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feat(ml): add preload and fp16 settings for ocr (#23576)

pull/23580/head^2
Mert 2025-11-06 12:55:11 +07:00 committed by GitHub
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48
docs/docs/install/environment-variables.md
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@ -149,29 +149,31 @@ 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__TEXTUAL` | Comma-separated list of (textual) CLIP model(s) to preload and cache | | machine learning |
| `MACHINE_LEARNING_PRELOAD__CLIP__VISUAL` | Comma-separated list of (visual) CLIP model(s) to preload and cache | | machine learning |
| `MACHINE_LEARNING_PRELOAD__FACIAL_RECOGNITION__RECOGNITION` | Comma-separated list of (recognition) facial recognition model(s) to preload and cache | | machine learning |
| `MACHINE_LEARNING_PRELOAD__FACIAL_RECOGNITION__DETECTION` | Comma-separated list of (detection) facial recognition model(s) to preload and 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 |
| `MACHINE_LEARNING_MODEL_ARENA` | Pre-allocates CPU memory to avoid memory fragmentation | true | 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` | Comma-separated list of (textual) CLIP model(s) to preload and cache | | machine learning |
| `MACHINE_LEARNING_PRELOAD__CLIP__VISUAL` | Comma-separated list of (visual) CLIP model(s) to preload and cache | | machine learning |
| `MACHINE_LEARNING_PRELOAD__FACIAL_RECOGNITION__RECOGNITION` | Comma-separated list of (recognition) facial recognition model(s) to preload and cache | | machine learning |
| `MACHINE_LEARNING_PRELOAD__FACIAL_RECOGNITION__DETECTION` | Comma-separated list of (detection) facial recognition model(s) to preload and 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_MAX_BATCH_SIZE__OCR` | Set the maximum number of boxes that will be processed at once by the OCR model | `6` | 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 spun up while inferencing. | `1` | machine learning |
| `MACHINE_LEARNING_MODEL_ARENA` | Pre-allocates CPU memory to avoid memory fragmentation | true | machine learning |
| `MACHINE_LEARNING_OPENVINO_PRECISION` | If set to FP16, uses half-precision floating-point operations for faster inference with reduced accuracy (one of [`FP16`, `FP32`], applies only to OpenVINO) | `FP32` | 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.

9
machine-learning/immich_ml/config.py
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@ -13,6 +13,8 @@ from rich.logging import RichHandler
from uvicorn import Server
from uvicorn.workers import UvicornWorker
from .schemas import ModelPrecision
class ClipSettings(BaseModel):
textual: str | None = None
@ -24,6 +26,11 @@ class FacialRecognitionSettings(BaseModel):
detection: str | None = None
class OcrSettings(BaseModel):
recognition: str | None = None
detection: str | None = None
class PreloadModelData(BaseModel):
clip_fallback: str | None = os.getenv("MACHINE_LEARNING_PRELOAD__CLIP", None)
facial_recognition_fallback: str | None = os.getenv("MACHINE_LEARNING_PRELOAD__FACIAL_RECOGNITION", None)
@ -37,6 +44,7 @@ class PreloadModelData(BaseModel):
del os.environ["MACHINE_LEARNING_PRELOAD__FACIAL_RECOGNITION"]
clip: ClipSettings = ClipSettings()
facial_recognition: FacialRecognitionSettings = FacialRecognitionSettings()
ocr: OcrSettings = OcrSettings()
class MaxBatchSize(BaseModel):
@ -70,6 +78,7 @@ class Settings(BaseSettings):
rknn_threads: int = 1
preload: PreloadModelData | None = None
max_batch_size: MaxBatchSize | None = None
openvino_precision: ModelPrecision = ModelPrecision.FP32
@property
def device_id(self) -> str:

14
machine-learning/immich_ml/main.py
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@ -103,6 +103,20 @@ async def preload_models(preload: PreloadModelData) -> None:
ModelTask.FACIAL_RECOGNITION,
)
if preload.ocr.detection is not None:
await load_models(
preload.ocr.detection,
ModelType.DETECTION,
ModelTask.OCR,
)
if preload.ocr.recognition is not None:
await load_models(
preload.ocr.recognition,
ModelType.RECOGNITION,
ModelTask.OCR,
)
if preload.clip_fallback is not None:
log.warning(
"Deprecated env variable: 'MACHINE_LEARNING_PRELOAD__CLIP'. "

5
machine-learning/immich_ml/schemas.py
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@ -46,6 +46,11 @@ class ModelSource(StrEnum):
PADDLE = "paddle"
class ModelPrecision(StrEnum):
FP16 = "FP16"
FP32 = "FP32"
ModelIdentity = tuple[ModelType, ModelTask]

8
machine-learning/immich_ml/sessions/ort.py
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@ -93,10 +93,12 @@ class OrtSession:
case "CUDAExecutionProvider" | "ROCMExecutionProvider":
options = {"arena_extend_strategy": "kSameAsRequested", "device_id": settings.device_id}
case "OpenVINOExecutionProvider":
openvino_dir = self.model_path.parent / "openvino"
device = f"GPU.{settings.device_id}"
options = {
"device_type": f"GPU.{settings.device_id}",
"precision": "FP32",
"cache_dir": (self.model_path.parent / "openvino").as_posix(),
"device_type": device,
"precision": settings.openvino_precision.value,
"cache_dir": openvino_dir.as_posix(),
}
case "CoreMLExecutionProvider":
options = {

