immich/machine-learning/README.md

# Immich Machine Learning

- CLIP embeddings
- Facial recognition

# Setup

This project uses [Poetry](https://python-poetry.org/docs/#installation), 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](https://locust.io/) using the provided `locustfile.py`.
Locust works by querying the model endpoints and aggregating their statistics, meaning the app must be deployed.
You can change the models or adjust options like score thresholds through the Locust UI.

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. 

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.
feat(machine-learning)!: move machine learning to Python based image (#1774) BREAKING CHANGES * Users have to update the docker-compose file, machine-learning portion. * Temporary dropping machine-learning support for Arm64 and Armv7 2023-02-18 15:13:37 +00:00			`# Immich Machine Learning`
Added nestjs microservice 2022-03-25 20:26:55 +00:00
chore(ml): updated dockerfile, added typing, packaging (#2642) * updated dockerfile, added typing, packaging apply env change * added arm64 support * added ml version pump, second try for arm64 * added linting config to pyproject.toml * renamed ml input field * fixed linter config * fixed dev docker compose 2023-06-05 14:40:48 +00:00			`- CLIP embeddings`
			`- Facial recognition`

			`# Setup`

			`This project uses [Poetry](https://python-poetry.org/docs/#installation), 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.
added locustfile (#2926) 2023-06-25 18:20:45 +00:00

			`# Load Testing`

			To measure inference throughput and latency, you can use [Locust](https://locust.io/) using the provided `locustfile.py`.
			`Locust works by querying the model endpoints and aggregating their statistics, meaning the app must be deployed.`
chore(docs): updated ML documentation (#4063) 2023-09-12 06:22:42 +00:00			`You can change the models or adjust options like score thresholds through the Locust UI.`
added locustfile (#2926) 2023-06-25 18:20:45 +00:00
chore(docs): updated ML documentation (#4063) 2023-09-12 06:22:42 +00:00			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.

			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.