pythonfix
fvcore 0.1.5.post20221221
Collection of common code shared among different research projects in FAIR computer vision team
Collection of common code shared among different research projects in FAIR computer vision team
Stars: 1869, Watchers: 1869, Forks: 227, Open Issues: 47The facebookresearch/fvcore repo was created 4 years ago and the last code push was 1 weeks ago.
The project is very popular with an impressive 1869 github stars!
How to Install fvcore
You can install fvcore using pip
pip install fvcore
or add it to a project with poetry
poetry add fvcore
Package Details
- Author
- FAIR
- License
- Apache 2.0
- Homepage
- https://github.com/facebookresearch/fvcore
- PyPi:
- https://pypi.org/project/fvcore/
- GitHub Repo:
- https://github.com/facebookresearch/fvcore
Related Packages
Errors
A list of common fvcore errors.
Code Examples
Here are some fvcore code examples and snippets.
GitHub Issues
The fvcore package has 47 open issues on GitHub
- ValueError: Invalid type <class 'numpy.int32'> for the flop count! Please use a wider type to avoid overflow.
- ShapelyDeprecationWarning in CropTransform
- fix pypi package release
- add lstm layer support
Related Packages & Articles
datasets 2.18.0
HuggingFace community-driven open-source library of datasets
autogluon.core 1.0.0
AutoML for Image, Text, and Tabular Data
nlp 0.4.0
HuggingFace/NLP is an open library of NLP datasets.
flair 0.13.1
A very simple framework for state-of-the-art NLP
fastai 2.7.14
fastai simplifies training fast and accurate neural nets using modern best practices
einops 0.7.0
A new flavour of deep learning operations
easyocr 1.7.1
End-to-End Multi-Lingual Optical Character Recognition (OCR) Solution
dlib 19.24.4
A toolkit for making real world machine learning and data analysis applications
deepspeed 0.14.0
DeepSpeed is a Python package developed by Microsoft that provides a deep learning optimization library designed to scale across multiple GPUs and servers. It is capable of training models with billions or even trillions of parameters, achieving excellent system throughput and efficiently scaling to thousands of GPUs.
DeepSpeed is particularly useful for training and inference of large language models, and it falls under the category of Machine Learning Frameworks and Libraries. It is designed to work with PyTorch and offers system innovations such as Zero Redundancy Optimizer (ZeRO), 3D parallelism, and model-parallelism to enable efficient training of large models.