Episode

GPU Accelerated Machine Learning with WSL 2

Tabs vs Spaces

Clarke Rahig will explain a bit about what it means to accelerate your GPU to help with training Machine Learning (ML) models, introducing concepts like parallelism, and then showing how to set up and run your full ML workflow (including GPU acceleration) with NVIDIA CUDA and TensorFlow in WSL 2.

Additionally, he'll demonstrate how students and beginners can start building knowledge in the Machine Learning (ML) space on their existing hardware by using the TensorFlow with DirectML package.

Chapters
  • 00:00 - Introduction
  • 00:49 - What is Machine Learning (ML)?
  • 01:24 - What is GPU acceleration?
  • 02:18 - Can I run my full ML workflow inside WSL?
  • 02:52 - How can I leverage NVIDIA CUDA inside WSL?
  • 03:39 - How do I set up NVIDIA CUDA inside WSL?
  • 11:07 - Is there a way to leverage my existing GPU?
  • 11:26 - How do I set up Tensorflow with DirectML?
  • 14:00 - Tabs vs. Spaces?
  • 14:29 - What's next? Where can I learn more?
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