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https://github.com/blakeblackshear/frigate.git
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Add network requirements docs (#22874)
* Add network requirements docs * shorten title * add note about network requirements in each section
This commit is contained in:
Josh Hawkins
@@ -119,6 +119,12 @@ audio:
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Frigate supports fully local audio transcription using either `sherpa-onnx` or OpenAI's open-source Whisper models via `faster-whisper`. The goal of this feature is to support Semantic Search for `speech` audio events. Frigate is not intended to act as a continuous, fully-automatic speech transcription service — automatically transcribing all speech (or queuing many audio events for transcription) requires substantial CPU (or GPU) resources and is impractical on most systems. For this reason, transcriptions for events are initiated manually from the UI or the API rather than being run continuously in the background.
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:::info
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Audio transcription requires a one-time internet connection to download the Whisper or Sherpa-ONNX model on first use. Once cached, transcription runs fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
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:::
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Transcription accuracy also depends heavily on the quality of your camera's microphone and recording conditions. Many cameras use inexpensive microphones, and distance to the speaker, low audio bitrate, or background noise can significantly reduce transcription quality. If you need higher accuracy, more robust long-running queues, or large-scale automatic transcription, consider using the HTTP API in combination with an automation platform and a cloud transcription service.
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#### Configuration
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@@ -9,6 +9,12 @@ import NavPath from "@site/src/components/NavPath";
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Bird classification identifies known birds using a quantized Tensorflow model. When a known bird is recognized, its common name will be added as a `sub_label`. This information is included in the UI, filters, as well as in notifications.
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:::info
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Bird classification requires a one-time internet connection to download the classification model and label map from GitHub. Once cached, models work fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
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:::
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## Minimum System Requirements
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Bird classification runs a lightweight tflite model on the CPU, there are no significantly different system requirements than running Frigate itself.
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@@ -9,6 +9,12 @@ import NavPath from "@site/src/components/NavPath";
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Object classification allows you to train a custom MobileNetV2 classification model to run on tracked objects (persons, cars, animals, etc.) to identify a finer category or attribute for that object. Classification results are visible in the Tracked Object Details pane in Explore, through the `frigate/tracked_object_details` MQTT topic, in Home Assistant sensors via the official Frigate integration, or through the event endpoints in the HTTP API.
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:::info
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Training a custom object classification model requires a one-time internet connection to download MobileNetV2 base weights. Once trained, the model runs fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
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:::
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## Minimum System Requirements
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Object classification models are lightweight and run very fast on CPU.
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@@ -9,6 +9,12 @@ import NavPath from "@site/src/components/NavPath";
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State classification allows you to train a custom MobileNetV2 classification model on a fixed region of your camera frame(s) to determine a current state. The model can be configured to run on a schedule and/or when motion is detected in that region. Classification results are available through the `frigate/<camera_name>/classification/<model_name>` MQTT topic and in Home Assistant sensors via the official Frigate integration.
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:::info
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Training a custom state classification model requires a one-time internet connection to download MobileNetV2 base weights. Once trained, the model runs fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
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:::
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## Minimum System Requirements
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State classification models are lightweight and run very fast on CPU.
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@@ -9,6 +9,12 @@ import NavPath from "@site/src/components/NavPath";
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Face recognition identifies known individuals by matching detected faces with previously learned facial data. When a known `person` is recognized, their name will be added as a `sub_label`. This information is included in the UI, filters, as well as in notifications.
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:::info
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Face recognition requires a one-time internet connection to download detection and embedding models from GitHub. Once cached, models work fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
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:::
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## Model Requirements
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### Face Detection
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@@ -193,6 +193,12 @@ To use a different OpenAI-compatible API endpoint, set the `OPENAI_BASE_URL` env
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Cloud providers run on remote infrastructure and require an API key for authentication. These services handle all model inference on their servers.
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:::info
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Cloud Generative AI providers require an active internet connection to send images and prompts for processing. Local providers like llama.cpp and Ollama (with local models) do not require internet. See [Network Requirements](/frigate/network_requirements#generative-ai) for details.
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:::
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### Ollama Cloud
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Ollama also supports [cloud models](https://ollama.com/cloud), where your local Ollama instance handles requests from Frigate, but model inference is performed in the cloud. Set up Ollama locally, sign in with your Ollama account, and specify the cloud model name in your Frigate config. For more details, see the Ollama cloud model [docs](https://docs.ollama.com/cloud).
