Cutlery/immich
1
0
Fork 1
mirror of https://github.com/immich-app/immich.git synced 2025-07-09 03:04:16 -04:00
Code Issues Packages Projects Releases Wiki Activity

Sort them by alphablet

Browse Source
This commit is contained in:
yoni13 2025-01-13 01:38:14 +08:00
parent 68fccad462
commit 1775397a84
5 changed files with 32 additions and 41 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
docker/docker-compose.dev.yml
View File

@ -85,12 +85,12 @@ services:
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
# service: cpu # set to one of [armnn, cuda, openvino, openvino-wsl, rknn] for accelerated inference
build:
context: ../machine-learning
dockerfile: Dockerfile
args:
- DEVICE=cpu # set to one of [armnn, cuda, openvino, openvino-wsl] for accelerated inference
- DEVICE=cpu # set to one of [armnn, cuda, openvino, openvino-wsl, rknn] for accelerated inference
ports:
- 3003:3003
volumes:

20
docker/docker-compose.prod.yml
View File

@ -29,12 +29,12 @@ services:
image: immich-machine-learning:latest
# extends:
# file: hwaccel.ml.yml
# service: cpu # set to one of [armnn, cuda, openvino, openvino-wsl] for accelerated inference
# service: cpu # set to one of [armnn, cuda, openvino, openvino-wsl, rknn] for accelerated inference
build:
context: ../machine-learning
dockerfile: Dockerfile
args:
- DEVICE=cpu # set to one of [armnn, cuda, openvino, openvino-wsl] for accelerated inference
- DEVICE=cpu # set to one of [armnn, cuda, openvino, openvino-wsl, rknn] for accelerated inference
ports:
- 3003:3003
volumes:
@ -68,22 +68,12 @@ services:
- 5432:5432
healthcheck:
test: >-
pg_isready --dbname="$${POSTGRES_DB}" --username="$${POSTGRES_USER}" || exit 1;
Chksum="$$(psql --dbname="$${POSTGRES_DB}" --username="$${POSTGRES_USER}" --tuples-only --no-align
--command='SELECT COALESCE(SUM(checksum_failures), 0) FROM pg_stat_database')";
echo "checksum failure count is $$Chksum";
[ "$$Chksum" = '0' ] || exit 1
pg_isready --dbname="$${POSTGRES_DB}" --username="$${POSTGRES_USER}" || exit 1; Chksum="$$(psql --dbname="$${POSTGRES_DB}" --username="$${POSTGRES_USER}" --tuples-only --no-align --command='SELECT COALESCE(SUM(checksum_failures), 0) FROM pg_stat_database')"; echo "checksum failure count is $$Chksum"; [ "$$Chksum" = '0' ] || exit 1
interval: 5m
start_interval: 30s
start_period: 5m
command: >-
postgres
-c shared_preload_libraries=vectors.so
-c 'search_path="$$user", public, vectors'
-c logging_collector=on
-c max_wal_size=2GB
-c shared_buffers=512MB
-c wal_compression=on
postgres -c shared_preload_libraries=vectors.so -c 'search_path="$$user", public, vectors' -c logging_collector=on -c max_wal_size=2GB -c shared_buffers=512MB -c wal_compression=on
restart: always
# set IMMICH_TELEMETRY_INCLUDE=all in .env to enable metrics
@ -100,7 +90,7 @@ services:
# add data source for http://immich-prometheus:9090 to get started
immich-grafana:
container_name: immich_grafana
command: ['./run.sh', '-disable-reporting']
command: [ './run.sh', '-disable-reporting' ]
ports:
- 3000:3000
image: grafana/grafana:11.4.0-ubuntu@sha256:afccec22ba0e4815cca1d2bf3836e414322390dc78d77f1851976ffa8d61051c

