Raspberry Pi AI Kit - Unboxing and Installation Guide

Hailo
6 Jun 202416:20

TLDRGilad introduces the new Raspberry Pi AI Kit, featuring the Halo 8l AI accelerator with 13 TOPS performance at 2W power consumption. The kit, available at Raspberry Pi resellers, includes an active cooler, camera, and power supply. The video demonstrates the installation process, showcases three AI pipelines for detection, pose estimation, and segmentation, and encourages joining the Halo Community platform for support and examples. The kit aims to optimize performance, manage costs, and ensure local data processing for privacy.

Takeaways

  • 😀 The video introduces the new Raspberry Pi AI Kit featuring the Halo 8l AI accelerator, capable of delivering 13 TOPS with a power consumption of around 2 Watts.
  • 🎉 The kit is available for purchase at official Raspberry Pi resellers and comes with the launch of the Halo Community platform and developer Zone.
  • 🔒 The installation process and examples are designed to be straightforward, ensuring local data processing for privacy, performance optimization, and cost management.
  • 🌐 All Halo examples are open source, encouraging users to integrate them into their projects and products.
  • 🛠️ The kit includes the Raspberry Pi 5, the Halo AI accelerator, a micro HDMI to HDMI adapter, an active cooler, a Raspberry Pi camera, a display cable, and a 27W USB-C power supply.
  • 🔧 The unboxing and setup guide includes connecting the active cooler, preparing the camera, and ensuring the Raspberry Pi OS is up to date with the latest drivers and support for the Halo device.
  • 📝 The video provides a step-by-step installation guide, including updating the Raspberry Pi OS, setting the PCI speed to Gen 3 for optimal performance, and installing the Halo software packages.
  • 🔎 The installation verification includes running commands to identify the chip and check the installation of the Halo elements and tools.
  • 📚 The video script explains how to set up the environment for the basic pipelines, including cloning the repo, configuring environment variables, and installing dependencies.
  • 👾 The demo applications showcase different tasks such as detection, pose estimation, and instance segmentation, using Python and integrated with the Raspberry Pi's official camera framework.
  • 🔄 The video also covers how to run the applications with different inputs, such as video files, USB cameras, and the Raspberry Pi camera, and how to customize the applications with various flags and options.
  • 🔍 For troubleshooting and community engagement, the video points to the Halo Community forum and encourages joining the community for updates and new project ideas.

Q & A

  • What is the main focus of the video?

    -The video focuses on unboxing and installing the new Raspberry Pi AI Kit featuring the Halo 8l AI accelerator.

  • Who is presenting the video?

    -The video is presented by Gilad, who leads the makers and developers community at Halo.

  • What are the key features of the Halo 8l AI accelerator?

    -The Halo 8l AI accelerator delivers 13 TOPS with a typical power consumption of around 2 Watts.

  • Where can you purchase the Raspberry Pi AI Kit?

    -The Raspberry Pi AI Kit is available at official Raspberry Pi resellers.

  • What platforms are being launched alongside the new AI Kit?

    -The Halo Community platform and the developer Zone are being launched alongside the new AI Kit.

  • What tasks do the three basic pipelines released with the AI Kit perform?

    -The three basic pipelines perform detection, pose estimation, and instant segmentation.

  • Which programming language are the example pipelines built in?

    -The example pipelines are built in Python.

  • What additional hardware components are needed to set up the Raspberry Pi AI Kit?

    -Additional components needed include a micro HDMI to HDMI adapter, an active cooler, Raspberry Pi camera model 3 or the high-quality camera, Raspberry Pi display cable, and a 27W USB-C power supply.

  • What should be done after installing the new Pi OS on the Raspberry Pi?

    -After installing the new Pi OS, you need to update the system by running 'sudo apt update' and 'sudo apt full-upgrade', then configure PCIe to Gen 3 in the raspi-config UI.

  • What software components are installed by running 'sudo apt install halo'?

    -Running 'sudo apt install halo' installs the Halo firmware, Halo RT runtime software, Halo Tapas core package, and the rpy-cam apps Halo postprocessing software stages.

  • How can the installation be verified?

    -The installation can be verified by running the commands 'halo-rt-cli firmware control identify', 'tapas halo test', and checking the Halo element installation.

  • What are the three main parts of the application structure?

    -The three main parts of the application structure are the user-defined data class, the application callback function, and the gamer application class.

  • How can the detection example be run and what network does it use by default?

    -The detection example can be run by executing the corresponding command, and it uses YOLO v6n by default.

  • What options are available for running the detection application?

    -Options include controlling the input source (file, USB camera, Raspberry Pi camera), enabling additional postprocessing, showing FPS, disabling sync, and selecting different networks (YOLO v6n, YOLO v8s, YOLO x s).

  • How is the pose estimation example run?

    -The pose estimation example is run using the provided command, which uses the YOLO V8 pose network.

  • What are the next steps after running the detection, pose estimation, and instant segmentation examples?

    -After running these examples, users can explore more sophisticated projects and examples, join the Halo community, and follow the channel for updates.

Outlines

00:00

😀 Introduction to Halo AI Kit for Raspberry Pi

Gilad introduces the new AI kit from Halo, which includes an 8l entry-level AI accelerator for the Raspberry Pi, offering 13 trillion operations per second (TOPS) with a power consumption of approximately 2 Watts. The kit is available through official Raspberry Pi resellers. The launch is accompanied by the Halo Community platform and a developer zone, with installation guides and open-source examples for easy integration and privacy-focused data processing. The video will demonstrate the installation process and showcase three basic AI pipelines for tasks such as detection, pose estimation, and instant segmentation.