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@@ -11,6 +11,12 @@ Frigate can recognize license plates on vehicles and automatically add the detec
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LPR works best when the license plate is clearly visible to the camera. For moving vehicles, Frigate continuously refines the recognition process, keeping the most confident result. When a vehicle becomes stationary, LPR continues to run for a short time after to attempt recognition.
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:::info
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License plate recognition requires a one-time internet connection to download OCR and detection models from GitHub. Once cached, models work fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
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:::
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When a plate is recognized, the details are:
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- Added as a `sub_label` (if [known](#matching)) or the `recognized_license_plate` field (if unknown) to a tracked object.
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@@ -21,6 +21,12 @@ The jsmpeg live view will use more browser and client GPU resources. Using go2rt
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| mse | native | native | yes (depends on audio codec) | yes | iPhone requires iOS 17.1+, Firefox is h.264 only. This is Frigate's default when go2rtc is configured. |
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| webrtc | native | native | yes (depends on audio codec) | yes | Requires extra configuration. Frigate attempts to use WebRTC when MSE fails or when using a camera's two-way talk feature. |
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:::info
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WebRTC may use an external STUN server for NAT traversal. MSE and HLS streaming do not require any internet access. See [Network Requirements](/frigate/network_requirements#webrtc-stun) for details.
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:::
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### Camera Settings Recommendations
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If you are using go2rtc, you should adjust the following settings in your camera's firmware for the best experience with Live view:
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@@ -11,6 +11,12 @@ import NavPath from "@site/src/components/NavPath";
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Frigate offers native notifications using the [WebPush Protocol](https://web.dev/articles/push-notifications-web-push-protocol) which uses the [VAPID spec](https://tools.ietf.org/html/draft-thomson-webpush-vapid) to deliver notifications to web apps using encryption.
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:::info
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Push notifications require internet access from the Frigate server to the browser vendor's push service (e.g., Google FCM, Mozilla autopush). See [Network Requirements](/frigate/network_requirements#push-notifications) for details.
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:::
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## Setting up Notifications
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In order to use notifications the following requirements must be met:
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@@ -288,6 +288,12 @@ This detector is available for use with both Hailo-8 and Hailo-8L AI Acceleratio
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See the [installation docs](../frigate/installation.md#hailo-8) for information on configuring the Hailo hardware.
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:::info
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If no custom model is provided, the Hailo detector downloads a default model from the Hailo Model Zoo on first startup. Once cached, the model works fully offline. See [Network Requirements](/frigate/network_requirements#hardware-specific-detector-models) for details.
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:::
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### Configuration
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When configuring the Hailo detector, you have two options to specify the model: a local **path** or a **URL**.
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@@ -1793,6 +1799,12 @@ Hardware accelerated object detection is supported on the following SoCs:
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This implementation uses the [Rockchip's RKNN-Toolkit2](https://github.com/airockchip/rknn-toolkit2/), version v2.3.2.
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:::info
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If no custom model is provided, the RKNN detector downloads a default model from GitHub on first startup. Once cached, the model works fully offline. See [Network Requirements](/frigate/network_requirements#hardware-specific-detector-models) for details.
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:::
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:::tip
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When using many cameras one detector may not be enough to keep up. Multiple detectors can be defined assuming NPU resources are available. An example configuration would be:
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@@ -2176,6 +2188,12 @@ This implementation uses the [AXera Pulsar2 Toolchain](https://huggingface.co/AX
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See the [installation docs](../frigate/installation.md#axera) for information on configuring the AXEngine hardware.
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:::info
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The AXEngine detector downloads its default model from HuggingFace on first startup. Once cached, the model works fully offline. See [Network Requirements](/frigate/network_requirements#hardware-specific-detector-models) for details.
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:::
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### Configuration
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When configuring the AXEngine detector, you have to specify the model name.
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@@ -13,6 +13,12 @@ Frigate uses models from [Jina AI](https://huggingface.co/jinaai) to create and
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Semantic Search is accessed via the _Explore_ view in the Frigate UI.
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:::info
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Semantic search requires a one-time internet connection to download embedding models from HuggingFace. Once cached, models work fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
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:::
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## Minimum System Requirements
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Semantic Search works by running a large AI model locally on your system. Small or underpowered systems like a Raspberry Pi will not run Semantic Search reliably or at all.
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@@ -0,0 +1,155 @@
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---
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id: network_requirements
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title: Network Requirements
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---
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# Network Requirements
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Frigate is designed to run locally and does not require a persistent internet connection for core functionality. However, certain features need internet access for initial setup or ongoing operation. This page describes what connects to the internet, when, and how to control it.
