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‎content/learning-paths/automotive/openadkit1_container/_index.md‎

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@@ -15,9 +15,50 @@ prerequisites:
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- An Arm Neoverse cloud instance, or a local Arm Neoverse Linux computer with at least 16 CPUs and 32GB of RAM
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- Familiarity with Docker and Docker Compose
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# START generated_summary_faq
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generated_summary_faq:
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template_version: summary-faq-v3
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generated_at: '2026-06-24T15:35:34Z'
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generator: ai
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ai_assisted: true
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ai_review_required: true
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model: gpt-5
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prompt_template: summary-faq-v3
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source_hash: 37913b2c4aed914d32dbdad054ebdd2b1d4587da3ede1a33ba81e3e68bf504a3
28+
summary_generated_at: '2026-06-24T15:35:34Z'
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summary_source_hash: 37913b2c4aed914d32dbdad054ebdd2b1d4587da3ede1a33ba81e3e68bf504a3
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faq_generated_at: '2026-06-24T15:35:34Z'
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faq_source_hash: 37913b2c4aed914d32dbdad054ebdd2b1d4587da3ede1a33ba81e3e68bf504a3
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summary: >-
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You'll deploy a containerized Autoware
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Open AD Kit simulation on Arm Neoverse using Docker and Docker Compose, within a SOAFEE-aligned Shift-Left workflow. First, you'll learn about software-defined vehicles (SDVs), SOAFEE, ROS 2, and the Open AD Kit components used in the demo. Then, you'll prepare an Arm Neoverse Linux system and use Docker Compose to start the Open AD Kit visualizer, planning, and simulation services. By the end, you'll review a running simulation. The workflow has been tested on both cloud (Amazon EC2) and on-premise Arm Neoverse platforms.
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faqs:
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- question: What result should I expect after launching the Docker Compose stack?
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answer: >-
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The visualizer service starts in detached mode, followed by continuously running planning
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and simulation services. Active containers for these components indicate the demo is operating
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as intended.
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- question: Where are the ROS 2 commands and service configurations defined?
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answer: >-
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They are defined in the docker/docker-compose.yml file. Reviewing that file shows the launch
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order, container settings, and ROS 2 commands used by the demo.
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- question: Can I run the same workflow on cloud and on-prem Arm Neoverse systems?
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answer: >-
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Yes. The worklow has been tested on Amazon EC2 and an Ampere Altra workstation, so you can
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choose either a cloud instance or an on-premise Arm Neoverse system.
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- question: What should I check before starting the demo to avoid resource-related failures?
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answer: >-
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Verify the Arm Neoverse system provides at least 16 CPUs and 32 GB of RAM. Ensure Docker
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and Docker Compose are installed and available.
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- question: If I stop and restart the demo, do I need to reconfigure anything?
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answer: >-
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No. Docker Compose allows you to start with the previous session’s settings without modifications,
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so the configuration persists between runs.
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# END generated_summary_faq
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author: Odin Shen
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generate_summary_faq: true
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generate_summary_faq: false
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rerun_summary: false
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rerun_faqs: false
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‎content/learning-paths/automotive/openadkit2_safetyisolation/_index.md‎

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@@ -31,14 +31,14 @@ generated_summary_faq:
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faq_generated_at: '2026-06-24T15:35:59Z'
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faq_source_hash: 92a6dac2b1674a44cd623a0c8c3189b38438124a6067ed7ca776999ff1d8b5bf
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summary: >-
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In this Learning Path, you'll prototype safety‑critical isolation for autonomous
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You'll learn about prototyping safety‑critical isolation for autonomous
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driving workloads on Arm Neoverse by applying functional safety concepts, ISO 26262 and ASIL
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guidance, and a safety-island architecture. You'll separate safety-critical control
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logic from non-safety functions, then connect components using a publish‑subscribe model (DDS/ROS
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2) within containerized deployments or across Arm‑based instances. You'll learn about lifecycle
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guidance, and a safety‑island architecture. First, you'll understand how to separate safety‑critical control
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logic from non‑safety functions. Then, you'll connect components using a publish‑subscribe model (DDS/ROS
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2) within containerized deployments or across Arm‑based instances. You'll explore lifecycle
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practices aligned with the V‑model, including clear requirements, version control, impact
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analysis, and regression testing. By the end, you'll organize simulation components into
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isolated units with defined interfaces and documentation suitable for advancing ISO 26262-oriented
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isolated units with defined interfaces and documentation suitable for advancing ISO 26262‑oriented
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development on Arm Neoverse.