18
docker/docker-compose.yml
View File

@ -32,12 +32,12 @@ services:
immich-machine-learning:
container_name: immich_machine_learning
# For hardware acceleration, add one of -[armnn, cuda, openvino] to the image tag.
# For hardware acceleration, add one of -[armnn, cuda, openvino, rknn] 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
# service: cpu # set to one of [armnn, cuda, openvino, openvino-wsl, rknn] for accelerated inference - use the `-wsl` version for WSL2 where applicable
volumes:
- model-cache:/cache
env_file:
@ -66,22 +66,12 @@ services:
- ${DB_DATA_LOCATION}:/var/lib/postgresql/data
healthcheck:
test: >-
pg_isready --dbname="$${POSTGRES_DB}" --username="$${POSTGRES_USER}" || exit 1;
Chksum="$$(psql --dbname="$${POSTGRES_DB}" --username="$${POSTGRES_USER}" --tuples-only --no-align
--command='SELECT COALESCE(SUM(checksum_failures), 0) FROM pg_stat_database')";
echo "checksum failure count is $$Chksum";
[ "$$Chksum" = '0' ] || exit 1
pg_isready --dbname="$${POSTGRES_DB}" --username="$${POSTGRES_USER}" || exit 1; Chksum="$$(psql --dbname="$${POSTGRES_DB}" --username="$${POSTGRES_USER}" --tuples-only --no-align --command='SELECT COALESCE(SUM(checksum_failures), 0) FROM pg_stat_database')"; echo "checksum failure count is $$Chksum"; [ "$$Chksum" = '0' ] || exit 1
interval: 5m
start_interval: 30s
start_period: 5m
command: >-
postgres
-c shared_preload_libraries=vectors.so
-c 'search_path="$$user", public, vectors'
-c logging_collector=on
-c max_wal_size=2GB
-c shared_buffers=512MB
-c wal_compression=on
postgres -c shared_preload_libraries=vectors.so -c 'search_path="$$user", public, vectors' -c logging_collector=on -c max_wal_size=2GB -c shared_buffers=512MB -c wal_compression=on
restart: always
volumes:

12
docker/hwaccel.ml.yml
View File

@ -13,6 +13,18 @@ services:
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)
rknn:
security_opt:
- systempaths=unconfined
- apparmor=unconfined
devices:
- /dev/rga:/dev/rga
- /dev/dri:/dev/dri
- /dev/dma_heap:/dev/dma_heap
- /dev/mpp_service:/dev/mpp_service
volumes:
- /sys/kernel/debug/:/sys/kernel/debug/:ro
cpu: {}

19
docs/docs/features/ml-hardware-acceleration.md
View File

@ -10,9 +10,9 @@ You do not need to redo any machine learning jobs after enabling hardware accele
## Supported Backends
- ARM NN (Mali)
- RKNN (Rockchip)
- CUDA (NVIDIA GPUs with [compute capability](https://developer.nvidia.com/cuda-gpus) 5.2 or higher)
- OpenVINO (Intel discrete GPUs such as Iris Xe and Arc)
- RKNN (Rockchip)
## Limitations
@ -35,15 +35,6 @@ You do not need to redo any machine learning jobs after enabling hardware accele
- The `hwaccel.ml.yml` file assumes an additional file `/lib/firmware/mali_csffw.bin`, so update accordingly if your device's driver does not require this file
- Optional: Configure your `.env` file, see [environment variables](/docs/install/environment-variables) for ARM NN specific settings
#### RKNN
- You must have a supported Rockchip SoC, only RK3566 and RK3588 are supported at this moment.
- Make sure you have the appropriate linux kernel driver installed
- This is usually pre-installed on the device vendor's Linux images
- RKNPU driver V0.9.8 or later must be available in the host server
- You may confirm this by running `cat /sys/kernel/debug/rknpu/version` to check the version
- Optional: Configure your `.env` file, see [environment variables](/docs/install/environment-variables) for RKNN specific settings
#### CUDA
- The GPU must have compute capability 5.2 or greater.
@ -56,6 +47,14 @@ You do not need to redo any machine learning jobs after enabling hardware accele
- The server must have a discrete GPU, i.e. Iris Xe or Arc. Expect issues when attempting to use integrated graphics.
- Ensure the server's kernel version is new enough to use the device for hardware accceleration.
#### RKNN
- You must have a supported Rockchip SoC, only RK3566 and RK3588 are supported at this moment.
- Make sure you have the appropriate linux kernel driver installed
- This is usually pre-installed on the device vendor's Linux images
- RKNPU driver V0.9.8 or later must be available in the host server
- You may confirm this by running `cat /sys/kernel/debug/rknpu/version` to check the version
- Optional: Configure your `.env` file, see [environment variables](/docs/install/environment-variables) for RKNN specific settings
## Setup
1. If you do not already have it, download the latest [`hwaccel.ml.yml`][hw-file] file and ensure it's in the same folder as the `docker-compose.yml`.