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* added transcode configs for nvenc,qsv and vaapi * updated dev docker compose * added software fallback * working vaapi * minor fixes and added tests * updated api * compile libvips * move hwaccel settings to `hwaccel.yml` * changed default dockerfile, moved `readdir` call * removed unused import * minor cleanup * fix for arm build * added documentation, minor fixes * added intel driver * updated docs styling * uppercase codec and api names * formatting * added tests * updated docs * removed semicolons * added link to `hwaccel.yml` * added newlines * added `hwaccel` section to docker-compose.prod.yml * ensure mesa drivers are installed * switch to mimalloc for sharp * moved build version and sha256 to json * let libmfx set the render device * possible fix for vp9 on qsv * updated tests * formatting * review suggestions * semicolon * moved `LD_PRELOAD` to start script * switched to jellyfin's ffmpeg package * fixed dockerfile * use cqp instead of icq for qsv vp9 * updated dockerfile * added sha256sum for other platforms * fixtures
61 lines
2.9 KiB
Markdown
61 lines
2.9 KiB
Markdown
# Hardware Transcoding [Experimental]
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This feature allows you to use a GPU or Intel Quick Sync 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
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- NVIDIA GPUs
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- Quick Sync
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- Intel CPUs
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- VAAPI
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- GPUs
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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.
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- This is mainly because the original video may not be hardware-decodable.
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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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1. If you do not already have it, download the latest [`hwaccel.yml`][hw-file] file and ensure it's in the same folder as the `docker-compose.yml`.
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2. Uncomment the lines that apply to your system and desired usage.
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3. In the `docker-compose.yml` under `immich-microservices`, uncomment the lines relating to the `hwaccel.yml` file.
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4. Redeploy the `immich-microservices` container with these updated settings.
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5. In the Admin page under `FFmpeg settings`, change the hardware acceleration setting to the appropriate option and save.
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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 GPUs and Intel CPUs, 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.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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