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faqs:
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- question: How do I decide which components belong on the safety island versus the general

‎content/learning-paths/automotive/system76-auto/_index.md‎

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@@ -12,11 +12,54 @@ learning_objectives:
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- Build and run the Arm Automotive Solutions Software Reference Stack locally.
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prerequisites:
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- A System76 Thelio Astra desktop computer running Ubuntu 24.04.
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- A System76 Thelio Astra desktop computer running Ubuntu 24.04.
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# START generated_summary_faq
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generated_summary_faq:
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template_version: summary-faq-v3
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generated_at: '2026-06-24T15:36:29Z'
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generator: ai
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ai_assisted: true
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ai_review_required: true
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model: gpt-5
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prompt_template: summary-faq-v3
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source_hash: 2b758d2dcf28a683ab164e28578a736d6d730b81dfbc01a6765619052fcdebd0
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summary_generated_at: '2026-06-24T15:36:29Z'
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summary_source_hash: 2b758d2dcf28a683ab164e28578a736d6d730b81dfbc01a6765619052fcdebd0
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faq_generated_at: '2026-06-24T15:36:29Z'
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faq_source_hash: 2b758d2dcf28a683ab164e28578a736d6d730b81dfbc01a6765619052fcdebd0
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summary: >-
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You'll use a System76 Thelio Astra Arm desktop to build and run the
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Arm Automotive Solutions Software Reference Stack in a local Multipass virtual machine. You'll start by
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creating an Ubuntu 20.04 guest, isolating builds, and compiling Yocto-based
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components targeting a Fixed Virtual Platform that models the Arm Reference Design-1 AE. You'll review the Thelio Astra platform and the software stack context, then run a Parsec-enabled TLS demo that establishes an HTTPS session to transfer a web page.
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faqs:
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- question: Which Multipass install guide should I follow before creating the virtual machine?
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answer: >-
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Use the Multipass install guide for Arm Linux before starting the steps. This ensures Multipass
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is set up correctly on the Thelio Astra running Ubuntu.
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- question: Which Ubuntu release runs inside the Multipass virtual machine for this build?
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answer: >-
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The build is performed from the command line of an Ubuntu 20.04 Multipass virtual machine.
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- question: Why use a Multipass virtual machine on the Thelio Astra instead of building directly on the host?
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answer: >-
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A Multipass VM creates an isolated automotive development environment and lets you split
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the resources of the Thelio Astra between development tasks. It keeps the build and test
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process contained.
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- question: What target platform is used when running the software stack examples?
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answer: >-
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The examples run on a Fixed Virtual Platform that models the Arm Reference Design‑1 AE (RD‑1
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AE) hardware system.
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- question: What result should I expect from the Parsec-enabled TLS demo?
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answer: >-
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The demo establishes an HTTPS session and transfers a simple web page over a TLS connection.
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Parsec provides the common API to the underlying security and cryptographic services used
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by the demo.
