Update ExecuTorch learning path: fix Dockerfile name, add build flags table, update docker paths, and add troubleshooting notes

Author: fidel-makatia

content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/4-environment-setup.md

@@ -28,10 +28,10 @@ Use a Docker container to build ExecuTorch:
 
    ```bash
    cd ubuntu-24-container
-   touch dockerfile
+   touch Dockerfile
    ```
 
-4. Add the following commands to your `dockerfile`:
+4. Add the following commands to your `Dockerfile`:
 
    ```dockerfile
    FROM ubuntu:24.04

content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/7-build-executorch-pte.md

@@ -109,17 +109,31 @@ Now you will build the `.pte` file, that will be used on the NXP board.
 1. Build the [MobileNet V2](https://pytorch.org/hub/pytorch_vision_mobilenet_v2/) ExecuTorch `.pte` runtime file using [aot_arm_compiler](https://github.com/pytorch/executorch/blob/2bd96df8de07bc86f2966a559e3d6c80fc324896/examples/arm/aot_arm_compiler.py):
 
    ```bash
-   python3 -m examples.arm.aot_arm_compiler \
-     --model_name="mv2" \
-     --quantize \
-     --delegate \
-     --debug
+      python3 -m examples.arm.aot_arm_compiler \
+         --model_name="mv2" \
+         --quantize \
+         --delegate \
+         --debug \
+         --target ethos-u55-256
+
    ```
 
-3. Check that the `mv2_arm.pte` file was generated:
+{{% notice Note %}}
+| Flag                     | Meaning                                             |
+| ------------------------ | --------------------------------------------------- |
+| `--model_name="mv2"`     | Example model: MobileNetV2 (small, efficient)       |
+| `--quantize`             | Enables int8 quantization (required for Ethos-NPUs) |
+| `--delegate`             | Enables offloading layers to the Ethos backend      |
+| `--debug`                | Verbose build output                                |
+| `--target ethos-u55-256` | Targets the Ethos-U55      |
+
+The `--quantize` flag uses one input example, so the resulting model will likely have poor classification performance.
+{{% /notice %}}
+
+3. Check that the `mv2_arm_delegate_ethos-u55-256.pte` file was generated:
 
    ```bash
-   ls mv2/mv2_arm.pte
+   ls mv2_arm_delegate_ethos-u55-256.pte
    ```
 
 ## Troubleshooting

content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/9-build-executorch-runner-for-cm33.md

@@ -85,12 +85,22 @@ abc123def456   executorch     "/bin/bash"   2 hours ago    Exited
 Copy the libraries:
 
 ```bash
-docker cp abc123def456:/root/executorch/cmake-out/lib/. ./executorch/lib/
-docker cp abc123def456:/root/executorch/. ./executorch/include/executorch/
+docker cp abc123def456:/home/ubuntu/executorch/cmake-out/lib/. ./executorch/lib/
+docker cp abc123def456:/home/ubuntu/executorch/. ./executorch/include/executorch/
 ```
 
 Replace `abc123def456` with your actual container ID.
 
+{{% notice Note %}}
+In some Docker containers, the `cmake-out` folder might not exist. If you don't see the libraries, run the following command to build them:
+
+```bash
+./examples/arm/run.sh --build-only
+```
+
+The libraries will be generated in `arm_test/cmake-out`.
+{{% /notice %}}
+
 Verify the libraries:
 
 ```bash { output_lines = "2-5" }