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[P] 2,000x Faster RAPIDS TSNE – 3 hours down to 5 seconds on NVIDIA GPUs

[P] 2,000x Faster RAPIDS TSNE - 3 hours down to 5 seconds on NVIDIA GPUs

TSNE is a very popular data visualization algorithm used alongside PCA and UMAP.

Sklearn’s TSNE is very effective for small datasets, but on the 60,000 MNIST Digits dataset, expect to wait 1 hour. With RAPIDS cuML, TSNE on MNIST runs in 3 seconds!

On 200,000 rows, Sklearn takes a whopping 3 hours, whilst RAPIDS takes 5 seconds! (2,000x faster).

Figure 1. cuML TSNE on MNIST Fashion takes 3 seconds. Scikit-Learn takes 1 hour.

Check out my blog showcasing how cuML achieves this massive performance boost, and how NVIDIA GPUs can help scientists and engineers save their precious time. https://medium.com/rapids-ai/tsne-with-gpus-hours-to-seconds-9d9c17c941db

Figure 2. TSNE used on the 60,000 Fashion MNIST dataset (3 seconds)

Give cuML a try! You might know me as the author of HyperLearn, and I can say cuML is the gold standard package for machine learning on GPUs! https://github.com/rapidsai/cuml

Linear Regression, UMAP, K-Means, DBSCAN etc are all sped up on the GPU! If you have any questions, feel free to ask!

Table 1. cuML’s TSNE time running on an NVIDIA DGX-1 with using 1 V100 GPU.

Finally, a big drawback of current GPU implementations is its memory consumption. With cuML TSNE, we use 30% less GPU memory! In a future release, this will be shaved by 33% again to a total of 50% memory reductions! We will also support PCA initialization.

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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.