jsbueno

import re, pickle, pprint, sys
from types import ModuleType
from collections.abc import Sequence, Mapping, Set
from contextlib import contextmanager


def pythonize(obj):
    if isinstance(obj, (str, bytes)):
        return obj

ljaraque

Install tensorflow-gpu1.8 in ubuntu18.04 with CUDA9.2, cuDNN7.2.1 and NVIDIA Driver 396

[email protected]

Overview

This is a summary of the process I lived in order to enable my system with CUDA9.2, cuDNN7.2.1, Tensorflow1.8 and NVIDIA GEFORCE GTX860M GPU. You can just skip the steps marked with FAILED. I decided to keep them there in order to be useful for others who tried those paths too.

FAILED (Next section is successfull) Install NVIDIA driver (FAILED, THIS WILL INSTALL DRIVER 390 which is not compatible with CUDA9.2):

ubuntu-drivers devices

Fleshgrinder

#!/bin/sh
set -eu

#
# Commands for generating a new GPG key for GitHub commit signing.
#
# https://help.github.com/articles/generating-a-new-gpg-key/
#

gpg --full-gen-key

shodanshok

#!/usr/bin/env python
"""
Synchronise block devices over the network
 
Copyright 2006-2008 Justin Azoff <[email protected]>
Copyright 2011 Robert Coup <[email protected]>
License: GPL
 
Getting started:
 

chrishokamp

alopresto

If anyone is interested in setting up their system to automatically (or manually) sign their git commits with their GPG key, here are the steps:

1. Generate and add your key to GitHub
2. $ git config --global commit.gpgsign true ([OPTIONAL] every commit will now be signed)
3. $ git config --global user.signingkey ABCDEF01 (where ABCDEF01 is the fingerprint of the key to use)
4. $ git config --global alias.logs "log --show-signature" (now available as $ git logs)
5. $ git config --global alias.cis "commit -S" (optional if global signing is false)
6. $ echo "Some content" >> example.txt
7. $ git add example.txt
8. $ git cis -m "This commit is signed by a GPG key." (regular commit will work if global signing is enabled)

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

ramcq

#!/usr/bin/env python
"""
Synchronise block devices over the network

Copyright 2006-2008 Justin Azoff <[email protected]>
Copyright 2011 Robert Coup <[email protected]>
Copyright 2012 Holger Ernst <[email protected]>
Copyright 2014 Robert McQueen <[email protected]>
License: GPL

blackfalcon

Git-workflow vs feature branching

When working with Git, there are two prevailing workflows are Git workflow and feature branches. IMHO, being more of a subscriber to continuous integration, I feel that the feature branch workflow is better suited, and the focus of this article.

If you are new to Git and Git-workflows, I suggest reading the atlassian.com Git Workflow article in addition to this as there is more detail there than presented here.

I admit, using Bash in the command line with the standard configuration leaves a bit to be desired when it comes to awareness of state. A tool that I suggest using follows these instructions on setting up GIT Bash autocompletion. This tool will assist you to better visualize the state of a branc

tkuchiki

$ autossh -M 33333 -N -f -L 13260:10.150.1.26:3260 sg

$ yum install -y iscsi-initiator-utils

$ iscsiadm -m discovery -t sendtargets -p 127.0.0.1:13260

$ vim /var/lib/iscsi/send_targets/127.0.0.1\,13260/iqn.1997-05.com.amazon\:myvolume\,10.150.1.26\,3260\,1\,default/default
#node.conn[0].address = 10.150.1.26
#node.conn[0].port = 3260
node.conn[0].address = 127.0.0.1