hartfordfive

#!/bin/bash

COMMAND='puppet parser validate'
TEMPDIR=`mktemp -d`

echo "### Attempting to validate puppet files... ####"

# See https://www.kernel.org/pub/software/scm/git/docs/githooks.html#pre-receive    
oldrev=$1
newrev=$2

wangruohui

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

mroderick

How to add an image to a gist

1. Create a gist if you haven't already.
2. Clone your gist:

   # make sure to replace `<hash>` with your gist's hash
   git clone https://gist.github.com/<hash>.git # with https
   git clone git@gist.github.com:<hash>.git     # or with ssh

a-maumau

if you can access to the GUI environment, It is very easy using "disks" application.

or might be easy with gdisk/fdisk ?   I'm not sure about this.  

------------------------------------------------------------------------  

see the reference for more detail, if you want.
https://richardstechnotes.wordpress.com/2015/12/18/setting-up-an-nvme-ssd-on-ubuntu-14-04-lts/   http://takatakamanbou.hatenablog.com/entry/2015/10/25/235600 : Japanese website

hdamron17

#!/usr/bin/python

import cv2
import numpy as np
import yaml
from os.path import join
from collections import OrderedDict

def load_cam(cam_dict):
    intrinsics = cam_dict["intrinsics"]

mikhailov-work

// Copyright 2019 Google LLC.
// SPDX-License-Identifier: Apache-2.0

// Author: Anton Mikhailov

// The look-up tables contains 256 entries. Each entry is a an sRGB triplet.

float turbo_srgb_floats[256][3] = {{0.18995,0.07176,0.23217},{0.19483,0.08339,0.26149},{0.19956,0.09498,0.29024},{0.20415,0.10652,0.31844},{0.20860,0.11802,0.34607},{0.21291,0.12947,0.37314},{0.21708,0.14087,0.39964},{0.22111,0.15223,0.42558},{0.22500,0.16354,0.45096},{0.22875,0.17481,0.47578},{0.23236,0.18603,0.50004},{0.23582,0.19720,0.52373},{0.23915,0.20833,0.54686},{0.24234,0.21941,0.56942},{0.24539,0.23044,0.59142},{0.24830,0.24143,0.61286},{0.25107,0.25237,0.63374},{0.25369,0.26327,0.65406},{0.25618,0.27412,0.67381},{0.25853,0.28492,0.69300},{0.26074,0.29568,0.71162},{0.26280,0.30639,0.72968},{0.26473,0.31706,0.74718},{0.26652,0.32768,0.76412},{0.26816,0.33825,0.78050},{0.26967,0.34878,0.79631},{0.27103,0.35926,0.81156},{0.27226,0.36970,0.82624},{0.27334,0.38008,0.84037},{0.27429,0.39043,0.85393},{0.27509,0.40072,0.86692},{0.2757

YashasSamaga

import cv2
import time

CONFIDENCE_THRESHOLD = 0.2
NMS_THRESHOLD = 0.4
COLORS = [(0, 255, 255), (255, 255, 0), (0, 255, 0), (255, 0, 0)]

class_names = []
with open("classes.txt", "r") as f:
    class_names = [cname.strip() for cname in f.readlines()]

rmccorm4

import argparse
import torch
import torchvision

parser = argparse.ArgumentParser()
parser.add_argument("--opset", type=int, default=11, help="ONNX opset version to generate models with.")
args = parser.parse_args()

dummy_input = torch.randn(10, 3, 224, 224, device='cuda')
model = torchvision.models.alexnet(pretrained=True).cuda()

daigo0927

import numpy as np
import torch
import torch.nn.functional as F

import onnx
import onnxruntime as ort

from torch.onnx import register_custom_op_symbolic
import torch.onnx.symbolic_helper as sym_help