Code for ICML2019 paper

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

• 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)

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

4. 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.

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