Training an NLP Engine in Go: Stability, GPU Optimization, and Overcoming "Word Salad"
About this lesson
Made a mistake on this video. I was not using the gpu. An update is here in this video. https://www.youtube.com/watch?v=hVSfiuoLxgs In this update, I’m taking a deep dive into the current state of Gollemer, specifically the transition back to a stable Linux-based training environment and the implementation of a pure-Go GPU workflow. The focus of this session is on the "boring" but essential work of training: monitoring resource usage, managing memory, and observing how the model reacts to long-duration runs. While the output is currently in the "word salad" phase, I discuss previous benchmarks where 4–5 hour training windows began producing coherent, structured results. substack - https://substack.com/@zachend1?utm_campaign=profile&utm_medium=profile-page In this video: Real-Time Training & GPU Waves: Watch how the model revs up through batches and sinks back down, utilizing a more natural resource cycle on Linux. The "Word Salad" Stage: A candid look at the challenges of epoch duration. I talk about why the model isn't quite where I want it yet and the results I saw during previous long-form training runs. Switching to GoGPU: Why moving away from Rust/CGO dependencies was a "thank god" moment for stability and debugging. Hardware & Memory Management: How I’m balancing 16GB of RAM and optimizing the training bash files for different OS environments. Intent Guessing Progress: Despite the current text generation being raw, the underlying intent guessing remains a core strength of the architecture. Real-Time Training Monitoring: Watch the training loops in action, utilizing the GPU in efficient "waves" to process batches. [04:33] GPU Implementation in Go: Why I moved to GoGPU for a more seamless, pure-Go experience that avoids the "horror" of complex stack traces and CGO stability issues. [01:09] Linux vs. Windows Workflow: A comparison of training stability and memory management between the two systems, including hardware awareness and profiling readout
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