Merge branch 'main' into rknn-toolkit-lite2

This commit is contained in:
Yoni Yang
2025-01-18 16:45:38 +08:00
committed by GitHub
141 changed files with 4341 additions and 2242 deletions

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@@ -63,6 +63,17 @@ If you only want to do web development connected to an existing, remote backend,
IMMICH_SERVER_URL=https://demo.immich.app/ npm run dev
```
#### `@immich/ui`
To see local changes to `@immich/ui` in Immich, do the following:
1. Install `@immich/ui` as a sibling to `immich/`, for example `/home/user/immich` and `/home/user/ui`
1. Build the `@immich/ui` project via `npm run build`
1. Uncomment the corresponding volume in web service of the `docker/docker-compose.dev.yaml` file (`../../ui:/usr/ui`)
1. Uncomment the corresponding alias in the `web/vite.config.js` file (`'@immich/ui': path.resolve(\_\_dirname, '../../ui')`)
1. Start up the stack via `make dev`
1. After making changes in `@immich/ui`, rebuild it (`npm run build`)
### Mobile app
The mobile app `(/mobile)` will required Flutter toolchain 3.13.x to be installed on your system.

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@@ -148,26 +148,29 @@ Redis (Sentinel) URL example JSON before encoding:
## Machine Learning
| Variable | Description | Default | Containers |
| :-------------------------------------------------------- | :-------------------------------------------------------------------------------------------------- | :-----------------------------: | :--------------- |
| `MACHINE_LEARNING_MODEL_TTL` | Inactivity time (s) before a model is unloaded (disabled if \<= 0) | `300` | machine learning |
| `MACHINE_LEARNING_MODEL_TTL_POLL_S` | Interval (s) between checks for the model TTL (disabled if \<= 0) | `10` | machine learning |
| `MACHINE_LEARNING_CACHE_FOLDER` | Directory where models are downloaded | `/cache` | machine learning |
| `MACHINE_LEARNING_REQUEST_THREADS`<sup>\*1</sup> | Thread count of the request thread pool (disabled if \<= 0) | number of CPU cores | machine learning |
| `MACHINE_LEARNING_MODEL_INTER_OP_THREADS` | Number of parallel model operations | `1` | machine learning |
| `MACHINE_LEARNING_MODEL_INTRA_OP_THREADS` | Number of threads for each model operation | `2` | machine learning |
| `MACHINE_LEARNING_WORKERS`<sup>\*2</sup> | Number of worker processes to spawn | `1` | machine learning |
| `MACHINE_LEARNING_HTTP_KEEPALIVE_TIMEOUT_S`<sup>\*3</sup> | HTTP Keep-alive time in seconds | `2` | machine learning |
| `MACHINE_LEARNING_WORKER_TIMEOUT` | Maximum time (s) of unresponsiveness before a worker is killed | `120` (`300` if using OpenVINO) | machine learning |
| `MACHINE_LEARNING_PRELOAD__CLIP` | Name of a CLIP model to be preloaded and kept in cache | | machine learning |
| `MACHINE_LEARNING_PRELOAD__FACIAL_RECOGNITION` | Name of a facial recognition model to be preloaded and kept in cache | | machine learning |
| `MACHINE_LEARNING_ANN` | Enable ARM-NN hardware acceleration if supported | `True` | machine learning |
| `MACHINE_LEARNING_ANN_FP16_TURBO` | Execute operations in FP16 precision: increasing speed, reducing precision (applies only to ARM-NN) | `False` | machine learning |
| `MACHINE_LEARNING_ANN_TUNING_LEVEL` | ARM-NN GPU tuning level (1: rapid, 2: normal, 3: exhaustive) | `2` | machine learning |
| `MACHINE_LEARNING_DEVICE_IDS`<sup>\*4</sup> | Device IDs to use in multi-GPU environments | `0` | machine learning |
