Jieru Mei meijieru

THIS GIST WAS MOVED TO TERMSTANDARD/COLORS REPOSITORY.

PLEASE ASK YOUR QUESTIONS OR ADD ANY SUGGESTIONS AS A REPOSITORY ISSUES OR PULL REQUESTS INSTEAD!

Smart Caps Lock: Remap to Control AND Escape (Linux, Mac, Windows)

把 Caps Lock 變成智慧的 Control 以及 Escape ：

單獨輕按一下就是 Escape 。
若按下時同時按著其他鍵，就會是 Control 。

這應該是 Vim 和 Emacs 的最佳解了！(Emacs? Bash 的快捷鍵就是 Emacs 系列的)

Send Escape if you tap Caps Lock alone.

A Tour of PyTorch Internals (Part I)

The fundamental unit in PyTorch is the Tensor. This post will serve as an overview for how we implement Tensors in PyTorch, such that the user can interact with it from the Python shell. In particular, we want to answer four main questions:

How does PyTorch extend the Python interpreter to define a Tensor type that can be manipulated from Python code?
How does PyTorch wrap the C libraries that actually define the Tensor's properties and methods?
How does PyTorch cwrap work to generate code for Tensor methods?
How does PyTorch's build system take all of these components to compile and generate a workable application?

Extending the Python Interpreter

PyTorch defines a new package torch. In this post we will consider the ._C module. This module is known as an "extension module" - a Python module written in C. Such modules allow us to define new built-in object types (e.g. the Tensor) and to call C/C++ functions.

WORK IN PROGRESS

PyTorch Internals Part II - The Build System

In the first post I explained how we generate a torch.Tensor object that you can use in your Python interpreter. Next, I will explore the build system for PyTorch. The PyTorch codebase has a variety of components:

The core Torch libraries: TH, THC, THNN, THCUNN
Vendor libraries: CuDNN, NCCL
Python Extension libraries
Additional third-party libraries: NumPy, MKL, LAPACK

	#cython: boundscheck=False, wraparound=False
	import numpy as np
	cimport numpy as np

	from cython.parallel cimport prange

	def dot(np.ndarray[np.float32_t, ndim=2] a not None,
	np.ndarray[np.float32_t, ndim=2] b not None,
	np.ndarray[np.float32_t, ndim=2] out=None):
	"""Naive O(N**3) 2D np.dot() implementation."""

	g_LastCtrlKeyDownTime := 0
	g_AbortSendEsc := false
	g_ControlRepeatDetected := false

	*CapsLock::
	if (g_ControlRepeatDetected)
	{
	return
	}

	{0: 'tench, Tinca tinca',
	1: 'goldfish, Carassius auratus',
	2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias',
	3: 'tiger shark, Galeocerdo cuvieri',
	4: 'hammerhead, hammerhead shark',
	5: 'electric ray, crampfish, numbfish, torpedo',
	6: 'stingray',
	7: 'cock',
	8: 'hen',
	9: 'ostrich, Struthio camelus',