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# END generated_summary_faq
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author: Jason Andrews
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generate_summary_faq: true
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generate_summary_faq: false
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rerun_summary: false
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rerun_faqs: false
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‎content/learning-paths/automotive/zenacssdebug/_index.md‎

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@@ -18,9 +18,54 @@ prerequisites:
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- Arm Development Studio 2024.1 or later with a valid license - for support see the [Install Guide for Arm DS](/install-guides/armds/)
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- Basic understanding of the Arm Zena CSS software stack, Armv8-A/Armv9-A cores, and Linux
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# START generated_summary_faq
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generated_summary_faq:
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template_version: summary-faq-v3
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generated_at: '2026-06-24T15:37:11Z'
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generator: ai
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ai_assisted: true
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ai_review_required: true
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model: gpt-5
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prompt_template: summary-faq-v3
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source_hash: 74740788edbfe7ea09bf955455b4aeaed00e667fa8c9d68467353c1f64528b08
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summary_generated_at: '2026-06-24T15:37:11Z'
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summary_source_hash: 74740788edbfe7ea09bf955455b4aeaed00e667fa8c9d68467353c1f64528b08
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faq_generated_at: '2026-06-24T15:37:11Z'
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faq_source_hash: 74740788edbfe7ea09bf955455b4aeaed00e667fa8c9d68467353c1f64528b08
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summary: >-
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You'll debug the Arm Zena Compute Subsystem (CSS) reference
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software stack on a Fixed Virtual Platform using Arm Development Studio. First, you'll launch the
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FVP with the Iris debug server enabled, then create and save a custom Arm DS configuration. You'll establish connections
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to each heterogeneous component within Zena CSS to debug the Linux kernel and user processes. By the end, you'll create reusable `.launch`
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files, step through early RSE boot, and attach to Safety Island and Linux targets to inspect
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execution across the system.
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faqs:
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- question: Which FVP launch method should I use for debugging?
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answer: >-
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Use the launch invocation that enables the Iris debug server. The default build-environment
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command runs the stack but does not enable Iris, so Arm Development Studio cannot connect.
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- question: How should I organize and save my debug connections in Arm Development Studio?
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answer: >-
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Create a General Project to store the connection files and save each connection as a `.launch`
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file. This makes it easy to reuse and enhance configurations for each subsystem.
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- question: What is the expected workflow to debug the RSE from reset?
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answer: >-
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Start the FVP with Iris enabled and hold the model at reset, then connect from Arm Development
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Studio. Load Trusted Firmware‑M symbols and step through the early boot code.
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- question: Can I connect to all Zena CSS processors at the same time?
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answer: >-
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Yes. Arm Development Studio supports heterogeneous systems, so you can connect to the RSE,
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Safety Island, and primary compute cores simultaneously, though you might prefer to set up
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one connection fully before adding others.
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- question: Why isn’t there a predefined Zena CSS target in Arm Development Studio?
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answer: >-
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As of Arm Development Studio 2025.0, there is no out-of-the-box configuration for the Zena
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CSS FVP. Create one using the Iris interface as shown in the Learning Path.
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# END generated_summary_faq
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author: Ronan Synnott
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generate_summary_faq: true
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generate_summary_faq: false
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rerun_summary: false
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rerun_faqs: false
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‎content/learning-paths/servers-and-cloud-computing/rafay-eks/_index.md‎

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---
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title: "Deploy an EKS cluster with Graviton nodes using Rafay"
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title: Deploy an Amazon EKS cluster with AWS Graviton-based nodes using Rafay
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description: Use the Rafay Kubernetes Operations Platform to provision an Amazon EKS cluster with an Arm Graviton node group and deploy NGINX to verify the setup.
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description: Use the Rafay Kubernetes Operations Platform to provision an Amazon EKS cluster with an AWS Graviton-based node group and deploy NGINX to verify the setup.
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draft: true
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cascade:
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draft: true
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minutes_to_complete: 60
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who_is_this_for: >
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This is an advanced topic for software developers familiar with Kubernetes and AWS who want to learn how to use the Rafay platform to provision and manage EKS clusters backed by Arm Graviton instances.
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This is an advanced topic for software developers familiar with Kubernetes and AWS who want to learn how to use the Rafay platform to provision and manage EKS clusters backed by AWS Graviton-based instances.
