Deep Learning with PyTorch made easy 🚀 !
v0.5.x WIP!
Here are the main design principles:
- The codes should be '
modulefirst', which means all previousmodels should be a simplemodulenow.- And
modelshould only be related to the training stuffs. If we only want to use the fancy AI models at inference stage,moduleshould be all we need.
- And
- The
modules should be as 'native' as possible: no inheritance from base classes exceptnn.Moduleshould be the best, and previous inheritance-based features should be achieved by dependency injection.- This helps the
modules to be moretorch.compilefriendly.
- This helps the
- Training stuffs are not considered at the first place, but they will definitely be added later on, based on the modern AI developments.
- APIs will be as BC as possible.
License
carefree-learn is MIT licensed, as found in the LICENSE file.
1 maintainer
Metadata
1 maintainer
Release files for carefree-learn 0.5.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| carefree-learn-0.5.0.tar.gz | 312.5 kB | Without provenance | Details |
Release files / carefree-learn-0.5.0.tar.gz
| Download URL | carefree-learn-0.5.0.tar.gz |
|---|---|
| Size | 312.5 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
5a2fbf4ee28387402be2e445830fafe6feddd017ec4504fc90cf7b9f6f977f79
|
|
BLAKE2b-256 checksum How to use checksums |
57ca739b9190c223967dfcd2b0af12b0783b36a3027f8d802ae3b174ad6633ff
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.11.5
|
Security for carefree-learn 0.5.0
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.