55
machine-learning/test_main.py
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@ -26,7 +26,7 @@ from immich_ml.models.clip.textual import MClipTextualEncoder, OpenClipTextualEn
from immich_ml.models.clip.visual import OpenClipVisualEncoder
from immich_ml.models.facial_recognition.detection import FaceDetector
from immich_ml.models.facial_recognition.recognition import FaceRecognizer
from immich_ml.schemas import ModelFormat, ModelTask, ModelType
from immich_ml.schemas import ModelFormat, ModelPrecision, ModelTask, ModelType
from immich_ml.sessions.ann import AnnSession
from immich_ml.sessions.ort import OrtSession
from immich_ml.sessions.rknn import RknnSession, run_inference
@ -240,11 +240,16 @@ class TestOrtSession:
@pytest.mark.ov_device_ids(["GPU.0", "CPU"])
def test_sets_default_provider_options(self, ov_device_ids: list[str]) -> None:
model_path = "/cache/ViT-B-32__openai/model.onnx"
model_path = "/cache/ViT-B-32__openai/textual/model.onnx"
session = OrtSession(model_path, providers=["OpenVINOExecutionProvider", "CPUExecutionProvider"])
assert session.provider_options == [
{"device_type": "GPU.0", "precision": "FP32", "cache_dir": "/cache/ViT-B-32__openai/openvino"},
{
"device_type": "GPU.0",
"precision": "FP32",
"cache_dir": "/cache/ViT-B-32__openai/textual/openvino",
},
{"arena_extend_strategy": "kSameAsRequested"},
]
@ -262,6 +267,21 @@ class TestOrtSession:
}
]
def test_sets_openvino_to_fp16_if_enabled(self, mocker: MockerFixture) -> None:
model_path = "/cache/ViT-B-32__openai/textual/model.onnx"
os.environ["MACHINE_LEARNING_DEVICE_ID"] = "1"
mocker.patch.object(settings, "openvino_precision", ModelPrecision.FP16)
session = OrtSession(model_path, providers=["OpenVINOExecutionProvider"])
assert session.provider_options == [
{
"device_type": "GPU.1",
"precision": "FP16",
"cache_dir": "/cache/ViT-B-32__openai/textual/openvino",
}
]
def test_sets_provider_options_for_cuda(self) -> None:
os.environ["MACHINE_LEARNING_DEVICE_ID"] = "1"
@ -417,7 +437,7 @@ class TestRknnSession:
session.run(None, input_feed)
rknn_session.return_value.put.assert_called_once_with([input1, input2])
np_spy.call_count == 2
assert np_spy.call_count == 2
np_spy.assert_has_calls([mock.call(input1), mock.call(input2)])
@ -925,11 +945,34 @@ class TestCache:
any_order=True,
)
async def test_preloads_ocr_models(self, monkeypatch: MonkeyPatch, mock_get_model: mock.Mock) -> None:
os.environ["MACHINE_LEARNING_PRELOAD__OCR__DETECTION"] = "PP-OCRv5_mobile"
os.environ["MACHINE_LEARNING_PRELOAD__OCR__RECOGNITION"] = "PP-OCRv5_mobile"
settings = Settings()
assert settings.preload is not None
assert settings.preload.ocr.detection == "PP-OCRv5_mobile"
assert settings.preload.ocr.recognition == "PP-OCRv5_mobile"
model_cache = ModelCache()
monkeypatch.setattr("immich_ml.main.model_cache", model_cache)
await preload_models(settings.preload)
mock_get_model.assert_has_calls(
[
mock.call("PP-OCRv5_mobile", ModelType.DETECTION, ModelTask.OCR),
mock.call("PP-OCRv5_mobile", ModelType.RECOGNITION, ModelTask.OCR),
],
any_order=True,
)
async def test_preloads_all_models(self, monkeypatch: MonkeyPatch, mock_get_model: mock.Mock) -> None:
os.environ["MACHINE_LEARNING_PRELOAD__CLIP__TEXTUAL"] = "ViT-B-32__openai"
os.environ["MACHINE_LEARNING_PRELOAD__CLIP__VISUAL"] = "ViT-B-32__openai"
os.environ["MACHINE_LEARNING_PRELOAD__FACIAL_RECOGNITION__RECOGNITION"] = "buffalo_s"
os.environ["MACHINE_LEARNING_PRELOAD__FACIAL_RECOGNITION__DETECTION"] = "buffalo_s"
os.environ["MACHINE_LEARNING_PRELOAD__OCR__DETECTION"] = "PP-OCRv5_mobile"
os.environ["MACHINE_LEARNING_PRELOAD__OCR__RECOGNITION"] = "PP-OCRv5_mobile"
settings = Settings()
assert settings.preload is not None
@ -937,6 +980,8 @@ class TestCache:
assert settings.preload.clip.textual == "ViT-B-32__openai"
assert settings.preload.facial_recognition.recognition == "buffalo_s"
assert settings.preload.facial_recognition.detection == "buffalo_s"
assert settings.preload.ocr.detection == "PP-OCRv5_mobile"
assert settings.preload.ocr.recognition == "PP-OCRv5_mobile"
model_cache = ModelCache()
monkeypatch.setattr("immich_ml.main.model_cache", model_cache)
@ -948,6 +993,8 @@ class TestCache:
mock.call("ViT-B-32__openai", ModelType.VISUAL, ModelTask.SEARCH),
mock.call("buffalo_s", ModelType.DETECTION, ModelTask.FACIAL_RECOGNITION),
mock.call("buffalo_s", ModelType.RECOGNITION, ModelTask.FACIAL_RECOGNITION),
mock.call("PP-OCRv5_mobile", ModelType.DETECTION, ModelTask.OCR),
mock.call("PP-OCRv5_mobile", ModelType.RECOGNITION, ModelTask.OCR),
],
any_order=True,
)