schlamar

import logging
import multiprocessing
import time

import mplog

FORMAT = '%(asctime)s - %(processName)s - %(levelname)s - %(message)s'
logging.basicConfig(level=logging.DEBUG, format=FORMAT)
existing_logger = logging.getLogger('x')

withr

library(shiny)
library(datasets)
Logged = FALSE;
PASSWORD <- data.frame(Brukernavn = "withr", Passord = "25d55ad283aa400af464c76d713c07ad")
# Define server logic required to summarize and view the selected dataset
shinyServer(function(input, output) {
  source("www/Login.R",  local = TRUE)
  
  observe({
    if (USER$Logged == TRUE) {

slaypni

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

import sys
import math

import numpy as np

sys.path.append('xgboost/wrapper/')
import xgboost as xgb

jarutis

## install Catalyst proprietary
sudo ntfsfix /dev/sda2
sudo cp /etc/X11/xorg.conf /etc/X11/xorg.conf.BAK
sudo apt-get remove --purge fglrx*
sudo apt-get install linux-headers-generic
sudo apt-get install fglrx xvba-va-driver libva-glx1 libva-egl1 vainfo 
sudo amdconfig --initial

## install build essentials
sudo apt-get install cmake

Alexey-Kamenev

// -------------
// in gstCamera.cpp, gstCamera::buildLaunchStr():
	ss << "v4l2src device=\"/dev/video1\" ! video/x-raw, width=(int)" << mWidth << ", height=(int)" << mHeight << ", format=(string)YUY2 ! appsink name=mysink";


// -------------
// in gstCamera.cpp, gstCamera::ConvertRGBA:
	if(CUDA_FAILED(cudaYUYVToRGBAf((uchar2*)input, (float4*)mRGBA, mWidth, mHeight)))
		return false;

dusty-nv

#!/bin/bash
#
# EDIT:  this script is outdated, please see https://forums.developer.nvidia.com/t/pytorch-for-jetson-nano-version-1-6-0-now-available
#
sudo apt-get install python-pip

# upgrade pip
pip install -U pip
pip --version
# pip 9.0.1 from /home/ubuntu/.local/lib/python2.7/site-packages (python 2.7)

daerduoCarey

import os
import sys
import numpy as np
from data_util import *
import datetime

ply_filelist = 'scripts/modelnet40_ply_filelist_shuffled.txt'

H5_BATCH_SIZE = 2000

kyamagu

"""
Use in PyTorch.
"""

def accuracy(output, target):
    """Computes the accuracy for multiple binary predictions"""
    pred = output >= 0.5
    truth = target >= 0.5
    acc = pred.eq(truth).sum() / target.numel()
    return acc

seanjtaylor

zhanwenchen

Updated 4/11/2018

Here's my experience of installing the NVIDIA CUDA kit 9.0 on a fresh install of Ubuntu Desktop 16.04.4 LTS.

Table of Contents

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

Table of contents generated with markdown-toc