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learning_objectives:
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- Connect your AWS account to the Rafay platform using a cross-account IAM role
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- Provision an Amazon EKS cluster with an Arm Graviton node group using Rafay
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- Deploy and verify NGINX on Arm nodes and clean up all cloud resources
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- Provision an Amazon EKS cluster with an AWS Graviton-based node group using Rafay
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- Deploy and verify workloads on arm64 nodes and clean up all cloud resources
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prerequisites:
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- An Amazon Web Services (AWS) [account](https://aws.amazon.com/)
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- A [Rafay account](https://rafay.co)
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- The [AWS CLI](/install-guides/aws-cli/) installed and configured
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- An Amazon Web Services (AWS) [account](https://aws.amazon.com/) with sufficient IAM permissions to create roles, EKS clusters, EC2 instances, CloudFormation stacks, and related resources.
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- A [Rafay account](https://rafay.co).
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- The [AWS CLI](/install-guides/aws-cli/) installed and configured.
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# START generated_summary_faq
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generated_summary_faq:
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template_version: summary-faq-v3
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generated_at: '2026-06-24T20:45:31Z'
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generator: ai
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ai_assisted: true
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ai_review_required: true
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model: gpt-5
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prompt_template: summary-faq-v3
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source_hash: 76cbd5e4b3a61e9604e0df9b3ba2a5e5af4aa9a0c295ece3c4ee5a98ec365630
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summary_generated_at: '2026-06-24T20:45:31Z'
33+
summary_source_hash: 76cbd5e4b3a61e9604e0df9b3ba2a5e5af4aa9a0c295ece3c4ee5a98ec365630
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faq_generated_at: '2026-06-24T20:45:31Z'
35+
faq_source_hash: 76cbd5e4b3a61e9604e0df9b3ba2a5e5af4aa9a0c295ece3c4ee5a98ec365630
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summary: >-
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You'll provision an Amazon EKS cluster on Arm using
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the Rafay Kubernetes Operations Platform and validate workloads on AWS Graviton-based nodes.
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First, you'll define a declarative cluster manifest in Rafay referencing an existing project,
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blueprint, and cloud credential. Then, you'll create the EKS cluster and deploy NGINX pinned to arm64
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to confirm scheduling on Graviton-based instances. Finally, you'll remove the NGINX workload and deprovision the EKS resources to avoid ongoing cloud costs.
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faqs:
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- question: How do I know the AWS connection to Rafay is set up correctly before creating the
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cluster?
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answer: >-
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Ensure the cross-account IAM role is configured in AWS and added to Rafay as a cloud credential.
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In the cluster manifest, reference this credential by name. If it's missing or has insufficient
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permissions, cluster creation will fail.
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- question: Which fields in the Rafay cluster manifest must match existing configuration?
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answer: >-
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The project, blueprint name and version, and the cloud credential must already exist in
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Rafay. If any of these don't match, the cluster won't be created.
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- question: What result should I expect when the EKS cluster is ready to use?
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answer: >-
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A running cluster with a Graviton-based (`arm64`) node group will be available for workloads.
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Nodes should advertise the label `kubernetes.io/arch=arm64`, indicating they can run `arm64`
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pods.
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- question: How do I verify that the NGINX deployment is running on Graviton nodes?
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answer: >-
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The provided manifest pins the pods using `nodeSelector: kubernetes.io/arch: arm64`. After
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deployment, the pod should schedule and run on nodes labeled `arm64`. If it remains `Pending`,
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verify the node group is active and the selector matches node labels.
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- question: What should I clean up to avoid ongoing AWS charges?
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answer: >-
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Delete the NGINX workload and namespace created for the test, then deprovision the EKS cluster
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from Rafay. This releases the associated AWS resources.
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# END generated_summary_faq
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generate_summary_faq: true
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generate_summary_faq: false
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rerun_summary: false
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rerun_faqs: false
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type: documentation
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- resource:
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title: Amazon EKS documentation
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link: https://aws.amazon.com/eks/
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link: https://docs.aws.amazon.com/eks/latest/userguide/what-is-eks.html
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type: documentation
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- resource:
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title: AWS Graviton processors
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layout: "learningpathall" # All files under learning paths have this same wrapper
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learning_path_main_page: "yes" # Indicates this should be surfaced when looking for related content. Only set for _index.md of learning path content.
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---
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