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## How Frigate Uses the Internet
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Frigate's internet usage falls into three categories:
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1. **One-time model downloads** — ML models are downloaded the first time a feature is enabled, then cached locally. No internet is needed on subsequent startups.
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2. **Optional cloud services** — Features like Frigate+ and Generative AI connect to external APIs only when explicitly configured.
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3. **Build-time dependencies** — Components bundled into the Docker image during the build process. These require no internet at runtime.
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:::tip
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After initial setup, Frigate can run fully offline as long as all required models have been downloaded and no cloud-dependent features are enabled.
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:::
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## One-Time Model Downloads
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The following models are downloaded automatically the first time their associated feature is enabled. Once cached in `/config/model_cache/`, they do not require internet again.
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| Feature | Models Downloaded | Source |
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| --------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------- | -------------------- |
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| [Semantic search](/configuration/semantic_search) | Jina CLIP v1 or v2 (ONNX) + tokenizer | HuggingFace |
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| [Face recognition](/configuration/face_recognition) | FaceNet, ArcFace, face detection model | GitHub |
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| [License plate recognition](/configuration/license_plate_recognition) | PaddleOCR (detection, classification, recognition) + YOLOv9 plate detector | GitHub |
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| [Bird classification](/configuration/bird_classification) | MobileNetV2 bird model + label map | GitHub |
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| [Custom classification](/configuration/custom_classification/state_classification) (training) | MobileNetV2 ImageNet base weights (via Keras) | Google storage |
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| [Audio transcription](/configuration/advanced) | Whisper or Sherpa-ONNX streaming model | HuggingFace / OpenAI |
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### Hardware-Specific Detector Models
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If you are using one of the following hardware detectors and have not provided your own model file, a default model will be downloaded on first startup:
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| Detector | Model Downloaded | Source |
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| ------------------------------------------------------------------ | -------------------- | ------------------------ |
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| [Rockchip RKNN](/configuration/object_detectors#rockchip-platform) | RKNN detection model | GitHub |
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| [Hailo 8 / 8L](/configuration/object_detectors#hailo-8) | YOLOv6n (.hef) | Hailo Model Zoo (AWS S3) |
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| [AXERA AXEngine](/configuration/object_detectors) | Detection model | HuggingFace |
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:::note
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The default CPU, EdgeTPU, and OpenVINO object detection models are bundled into the Docker image and do not require any download at runtime.
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:::
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### Preventing Model Downloads
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If you have already downloaded all required models and want to prevent Frigate from attempting any outbound connections to HuggingFace or the Transformers library, set the following environment variables on your Frigate container:
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```yaml
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environment:
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HF_HUB_OFFLINE: "1"
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TRANSFORMERS_OFFLINE: "1"
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```
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:::warning
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Setting these variables without having the correct model files already cached in `/config/model_cache/` will cause failures. Only use these after a successful initial setup with internet access.
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:::
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### Mirror Support
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If your Frigate instance has restricted internet access, you can point model downloads at internal mirrors using environment variables:
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| Environment Variable | Default | Used By |
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| ----------------------------------- | ----------------------------------- | --------------------------------------------- |
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| `HF_ENDPOINT` | `https://huggingface.co` | Semantic search, Sherpa-ONNX, AXEngine models |
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| `GITHUB_ENDPOINT` | `https://github.com` | Face recognition, LPR, RKNN models |
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| `GITHUB_RAW_ENDPOINT` | `https://raw.githubusercontent.com` | Bird classification |
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| `TF_KERAS_MOBILENET_V2_WEIGHTS_URL` | Google storage (Keras default) | Custom classification training |
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## Optional Cloud Services
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These features connect to external services during normal operation and require internet whenever they are active.
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### Frigate+
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When a Frigate+ API key is configured, Frigate communicates with `https://api.frigate.video` to download models, upload snapshots for training, submit annotations, and report false positives. Remove the API key to disable all Frigate+ network activity.
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See [Frigate+](/integrations/plus) for details.
