VAAPI (AMD / NVIDIA / Intel)
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## Limitations
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- The instructions and configurations here are specific to Docker Compose. Other container engines may require different configuration.
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- Only Linux and Windows (through WSL2) servers are supported.
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- WSL2 does not support Quick Sync.
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- Raspberry Pi is currently not supported.
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- Two-pass mode is only supported for NVENC. Other APIs will ignore this setting.
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- Only encoding is currently hardware accelerated, so the CPU is still used for software decoding and tone-mapping.
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- Hardware dependent
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- Codec support varies, but H.264 and HEVC are usually supported.
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- Notably, NVIDIA and AMD GPUs do not support VP9 encoding.
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- Newer devices tend to have higher transcoding quality.
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## Prerequisites
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#### NVENC
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- You must have the official NVIDIA driver installed on the server.
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- On Linux (except for WSL2), you also need to have [NVIDIA Container Runtime][nvcr] installed.
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#### QSV
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- For VP9 to work:
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- You must have a 9th gen Intel CPU or newer
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- If you have an 11th gen CPU or older, then you may need to follow [these][jellyfin-lp] instructions as Low-Power mode is required
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- Additionally, if the server specifically has an 11th gen CPU and is running kernel 5.15 (shipped with Ubuntu 22.04 LTS), then you will need to upgrade this kernel (from [Jellyfin docs][jellyfin-kernel-bug])
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## Setup
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#### Basic Setup
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1. If you do not already have it, download the latest [`hwaccel.transcoding.yml`][hw-file] file and ensure it's in the same folder as the `docker-compose.yml`.
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2. In the `docker-compose.yml` under `immich-microservices`, uncomment the `extends` section and change `cpu` to the appropriate backend.
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- For VAAPI on WSL2, be sure to use `vaapi-wsl` rather than `vaapi`
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3. Redeploy the `immich-microservices` container with these updated settings.
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4. In the Admin page under `Video transcoding settings`, change the hardware acceleration setting to the appropriate option and save.
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#### All-In-One - Unraid Setup
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##### NVENC - NVIDIA GPUs
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1. In the container app, add this environmental variable: Key=`NVIDIA_VISIBLE_DEVICES` Value=`all`
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2. While still in the container app, change the container from Basic Mode to Advanced Mode and add the following parameter to the Extra Parameters field: `--runtime=nvidia`
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3. Restart the container app.
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4. Continue to step 4 of "Basic Setup".
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##### Other APIs
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Unraid does not currently support multiple Compose files. As an alternative, you can "inline" the relevant contents of the [`hwaccel.transcoding.yml`][hw-file] file into the `immich-microservices` service directly.
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For example, the `qsv` section in this file is:
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```
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devices:
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- /dev/dri:/dev/dri
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```
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You can add this to the `immich-microservices` service instead of extending from `hwaccel.transcoding.yml`.
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Once this is done, you can continue to step 3 of "Basic Setup".
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## Tips
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- You may want to choose a slower preset than for software transcoding to maintain quality and efficiency
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- While you can use VAAPI with NVIDIA and Intel devices, prefer the more specific APIs since they're more optimized for their respective devices
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[hw-file]: https://github.com/immich-app/immich/releases/latest/download/hwaccel.transcoding.yml
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[nvcr]: https://github.com/NVIDIA/nvidia-container-runtime/
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[jellyfin-lp]: https://jellyfin.org/docs/general/administration/hardware-acceleration/intel/#configure-and-verify-lp-mode-on-linux
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[jellyfin-kernel-bug]: https://jellyfin.org/docs/general/administration/hardware-acceleration/intel/#known-issues-and-limitations
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