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# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
d = 64 # dimension
nb = 100000 # database size
nq = 10000 # nb of queries
np.random.seed(1234) # make reproducible
xb = np.random.random((nb, d)).astype('float32')
xb[:, 0] += np.arange(nb) / 1000.
xq = np.random.random((nq, d)).astype('float32')
xq[:, 0] += np.arange(nq) / 1000.
import faiss # make faiss available
ngpus = faiss.get_num_gpus()
print("number of GPUs:", ngpus)
cpu_index = faiss.IndexFlatL2(d)
gpu_index = faiss.index_cpu_to_all_gpus( # build the index
cpu_index
)
gpu_index.add(xb) # add vectors to the index
print(gpu_index.ntotal)
k = 4 # we want to see 4 nearest neighbors
D, I = gpu_index.search(xq, k) # actual search
print(I[:5]) # neighbors of the 5 first queries
print(I[-5:]) # neighbors of the 5 last queries
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