pythonfix
pytorch-tabular 1.2.0
A standard framework for using Deep Learning for tabular data
A standard framework for using Deep Learning for tabular data
Stars: 1630, Watchers: 1630, Forks: 167, Open Issues: 21The pytorch-tabular/pytorch_tabular repo was created 5 years ago and the last code push was Yesterday.
The project is very popular with an impressive 1630 github stars!
How to Install pytorch-tabular
You can install pytorch-tabular using pip
pip install pytorch-tabular
or add it to a project with poetry
poetry add pytorch-tabular
Package Details
- Author
- None
- License
- MIT
- Homepage
- None
- PyPi:
- https://pypi.org/project/pytorch-tabular/
- GitHub Repo:
- https://github.com/manujosephv/pytorch_tabular
Classifiers
- Scientific/Engineering
- Scientific/Engineering/Artificial Intelligence
- Software Development
- Software Development/Libraries
- Software Development/Libraries/Python Modules
Related Packages
Errors
A list of common pytorch-tabular errors.
Code Examples
Here are some pytorch-tabular code examples and snippets.
GitHub Issues
The pytorch-tabular package has 21 open issues on GitHub
- [ENH] Move to
lightning.pytorchin place ofpytorch_lightning. - [MNT] Dependabot: Update pytorch-lightning requirement from <2.5.0,>=2.0.0 to >=2.0.0,<2.7.0
- [ENH] deal with
pytorch-tabnetlapsed soft dependency - [MNT] reduce data size of test data to allow faster tests
- [ENH] refactor progress bar backend to allow user choice, decouple from
rich, investigaterichproblems - [ENH] address long test run times
- Is it necessary to manually specify the loss weight for data with a large difference in the number of positive and negative samples, and how to do it
- [BUG] failing test fixtures -
load_classification_dataetc - [MNT] integration plan with GC.OS
- [Maintenance] Updating the dependency versions and migration to
pyproject.toml - add custom loss, optim, metrics for model_sweep
- Bump pypa/gh-action-pypi-publish from 1.12.2 to 1.13.0
- Help: custom loss for model_sweep
- Working with huge datasets
- Re-write DataModule from scratch enabling support for Spark DataFrames, Polars, and larger than memory dataframes
Related Packages & Articles
autogluon.core 1.5.0
Fast and Accurate ML in 3 Lines of Code
pytorch-lightning 2.6.1
PyTorch Lightning is the lightweight PyTorch wrapper for ML researchers. Scale your models. Write less boilerplate.
petastorm 0.13.1
Petastorm is a library enabling the use of Parquet storage from Tensorflow, Pytorch, and other Python-based ML training frameworks.
onnx 1.20.1
Open Neural Network Exchange (ONNX) is an open ecosystem that empowers AI developers to choose the right tools as their project evolves. ONNX provides an open source format for AI models, both deep learning and traditional ML. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types. Currently we focus on the capabilities needed for inferencing (scoring).
kornia 0.8.2
Open Source Differentiable Computer Vision Library for PyTorch
interpret-community 0.32.0
Microsoft Interpret Extensions SDK for Python
huggingface-hub 1.4.1
Client library to download and publish models, datasets and other repos on the huggingface.co hub
horovod 0.28.1
Horovod is a powerful distributed training framework for Python that allows you to train deep learning models across multiple GPUs and servers quickly and efficiently. It falls under the category of distributed computing libraries. Built on top of TensorFlow, PyTorch, and other popular deep learning frameworks, Horovod simplifies the process of scaling up your model training by handling the complexities of distributed training under the hood.
gluonts 0.16.2
Probabilistic time series modeling in Python.