michaelkamprath · August 22, 2025 18:03
diff --git a/frigate-ollama-docker-compose.yaml b/frigate-ollama-docker-compose.yaml
 # This docker compose file will deploy an integrated Frigate / Ollama / Open WebUI deployment
 # using an NVIDIA GPU for Frigate model inferencing the Ollama LLMs.
 #
 # Installation
 #
 #   * Ensure your docker deployment is enabled to provide GPU services:
 #     - https://docs.docker.com/compose/how-tos/gpu-support/
 #
 #   * Create a directory containing the following sub-directories:
 #     - frigate/config
 #     - ollama
 #     - open-webui
 #
 #   * Place this compose.yaml file in that directory with the above sub-directories.
 #
 #   * Identify where you want Frigate to store its history recordings. This location
 #     should be several hundred GBs if not several TBs in size. Update the
 #     "/mnt/storage/frigate" path below to the location you select.
 #
 #   * Add an environment variable file at frigate/frigate.env that contains
 #     the environment variables you need in your Frigate config, such as
 #     camera passwords.
 #
 #   * Place your Frigate configuration file in "frigate/config/config.yml". To take advantage
 #     of Ollama for generative AI in Frigate, your Frigate config should have this section:
 #
 #         genai:
 #           enabled: true
 #           provider: ollama
 #           base_url: http://ollama:11434
 #           model: <<select a pulled model>>
 #
 #     Follow Frigate's instructions on how to then use the generative AI feature:
 #     - https://docs.frigate.video/configuration/genai/
 #
 #   * You may reduce the model list in the YOLO_MODELS variable below to be the
 #     one you selected in your Frigate config. This will save time in the initial launch
 #     of Frigate in this deployment.
 #
 #   * Launch service with:
 #
 #       docker compose up -d
 #
 #     The first time you do this Frigate needs to build the YOLO models, which will take a
 #     while. You will not be able to view the Frigate website until it is complete. Check
 #     logs with
 #
 #       docker compose logs --tail 100 -f
 #
 #     Note that "docker ps" may report the Frigate container as unhealthy while this model
 #     build process is occurring.
 #
 #   * Pull models for Ollama using its running Docker instance with:
 #
 #       docker exec -it ollama ollama pull <<model name>>
 #
 #   * Monitor Ollama's model load (thus identifying if GPU is being used) with:
 #
 #       watch docker exec -it ollama ollama ps
 #
 #   * You may view the services at the following URLs:
 #     - Frigate: http://<<your-server>>:5000/
 #     - Open WebUI (Ollama chat UI): http://<<your-server>>:3000/
 #

 services:
  frigate:
    container_name: frigate
    privileged: true
    restart: unless-stopped
    pull_policy: always
    image: ghcr.io/blakeblackshear/frigate:stable-tensorrt
    shm_size: "2000mb" # update for your cameras. See frigate documentation.
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]
 # Other inferencing devices you might consider
 #    devices:
 #      - /dev/bus/usb:/dev/bus/usb # passes the USB Coral, needs to be modified for other versions
 #      - /dev/apex_0:/dev/apex_0 # passes a PCIe Coral, follow driver instructions here https://coral.ai/docs/m2/get-started/#2a-on-linux
 #      - /dev/dri/renderD128 # for intel hwaccel, needs to be updated for your hardware
    volumes:
      - /etc/localtime:/etc/localtime:ro
      - ./frigate/config:/config
      # change the file path to where you want history to be stored
      - /mnt/storage/frigate:/media/frigate
      - type: tmpfs # Optional: 2GB of memory, reduces SSD wear
        target: /tmp/cache
        tmpfs:
          size: 2000000000
    ports:
      - "5000:5000"
      - "8554:8554" # RTSP feeds
      - "8555:8555/tcp" # WebRTC over tcp
      - "8555:8555/udp" # WebRTC over udp
      - "8971:8971"
    env_file:
      - ./frigate/frigate.env
    environment:
      - YOLO_MODELS=yolov7-320,yolov7-640,yolov7-tiny-416,yolov7x-320,yolov7x-640
  ollama:
    container_name: ollama
    restart: unless-stopped
    pull_policy: always
    image: ollama/ollama
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]
    volumes:
      - ./ollama:/root/.ollama
    ports:
      - "11434:11434"
  open-webui:
    container_name: open-webui
    pull_policy: always
    restart: unless-stopped
    image: ghcr.io/open-webui/open-webui:main
    volumes:
      - ./open-webui:/app/backend/data
    environment:
      - OLLAMA_BASE_URL=http://ollama:11434
    ports:
      - "3000:8080"
