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chore(ml): set higher worker timeout for openvino (#11174)
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@ -155,18 +155,18 @@ Redis (Sentinel) URL example JSON before encoding:
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## Machine Learning
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| Variable | Description | Default | Containers |
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| :----------------------------------------------- | :------------------------------------------------------------------- | :-----------------: | :--------------- |
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| `MACHINE_LEARNING_MODEL_TTL` | Inactivity time (s) before a model is unloaded (disabled if \<= 0) | `300` | machine learning |
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| `MACHINE_LEARNING_MODEL_TTL_POLL_S` | Interval (s) between checks for the model TTL (disabled if \<= 0) | `10` | machine learning |
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| `MACHINE_LEARNING_CACHE_FOLDER` | Directory where models are downloaded | `/cache` | machine learning |
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| `MACHINE_LEARNING_REQUEST_THREADS`<sup>\*1</sup> | Thread count of the request thread pool (disabled if \<= 0) | number of CPU cores | machine learning |
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| `MACHINE_LEARNING_MODEL_INTER_OP_THREADS` | Number of parallel model operations | `1` | machine learning |
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| `MACHINE_LEARNING_MODEL_INTRA_OP_THREADS` | Number of threads for each model operation | `2` | machine learning |
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| `MACHINE_LEARNING_WORKERS`<sup>\*2</sup> | Number of worker processes to spawn | `1` | machine learning |
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| `MACHINE_LEARNING_WORKER_TIMEOUT` | Maximum time (s) of unresponsiveness before a worker is killed | `120` | machine learning |
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| `MACHINE_LEARNING_PRELOAD__CLIP` | Name of a CLIP model to be preloaded and kept in cache | | machine learning |
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| `MACHINE_LEARNING_PRELOAD__FACIAL_RECOGNITION` | Name of a facial recognition model to be preloaded and kept in cache | | machine learning |
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| Variable | Description | Default | Containers |
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| :----------------------------------------------- | :------------------------------------------------------------------- | :-----------------------------------: | :--------------- |
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| `MACHINE_LEARNING_MODEL_TTL` | Inactivity time (s) before a model is unloaded (disabled if \<= 0) | `300` | machine learning |
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| `MACHINE_LEARNING_MODEL_TTL_POLL_S` | Interval (s) between checks for the model TTL (disabled if \<= 0) | `10` | machine learning |
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| `MACHINE_LEARNING_CACHE_FOLDER` | Directory where models are downloaded | `/cache` | machine learning |
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| `MACHINE_LEARNING_REQUEST_THREADS`<sup>\*1</sup> | Thread count of the request thread pool (disabled if \<= 0) | number of CPU cores | machine learning |
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| `MACHINE_LEARNING_MODEL_INTER_OP_THREADS` | Number of parallel model operations | `1` | machine learning |
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| `MACHINE_LEARNING_MODEL_INTRA_OP_THREADS` | Number of threads for each model operation | `2` | machine learning |
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| `MACHINE_LEARNING_WORKERS`<sup>\*2</sup> | Number of worker processes to spawn | `1` | machine learning |
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| `MACHINE_LEARNING_WORKER_TIMEOUT` | Maximum time (s) of unresponsiveness before a worker is killed | `120` (`300` if using OpenVINO image) | machine learning |
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| `MACHINE_LEARNING_PRELOAD__CLIP` | Name of a CLIP model to be preloaded and kept in cache | | machine learning |
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| `MACHINE_LEARNING_PRELOAD__FACIAL_RECOGNITION` | Name of a facial recognition model to be preloaded and kept in cache | | machine learning |
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\*1: It is recommended to begin with this parameter when changing the concurrency levels of the machine learning service and then tune the other ones.
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@ -77,6 +77,7 @@ RUN apt-get update && \
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rm -rf /var/lib/apt/lists/*
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WORKDIR /usr/src/app
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ARG DEVICE
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ENV TRANSFORMERS_CACHE=/cache \
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PYTHONDONTWRITEBYTECODE=1 \
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PYTHONUNBUFFERED=1 \
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@ -5,12 +5,13 @@ lib_path="/usr/lib/$(arch)-linux-gnu/libmimalloc.so.2"
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if ! [ "$DEVICE" = "openvino" ]; then
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export LD_PRELOAD="$lib_path"
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export LD_BIND_NOW=1
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: "${MACHINE_LEARNING_WORKER_TIMEOUT:=120}"
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else
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: "${MACHINE_LEARNING_WORKER_TIMEOUT:=300}"
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fi
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: "${IMMICH_HOST:=[::]}"
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: "${IMMICH_PORT:=3003}"
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: "${MACHINE_LEARNING_WORKERS:=1}"
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: "${MACHINE_LEARNING_WORKER_TIMEOUT:=120}"
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gunicorn app.main:app \
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-k app.config.CustomUvicornWorker \
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