> ## Documentation Index
> Fetch the complete documentation index at: https://docs.edgeimpulse.com/llms.txt
> Use this file to discover all available pages before exploring further.

# macOS devices

You can use your Intel or M1-based Mac computer as a fully-supported development environment for Edge Impulse for Linux. This lets you sample raw data, build models, and deploy trained machine learning models directly from the Studio. If you have a Macbook, the webcam and microphone of your system are automatically detected and can be used to build models.

<Frame caption="Macbook Pro">
  <img src="https://mintcdn.com/edgeimpulse/d_Ddn0QCYskBYT93/.assets/images/45c6d20-6418601_sd.jpg?fit=max&auto=format&n=d_Ddn0QCYskBYT93&q=85&s=c7bdea034548b56e9d7e0adbe89eb068" width="1600" height="928" data-path=".assets/images/45c6d20-6418601_sd.jpg" />
</Frame>

### Installing dependencies

To connect your Mac to Edge Impulse:

1. Install [Node.js](https://nodejs.org/en/).
2. Install [Homebrew](https://brew.sh).
3. Open a terminal window and install the dependencies:

```
$ brew install sox
$ brew install imagesnap
```

1. Last, install the Edge Impulse CLI:

```
$ npm install edge-impulse-linux -g
```

<Warning>
  **Problems installing the CLI?**

  See the [Installation and troubleshooting](/tools/clis/edge-impulse-cli/installation) guide.
</Warning>

### Connecting to Edge Impulse

With the software installed, open a terminal window and run::

```
$ edge-impulse-linux
```

This will start a wizard which will ask you to log in, and choose an Edge Impulse project. If you want to switch projects run the command with `--clean`.

#### Verifying that your device is connected

That's all! Your Mac is now connected to Edge Impulse. To verify this, go to [your Edge Impulse project](https://studio.edgeimpulse.com/studio/profile/projects), and click **Devices**. The device will be listed here.

<Frame caption="Device connected to Edge Impulse.">
  <img src="https://mintcdn.com/edgeimpulse/yaErNg6BwcJv1FZd/.assets/images/22c1094-screenshot_2021-02-23_at_161245.png?fit=max&auto=format&n=yaErNg6BwcJv1FZd&q=85&s=507890ccf72c2ca09642112040126daf" width="1229" height="290" data-path=".assets/images/22c1094-screenshot_2021-02-23_at_161245.png" />
</Frame>

### Next steps: building a machine learning model

With everything set up you can now build your first machine learning model with these tutorials:

* [Keyword spotting](/tutorials/end-to-end/keyword-spotting)
* [Sound recognition](/tutorials/end-to-end/sound-recognition)
* [Image classification](/tutorials/end-to-end/image-classification)
* [object detection](/tutorials/end-to-end/object-detection-bounding-boxes).
* [Object detection with centroids (FOMO)](/tutorials/end-to-end/object-detection-centroids)

Looking to connect different sensors? Our [Linux SDK](/tools/libraries/sdks/inference/linux) lets you easily send data from any sensor and any programming language (with examples in Node.js, Python, Go and C++) into Edge Impulse.

### Deploying back to device

To run your impulse locally, just open a terminal and run:

```
$ edge-impulse-linux-runner
```

This will automatically compile your model with full hardware acceleration, download the model to your Raspberry Pi, and then start classifying. Our [Linux SDK](/tools/libraries/sdks/inference/linux) has examples on how to integrate the model with your favourite programming language.

#### Image model?

If you have an image model then you can get a peek of what your device sees by being on the same network as your device, and finding the 'Want to see a feed of the camera and live classification in your browser' message in the console. Open the URL in a browser and both the camera feed and the classification are shown:

<Frame caption="Live feed with classification results">
  <img src="https://mintcdn.com/edgeimpulse/pssAI2UB-dmQQKtc/.assets/images/demo1.png?fit=max&auto=format&n=pssAI2UB-dmQQKtc&q=85&s=f5d180c10366b2d54c7aec5f6c2187cc" width="400" height="440" data-path=".assets/images/demo1.png" />
</Frame>

#### Troubleshooting

If you get a warning from macOS about the app being from an unidentified developer for your .eim file you can adjust the quarantine attribute on the file like so:

```
% xattr -d com.apple.quarantine <path-to-your-model.eim>
```