# Immich
Self-hosted photo and video backup solution directly from your mobile phone.
Loading ~4000 images/videos
## Screenshots
# Note
**!! NOT READY FOR PRODUCTION! DO NOT USE TO STORE YOUR ASSETS !!**
This project is under heavy development, there will be continous functions, features and api changes.
# Features
- Upload and view assets (videos/images).
- Auto Backup.
- Download asset to local device.
- Multi-user supported.
- Quick navigation with drag scroll bar.
- Support HEIC/HEIF Backup.
- Extract and display EXIF info.
- Real-time render from multi-device upload event.
- Image Tagging/Classification based on ImageNet dataset
- Object detection based on COCO SSD.
- Search assets based on tags and exif data (lens, make, model, orientation)
- [Optional] Reverse geocoding using Mapbox (Generous free-tier of 100,000 search/month)
- Show asset's location information on map (OpenStreetMap).
- Show curated places on the search page
- Show curated objects on the search page
- Shared album with users on the same server
- Selective backup - albums can be included and excluded during the backup process.
# System Requirement
**OS**: Preferred Linux-based operating system (Ubuntu, Debian, MacOS...etc).
I haven't tested with `Docker for Windows` as well as `WSL` on Windows
*Raspberry Pi can be used but `microservices` container has to be comment out in `docker-compose` since TensorFlow has not been supported in Dockec image on arm64v7 yet.*
**RAM**: At least 2GB, preffered 4GB.
**Core**: At least 2 cores, preffered 4 cores.
# Development and Testing out the application
You can use docker compose for development and testing out the application, there are several services that compose Immich:
1. **NestJs** - Backend of the application
2. **PostgreSQL** - Main database of the application
3. **Redis** - For sharing websocket instance between docker instances and background tasks message queue.
4. **Nginx** - Load balancing and optimized file uploading.
5. **TensorFlow** - Object Detection and Image Classification.
## Step 1: Populate .env file
Navigate to `docker` directory and run
```
cp .env.example .env
```
Then populate the value in there.
Notice that if set `ENABLE_MAPBOX` to `true`, you will have to provide `MAPBOX_KEY` for the server to run.
Pay attention to the key `UPLOAD_LOCATION`, this directory must exist and is owned by the user that run the `docker-compose` command below.
**Example**
```bash
# Database
DB_USERNAME=postgres
DB_PASSWORD=postgres
DB_DATABASE_NAME=immich
# Upload File Config
UPLOAD_LOCATION=
# JWT SECRET
JWT_SECRET=randomstringthatissolongandpowerfulthatnoonecanguess
# MAPBOX
## ENABLE_MAPBOX is either true of false -> if true, you have to provide MAPBOX_KEY
ENABLE_MAPBOX=false
MAPBOX_KEY=
```
## Step 2: Start the server
To start, run
```bash
docker-compose -f ./docker/docker-compose.yml up
```
If you have a few thousand photos/videos, I suggest running docker-compose with scaling option for the `immich_server` container to handle high I/O load when using fast scrolling.
```bash
docker-compose -f ./docker/docker-compose.yml up --scale immich_server=5
```
The server will be running at `http://your-ip:2283` through `Nginx`
## Step 3: Register User
Use the command below on your terminal to create user as we don't have user interface for this function yet.
```bash
curl --location --request POST 'http://your-server-ip:2283/auth/signUp' \
--header 'Content-Type: application/json' \
--data-raw '{
"email": "testuser@email.com",
"password": "password"
}'
```
## Step 4: Run mobile app
The app is distributed on several platforms below.
## F-Droid
You can get the app on F-droid by clicking the image below.
[
](https://f-droid.org/packages/app.alextran.immich)
## Android
#### Get the app on Google Play Store [here](https://play.google.com/store/apps/details?id=app.alextran.immich)
*The App version might be lagging behind the latest release due to the review process.*
## iOS
#### Get the app on Apple AppStore [here](https://apps.apple.com/us/app/immich/id1613945652):
*The App version might be lagging behind the latest release due to the review process.*
# Support
If you like the app, find it helpful, and want to support me to offset the cost of publishing to AppStores, you can sponsor the project with [**Github Sponsore**](https://github.com/sponsors/alextran1502), or one time donation with Buy Me a coffee link below.
[](https://www.buymeacoffee.com/altran1502)
This is also a meaningful way to give me motivation and encounragment to continue working on the app.
Cheer! 🎉
# Known Issue
## TensorFlow Build Issue
*This is a known issue on RaspberryPi 4 arm64-v7 and incorrect Promox setup*
TensorFlow doesn't run with older CPU architecture, it requires CPU with AVX and AVX2 instruction set. If you encounter the error `illegal instruction core dump` when running the docker-compose command above, check for your CPU flags with the command and make sure you see `AVX` and `AVX2`:
```bash
more /proc/cpuinfo | grep flags
```
If you are running virtualization in Promox, the VM doesn't have the flag enable.
You need to change the CPU type from `kvm64` to `host` under VMs hardware tab.
`Hardware > Processors > Edit > Advanced > Type (dropdown menu) > host`
Otherwise you can:
- edit `docker-compose.yml` file and comment the whole `immich_microservices` service **which will disable machine learning features like object detection and image classification**
- switch to a different VM/desktop with different architecture.