05:03

🛠️ Setting Up the Raspberry Pi with Halo AI Kit

The script details the setup process for the Raspberry Pi with the Halo AI kit, starting with unboxing and connecting the components like the active cooler, camera, and AI kit. It emphasizes the ease of installation and the availability of a GitHub repository with examples. The user is guided through updating the Raspberry Pi OS, installing the necessary packages for the Halo device, and configuring the system for optimal performance. The installation includes the Halo firmware, runtime software, and the Tapas core package, which is a streamlined version of the Tapas repository for faster app development with the GStreamer framework.

10:05

🔍 Exploring the Halo AI Kit's Features and Demo Applications

This section delves into the features of the Halo AI Kit, explaining the structure of the demo applications and how they utilize the Halo metadata object for tasks such as detection, pose estimation, and instance segmentation. It outlines the user-defined data class, the application callback function, and the pipeline replication class, highlighting how these components work together in the demo applications. The video also demonstrates how to run the detection example using different YOLO networks and how to adjust settings for optimal performance.

15:06

📹 Running AI Demos and Expanding the Halo Community

The final paragraph focuses on running the demo applications for pose estimation and instance segmentation, showing how to execute them with video files and USB camera inputs. It provides guidance on identifying the correct camera device and running the demos with various options. The script concludes with an invitation to join the Halo community, follow the channel for updates, and contribute ideas for future projects, emphasizing the ongoing development of more sophisticated projects and examples.

Mindmap

Keywords

💡Raspberry Pi

Raspberry Pi is a series of small single-board computers developed to promote the teaching of basic computer science in schools and in part for self-learning. It is central to the video's theme as the AI Kit being introduced is designed for use with Raspberry Pi. In the script, Raspberry Pi is mentioned as the platform for the new AI Kit, which includes a camera and other accessories for creating AI-driven projects.

💡AI Kit

An AI Kit typically refers to a collection of hardware and software components designed to facilitate the development of artificial intelligence applications. In this video, the AI Kit is a new product featuring the Halo 8L AI accelerator, which is a key component for achieving the video's focus on AI capabilities for the Raspberry Pi platform.

💡Halo 8L

Halo 8L is an entry-level AI accelerator mentioned in the script as part of the AI Kit. It is designed to enhance the AI processing capabilities of the Raspberry Pi. The term is used to highlight the hardware component that provides the computational power needed for the AI applications discussed in the video.

💡TOPS

TOPS stands for trillions of operations per second and is a unit of measurement for the computational performance of AI accelerators. In the context of the video, the Halo 8L is said to deliver 13 TOPS, indicating its processing power for AI tasks, which is a critical aspect of the kit's capabilities.

💡Data Processing

Data processing refers to the manipulation and analysis of data. In the video, keeping data processing local is emphasized to ensure privacy, which means that the data is analyzed on the user's own device rather than being sent to external servers. This concept is important for understanding the video's focus on maintaining user privacy and control over data.

💡Open Source

Open source describes a type of software whose source code is available to the public for use and modification. The video mentions that all Halo examples are open source, encouraging users to incorporate them into their projects. This reflects the video's promotion of community engagement and collaborative development.

💡Pipelines

In the context of this video, pipelines refer to pre-defined sequences of processing steps for AI tasks. The script introduces three basic pipelines for tasks such as detection, pose estimation, and instance segmentation, which are built in Python for easy integration into projects.

💡Pose Estimation

Pose estimation is an AI technique used to determine the position and orientation of objects within an image or video, often referring to human body parts. The video script describes a pipeline for pose estimation, highlighting its application in identifying and locating human poses using the AI Kit.

💡Instance Segmentation

Instance segmentation is a process in computer vision where each object instance in an image is identified and segmented. The video demonstrates an instance segmentation example, showing how the AI Kit can differentiate and label multiple objects within a scene.

💡RPY Cam Apps

RPY Cam Apps refer to the official Raspberry Pi camera applications, which are used for capturing and processing images or video from the Raspberry Pi camera. The script mentions that Raspberry Pi has integrated Halo inference into its RPY Cam Apps repository, indicating a seamless integration between the camera software and the AI Kit's capabilities.

💡GStreamer

GStreamer is a framework for constructing graphs of media-handling components. In the video, the Tapas core package, which is used for developing applications faster with GStreamer, is installed as part of the Halo software components. This shows the video's emphasis on streamlining the development process for AI applications.

Highlights

Introduction of the new Raspberry Pi AI Kit featuring the Halo 8l entry level AI accelerator.

The AI accelerator delivers 13 TOPS with a typical power consumption of around 2 Watts.

Availability of the kit at official Raspberry Pi resellers.

Launch of the Halo Community platform and opening of the developer Zone.

Efforts to ensure straightforward installation flow and examples for local data processing, privacy, performance optimization, and cost management.

All Halo examples are open source and can be used in projects and products.

Release of three basic pipelines for detection, pose estimation, and instant segmentation.

Pipelines are built in Python for easy integration.

Raspberry Pi has integrated Halo inference into its official rpy cam apps repo.

Overview of the necessary components for the Raspberry Pi setup.

Instructions for unboxing and setting up the Raspberry Pi 5 and AI Kit.

Details on updating the Raspberry Pi OS and installing the latest Raspberry Pi Core which includes Halo driver support.

Guidance on setting PCIe to Gen 3 for optimal performance on the Halo device.

Installation of Halo software components including firmware, runtime software, and the Halo core package.

Verification of installation through specific commands.

Instructions for installing the application and configuring the environment for the demos.

Explanation of the application structure including the user-defined data class, application callback function, and the pipeline replication class.

Demonstration of the detection application using YOLO v6n and its capabilities.

Options for running the application with different inputs and additional flags for customization.

Demonstration of pose estimation and instance segmentation with sample outputs.

Guide on running the application with USB input and the process of identifying the correct device.

Invitation to join the Halo community and follow for updates on future projects and integrations.