henryiii

MIT License

Copyright (c) 2023 Françoise CONIL

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

ntustison

ANTsX ecosystem

ANTs intro

• ANTs --- Set of C++ programs built on top of the Insight Toolkit
• ANTsR --- R-based ANTs interface (dependencies include ITKR, ANTsRCore)
• ANTsPy --- Python-based ANTs interface
• ANTsXNet --- deep learning library built on Keras
  • ANTsRNet
• ANTsPyNet

mingfeima

PyTorch Channels Last Memory Format Performance Optimization on CPU Path

("mkldnn" has been renamed to "oneDNN", but exsiting PyTorch APIs still use "mkldnn", future work will align PyTorch user level APIs to "oneDNN")

Table of Contents

• PyTorch Channels Last memory format introduction
• oneDNN API for NHWC layout
• Generic Channels Last memory format optimization with ATen native
• oneDNN NHWC integration

NB: Memory format refers to data representation that describes how multidimensional arrays (nD) are stored in linear (1D) memory address space. Memory format has the same semantic with layout in oneDNN. Layout in PyTorch has other semantic ofdescribing dense or sparse with the attributes: 'torch.strided', 'torch.sparse_coo'.

rabernat

mrocklin

jakirkham

dholth

#!/usr/bin/env python
# wgc "wheel greater compression"
# puts everything but *.dist-info/ in an interior archive

import pathlib
import os.path
import sys
import hashlib
import zipfile
import base64

N-Dekker

/*
Compares performance of "old-school" neighborhood iteration, using
itk::ConstantBoundaryCondition, and range-based neighborhood iteration,
using the patch "WIP: Added policy for constant NeighborhoodRange values
outside image", http://review.source.kitware.com/#/c/23795/7.

Niels Dekker, LKEB, Leiden University Medical Center, 2018

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,

F21

This is a quick guide of the commands we use to sign someone's GPG key in a virtual key signing party.

Note: The steps cover only the technical aspects of signing someone's key. Before signing someone's key, you must verify their identity. This is usually done by showing government-issued ID and confirming the key's fingerprint

The commands will work for both GPG and GPG2.

I use Julian's key for the examples. His key id is 2AD3FAE3. You should substitute with the appropriate key id when running the commands.

Signing the key

1. List the keys currently in your keyring: gpg --list-keys.

linar-jether

from __future__ import absolute_import, division, print_function

import multiprocessing
import pickle
from multiprocessing.pool import ThreadPool

from celery import shared_task
from dask.local import get_async  # TODO: get better get
from dask.context import _globals
from dask.optimize import fuse, cull