TensorFlow lite micro with WSL

I have WSL in my windows OS and already clone the repo from tflite-micro. I have everything in the Ubuntu distribution and installed dependencies asked (numpy and Pillow). However, whenever in tflite-micro, calling the Makefile I get this:

:~/tflite-micro$ sudo make -f tensorflow/lite/micro/tools/make/Makefile test_hello_world_test

tensorflow/lite/micro/tools/make/downloads/flatbuffers already exists, skipping the download.
tensorflow/lite/micro/tools/make/downloads/kissfft already exists, skipping the download.
tensorflow/lite/micro/tools/make/downloads/pigweed already exists, skipping the download.
make: *** No rule to make target ‘test_hello_world_test’. Stop.

hello_world folder exists in examples but inside there are evaluate files (.py evaluate_test.cc etc)
I’m getting a bit crazy about this and a hand would be appreciated.
All the best,

It seems you’re encountering an issue with the Makefile not recognizing the test_hello_world_test target within the TensorFlow Lite Micro (TFLM) repository in your WSL environment. This issue could be due to several reasons, such as changes in the repository structure, outdated documentation, or a misconfiguration in your setup. Here’s how you can troubleshoot and potentially resolve the issue:

1.	Check Repository Structure and Documentation:
•	Ensure you’re working with the latest version of the TFLM repository. The TFLM project is actively developed, and its structure can change over time. If you cloned the repository a while ago, consider pulling the latest changes.
•	Verify that the documentation you’re following is up-to-date with the current version of the repository. Sometimes, the build or test commands may change, and outdated documentation could lead to confusion.
2.	Verify Makefile Targets:
•	Inspect the tensorflow/lite/micro/tools/make/Makefile and other relevant Makefiles for the correct target names. The target test_hello_world_test might have been renamed, moved, or removed in newer versions.
•	Look for a list of available targets by scanning the Makefile or using commands like grep to search for keywords related to “hello_world” or “test”.
3.	Examine the Examples Directory:
•	Navigate to the tensorflow/lite/micro/examples/hello_world directory and check its contents. Verify that there are make targets intended for testing within this directory or in associated Makefiles.
•	Some examples might not have a direct make command for testing and instead require you to build and then manually run the tests.
4.	Consult TFLM Documentation and Community:
•	Check the official TensorFlow Lite Micro documentation for updated instructions on building and testing examples. The TensorFlow website or GitHub repository might have guides or tutorials relevant to your issue.
•	If you’re still stuck, consider reaching out to the TensorFlow community, such as TensorFlow’s GitHub issues, forums, or Stack Overflow. Someone might have faced a similar issue or could offer insights based on the latest repository structure.
5.	General Makefile Troubleshooting:
•	Ensure that all dependencies required by the Makefile are installed and accessible within your WSL environment. Missing tools or libraries could sometimes cause make to fail.
•	Try running make commands without sudo if it’s not necessary, as using sudo might change the environment in ways that affect how make finds files and executes targets.
6.	Alternative Testing Approach:
•	If direct Makefile targets for testing are not working or available, consider manually compiling the example and then running the test scripts or binaries as separate steps. This might require a deeper dive into the documentation for instructions on manual compilation and execution.
7.	WSL-Specific Checks:
•	Ensure that your WSL environment has all the necessary tools and permissions to build and execute the TFLM projects. Sometimes, the WSL environment might have subtle differences from a native Linux installation that could affect building and testing complex projects like TFLM.

By systematically checking these aspects, you should be able to identify where the issue lies and take steps to resolve it, allowing you to build and test the TensorFlow Lite Micro examples successfully.