cgmb

cmake_minimum_required(VERSION 3.16)
project(example LANGUAGES CXX)
find_package(hip REQUIRED)
add_executable(example main.cpp)
target_link_libraries(example PRIVATE hip::device)

biggzlar

#!/bin/bash
## This gist contains step by step instructions to install cuda v9.0 and cudnn 7.2 in ubuntu 18.04

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

xavierpriour

#!/usr/bin/env bash
#
# Builds the docker image used in deployments.
# - embedding most files inside the image
# - turning on compilation and optimization
# - this requires the dev image as a tool (will pull it if not already available)
#
# This WILL wipe out any previous build and replace with a new one

# fetch build info to embed in the image

timvisee

Falsehoods programmers believe about time

This is a compiled list of falsehoods programmers tend to believe about working with time.

Don't re-invent a date time library yourself. If you think you understand everything about time, you're probably doing it wrong.

Falsehoods

• There are always 24 hours in a day.
• February is always 28 days long.
• Any 24-hour period will always begin and end in the same day (or week, or month).

fedden

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

spalladino

# Backup
docker exec CONTAINER /usr/bin/mysqldump -u root --password=root DATABASE > backup.sql

# Restore
cat backup.sql | docker exec -i CONTAINER /usr/bin/mysql -u root --password=root DATABASE

seanmckaybeck

Servo serv;
int pos = 0;

void setup() {
    serv.attach(D0);
    Spark.function("setpos", setPos);
    Spark.variable("getpos", &pos, INT);
}

void loop() {

bogger

name: "GoogleNet"

input: "data"
input_dim: 10
input_dim: 3
input_dim: 224
input_dim: 224

# hierarchy 1
# conv -> relu -> pool -> lrn

derricw

import numpy as np

def bin_ndarray(ndarray, new_shape, operation='sum'):
    """
    Bins an ndarray in all axes based on the target shape, by summing or
        averaging.

    Number of output dimensions must match number of input dimensions.

    Example