evoten
Find the documentation here.
Vectorized computation of tree likelihoods.
Features
- Supports multiple models
- Highly parallel, processes trees in layers, models and input examples are treated in parallel
- Multiple compute backends (TensorFlow, pytorch)
Installation
For users:
pip install evoten[tensorflow] # or [torch] for pytorch backend
For developers:
git clone https://github.com/felbecker/evoten
cd evoten
pip install -e .[tensorflow,torch,docs]
Minimal example
See here.
1 maintainer
Metadata
1 maintainer
Release files for evoten 0.0.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| evoten-0.0.2.tar.gz | 1.3 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| evoten-0.0.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 1.3 MB
Release files / evoten-0.0.2.tar.gz
| Download URL | evoten-0.0.2.tar.gz |
|---|---|
| Size | 1.3 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.12.7
|
Release files / evoten-0.0.2-py3-none-any.whl
| Download URL | evoten-0.0.2-py3-none-any.whl |
|---|---|
| Size | 34.6 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.12.7
|
