Topo Deploy a Machine Learning application with NPU acceleration

content/learning-paths/cross-platform/deploy-ml-model-to-npu-with-topo/_index.md

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+---
+title: Deploy a machine learning model to an NPU-capable system with Topo
+
+draft: true
+cascade:
+    draft: true
+
+description: Use Topo to deploy a web application on Cortex-A that triggers a MobileNetV2 image classifier running as Cortex-M firmware with Ethos-U65 NPU acceleration.
+
+minutes_to_complete: 60
+
+who_is_this_for: This is an introductory topic for embedded, edge, and cloud software developers who want to deploy machine learning workloads to heterogeneous Arm-based Linux targets using Topo.
+
+learning_objectives:
+    - Explain how Topo deploys an application that spans Cortex-A, Cortex-M, and Ethos-U
+    - Prepare an NXP FRDM i.MX 93 board for remoteproc-runtime and shared-memory inference
+    - Clone and deploy the topo-imx93-npu-deployment template
+    - Describe how the Template is bootstrapped from Compose services, Remoteproc Runtime metadata, and Topo arguments
+    - Run image classification from a browser and verify that inference is executed by the Cortex-M33 firmware
+
+prerequisites:
+    - A host machine (x86 or Arm) with Linux, macOS, or Windows
+    - An NXP FRDM i.MX 93 target board accessible over SSH with root access
+    - Docker installed on the host and target. For installation steps, see [Install Docker](/install-guides/docker/).
+    - lscpu installed on the target (pre-installed on most Linux distributions)
+    - Topo installed on the host. For installation steps, see [Deploy containerized workloads to Arm-based Linux targets with Topo](/learning-paths/cross-platform/deploy-containerized-workloads-with-topo/).
+    - Basic familiarity with containers, SSH, and CLI tools
+
+author: Tomas Agustin Gonzalez Orlando
+
+### Tags
+skilllevels: Introductory
+subjects: Containers and Virtualization
+armips:
+    - Cortex-A
+    - Cortex-M
+    - Ethos-U
+tools_software_languages:
+    - Topo
+    - Docker
+    - SSH
+    - ExecuTorch
+    - remoteproc-runtime
+operatingsystems:
+    - Linux
+    - macOS
+    - Windows
+
+### Cross-platform metadata only
+shared_path: true
+shared_between:
+    - servers-and-cloud-computing
+    - laptops-and-desktops
+    - embedded-and-microcontrollers
+
+further_reading:
+    - resource:
+        title: Topo repository
+        link: https://github.com/arm/topo
+        type: documentation
+    - resource:
+        title: Topo template format
+        link: https://github.com/arm/topo-template-format
+        type: documentation
+    - resource:
+        title: Topo releases
+        link: https://github.com/arm/topo/releases/latest
+        type: website
+    - resource:
+        title: remoteproc-runtime
+        link: https://github.com/arm/remoteproc-runtime
+        type: documentation
+    - resource:
+        title: ExecuTorch
+        link: https://docs.pytorch.org/executorch/stable/index.html
+        type: documentation
+
+### FIXED, DO NOT MODIFY
+# ================================================================================
+weight: 1                       # _index.md always has weight of 1 to order correctly
+layout: "learningpathall"       # All files under learning paths have this same wrapper
+learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
+---

content/learning-paths/cross-platform/deploy-ml-model-to-npu-with-topo/_next-steps.md

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+---
+# ================================================================================
+#       FIXED, DO NOT MODIFY THIS FILE
+# ================================================================================
+weight: 21                  # The weight controls the order of the pages. _index.md always has weight 1.
+title: "Next Steps"         # Always the same, html page title.
+layout: "learningpathall"   # All files under learning paths have this same wrapper for Hugo processing.
+---