lull 0.0.1

Package for the lulling all output

lull

Developer Guide

Setup

# create conda environment
$ mamba env create -f env.yml

# update conda environment
$ mamba env update -n lull --file env.yml

Install

pip install -e .

# install from pypi
pip install lull

nbdev

# activate conda environment
$ conda activate lull

# make sure the lull package is installed in development mode
$ pip install -e .

# make changes under nbs/ directory
# ...

# compile to have changes apply to the lull package
$ nbdev_prepare

Publishing

# publish to pypi
$ nbdev_pypi

# publish to conda
$ nbdev_conda --build_args '-c conda-forge'
$ nbdev_conda --mambabuild --build_args '-c conda-forge -c dsm-72'

Usage

Installation

Install latest from the GitHub repository:

$ pip install git+https://github.com/dsm-72/lull.git

or from conda

$ conda install -c dsm-72 lull

or from pypi

$ pip install lull

Documentation

Documentation can be found hosted on GitHub repository pages. Additionally you can find package manager specific guidelines on conda and pypi respectively.

PyTorch Documentation:

• TorchData
• How to Package PyTorch Models
• torch.monitor.Event
• torchvision
• torchvision.Datasets.VisionDataset
• torchvision.utils.flow_to_image

PyTorch Models to Consider:

• Diffusion Video AutoEncoders
• ConvLSTM AutoEncoder
• Recurrent All Pairs Field Transforms for Optical Flow
• Optical Flow Toolbox (mmflow)
• torchvision.models.optical_flow.raft