Gemini 3 Pro Mods: Gradio 6.0 & CUDA 13 Tweaks

Hello everyone! I've been experimenting with Gemini 3 Pro and made some modifications that I thought might be useful for others. This article details the changes I've made, specifically focusing on compatibility with Gradio 6.0 and CUDA 13. I'll walk you through the modifications to requirements.txt and app.py, and also address a minor issue I encountered after updating to Gradio 6. Let's get started!

1. Modified requirements.txt for Bleeding-Edge Repositories

In this section, we'll delve into the changes made to the requirements.txt file. This file is crucial for managing the project's dependencies, ensuring that all the necessary libraries and packages are installed for the application to run smoothly. The modifications implemented here are designed to optimize the environment for Gemini 3 Pro by utilizing bleeding-edge repositories and accommodating CUDA 13. Let's break down the specific adjustments:

Pointing to Bleeding-Edge Repositories

One of the primary changes involves updating the file to point to bleeding-edge repositories. This means that the project will be using the most recent versions of the libraries, which often include the latest features, bug fixes, and performance improvements. By utilizing bleeding-edge repositories, we aim to harness the cutting-edge advancements in the field, ensuring that our application remains up-to-date and competitive. This approach, however, comes with its own set of considerations. While bleeding-edge versions offer the newest enhancements, they may also introduce instability or compatibility issues due to their nascent nature. Therefore, careful testing and monitoring are essential to ensure that the application functions as expected after incorporating these updates. The decision to use bleeding-edge repositories reflects a commitment to staying at the forefront of technological progress, but it also necessitates a proactive approach to address any potential challenges that may arise.

Unpinning Versions for Flexibility

Another significant modification is the unpinning of versions, which provides greater flexibility in managing dependencies. In many software projects, specific versions of libraries are pinned to ensure consistent behavior and prevent unexpected issues caused by updates. However, unpinning versions can allow for more seamless integration of new features and improvements from updated libraries. In this context, version locks like ==5.49.1 for gradio and audio-separator have been removed to permit the installation of the latest versions. This adjustment is particularly beneficial when working with rapidly evolving technologies, as it enables the project to adapt quickly to changes in the ecosystem. By allowing the installation of the latest versions, the project can take advantage of optimizations, bug fixes, and new capabilities introduced in these updates. However, this approach also requires diligence in testing and validation to ensure that the application remains stable and functions correctly with the updated dependencies. The decision to unpin versions balances the desire for leveraging the newest advancements with the need for maintaining a reliable and robust system.

Added Extra Index URLs for PyTorch and Azure AI

To facilitate the installation of PyTorch with CUDA 13.0 and Azure AIInfra Nightly builds for ONNX Runtime CUDA 13, additional index URLs have been added. These URLs serve as alternative sources for downloading the required packages, ensuring that the project can access the necessary components even if the default repositories are unavailable or do not contain the specific versions needed. The inclusion of extra index URLs is a strategic measure to enhance the reliability and availability of the dependency installation process. By providing multiple sources for package downloads, the project is less likely to be affected by outages or disruptions in any single repository. This approach is particularly important when dealing with specialized libraries or nightly builds, which may not be readily available in the standard package repositories. Furthermore, the addition of these URLs reflects a proactive approach to addressing compatibility requirements, as it enables the project to specifically target versions optimized for CUDA 13.0 and ONNX Runtime. This level of precision ensures that the application can fully leverage the capabilities of the hardware and software environment, leading to improved performance and efficiency. The use of extra index URLs is a best practice in dependency management, particularly in complex projects with specific requirements.

Removed GPU Tag from Audio-Separator

The [gpu] tag was removed from audio-separator to prevent it from overwriting the custom nightly runtime with the standard CUDA 12 version. This adjustment is critical for maintaining the integrity of the project's configuration, as the custom nightly runtime is specifically tailored to meet the unique requirements of the application. By removing the [gpu] tag, we ensure that the audio-separator library does not inadvertently replace the custom runtime, which could lead to compatibility issues or performance degradation. This decision underscores the importance of carefully managing dependencies and configurations to avoid unintended consequences. The custom nightly runtime likely includes specific optimizations or features that are not present in the standard CUDA 12 version, making it essential for the application's functionality. Therefore, preventing the overwrite is a proactive measure to safeguard the application's stability and performance. This modification highlights the need for a deep understanding of the project's dependencies and the potential impact of changes in the environment.

You can find the modified requirements.txt here.

2. Heavily Modified app.py for Gradio 6.0 and CUDA 13

The app.py file underwent significant changes to ensure compatibility with Gradio 6.0 and to explicitly force CUDA 13 detection. These modifications are crucial for leveraging the latest features of Gradio and optimizing performance with CUDA 13. Let's explore the specific adjustments made.

A. DLL Path Injection (Top of File)

To ensure that Python finds the system DLLs before loading internal libraries, a code snippet was inserted at the very start of the script to explicitly register the CUDA 13 bin\x64 directory. This technique, known as DLL path injection, is a common practice when dealing with libraries that have dependencies on specific system DLLs. By adding the CUDA 13 directory to the system's DLL search path, we ensure that the necessary DLLs are loaded correctly, preventing potential runtime errors or compatibility issues. This is particularly important in environments where multiple versions of CUDA may be installed, as it allows us to explicitly specify which version should be used by the application. The code snippet accomplishes this by checking if the CUDA 13 directory exists and then using the os.add_dll_directory function (if available) or modifying the `os.environ[