Ilya Kavalerov ilyakava

Getting a linux virtual environment with GPU access on Windows 10

Follow these directions, everything before "Set up Python environment"
- Choose Ubuntu 18.04
Follow some sections in these directions
- Do "Setting up CUDA Toolkit"
- In "Running CUDA Applications", try to cd /usr/local/cuda/samples/0_Simple/matrixMulCUBLAS/, make, and ./matrixMulCUBLAS

importunate: adj. expressing earnest entreaty
convalescence: noun gradual healing (through rest) after sickness or injury
Copse: noun a dense growth of bushes
anfractuous: adj. full of twists and turns
Agnate: adj. related on the father's side; noun one related on the father's side
Sub-rosa: adj. designed and carried out secretly or confidentially
soused: adj. very drunk; wet from being plunged into liquid
Abulia: noun a loss of will power
Screed: noun an accurately levelled strip of material placed on a wall or floor as guide for the even application of plaster or concrete; a long piece of writing; a long monotonous harangue
Probity: noun complete and confirmed integrity; having strong moral principles

	% // References:
	% // http://www.ehu.eus/ccwintco/uploads/d/dc/LoadHypercubesMatlab.pdf

	% data can be downloaded from:
	% http://www.csr.utexas.edu/hyperspectral/data/KSC/KSC_data.bin
	% http://www.csr.utexas.edu/hyperspectral/data/KSC/KSC_data.bin.hdr

	% compare
	% cd /scratch0/ilya/locDoc/data/hyperspec/datasets/
	im = multibandread('KSC_data.bin', [512 614 176], 'uint16', 0, 'bsq', 'ieee-le');

	warm_starts = sorted(glob.glob(args.warm_start_from)) if args.warm_start_from is not None else None
	warm_start_from = warm_starts[-1] if (warm_starts is not None and len(warm_starts)) else None
	warm_start_from = os.path.join(warm_start_from, 'variables/variables')

	id_assignment_map = {}
	for myvar in tf.contrib.framework.get_variables_to_restore():
	myscope = os.path.dirname(myvar.name)
	if len(myscope):
	myscope += '/'
	id_assignment_map[myscope] = myscope

	<!DOCTYPE html>
	<html>
	<head>
	<title>Ilya Kavalerov</title>
	</head>
	<body>
	<h1>Ilya Kavalerov</h1>
	<img src="ilyak/headshot.jpg" width="300" height="300">
	<h2>PhD Candidate at University of Maryland, College Park, Department of Electrical and Computer Engineering</h2>
	<p>Advised by <a href="http://users.umiacs.umd.edu/~rama/people.html">Professor Rama Chellappa</a>, and <a href="https://www.math.umd.edu/~czaja/">Professor Wojciech Czaja</a></p>

	// http://cs.stanford.edu/people/karpathy/convnetjs//demo/classify2d.html

	(function xor_data(){
	data = [];
	labels = [];
	data.push([0 , 0 ]); labels.push(0);
	data.push([1 , -1 ]); labels.push(0);
	data.push([0 , -1 ]); labels.push(1);
	data.push([1 , 0 ]); labels.push(1);
	N = labels.length;

	# to cut first 3 seconds and length 8 seconds (custom step done for each file)
	ffmpeg -ss 3 -t 8 -i VID00080.MP4 -vcodec copy -acodec copy white.MP4

	# to burn in the timecode (r=framerate) (use in loop like below)
	# ffmpeg -i $MOVIE -vf "drawtext=fontfile=/Users/artsyinc/Library/Fonts/PxPlus_VGA_SquarePx.ttf: fontsize=128: timecode='00\:00\:00\:00': r=30: x=(w-tw)/2: y=h-(2*lh): fontcolor=white: box=1: boxcolor=0x00000000@1" timecode/$MOVIE

	# to speed up
	for MOVIE in $(ls \| grep MP4);
	do LENGTH=$(ffprobe -i $MOVIE -show_format -loglevel quiet \| egrep -oE 'duration=(\d+)' \| awk -F= '{print $2}');
	RATIO=$(echo 7.0/$LENGTH \| bc -l);

	import numpy as np
	import caffe

	MODEL_FILE = '../val.prototxt'
	PRETRAINED = '../food_alexnet_train_iter_25000.caffemodel'
	IMAGE_MEAN = '../imagenet_mean.binaryproto'
	INPUT_IMAGE = '~/code/fundus/data/train/cent_crop_227/1000016.png'

	net = caffe.Classifier(MODEL_FILE, PRETRAINED, image_dims=(256,256))
	# net = caffe.Classifier(MODEL_FILE, PRETRAINED, image_dims=(227,227))