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#!/usr/bin/env python
#ROS Node to convert a GPS waypoint published on the topic "waypoint" into a 2D Navigation Goal in SLAM to achieve autonomous navigation to a GPS Waypoint
#Converts Decimal GPS Coordinates of waypoint to ROS Position Vector relative to the current gps position of the robot
#Accounts for curvature of the earth using haversine formula

#Depends rospy, std_msgs, geographic_msgs, sensor_msgs, numpy
#Written by Alex McClung, 2015, [email protected], To be Released Open Source under Creative Commons Attribution Share-Alike Licence

import roslib
import rospy

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General guidelines for CPU performance on PyTorch

This file serves a BKM to get better performance on CPU for PyTorch, mostly focusing on inference or deployment. Chinese version available here.

1. Use mkldnn layout

layout refers to how data is organized in a tensor. PyTorch default layout is NCHW, from optimization perspective, MKL-DNN library (renamed as DNNL recently) may choose a different layout, sometimes refered to as internal layout or primitive layout. This is actually a normal technique for acceleration libraries, common knowledge is that NHWC runs faster than NCHW for convolution, changing the default NCHW to NHWC is called a reorder. MKL-DNN may choose different internal layouts based on the input pattern and the algorithm selected, e.g. nChw16c, a.k.a. reorder a 4-dim tensor into 5-dim by chop down dimension C by 16, for vectorization purpose (AVX512 instruction length is 16x32 bit).

By default on CPU, conv2d will ru

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package main

import (
	"github.com/kardianos/service"
	"log"
	"flag"
)

type Service struct {}

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#include <Python.h>
#include <stdio.h>
/*
 * gcc embpython.c -I/usr/include/python2.7 -lpython 
 **/
void loadModule()
{
    /* run objects with low-level calls */
    char *arg1="sir", *arg2="robin", *cstr;
    printf("Load Module err!\n");

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/* Example of embedding Python in another program */
// to compile run:
//  gcc -o test $(python-config --cflags) test.c $(python-config --ldflags) && ./test

#include<stdio.h>
#include "Python.h"

void initxyzzy(void); /* Forward */

main(int argc, char **argv)

Coderx7

Here's my experience of installing the NVIDIA CUDA kit 8.0 on a fresh install of Ubuntu Desktop 16.04.3 LTS.

Table of Contents

• Install NVIDIA Graphics Driver via apt-get
• Install CUDA
• Install cuDNN

Table of contents generated with markdown-toc

1. Install NVIDIA Graphics Driver via apt-get

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In this article, I will share some of my experience on installing NVIDIA driver and CUDA on Linux OS. Here I mainly use Ubuntu as example. Comments for CentOS/Fedora are also provided as much as I can.

Table of Contents

• Install NVIDIA Graphics Driver via apt-get
• Install NVIDIA Graphics Driver via runfile
  • Remove Previous Installations (Important)
  • Download the Driver
  • Install Dependencies
  • Creat Blacklist for Nouveau Driver

• Stop lightdm/gdm/kdm

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#برای نمایش بلادرنگ نمودار ترینینگ و تست ما 
import numpy as np
from matplotlib import pyplot as plt

class LivePlotNotebook(object):
    """
    Live plot using %matplotlib notebook in jupyter notebook
    original url : https://gist.github.com/wassname/04e77eb821447705b399e8e7a6d082ce
    """

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# Dinesh Jayaraman

# Based on code by
# Authors: Fabian Pedregosa <[email protected]>
#          Olivier Grisel <[email protected]>
#          Mathieu Blondel <[email protected]>
#          Gael Varoquaux
# License: BSD 3 clause (C) INRIA 2011

print(__doc__)

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import argparse
import time

start_time=time.time();
################## Argument Parsing #####################################
parser=argparse.ArgumentParser();
parser.add_argument('-s','--solver', default='', type=str); # if empty, solver is created, else read
parser.add_argument('-res', '--resume_from', default='', type=str); #if not empty, resumes training from given file
parser.add_argument('-ft', '--finetune_from', default='', type=str);