0leynik

# Stop all containers
docker stop `docker ps -qa`

# Remove all containers
docker rm `docker ps -qa`

# Remove all images
docker rmi -f `docker images -qa `

# Remove all volumes

0leynik

Building TensorFlow from source (TF 2.3.0, Ubuntu 20.04)

Why build from source?

The official instructions on installing TensorFlow are here: https://www.tensorflow.org/install. If you want to install TensorFlow just using pip, you are running a supported Ubuntu LTS distribution, and you're happy to install the respective tested CUDA versions (which often are outdated), by all means go ahead. A good alternative may be to run a Docker image.

I am usually unhappy with installing what in effect are pre-built binaries. These binaries are often not compatible with the Ubuntu version I am running, the CUDA version that I have installed, and so on. Furthermore, they may be slower than binaries optimized for the target architecture, since certain instructions are not being used (e.g. AVX2, FMA).

So installing TensorFlow from source becomes a necessity. The official instructions on building TensorFlow from source are here: ht

0leynik

Installing the NVIDIA driver, CUDA and cuDNN on Linux (Ubuntu 20.04)

This is a companion piece to my instructions on building TensorFlow from source. In particular, the aim is to install the following pieces of software

• NVIDIA graphics card driver (v450.57)
• CUDA (v11.0.2)
• cuDNN (v8.0.2.39)

on an Ubuntu Linux system, in particular Ubuntu 20.04.

0leynik

import sys
import re
import os

add_mode = sys.argv[1]

if add_mode == '--word':
    words = [sys.stdin.read().strip()]
elif add_mode == '--words':
    split_pattern = r'[\ \,\.\:\;\!\?\(\)\{\}\[\]\\\/\|\<\>\$\%\*]+'

0leynik

0leynik

0leynik

0leynik

(function(){
	'use strict';
	if (!confirm('Delete?')) return;
	
	//posts
	//var deletePostLink = document.body.querySelectorAll('a.ui_actions_menu_item[onclick^="wall.deletePost"]');

	//gifts
	// var deletePostLink = document.body.querySelectorAll('a.gift_delete[onclick^="Gifts.deleteGift"]');

0leynik

# imports
import numpy as np
import cv2
import matplotlib.pyplot as plt


# The Hough Transform is a popular algorithm for detecting any shape that can
# be represented in a parametric mathmatical form in binary images. This
# usually means that images need to be thresholded or filtered prior to running

0leynik

The error I get:
/usr/bin/ld: cannot find -lopenblas
collect2: error: ld returned 1 exit status
Makefile:568: recipe for target '.build_release/lib/libcaffe.so.1.0.0-rc3' failed

The changes I made:

Makefile.config

#INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include