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### Generative AI
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When a Generative AI provider is configured, Frigate sends images and prompts to the configured provider for event descriptions, chat, and camera monitoring. Available providers:
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| Provider | Internet Required |
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| ------------- | ---------------------------------------------------------------- |
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| OpenAI | Yes — connects to OpenAI API (or custom base URL) |
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| Google Gemini | Yes — connects to Google Generative AI API |
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| Azure OpenAI | Yes — connects to your Azure endpoint |
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| Ollama | Depends — typically local (`localhost:11434`), but can be remote |
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| llama.cpp | No — runs entirely locally |
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Disable Generative AI by removing the `genai` configuration from your cameras. See [Generative AI](/configuration/genai/genai_config) for details.
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### Version Check
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Frigate checks GitHub for the latest release version on startup by querying `https://api.github.com`. This can be disabled:
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```yaml
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telemetry:
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version_check: false
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```
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### Push Notifications
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When [notifications](/configuration/notifications) are enabled and users have registered for push notifications in the web UI, Frigate sends push messages through the browser vendor's push service (e.g., Google FCM, Mozilla autopush). This requires internet access from the Frigate server to these push endpoints.
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### MQTT
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If an [MQTT broker](/integrations/mqtt) is configured, Frigate maintains a connection to the broker's host and port. This is typically a local network connection, but will require internet if you use a cloud-hosted MQTT broker.
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### DeepStack / CodeProject.AI
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When using the [DeepStack detector plugin](/configuration/object_detectors), Frigate sends images to the configured API endpoint for inference. This is typically local but depends on where the service is hosted.
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## WebRTC (STUN)
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For [WebRTC live streaming](/configuration/live), Frigate uses STUN for NAT traversal:
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- **go2rtc** defaults to a local STUN listener (`stun:8555`) — no internet required.
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- **The web UI's WebRTC player** includes a fallback to Google's public STUN server (`stun:stun.l.google.com:19302`), which requires internet.
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## Home Assistant Supervisor
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When running as a Home Assistant add-on, the go2rtc startup script queries the local Supervisor API (`http://supervisor/`) to discover the host IP address and WebRTC port. This is a local network call to the Home Assistant host, not an internet connection.
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## What Does NOT Require Internet
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- **Object detection** — CPU, EdgeTPU, OpenVINO, and other bundled detector models are included in the Docker image.
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- **Recording and playback** — All video is stored and served locally.
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- **Live streaming** — Camera streams are pulled over your local network. MSE and HLS streaming work without any external connections.
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- **The web interface** — Fully self-contained with no external fonts, scripts, analytics, or CDN dependencies. All translations are bundled locally.
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- **Custom classification inference** — After training, custom models run entirely locally.
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- **Audio detection** — The YAMNet audio classification model is bundled in the Docker image.
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## Running Frigate Offline
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To run Frigate in an air-gapped or offline environment:
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1. **Pre-download models** — Start Frigate with internet access once with all desired features enabled. Models will be cached in `/config/model_cache/`.
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2. **Disable version check** — Set `telemetry.version_check: false` in your configuration.
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3. **Block outbound model requests** — Set the `HF_HUB_OFFLINE=1` and `TRANSFORMERS_OFFLINE=1` environment variables to prevent HuggingFace and Transformers from attempting any network requests.
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4. **Avoid cloud features** — Do not configure Frigate+, Generative AI providers that require internet, or cloud MQTT brokers.
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5. **Use local model mirrors** — If limited internet is available, set the `HF_ENDPOINT`, `GITHUB_ENDPOINT`, and `GITHUB_RAW_ENDPOINT` environment variables to point to local mirrors.
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After these steps, Frigate will operate with no outbound internet connections.
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@@ -5,6 +5,12 @@ title: MQTT
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These are the MQTT messages generated by Frigate. The default topic_prefix is `frigate`, but can be changed in the config file.
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:::info
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MQTT requires a network connection to your broker. This is typically local, but will require internet if using a cloud-hosted MQTT broker. See [Network Requirements](/frigate/network_requirements#mqtt) for details.
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:::
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## General Frigate Topics
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### `frigate/available`
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@@ -5,6 +5,12 @@ title: Frigate+
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For more information about how to use Frigate+ to improve your model, see the [Frigate+ docs](/plus/).
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:::info
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Frigate+ requires an active internet connection to communicate with `https://api.frigate.video` for model downloads, image uploads, and annotations. See [Network Requirements](/frigate/network_requirements#frigate) for details.
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:::
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## Setup
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### Create an account
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@@ -12,6 +12,7 @@ const sidebars: SidebarsConfig = {
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"frigate/updating",
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"frigate/camera_setup",
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"frigate/video_pipeline",
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"frigate/network_requirements",
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"frigate/glossary",
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],
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Guides: [
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Reference in New Issue
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