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Copy file name to clipboardExpand all lines: content/learning-paths/automotive/tinkerblox_ultraedge/_index.md
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cascade:
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draft: true
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minutes_to_complete: 60
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minutes_to_complete: 60
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who_is_this_for: Business teams targeting lower TCO of computing infrastructure through higher utilization of CPU and/or CPU-GPU install bases. R&D and Engineering teams looking for most efficient use of CPU/CPU-GPU infrastructure. Innovation teams looking to maximize edge resources to host new-age AI on constrained environments. Development teams looking at alternative packaging tech – run-time environments to build next generation workloads.
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learning_objectives:
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- Understand the layered architecture of UltraEdge **core**, **boost**, and **prime**.
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- Understand the layered architecture of UltraEdge **core**, **boost**, and **prime**
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- Build applications using the **UltraEdge MicroStack**
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- Deploy the MicroPacs on **Linux-based compute systems** and scale to
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cloud or data-centre environments.
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- Optimize performance for **edge-cloud scenarios**, enabling near
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real-time data flows.
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- Deploy the MicroPacs on **Linux-based compute systems** and scale to cloud or data-center environments
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- Optimize performance for **edge-cloud scenarios**, enabling near real-time data flows
Copy file name to clipboardExpand all lines: content/learning-paths/automotive/tinkerblox_ultraedge/background.md
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UltraEdge takes a fundamentally different approach. It replaces heavyweight container runtimes with a lean, deterministic execution stack purpose-built for performance-oriented compute. This enables millisecond-level startup times, predictable performance, and a dramatically smaller resource footprint—allowing workloads to start faster, run closer to the hardware, and make full use of available CPU and GPU resources.
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At the core of UltraEdge are two specialized execution systems:
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At the core of UltraEdge are two specialized execution systems:
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· **MicroStack**, optimized for enterprise and mixed workloads
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This learning path introduces the architecture, principles, and components that make UltraEdge a high-performance execution fabric for modern edge infrastructure.
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### Ultraedge Overview
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### Ultraedge overview
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UltraEdge was built with the vision of **orchestrating the edge-native execution fabric for high-performance compute infrastructure**
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Demonstrable reduction in CPU/GPU cluster costs through faster startup, higher utilization, and reduced runtime overhead.
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### UltraEdge High-Level Architecture
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### UltraEdge high-level architecture
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UltraEdge is composed of layered systems, each responsible for a distinct aspect of execution and orchestration:
MicroPac is the core tooling used to build and manage **MicroStack** (general microservices) and **NeuroStack** (AI-native services).
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***Platform Agnostic:** MicroPac is not restricted to a specific operating system; it is fully compatible with both **Debian** and **Yocto** environments, providing a consistent execution layer across different Linux distributions.
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* Platform agnostic: MicroPac is not restricted to a specific operating system; it is fully compatible with both **Debian** and **Yocto** environments, providing a consistent execution layer across different Linux distributions.
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***Build System:** To create a service, the system utilizes a **MicroPacFile** (the declarative configuration) and the **MicroPac Builder** (the high-performance packaging engine).
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* Build system: To create a service, the system utilizes a **MicroPacFile** (the declarative configuration) and the **MicroPac Builder** (the high-performance packaging engine).
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***Validation:** The ecosystem includes a **MicroPac Validator**, which verifies the integrity and security of the package created by the builder to ensure it is ready for edge deployment.
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* Validation: The ecosystem includes a **MicroPac Validator**, which verifies the integrity and security of the package created by the builder to ensure it is ready for edge deployment.
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#### System Requirements
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#### System requirements
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- Linux host (aarch64)
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- Sudo permissions
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- Overlay filesystem support
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- Internet connection
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#### Required Packages
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#### Required packages
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```bash
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sudo apt-get update
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sudo apt-get install -y tar curl qemu-user-static binfmt-support
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```
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### Cross-Architecture Support
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### Cross-architecture support
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The **MicroPacFile** is the central declarative configuration used by the builder to define the environment and behavior of your service. This configuration is essential for orchestrating both **MicroStack** (general microservices) and **NeuroStack** (AI/ML) services.
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***Multi-Language Support:** You can configure MicroPacFiles for applications written in **Python, C, and C++**, making it highly versatile for both high-level AI workloads and low-level embedded system tasks.
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* Multi-language support: You can configure MicroPacFiles for applications written in **Python, C, and C++**, making it highly versatile for both high-level AI workloads and low-level embedded system tasks.
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***Unified Workloads:** It bridges the gap between complex ML models and resource-constrained embedded software, ensuring consistent execution across diverse hardware.
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* Unified workloads: It bridges the gap between complex ML models and resource-constrained embedded software, ensuring consistent execution across diverse hardware.
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To build MicroPac for different architectures:
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sudo apt install ./<package_name>.deb
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```
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### MicroPac File Schema file creation/setup
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### MicroPac file schema file creation/setup
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#### File Placement
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#### File placement
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For the MicroPac Builder to function correctly, the **MicroPacFile** must be placed in the root directory alongside your source code and dependency files.
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Place a `MicroPacFile` in your project directory as mentioned in above example.
