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