While we move forward with the ‘Hive Mind’ project, there’s more to do than just installing Unreal Engine 5 (UE5). We need to set up the right tools for coding and debugging to make our workflow smoother and more efficient. Whether you’re following along with this project or just curious about the tools we’re using, here’s a breakdown of the next steps.

Choosing a Code Editor

For the coding side of this project, I’m using tools from the JetBrains suite, which I’ve found incredibly powerful and user-friendly. However, if you’re looking for a free alternative, Visual Studio Code from Microsoft is another great choice. It’s lightweight, fast, and has an extensive library of plugins for all kinds of development needs.

My Setup for C++ and Unreal Engine

When working with C++ in Unreal Engine, I’ve installed Visual Studio Community 2022. This is a free, feature-rich version of Visual Studio that is perfect for C++ development with Unreal. But my preferred tool for coding in Unreal Engine is JetBrains Rider, which integrates seamlessly with Unreal Engine, making debugging and coding easier. The built-in features in Rider allow you to navigate Unreal Engine’s large codebase effortlessly, and its debugger is incredibly useful when hunting down bugs in your code.

To use Rider as your code editor in Unreal Engine, simply head to the Editor Preferences menu and search for “Source Code Editor.” There, you’ll find an option to select the code editor of your choice—just choose Rider, and you’re all set. This small adjustment makes working with Unreal a lot smoother.

Python Development in WSL2 with CUDA for TensorFlow

On the Python side of things, I’ve opted for JetBrains PyCharm, which is perfect for handling our machine learning setup. The great thing about PyCharm is that you can open projects directly within WSL2 (Windows Subsystem for Linux 2), allowing you to run Linux commands seamlessly from within your editor. This makes updating or installing Python packages just as easy as if you were running everything natively on Windows.

If you’re planning to use CUDA for TensorFlow to leverage your NVIDIA GPU, you’ll need to follow NVIDIA’s setup instructions to get it working correctly within WSL2. Here’s a quick summary of the steps:

1. Install the NVIDIA Driver: First, ensure that you have the latest NVIDIA driver installed on your Windows machine that supports WSL2. This driver is specifically designed for WSL and supports CUDA.
2. Install CUDA Toolkit: Once the driver is in place, install the CUDA Toolkit inside your WSL2 environment. NVIDIA provides a package repository for Ubuntu that allows you to install the necessary CUDA libraries.
3. Install cuDNN: TensorFlow also requires cuDNN (CUDA Deep Neural Network library). This can be installed alongside the CUDA Toolkit from NVIDIA’s repository.
4. Configure Environment Variables: After installing CUDA and cuDNN, you’ll need to set up environment variables to ensure TensorFlow can locate these libraries. You can do this by adding the paths to your .bashrc file.
5. Install TensorFlow with GPU Support: Finally, install the version of TensorFlow that includes GPU support by running pip install tensorflow-gpu. This will ensure that TensorFlow can take advantage of CUDA for faster computation.

By following these steps, you’ll be able to run TensorFlow with GPU acceleration inside your WSL2 environment, taking full advantage of NVIDIA GPU’s power.

For more information use these links:

https://docs.nvidia.com/cuda/wsl-user-guide/index.html

https://www.tensorflow.org/install/pip