Dani El-Ayyass dayyass

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The count of contributions (summary of Pull Requests, opened issues and commits) to public repos at GitHub.com from Wed, 21 Sep 2022 till Thu, 21 Sep 2023.

Because of GitHub search limitations, only 1000 first users according to amount of followers are included. If you are not in the list you don't have enough followers. See raw data and source code. Algorithm in pseudocode:

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In general, check the crt/host_config.h file to find out which versions are supported. Sometimes it is possible to hack the requirements there to get some newer versions working, too :)

Thrust version can be found in $CUDA_ROOT/include/thrust/version.h.

Download Archives: https://developer.nvidia.com/cuda-toolkit-archive

Release notes for CUDA Toolkit (CTK):

latest: https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html
archive: https://docs.nvidia.com/cuda/archive/

Docker Completion for Zsh (Official)

mkdir -p ~/.oh-my-zsh/plugins/docker/
curl -fLo ~/.oh-my-zsh/plugins/docker/_docker https://raw.githubusercontent.com/docker/cli/master/contrib/completion/zsh/_docker
Add docker to plugins section in ~/.zshrc
exec zsh

	$ curl -O ftp://ftp.gnu.org/gnu/ncurses/ncurses-5.9.tar.gz
	$ tar -xzvf ncurses-5.9.tar.gz
	$ cd ./ncurses-5.9
	$ ./configure --prefix=/usr/local \
	--without-cxx --without-cxx-binding --without-ada --without-progs --without-curses-h \
	--with-shared --without-debug \
	--enable-widec --enable-const --enable-ext-colors --enable-sigwinch --enable-wgetch-events \
	&& make
	$ sudo make install

	#!/usr/bin/env python

	import sys

	class Lexer:

	NUM, ID, IF, ELSE, WHILE, DO, LBRA, RBRA, LPAR, RPAR, PLUS, MINUS, LESS, \
	EQUAL, SEMICOLON, EOF = range(16)

	SYMBOLS = { '{': LBRA, '}': RBRA, '=': EQUAL, ';': SEMICOLON, '(': LPAR,

	"""
	ATTENTION:
	When using executemany with a list of tuples, the numbers representing the rows has to be strictly from 1 to the last. Or else it won't work.
	I really don't understand why.
	"""

	import cx_Oracle
	from parserFWF import getConfigDF

	HOTEL_CONFIG = getConfigDF() #dataframe

	# problem: when presenting, I want to obscure
	# my prompt to act like it's at root of file system
	# and be very basic with no git info, etc.

	# solution: this theme lets you set a ENV to the path
	# of your presentation, which will help remove unneeded prompt
	# features while in that path

	# oh-my-zsh theme for presenting demos
	# based off the default rubbyrussell theme

	import io
	from flask import Flask, send_file

	app = Flask(__name__)

	@app.route('/logo.jpg')
	def logo():
	"""Serves the logo image."""

	with open("logo.jpg", 'rb') as bites:

	import torch
	from torch import LongTensor
	from torch.nn import Embedding, LSTM
	from torch.autograd import Variable
	from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence

	## We want to run LSTM on a batch of 3 character sequences ['long_str', 'tiny', 'medium']
	#
	# Step 1: Construct Vocabulary
	# Step 2: Load indexed data (list of instances, where each instance is list of character indices)

	import torch
	import torch.nn as nn
	import torch.nn.functional as F

	import torchvision.models as tmodels
	from functools import partial
	import collections

	# dummy data: 10 batches of images with batch size 16
	dataset = [torch.rand(16,3,224,224).cuda() for _ in range(10)]