forked from Cutlery/immich
22 lines
1.4 KiB
Markdown
22 lines
1.4 KiB
Markdown
# Immich Machine Learning
|
|
|
|
- Image classification
|
|
- 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 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](https://docs.locust.io/en/stable/index.html). 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. |