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:
 

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

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

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

vgel

import argparse
import random
import sys

from transformers import AutoModelForCausalLM, AutoTokenizer, DynamicCache
import torch

parser = argparse.ArgumentParser()
parser.add_argument("question", type=str)
parser.add_argument(