| `MACHINE_LEARNING_MAX_BATCH_SIZE__FACIAL_RECOGNITION` | Set the maximum number of faces that will be processed at once by the facial recognition model | None (`1` if using OpenVINO) | machine learning |
| `MACHINE_LEARNING_RKNN` | Enable RKNN hardware acceleration if supported | `True` | machine learning |
| `MACHINE_LEARNING_RKNN_THREADS` | How many threads of RKNN runtime should be spinned up while inferencing. | `1` | machine learning |
| Variable | Description | Default | Containers |
| :---------------------------------------------------------- | :-------------------------------------------------------------------------------------------------- | :-----------------------------: | :--------------- |
| `MACHINE_LEARNING_MODEL_TTL` | Inactivity time (s) before a model is unloaded (disabled if \<= 0) | `300` | machine learning |
| `MACHINE_LEARNING_MODEL_TTL_POLL_S` | Interval (s) between checks for the model TTL (disabled if \<= 0) | `10` | machine learning |
| `MACHINE_LEARNING_CACHE_FOLDER` | Directory where models are downloaded | `/cache` | machine learning |
| `MACHINE_LEARNING_REQUEST_THREADS`<sup>\*1</sup> | Thread count of the request thread pool (disabled if \<= 0) | number of CPU cores | machine learning |
| `MACHINE_LEARNING_MODEL_INTER_OP_THREADS` | Number of parallel model operations | `1` | machine learning |
| `MACHINE_LEARNING_MODEL_INTRA_OP_THREADS` | Number of threads for each model operation | `2` | machine learning |
| `MACHINE_LEARNING_WORKERS`<sup>\*2</sup> | Number of worker processes to spawn | `1` | machine learning |
| `MACHINE_LEARNING_HTTP_KEEPALIVE_TIMEOUT_S`<sup>\*3</sup> | HTTP Keep-alive time in seconds | `2` | machine learning |
| `MACHINE_LEARNING_WORKER_TIMEOUT` | Maximum time (s) of unresponsiveness before a worker is killed | `120` (`300` if using OpenVINO) | machine learning |
| `MACHINE_LEARNING_PRELOAD__CLIP__TEXTUAL` | Name of the textual CLIP model to be preloaded and kept in cache | | machine learning |
| `MACHINE_LEARNING_PRELOAD__CLIP__VISUAL` | Name of the visual CLIP model to be preloaded and kept in cache | | machine learning |
| `MACHINE_LEARNING_PRELOAD__FACIAL_RECOGNITION__RECOGNITION` | Name of the recognition portion of the facial recognition model to be preloaded and kept in cache | | machine learning |
| `MACHINE_LEARNING_PRELOAD__FACIAL_RECOGNITION__DETECTION` | Name of the detection portion of the facial recognition model to be preloaded and kept in cache | | machine learning |
| `MACHINE_LEARNING_ANN` | Enable ARM-NN hardware acceleration if supported | `True` | machine learning |
| `MACHINE_LEARNING_ANN_FP16_TURBO` | Execute operations in FP16 precision: increasing speed, reducing precision (applies only to ARM-NN) | `False` | machine learning |
| `MACHINE_LEARNING_ANN_TUNING_LEVEL` | ARM-NN GPU tuning level (1: rapid, 2: normal, 3: exhaustive) | `2` | machine learning |
| `MACHINE_LEARNING_DEVICE_IDS`<sup>\*4</sup> | Device IDs to use in multi-GPU environments | `0` | machine learning |
| `MACHINE_LEARNING_MAX_BATCH_SIZE__FACIAL_RECOGNITION` | Set the maximum number of faces that will be processed at once by the facial recognition model | None (`1` if using OpenVINO) | machine learning |
| `MACHINE_LEARNING_RKNN` | Enable RKNN hardware acceleration if supported | `True` | machine learning |
| `MACHINE_LEARNING_RKNN_THREADS` | How many threads of RKNN runtime should be spinned up while inferencing. | `1` | machine learning |
\*1: It is recommended to begin with this parameter when changing the concurrency levels of the machine learning service and then tune the other ones.