Laptop Hardware Acceleration
Enabling TensorFlow Environments on Windows 11
Christopher A. Murphy
Summer Term, 2025
Project Goal
Give Windows-based data-science students a repeatable, minimal-friction path to modern GPU-accelerated TensorFlow using Microsoft Windows Subsystem for Linux 2 (WSL 2), NVIDIA’s CUDA toolchain, and a Conda-managed Python stack.
Video Tutorial
Problem Statement
TensorFlow versions 2.11 and newer no longer provide native, direct GPU support for Windows. A streamlined resource for a complete installation and configuration guide for enabling TensorFlow workloads on GPUs on Windows 11 is challenging to locate.
Key Learnings:
GPU Driver to CUDA compatibility mismatch is the #1 failure mode.
Conda + pip install tensorflow[and-cuda] is the fastest, conflict-free combo.
Azure TTS + PowerPoint timings provide an alternative to generate polished training videos without voice-acting or video-editing software.
Visualizations
Competencies Employed
Python Coding
Developing data pipelines, machine learning models, and automation tools using Python’s data science ecosystem (e.g., pandas, scikit-learn, TensorFlow)
Research
Locate and leverage active research to communicate technology trends.
LLM Integration
Connecting large language models (LLMs) to applications via APIs (e.g., OpenAI, Anthropic, Gemini).
Python Environment Management
Creating data science environments using Python.
Insights & Recommendations
Turn analysis into clear, prioritized stakeholder actions with rationale, trade-offs, and measurable outcomes.
Hardware Acceleration (GPU)
Leveraging GPU to process data science workloads.
LLM-Orchestrated Workflows
Designing multi-step pipelines where LLMs control logic, reasoning, and sequencing.
Prompt Engineering
Crafting and optimizing inputs to guide LLM behavior effectively and reliably.
Communication
Succinctly communicate complicated technical concepts.
Project Management
Planning, executing, and managing data science projects across teams and phases.
LLM Evaluation & Feedback
Capturing human feedback and analyzing LLM outputs to improve quality, relevance, and safety.
Additional Technical Information
Environment Information
Host: Windows 11 Home 23H2, NVIDIA Studio driver 576.80
WSL 2 guest: Ubuntu 22.04, CUDA 12.9.1 Toolkit & cuDNN
Python env: Conda 3.10, TensorFlow 2.16 (GPU wheel), NumPy, Pandas, scikit-learn, Jupyter
IDE: VS Code (Remote WSL)
Results Summary
Created a working TensorFlow GPU environment and successfully replicated the setup.
Produced a 15-minute narrated video suitable for YouTube or LMS upload.
Demonstrated ~18× speed-up vs CPU.
Future Improvements
Add an auto-detection script to probe GPU/driver and select/install the matching CUDA/cuDNN stack.
Build an AI agent that converts PPT notes to narration.
Generate a similar video guide for PyTorch.