BeanML
A framework for creating machine learning models.
BeanML provides a structured base for defining, registering, and loading machine learning models built on PyTorch Lightning. It handles model auto-discovery, checkpoint serialization, optimizer/scheduler configuration, and hyperparameter management.
Installation
pip install beanml
Usage
Read the Usage Guide to understand all of the features of BeanML and how to use them..
1 maintainer
Metadata
1 maintainer
Release files for beanml 1.0.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| beanml-1.0.0.tar.gz | 4.4 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| beanml-1.0.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 10.6 kB
Release files / beanml-1.0.0.tar.gz
| Download URL | beanml-1.0.0.tar.gz |
|---|---|
| Size | 4.4 kB |
| Tags | Source |
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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/6.2.0 CPython/3.11.9
|
Release files / beanml-1.0.0-py3-none-any.whl
| Download URL | beanml-1.0.0-py3-none-any.whl |
|---|---|
| Size | 6.1 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 | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.11.9
|
