# Get started with the Raspberry Pi 4

## In this learning path

- [Introduction](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/rpi/)
- [Introduction to the Raspberry Pi 4](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/rpi/intro/)
- [Setup a Raspberry Pi 4 and an Arm cloud instance](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/rpi/setup/)
- [Identifying the hardware](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/rpi/id/)
- [Linux Kernel Compile](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/rpi/kernel/)
- [TensorFlow](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/rpi/tf/)
- [Docker](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/rpi/docker/)
- [Linux Perf](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/rpi/perf/)
- [Next Steps](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/rpi/_next-steps/)

## About this Learning Path

| Skill level:      | Introductory        |
|--------------------|---------------------|
| Reading time:      | 1 hr 30 min         |
| Last updated:      | 14 Aug 2026         |

### Author:
Jason Andrews, Arm  
[GitHub](https://github.com/jasonrandrews)  
[LinkedIn](https://linkedin.com/in/jason-andrews-7b05a8)

### Arm IP:
- [Cortex-A](https://support.arm.com/?tab=compute-ip&Product%20Type=Application%20Processors)
- [Neoverse](https://support.arm.com/?tab=compute-ip&Product%20Type=Infrastructure%20Processors)

### Tags:
- [Embedded Linux](/tag/embedded-linux)
- [Linux](/tag/linux)
- [Raspberry Pi](/tag/raspberry-pi)
- [TensorFlow](/tag/tensorflow)
- [Docker](/tag/docker)

### Who is this for?
This is an introductory topic for software developers interested in the Raspberry Pi 4.

### What will you learn?
Upon completion of this Learning Path, you will be able to:
- Build and run multiple software examples on the Raspberry Pi 4
- Compare and contrast the Raspberry Pi 4 to an Arm cloud server

### Prerequisites
Before starting, you will need the following:
- A Raspberry Pi 4 board
- An [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider.

### Summary
You’ll compare a Raspberry Pi 4 and an Arm-based cloud instance by running the same workloads on both systems. First, you’ll install 64-bit Raspberry Pi OS and verify each system with `uname`. Then, you’ll build a Linux kernel, install TensorFlow, and run a quickstart example. You’ll use matching commands to compare platform behavior and build times.

## Frequently asked questions

<details>
<summary>How do I know both systems are configured for 64-bit Arm before comparing results?</summary>
Run `uname -a` on each machine and confirm the architecture shows `aarch64`. The exact kernel version string might differ, but the architecture should match on both.
</details>

<details>
<summary>Which Raspberry Pi OS image should I write to the SD card?</summary>
Use the 64-bit version of Raspberry Pi OS.
</details>

<details>
<summary>Where should I run the TensorFlow example, and which example should I use?</summary>
Run it on both the Raspberry Pi 4 and the Arm-based cloud server to compare behavior. Use the TensorFlow quickstart example or the quickstart code provided in the Learning Path.
</details>

<details>
<summary>What outcome should I expect from the Linux kernel compile comparison?</summary>
Every recent cloud server is faster than a Raspberry Pi 4. Record the elapsed build time on both systems to understand the relative difference.
</details>

<details>
<summary>How do I choose a cloud instance for this comparison?</summary>
Use an Arm-based instance from a cloud service provider. For instructions to select and provision an instance, follow the [Get started with Arm-based cloud instances](/learning-paths/servers-and-cloud-computing/csp/) Learning Path.
</details>