roachsinai

Table Of Content

Skip to the relevant sections if needed.

• 2-min tutorial to do it the quick-and-dirty-way
• Concepts for resolving Git conflicts
• Setting up different editors / tool for using git mergetool
  • Finding out what mergetool editors are supported
• mergetool simple code example for vimdiff
  • Resolving conflict from a Git pull
• Other great references and tutorials

roachsinai

#include <cstddef>
#include <cstdlib>

template <typename R, auto getter, auto setter, size_t (*offset)()>
struct property {
    inline R *self() {
        return reinterpret_cast<R *>(reinterpret_cast<size_t>(this) - offset());
    }
    inline operator auto() { return (self()->*getter)(); }
    inline void operator=(auto t) { (self()->*setter)(t); }

roachsinai

all:
	cc -shared -fPIC lib_one.c lib_one.h -o libone.so
	cc -shared -fPIC lib_two.c lib_two.h -o libtwo.so -L. -lone -Wl,-rpath=libs
	mkdir -p libs
	mv libone.so libs
	# -L. 是针对编译时的，使得链接器（ld）可以在 gcc 编译时找到 libtwo.so 让 name loopup 成功
	# -rpath=. 是针对运行时的，使得在执行 main 时让动态链接器（ld.so）可以找到 main 依赖的 so
	# Wl 表示后面的是一个链接器选项（针对 ld，而不是 gcc 的选项）
	cc main.c -o main -L. -ltwo -Wl,-rpath=.

roachsinai

/* recursive mkdir based on
http://nion.modprobe.de/blog/archives/357-Recursive-directory-creation.html
*/
#include <unistd.h>
#include <sys/types.h>
#include <sys/stat.h>
#include <string.h>

#define PATH_MAX_STRING_SIZE 256

roachsinai

#!/bin/bash
## This gist contains instructions about cuda v11.2 and cudnn8.1 installation in Ubuntu 20.04 for Pytorch 1.8 & Tensorflow 2.7.0

### steps ####
# verify the system has a cuda-capable gpu
# download and install the nvidia cuda toolkit and cudnn
# setup environmental variables
# verify the installation
###

roachsinai

#include <assert.h>
#include <stdint.h>
#include <stdio.h>
#include <string.h>
#include <windows.h> // 各种位图数据结构

class Converter
{
public:
    Converter() : pixels_(NULL), width_(0), height_(0) {}

roachsinai

#!/bin/sh
# Copy the contents of this file to the clipboard, then get a terminal open on your device and enter:  
#   $ cat > n.sh  
#  [Ctrl+V] or Right Click, Paste. Then [Ctrl+D].
#  chmod +x n.sh
#  To run: ./n.sh eth0
SLP=1 # display / sleep interval
DEVICE=$1
IS_GOOD=0
for GOOD_DEVICE in `grep \: /proc/net/dev | awk -F: '{print $1}'`; do

roachsinai

name: "YOLONET"
layer {
  name: "data"
  type: "Input"
  top: "data"
  input_param { shape: { dim: 1 dim: 3 dim: 416 dim: 416 } }
}
layer {
  name: "conv1"
  type: "Convolution"

roachsinai

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

roachsinai

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:

1. How does PyTorch extend the Python interpreter to define a Tensor type that can be manipulated from Python code?
2. How does PyTorch wrap the C libraries that actually define the Tensor's properties and methods?
3. How does PyTorch cwrap work to generate code for Tensor methods?
4. 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.