Learning to Route in Similarity Graphs

It learns a mapping for vertices in an HNSW graph so as to improve nearest neighbor search and avoid local optima.

What do I need to run it?

  • A machine with some CPU (preferably 8+) and a GPU
    • Running with no GPU or less than 4 CPU cores may cause premature senility;
  • Some popular Linux x64 distribution
    • Tested on Ubuntu16.04, should work fine on any popular linux64 and even MacOS;
    • Windows and x32 systems may require heavy wizardry to run;
    • When in doubt, use Docker, preferably GPU-enabled (i.e. nvidia-docker)

How do I run it?

  1. Clone or download this repo. cd yourself to it's root directory.
  2. Grab or build a working python enviromnent. Anaconda works fine.
  3. Install packages from requirements.txt
  • Notably, the code really requires joblib 0.9.4 and pytorch 1.0.0.
  • You will also need jupyter or some other way to work with .ipynb files
  1. Run jupyter notebook and open a notebook in ./notebooks/
  • Before you run the first cell, change %env CUDA_VISIBLE_DEVICES=# to an index that you plan to use.
  • First it downloads data from dropbox. You will need up to 50-70Gb (Precomputed optimal paths take most of the space. Patch with computing optimal routing on the fly coming soon).
  • Second, defines an experiment setup. We provide one example per dataset:
    • SIFT100K_dcs256_gcn_size64_routing352_verification16.ipynb - SIFT100K dataset, 256dcs budget, 128d vectors compressed to 64d, 352 routing dcs and 16 dcs for verification
    • DEEP100K_dcs128_gcn_size96_routing120_verification8.ipynb - DEEP100K dataset, 128dcs budget, 96d vectors, no compression, 120 routing dcs and 8 dcs for verification
    • GLOVE100K_dcs256_gcn_size75_routing596_verification32.ipynb - GLOVE100K dataset, 256dcs budget, 300d vectors compressed to 75d, 596 routing dcs and 32 dcs for verification
    • An experiment setup
  • Another time-consuming stage is preparing path_cache.
    • In[7] in both notebooks.
    • If the process was interrupted or you suspect something is broken, !rm -rf {cache_path} and start over.

Ways to improve training performance

  • Grab a bigger GPU and/or more CPU cores
  • Multi-GPU training using torch DataParallel module