# Guide to Compiling Your Custom Frigate Model to .dfp

**URL:** <https://community.memryx.com/t/guide-to-compiling-your-custom-frigate-model-to-dfp/191>\
**Category:** Frigate\
**Created:** [April 8, 2026, 4:55pm UTC](https://community.memryx.com/t/guide-to-compiling-your-custom-frigate-model-to-dfp/191 "2026-04-08T16:55:34Z")\
**Posts on this page:** 1\
**Page:** 1

<div class="post-metadata">

**Author:** ![abinila\_siva](https://avatars.discourse-cdn.com/v4/letter/a/c67d28/32.png) [@abinila\_siva](https://community.memryx.com/u/abinila_siva)\
**Post date:** [April 8, 2026, 4:55pm UTC](https://community.memryx.com/t/guide-to-compiling-your-custom-frigate-model-to-dfp/191/1 "2026-04-08T16:55:34Z")

</div>

To use your own custom model, first compile it into a [.dfp](https://developer.memryx.com/2p1/specs/files.html#dataflow-program) file, which is the format used by MemryX.

### Compile the Model

If you can export your ONNX model to the **host** , I’d recommend compiling it there instead.

1. Install the MemryX Neural Compiler tools from the [Install Tools](https://developer.memryx.com/2p1/get_started/install_tools.html) page on the host in a Python venv (Python 3.9–3.12).

2. Activate the MemryX environment, then run the `mx_nc` command to compile your model. For example:

```sh
mx_nc -m yolonas.onnx -c 4 --autocrop -v --dfp_fname yolonas.dfp

```

You can also refer to the [MemryX Compiler](https://developer.memryx.com/2p1/tools/neural_compiler.html#usage) documentation for more details on compiling your model.

> **Note:** We recommend compiling the model on the host machine, or on a separate machine, rather than inside the Frigate Docker container. Installing the compiler inside Docker may conflict with container packages. We also recommend using a Python virtual environment for the compiler installation.

* * *

### Package the Compiled Model

1. Package your compiled model into a `.zip` file.
2. The `.zip` file must contain the compiled `.dfp` file.
3. Depending on the model, the compiler may also generate a cropped post-processing network. If present, it will be named with the suffix `_post.onnx`.
4. Bind-mount the `.zip` file into the container and specify its path using `model.path` in your config.
5. Update `labelmap_path` to match your custom model’s labels.

**Example**

```yaml
path: /config/yolonas.zip

```

The `.zip` file must contain:

```auto
yolonas.zip
├── yolonas.dfp
└── yolonas_post.onnx (optional; only if the model includes a cropped post-processing network)

```