rappdw/tsne-perf-test repository overview

⁠Performance Comparision of Several TSNE Implementations

This docker image allows for head to head performance comparisions between different implementations of BH-tSNE.

The versions that are compared are:

• lvdmaaten⁠ (e53ec46d9...)
• 10XDev⁠ (1858079da...)
• danielfrg⁠ (v0.1.8) (This is the library on pypi)
• rappdw⁠ (v0.1.9) (This is now available on pypi as tsne-mp)

To run the test:

1. Build to docker image: docker build -t tsne-perf .
2. Run the image, specifying which data set to use, e.g. docker run -it tsne-perf iris NOTE: do not run -d, for some reason, this slows things down significatnly. if you need to detach, use ctl-p, ctrl-q

⁠vs. scikit-learn impl

Because of implementation differences I don't include scikit-learn in the performance test. scikit-learn performs significantly slower than any of these implementations (approximately twice as long as the lvdmaaten implementation with informal testing).

⁠On an EC2 m5.12xlarge instance

⁠2500 Instance MNIST Data

⁠70,000 Instance MNIST Data