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Description:

pipseq 0.0.2

pipseq

Developer Guide
Setup
# create conda environment
$ mamba env create -f env.yml

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

Install
pip install -e .

# install from pypi
pip install pipseq

nbdev
# activate conda environment
$ conda activate pipseq

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

# make changes under nbs/ directory
# ...

# compile to have changes apply to the pipseq 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/pipseq.git

or from conda
$ conda install -c dsm-72 pipseq

or from pypi
$ pip install pipseq

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