vlasenkov

# win32com multithreading example
import sys
import time
from threading import Thread
 
sys.coinit_flags = 0  # pythoncom.COINIT_MULTITHREADED == 0
from pythoncom import (CoInitializeEx, CoUninitialize,
                       COINIT_MULTITHREADED, PumpWaitingMessages)
from win32com.client import Dispatch, WithEvents
 

vlasenkov

# IDEs
*.sublime-project
*.sublime-workspace
*.vim
*~
*.swp
*.swo
.idea

# Data files

vlasenkov

{
	"translate_tabs_to_spaces": true,
	"tab_size": 4,
	"rulers": [72, 79],
	"indent_to_bracket": true,
}

vlasenkov

set encoding=utf-8

set nocompatible              " required
filetype off                  " required

" set the runtime path to include Vundle and initialize
set rtp+=~/.vim/bundle/Vundle.vim
call vundle#begin()

" alternatively, pass a path where Vundle should install plugins

vlasenkov

# Simple script for experiments with dsitributed TensorFlow.
# Requires jq utility for JSON parsing. Example JSON file can be found on the gist.
# Args:
#     name of script to run, example: train.py
#     file with cluster configuration, example: hosts.json
#!/bin/bash

script=$1
hosts=$2

vlasenkov

{
	"ps":[
		{
			"host":"localhost",
			"port":2222
		}
	],
	"worker":[
		{
			"host":"localhost",

vlasenkov

import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
import torch
import torch.nn as nn
import numpy as np


def get_upsampling_weight(in_channels, out_channels, kernel_size):
    """Make a 2D bilinear kernel suitable for upsampling"""
    factor = (kernel_size + 1) // 2

vlasenkov

import torch
torch.backends.cudnn.deterministic = True
import torch.nn as nn


class UNet3d(nn.Module):

    def __init__(self, activation=nn.ReLU, pooling=nn.MaxPool3d):
        super().__init__()
        self.down0 = nn.Sequential(

vlasenkov

set nocompatible              " be iMproved, required
filetype off                  " required

" set the runtime path to include Vundle and initialize
set rtp+=~/.vim/bundle/Vundle.vim
call vundle#begin()
" alternatively, pass a path where Vundle should install plugins
"call vundle#begin('~/some/path/here')

" let Vundle manage Vundle, required