Comments (1)
No, as of now, there is no option to run any of the algorithms on a GPU. Since most algorithms use bitparallelism it would theoretically be possible to implement them on a GPU. However at least right now I consider this out of scope of RapidFuzz/Levenshtein. It would be a lot of work to implement and a lot harder to distribute. So this would likely need to be implemented in a separate library.
I assume you're asking because you need to compare a lot of string. Did you already try to use rapidfuzz.cdist
for this? It allows comparing whole lists of strings, which is significantly faster, since it removes the overhead of calling the functions from Python. In addition it gives the option to use multithreading:
from rapidfuzz import process, string_metric
cdist(a, b, scorer=string_metric.levenshtein)
from levenshtein.
Related Issues (20)
- Mypy complaines with newest release (20.07), code still works HOT 3
- Extension to word-level HOT 1
- score_cutoff argument not seeming to work for ratio HOT 6
- Non-standard function signature for get_requires_for_build_wheel() HOT 2
- Mismatch between different implementations of Levenshtein HOT 5
- Support Java JNI call HOT 1
- Module 'Levenshtein' has no attribute 'distance' HOT 13
- Fails to build debian11/python 3.9: Could NOT find Python (missing: Interpreter Development.Module) HOT 15
- dependency rapidfuzz 3.0 HOT 1
- Compatibility with rapidfuzz-cpp 2.0.0 HOT 2
- Make it a rust crate HOT 6
- License HOT 1
- Levenshtein realisation counts substitution as 2 edits instead of 1 HOT 2
- Compatibility with rapidfuzz-cpp 3 HOT 2
- please provide a source tarball including external dependencies HOT 3
- `Callable` missing type argument, making some methods partially-Unknown HOT 6
- Citation HOT 1
- Damerau–Levenshtein distance HOT 1
- jaro_winkler gives values larger than 1. HOT 2
- Distance finds min cost not min number HOT 1
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from levenshtein.