denguir

How to install CUDA / cuDNN / TensorRT on Ubuntu

Install NVIDIA drivers

Update & upgrade

sudo apt update && sudo apt upgrade

Remove previous NVIDIA installation

MSFTserver

Install Disco Diffusion v5 for Windows

NOTE: Pytorch3d no longer has to be compiled i have stripped out the function we use to make this a lot easier and also so we do not have to use WSL2 with linux and can now run directly on your windows system.

Comments section is not checked often for issues please join the disco diffusion discord for assistance

https://discord.gg/mK4AneuycS

You may now use the official disco diffusion notebook with this tutorial as it has been uodated to reflect the changes here for better cross platform support

bstriner

"""
A series of speed tests on pytorch LSTMs.
- LSTM is fastest (no surprise)
- When you have to go timestep-by-timestep, LSTMCell is faster than LSTM
- Iterating using chunks is slightly faster than __iter__ or indexing depending on setup

**Results**

My Ubuntu server:
    OS: posix, pytorch version: 0.4.0a0+67bbf58

techpavan

var totalForkCount;
var curForkCount=0;
var errorForkArray = new Array();

function initProcess(){
	var user = window.location.pathname.split('/')[1];
	var pageNo = 1;
	var hrefArray = new Array();
	while(hrefArray.length % 30 == 0){
		var resp = sendSyncAjax("https://github.com/"+user+"?tab=repositories&page="+pageNo);

takluyver

#!/usr/bin/env python3
"""
To run: python3 nb2to3.py notebook-or-directory
"""
# Authors: Thomas Kluyver, Fernando Perez
# See: https://gist.github.com/takluyver/c8839593c615bb2f6e80

import argparse
import pathlib
from nbformat import read, write

diorahman

The natural way to start with CUDA

The natural way to start with CUDA if you plan to be self taught is:

1. CUDA by Example: An Introduction to General-Purpose GPU Programming by Jason Sanders and Edward Kandrot Nice introduction. It is more like playing with your GPU and admire its capabilities.

2. Programming Massively Parallel Processors, Second Edition: A Hands-on Approach by David B. Kirk and Wen-mei W. Hwu It explains a lot of things in GPU Programming. You simply can't go without it.

lexdene

# -*- coding: utf-8 -*-
'''
    Python在同一行输出内容。

    原理：
        输出\r
        在Linux中，\r的作用是回到一行的开头。

        这里不能使用print，因为print自带换行。而这里我们不能使用换行。
        但是如果禁用print的换行也不行。如果没有换行print就不会flush，导致什么也不输出。