c73832bd9c
* download facial recognition models * download hf models * simplified logic * updated `predict` for facial recognition * ensure download method is called * fixed repo_id for clip * fixed download destination * use st's own `snapshot_download` * conditional download * fixed predict method * check if loaded * minor fixes * updated mypy overrides * added pytest-mock * updated tests * updated lock |
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| .. | ||
| app | ||
| .dockerignore | ||
| .gitignore | ||
| Dockerfile | ||
| load_test.sh | ||
| locustfile.py | ||
| poetry.lock | ||
| pyproject.toml | ||
| README_es_ES.md | ||
| README.md | ||
Immich Machine Learning
- Image classification
- CLIP embeddings
- Facial recognition
Setup
This project uses Poetry, so be sure to install it first.
Running poetry install --no-root --with dev will install everything you need in an isolated virtual environment.
To add or remove dependencies, you can use the commands poetry add $PACKAGE_NAME and poetry remove $PACKAGE_NAME, respectively.
Be sure to commit the poetry.lock and pyproject.toml files to reflect any changes in dependencies.
Load Testing
To measure inference throughput and latency, you can use Locust using the provided locustfile.py.
Locust works by querying the model endpoints and aggregating their statistics, meaning the app must be deployed.
You can run load_test.sh to automatically deploy the app locally and start Locust, optionally adjusting its env variables as needed.
Alternatively, for more custom testing, you may also run locust directly: see the documentation. Note that in Locust's jargon, concurrency is measured in users, and each user runs one task at a time. To achieve a particular per-endpoint concurrency, multiply that number by the number of endpoints to be queried. For example, if there are 3 endpoints and you want each of them to receive 8 requests at a time, you should set the number of users to 24.