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Install CMake 3.5.2 on CentOS 7
Download Source distributions cmake-3.5.2.tar.gz from https://cmake.org/download/ ./bootstrap gmake sudo gmake install
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Install libgpuarray on Centos 7
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#!/bin/python3 | |
import os | |
import sys | |
import subprocess | |
import re | |
def run(): | |
arguments = sys.argv[1:] |
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# ================================================= | |
# SLURM UTILS | |
# ================================================= | |
# sbatlog: runs sbatch and automatically tail -f slurm-$number.out | |
function sbatlog | |
{ | |
result=$(command sbatch "$@") | |
outputid=$(echo $result | awk -F' ' '{print $NF}') | |
# fileout="slurm-$outputid.out" # assumes default file output name |
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################################################################################# | |
# Authors: Keiller Nogueira, Victor de Melo | |
################################################################################# | |
# nvidia-docker seems more stable now and several images are starting to adopt it (including | |
# [kaixhin](https://github.com/NVIDIA/nvidia-docker/issues/85). | |
# hence, we suggest to use it instead, as it solves a few issues with gpu passthrough. | |
# usage: it is only required when creating a container, i.e, with `run` and related arguments | |
# you're not required to provide $DOCKER_NVIDIA_DEVICES as it will find the devices itself |
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import av | |
import numpy as np | |
def write_packets(filename, packets, video_stream): | |
container = av.open(filename, mode='w') | |
stream = container.add_stream(template=video_stream) | |
stream.options = {} | |
for p in packets: | |
if p.dts is not None: |
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#!/usr/bin/env python3 | |
import sys | |
from hashlib import md5 | |
from base64 import b64encode | |
from binascii import hexlify, unhexlify | |
from time import time | |
def reduction_function(hash, max_password_length): |
L1 cache reference ......................... 0.5 ns
Branch mispredict ............................ 5 ns
L2 cache reference ........................... 7 ns
Mutex lock/unlock ........................... 25 ns
Main memory reference ...................... 100 ns
Compress 1K bytes with Zippy ............. 3,000 ns = 3 µs
Send 2K bytes over 1 Gbps network ....... 20,000 ns = 20 µs
SSD random read ........................ 150,000 ns = 150 µs
Read 1 MB sequentially from memory ..... 250,000 ns = 250 µs
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name: "VGG_FACE_16_layer" | |
input: "data" | |
input_dim: 1 | |
input_dim: 3 | |
input_dim: 224 | |
input_dim: 224 | |
layer { | |
name: "data" | |
type: "Data" | |
top: "data" |
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# A yaml constructor is for loading from a yaml node. | |
# This is taken from @misha 's answer: http://stackoverflow.com/a/15942429 | |
def opencv_matrix_constructor(loader, node): | |
mapping = loader.construct_mapping(node, deep=True) | |
mat = np.array(mapping["data"]) | |
mat.resize(mapping["rows"], mapping["cols"]) | |
return mat | |
yaml.add_constructor(u"tag:yaml.org,2002:opencv-matrix", opencv_matrix_constructor) | |
# A yaml representer is for dumping structs into a yaml node. |
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