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@dypsilon
dypsilon / frontendDevlopmentBookmarks.md
Last active May 7, 2024 01:27
A badass list of frontend development resources I collected over time.
@F21
F21 / signing-gpg-keys.md
Last active May 5, 2024 20:59
Signing someone's GPG key

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.
@myusuf3
myusuf3 / delete_git_submodule.md
Created November 3, 2014 17:36
How effectively delete a git submodule.

To remove a submodule you need to:

  • Delete the relevant section from the .gitmodules file.
  • Stage the .gitmodules changes git add .gitmodules
  • Delete the relevant section from .git/config.
  • Run git rm --cached path_to_submodule (no trailing slash).
  • Run rm -rf .git/modules/path_to_submodule (no trailing slash).
  • Commit git commit -m "Removed submodule "
  • Delete the now untracked submodule files rm -rf path_to_submodule
@ntustison
ntustison / a_background.md
Last active April 30, 2024 19:18
ANTsX Tutorial

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
@defunkt
defunkt / clients.md
Created April 18, 2010 14:09
A list of Gist clients.

Gist Clients

Want to create a Gist from your editor, the command line, or the Services menu? Here's how.

Editor Support

@henryiii
henryiii / LICENSE
Last active February 7, 2024 03:21
Download all pyproject.tomls
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:
@minrk
minrk / ipnbdoctest.py
Last active September 14, 2023 11:23
simple example script for running and testing notebooks.
#!/usr/bin/env python
"""
simple example script for running and testing notebooks.
Usage: `ipnbdoctest.py foo.ipynb [bar.ipynb [...]]`
Each cell is submitted to the kernel, and the outputs are compared with those stored in the notebook.
"""
# License: Public Domain, but credit is nice (Min RK).
@mingfeima
mingfeima / pytorch_channels_last_perf_optimization.md
Last active September 1, 2023 03:02
PyTorch Channels Last memory format perf optimization and oneDNN integration plan.

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'.

@linar-jether
linar-jether / dask_celery_scheduler.py
Created March 18, 2018 09:20
A dask distributed scheduler based on on Celery tasks - Allows reusing an existing celery cluster for ad-hoc computation
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