Contributing a new method

Under vibe/algorithms/<algorithm>/, add:

• image.def container definition
• module.py adapter
• config.yml parameter grid

See e.g. hnswlib for a small working example.

1. Build the image

Create image.def. Install the method and its dependencies here, pinning the method to a version or a Git commit:

Bootstrap: localimage
From: base.sif
%post
  apt-get install -y libomp-dev
  pip install --no-cache-dir example-ann==1.2.3

Build it with the directory name:

./install.sh --algorithm example

Use --force after changing image.def.

2. Implement the adapter

Subclass BaseANN in module.py:

import numpy as np
from example_ann import Index
from ..base.module import BaseANN
class ExampleANN(BaseANN):
    def __init__(self, metric, build_budget, graph_degree):
        self.metric = metric
        self.build_budget = build_budget
        self.graph_degree = graph_degree
    def fit(self, X):
        self.index = Index(
            X, metric=self.metric, graph_degree=self.graph_degree, threads=1
        )
        self.index.build(self.build_budget)
    def set_query_arguments(self, search_budget):
        self.search_budget = search_budget
    def query(self, vector, n):
        ids = self.index.search(vector, n, self.search_budget)
        return np.asarray(ids, dtype=np.int64)
    def __str__(self):
        return (
            f"ExampleANN(build={self.build_budget}, degree={self.graph_degree}, "
            f"search={self.search_budget})"
        )

The important adapter methods are:

• __init__(*build_args) stores build parameters; do not build the index yet.
• fit(X) builds the index using the corpus vectors stored in a NumPy array X. Do not modify X in place. All indexes are built using a single thread.
• set_query_arguments(*query_args) applies one query-time hyperparameter combination.
• query(vector, n) returns at most n unique row IDs from X, not distances.
• __str__() records every build and query parameter affecting the result.

GPU methods should implement batch_query(X, n) and get_batch_results().

OOD methods that leverage query samples should implement fit_ood(X_train, X_learn, X_learn_neighbors), where X_learn contains the query samples and X_learn_neighbors contains the 100 nearest neighbors for each query sample.

3. Configure the hyperparameters

Create config.yml:

float:
  any:
  - name: example
    module: vibe.algorithms.example
    constructor: ExampleANN
    singularity_image: example
    disabled: false
    gpu: false
    ood: false
    base_args: ['@metric']
    run_groups:
      default:
        args:
          build_budget: [100, 200]
          graph_degree: [16, 32]
        query_args:
          search_budget: [20, 40, 80, 160]

Fill in name, module, constructor, and singularity_image (usually just the name of the method for each).

The outer key selects the supported datatype: float for regular float vectors, int8, uint8, or binary for quantized vectors. The next key is a supported distance (euclidean, cosine, ip, normalized, or hamming) or any if the method supports all distances.

args and query_args are Cartesian-product grids passed positionally in YAML order. Put only index-building parameters in args. Generally, it's best to keep the number of indexing combinations below 20 and query combinations below 60.

Set gpu: true for GPU methods. They run only with run.py --gpu and must implement the batch-query methods described above. Set ood: true when the method uses the learning queries and neighbors supplied by out-of-distribution datasets; VIBE then calls fit_ood when that data is available. Both keys default to false and may be omitted.

4. Update the README and open a pull request

Add the method and pinned implementation version to the README, then open a pull request.