forked from Cutlery/immich
24 lines
1.5 KiB
Markdown
24 lines
1.5 KiB
Markdown
# Immich Machine Learning
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- Image classification
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- CLIP embeddings
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- Facial recognition
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# Setup
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This project uses [Poetry](https://python-poetry.org/docs/#installation), so be sure to install it first.
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Running `poetry install --no-root --with dev` will install everything you need in an isolated virtual environment.
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To add or remove dependencies, you can use the commands `poetry add $PACKAGE_NAME` and `poetry remove $PACKAGE_NAME`, respectively.
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Be sure to commit the `poetry.lock` and `pyproject.toml` files to reflect any changes in dependencies.
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# Load Testing
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To measure inference throughput and latency, you can use [Locust](https://locust.io/) using the provided `locustfile.py`.
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Locust works by querying the model endpoints and aggregating their statistics, meaning the app must be deployed.
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You can change the models or adjust options like score thresholds through the Locust UI.
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To get started, you can simply run `locust --web-host 127.0.0.1` and open `localhost:8089` in a browser to access the UI. See the [Locust documentation](https://docs.locust.io/en/stable/index.html) for more info on running Locust.
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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. |