ray
104 lines
3.3 KiB
Markdown
104 lines
3.3 KiB
Markdown
# Immich ML GPU Cache Paths and Model Names
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Known working for Immich v2.7.5.
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## Model Sources
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All models hosted on HuggingFace under `immich-app/` org.
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| Model Name | HuggingFace Repo | Files | Size |
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|---|---|---|---|
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| `ViT-B-32__openai` | `immich-app/ViT-B-32__openai` | visual/model.onnx (335 MB), textual/model.onnx (254 MB) | ~590 MB total |
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| `buffalo_l` | `immich-app/buffalo_l` | detection/model.onnx (16 MB), recognition/model.onnx (166 MB) | ~182 MB total |
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## Cache Directory Structure
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Cache root: `/cache` (from `MACHINE_LEARNING_CACHE_FOLDER` env var).
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### CLIP (smart search)
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| Field | Value |
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|---|---|
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| Model task | `SEARCH` → `"clip"` |
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| Model type | `VISUAL` → `"visual"`, `TEXTUAL` → `"textual"` |
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| Cache dir | `/cache/clip/ViT-B-32__openai` |
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| Model dir (visual) | `/cache/clip/ViT-B-32__openai/visual` |
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| Model path (visual) | `/cache/clip/ViT-B-32__openai/visual/model.onnx` |
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| Model dir (textual) | `/cache/clip/ViT-B-32__openai/textual` |
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| Model path (textual) | `/cache/clip/ViT-B-32__openai/textual/model.onnx` |
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### Face detection & recognition
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| Field | Value |
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| Model task | `FACIAL_RECOGNITION` → `"facial-recognition"` |
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| Cache dir | `/cache/facial-recognition/buffalo_l` |
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| Model dir (detection) | `/cache/facial-recognition/buffalo_l/detection` |
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| Model path (detection) | `/cache/facial-recognition/buffalo_l/detection/model.onnx` |
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| Model dir (recognition) | `/cache/facial-recognition/buffalo_l/recognition` |
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| Model path (recognition) | `/cache/facial-recognition/buffalo_l/recognition/model.onnx` |
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## Code Derivation
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Cache paths follow this pattern:
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```python
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from immich_ml.config import settings
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# settings.cache_folder = "/cache"
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# model_task.value = one of: "clip", "facial-recognition", "ocr"
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# model_name = e.g. "ViT-B-32__openai" or "buffalo_l"
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# model_type.value = one of: "visual", "textual", "detection", "recognition"
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cache_dir = settings.cache_folder / model_task.value / model_name
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model_dir = cache_dir / model_type.value
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model_path = model_dir / "model.onnx"
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```
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## Pre-download command
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```python
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from huggingface_hub import snapshot_download
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import os
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# For CLIP
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os.makedirs("/cache/clip/ViT-B-32__openai", exist_ok=True)
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snapshot_download(
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"immich-app/ViT-B-32__openai",
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cache_dir="/cache/clip/ViT-B-32__openai",
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local_dir="/cache/clip/ViT-B-32__openai",
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ignore_patterns=["*.armnn", "*.rknn"],
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)
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# For face detection/recognition
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os.makedirs("/cache/facial-recognition/buffalo_l", exist_ok=True)
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snapshot_download(
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"immich-app/buffalo_l",
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cache_dir="/cache/facial-recognition/buffalo_l",
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local_dir="/cache/facial-recognition/buffalo_l",
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ignore_patterns=["*.armnn", "*.rknn"],
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)
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```
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## docker-compose GPU config
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Required addition to `immich-machine-learning` service:
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```yaml
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deploy:
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resources:
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reservations:
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devices:
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- driver: nvidia
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count: all
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capabilities: [gpu]
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```
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## Environment variables
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| Variable | Value | Effect |
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|---|---|---|
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| `DEVICE` | `cuda` | Use CUDA (set by -cuda image) |
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| `MACHINE_LEARNING_MODEL_ARENA` | `false` | Load all models at once. Set `true` to load one at a time (saves VRAM but slower switching) |
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| `MACHINE_LEARNING_DEVICE_ID` | `0` | GPU device index |
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| `MACHINE_LEARNING_CACHE_FOLDER` | `/cache` | Where models are stored |
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