Extract insights from massive text datasets using cuML's GPU-accelerated BERTopic
Topic modeling helps you discover hidden themes in large document collections—but traditional methods crawl when datasets grow to millions of records. This playbook shows how to process 40 million Amazon product reviews in minutes using GPU-accelerated BERTopic.
BERTopic combines transformer embeddings with clustering to extract human-readable topics from text. By swapping CPU-based UMAP and HDBSCAN with GPU-accelerated versions from RAPIDS cuML, you get the same results dramatically faster—no code changes required.
cuml.accel and your existing UMAP/HDBSCAN code runs on GPU automaticallyYou'll run a complete topic modeling pipeline on 40 million product reviews and generate interactive visualizations of discovered topics.
By the end, you'll be able to:
Hardware Requirements:
Software Requirements:
conda --versionnvidia-smiAll required assets are in the playbook directory nvidia/station-topic-modeling/assets (see Instructions, Step 7). Key file:
video_notebook_for_GPU_Accelerated_Machine_Learning_BERTopic_RTX6000_40M.ipynb - Complete Jupyter notebook with GPU-accelerated topic modeling pipeline (filename reflects original demo hardware; the notebook runs on GB300 and other NVIDIA GPUs)