Install this package:
emerge -a sci-libs/faiss
<pkgmetadata>
<maintainer type="person">
<email>iohann.s.titov@gmail.com</email>
<name>Ivan S. Titov</name>
</maintainer>
<longdescription lang="en">
Faiss is a library for efficient similarity search and clustering of
dense vectors. It contains algorithms that search in sets of vectors
of any size, up to ones that possibly do not fit in RAM. It also
contains supporting code for evaluation and parameter tuning. Faiss
is written in C++ with complete wrappers for Python — installed
here under USE=python via SWIG-generated bindings. The PyPI
distribution is named "faiss-cpu"; the Python import name is
plain "faiss".
</longdescription>
<use>
<flag name="python">Build the SWIG-generated Python bindings (import faiss)</flag>
</use>
<upstream>
<remote-id type="github">facebookresearch/faiss</remote-id>
<remote-id type="pypi">faiss-cpu</remote-id>
</upstream>
</pkgmetadata>
Manage flags for this package:
euse -i <flag> -p sci-libs/faiss |
euse -E <flag> -p sci-libs/faiss |
euse -D <flag> -p sci-libs/faiss
| Flag | Description | 1.14.2 | 1.14.1 |
|---|---|---|---|
| cpu_flags_x86_avx2 | ⚠️ | ✓ | ✓ |
| cpu_flags_x86_avx512f | ⚠️ | ✓ | ✓ |
| python | Build the SWIG-generated Python bindings (import faiss) | ✓ | ✓ |
| test | Build and run unit tests ⚠️ | ✓ | ✓ |
| Type | File | Size | Versions |
|---|---|---|---|
| DIST | faiss-1.14.1.gh.tar.gz | 1377324 bytes | 1.14.1 |
| DIST | faiss-1.14.2.gh.tar.gz | 1595385 bytes | 1.14.2 |
| Type | File | Size |
|---|