	# This docker compose file will deploy an integrated Frigate / Ollama / Open WebUI deployment
	# using an NVIDIA GPU for Frigate model inferencing the Ollama LLMs.
	#
	# Installation
	#
	# * Ensure your docker deployment is enabled to provide GPU services:
	# - https://docs.docker.com/compose/how-tos/gpu-support/
	#
	# * Create a directory containing the following sub-directories:
	# - frigate/config
	# - ollama
	# - open-webui
	#
	# * Place this compose.yaml file in that directory with the above sub-directories.
	#
	# * Identify where you want Frigate to store its history recordings. This location
	# should be several hundred GBs if not several TBs in size. Update the
	# "/mnt/storage/frigate" path below to the location you select.
	#
	# * Add an environment variable file at frigate/frigate.env that contains
	# the environment variables you need in your Frigate config, such as
	# camera passwords.
	#
	# * Place your Frigate configuration file in "frigate/config/config.yml". To take advantage
	# of Ollama for generative AI in Frigate, your Frigate config should have this section:
	#
	# genai:
	# enabled: true
	# provider: ollama
	# base_url: http://ollama:11434
	# model: <<select a pulled model>>
	#
	# Follow Frigate's instructions on how to then use the generative AI feature:
	# - https://docs.frigate.video/configuration/genai/
	#
	# * You may reduce the model list in the YOLO_MODELS variable below to be the
	# one you selected in your Frigate config. This will save time in the initial launch
	# of Frigate in this deployment.
	#
	# * Launch service with:
	#
	# docker compose up -d
	#
	# The first time you do this Frigate needs to build the YOLO models, which will take a
	# while. You will not be able to view the Frigate website until it is complete. Check
	# logs with
	#
	# docker compose logs --tail 100 -f
	#
	# Note that "docker ps" may report the Frigate container as unhealthy while this model
	# build process is occurring.
	#
	# * Pull models for Ollama using its running Docker instance with:
	#
	# docker exec -it ollama ollama pull <<model name>>
	#
	# * Monitor Ollama's model load (thus identifying if GPU is being used) with:
	#
	# watch docker exec -it ollama ollama ps
	#
	# * You may view the services at the following URLs:
	# - Frigate: http://<<your-server>>:5000/
	# - Open WebUI (Ollama chat UI): http://<<your-server>>:3000/
	#

	services:
	frigate:
	container_name: frigate
	privileged: true
	restart: unless-stopped
	pull_policy: always
	image: ghcr.io/blakeblackshear/frigate:stable-tensorrt
	shm_size: "2000mb" # update for your cameras. See frigate documentation.
	deploy:
	resources:
	reservations:
	devices:
	- driver: nvidia
	count: 1
	capabilities: [gpu]
	# Other inferencing devices you might consider
	# devices:
	# - /dev/bus/usb:/dev/bus/usb # passes the USB Coral, needs to be modified for other versions
	# - /dev/apex_0:/dev/apex_0 # passes a PCIe Coral, follow driver instructions here https://coral.ai/docs/m2/get-started/#2a-on-linux
	# - /dev/dri/renderD128 # for intel hwaccel, needs to be updated for your hardware
	volumes:
	- /etc/localtime:/etc/localtime:ro
	- ./frigate/config:/config
	# change the file path to where you want history to be stored
	- /mnt/storage/frigate:/media/frigate
	- type: tmpfs # Optional: 2GB of memory, reduces SSD wear
	target: /tmp/cache
	tmpfs:
	size: 2000000000
	ports:
	- "5000:5000"
	- "8554:8554" # RTSP feeds
	- "8555:8555/tcp" # WebRTC over tcp
	- "8555:8555/udp" # WebRTC over udp
	- "8971:8971"
	env_file:
	- ./frigate/frigate.env
	environment:
	- YOLO_MODELS=yolov7-320,yolov7-640,yolov7-tiny-416,yolov7x-320,yolov7x-640
	ollama:
	container_name: ollama
	restart: unless-stopped
	pull_policy: always
	image: ollama/ollama
	deploy:
	resources:
	reservations:
	devices:
	- driver: nvidia
	count: all
	capabilities: [gpu]
	volumes:
	- ./ollama:/root/.ollama
	ports:
	- "11434:11434"
	open-webui:
	container_name: open-webui
	pull_policy: always
	restart: unless-stopped
	image: ghcr.io/open-webui/open-webui:main
	volumes:
	- ./open-webui:/app/backend/data
	environment:
	- OLLAMA_BASE_URL=http://ollama:11434
	ports:
	- "3000:8080"
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