nizhib

package main

import (
	"log"
	"math/rand"
	"os"
	"os/signal"
	"sync"
	"time"
)

nizhib

.highlight > pre > span,
.highlight > tbody > tr > td,
.highlight > table > tbody > tr > td,
.highlight > table > tbody > tr > td > span,
.jp-InputPrompt, .jp-OutputPrompt,
.CodeMirror-linenumber,
.preview > span,
.preview > span > span,
.line-number,

nizhib

[Unit]
Description=Keeps the ssh tunnel to %i open
After=network.target

[Service]
User=nizhib
Environment="AUTOSSH_PORT=0"
Environment="AUTOSSH_LOGFILE=/tmp/tunnel.%i.log"
Environment="AUTOSSH_PIDFILE=/tmp/tunnel.%i.pid"
Environment="SSH_OPTIONS=-o 'ServerAliveInterval=60' -o 'ServerAliveCountMax=3'"

nizhib

class DataPrefetcher:
    def __init__(self, loader, num_images=1, num_labels=1):
        self.loader = iter(loader)
        self.num_images = num_images
        self.num_labels = num_labels
        self.num_others = 1

        self.stream = torch.cuda.Stream()
        self.mean = (
            torch.tensor([0.485 * 255, 0.456 * 255, 0.406 * 255])

nizhib

*, a {
    font-family: Helvetica Neue, Helvetica, Arial, sans-serif;
    font-weight: 400;
}

h1, h2, h3, h4, h5, h6,
.blog .main-content-wrapper h4 a {
    font-family: Helvetica Neue, Helvetica, Arial, sans-serif;
    font-weight: 700;
}

nizhib

import threading

import torch
from torch.nn.modules import Module
from torch.nn.parallel.scatter_gather import scatter_kwargs, gather
from torch.nn.parallel.replicate import replicate
from torch.nn.parallel.parallel_apply import parallel_apply

__all__ = ['DataParallelModel', 'DataParallelCriterion']

nizhib

__author__ = "nizhib"

import torch
from torch import nn
from torch.autograd import Variable


def conv3x3(in_channels, out_channels, dilation=1):
    return nn.Conv2d(in_channels, out_channels, 3, padding=dilation, dilation=dilation)

nizhib

import multiprocessing
import os


class MultiprocessPipeline(object):
    """
    pipeline = MultiprocessPipeline(foo)
    pipeline.start()
    for a, b, c in something:
        pipeline.put((a, b, c))