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```console
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```yaml
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name: nginx
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version: 1.0.0.0
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target: aarch64
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mode: host
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name: nginx-net
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```
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### Configuration Fields
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### Configuration fields
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#### Required Fields
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#### Required fields
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- **name**: Application name (≤ 10 characters)
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- **version**: Application version
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- **entry**: Entry point command
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- **mode**: single-run
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#### Optional Fields
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#### Optional fields
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- **env**: Environment variable
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- **buildSteps**: Array of build instructions
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- **createdBy**: maintainer of the application
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- **description**: description of the application
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### Building the MicroPac
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### Build the MicroPac
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Navigate to your project directory and execute:
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This generates a file named `<project_name>.mpac`.
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### Verifying the Micropac Setup
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### Verify the Micropac setup
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To confirm that the Micropac has been generated properly, follow these steps:
Copy file name to clipboardExpand all lines: content/learning-paths/automotive/tinkerblox_ultraedge/instance.md
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## Overview
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In this section, you provision a Google Axion C4A virtual machine on Google Cloud Platform (GCP) using the `c4a-standard-32` machine type, which provides 16 vCPUs and 128 GB of memory. This size image will be used in the next steps to build our YOCTO based image.
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In this section, you provision a Google Axion C4A virtual machine on Google Cloud Platform (GCP) using the `c4a-standard-32` machine type, which provides 16 vCPUs and 128 GB of memory. This VM size is required for building a Yocto-based image in the next steps.
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{{% notice Note %}}
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For general guidance on setting up a Google Cloud account and project, see the Learning Path [Getting started with Google Cloud Platform](/learning-paths/servers-and-cloud-computing/csp/google/).
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For general guidance on setting up a Google Cloud account and project, see the Learning Path [Getting started with Google Cloud Platform](/learning-paths/servers-and-cloud-computing/csp/google/).
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{{% /notice %}}
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## Provision a Google Axion C4A VM in the Google Cloud Console
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- Set **Series** to **C4A**.
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- Select **c4a-standard-32** as the machine type.
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![Google Cloud Console VM creation page with the C4A machine series selected and the c4a-standard-32 machine type highlighted alt-text#center](images/gcp-vm.png"Creating a Google Axion C4A virtual machine in the Google Cloud Console")
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![Google Cloud Console VM creation page with the C4A machine series selected and the c4a-standard-32 machine type highlighted#center](images/gcp-vm.png"Creating a Google Axion C4A virtual machine in the Google Cloud Console")
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- Under **OS and storage**, select **Change**, and then choose an Arm64-based operating system image.
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- For this Learning Path, select **Ubuntu 22.04 LTS**.
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- Increase **Size (GB)** from **10** to **100** to allocate sufficient disk space.
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- Select **Choose** to apply the changes.
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![Selecting Ubuntu LTS 22.04 with 100GB disk space highlighted alt-text#center](images/gcp-ubuntu.png"Selecting Ubuntu 22.04 LTS with 100GB disk space")
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![Selecting Ubuntu LTS 22.04 with 100GB disk space highlighted#center](images/gcp-ubuntu.png"Selecting Ubuntu 22.04 LTS with 100GB disk space")
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- Under **Networking**, enable **Allow HTTP traffic** and **Allow HTTPS traffic** to simplify access for later Kubernetes testing.
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- Select **Create** to launch the virtual machine.
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![Configuring the required networking settings alt-text#center](images/gcp-network.png"Configuring the VM network")
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![Configuring the required networking settings#center](images/gcp-network.png"Configuring the VM network")
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After the instance starts, click **SSH** next to the VM in the instance list to open a browser-based terminal session.
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![Google Cloud Console VM instances list with the SSH button highlighted for a running C4A instance alt-text#center](images/gcp-ssh.png"Connecting to a running C4A virtual machine using SSH")
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![Google Cloud Console VM instances list with the SSH button highlighted for a running C4A instance#center](images/gcp-ssh.png"Connecting to a running C4A virtual machine using SSH")
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A new browser window opens with a terminal connected to your virtual machine.
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![Browser-based terminal window showing a command prompt on a SUSE Linux VM running on Google Axion C4A alt-text#center](images/gcp-shell.png"Terminal session connected to the virtual machine")
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![Browser-based terminal window showing a command prompt on an Ubuntu Linux VM running on Google Axion C4A#center](images/gcp-shell.png"Terminal session connected to the virtual machine")
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{{% notice Note %}}
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This is a rather expensive VM given its vCPU and memory usage. It is highly recommended to delete this VM once you have completed this learning path!
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{{% /notice %}}
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Next, lets build our Yocto image on this VM instance.
Installs a microservice. You must provide the path to the MPAC file as an argument.
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sudo tinkerblox-cli microboost uninstall <id>
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#### Diagnostics Management
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#### Diagnostics management
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Run diagnostics on the Edge platform.
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The following sections describe common errors you may encounter while running CLI commands or deploying microservices, along with guidance on how to resolve them. These are general errors and are not limited to any specific scenario; they may occur while executing commands or managing the system.
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**Permission Denied**
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Permission denied
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- Ensure `sudo` privileges.
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- Check directory ownership and permissions.
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- Verify overlay filesystem support.
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**Directory Creation Failed**
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Directory creation failed
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- Check disk space.
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- Verify parent directory permissions.
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- Ensure the path is valid.
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**Cross-Architecture Build Issues**
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Cross-architecture build issues
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Cross-architecture issues typically occur when binaries are built for a different CPU architecture than the host system.They are not related to yocto build issue.
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Cross-architecture issues typically occur when binaries are built for a different CPU architecture than the host system.They are not related to yocto build issues.
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