dongkwan-kim

import random

T = 596
R, C = 37, 16

numbers = list(range(1, T + 1))
random.shuffle(numbers)

sets = []
for _set in range(R):

dongkwan-kim

from typing import Tuple, List

try:
    import matplotlib.pyplot as plt
    from matplotlib import cm
    from matplotlib.ticker import LinearLocator
    from mpl_toolkits.mplot3d import Axes3D
except ImportError:
    pass
import numpy as np

dongkwan-kim

\newcommand\todo[1]{\textcolor{red}{#1}}
\newcommand\g[1]{\textcolor{gray}{#1}}
\newcommand*{\scale}[2][4]{\scalebox{#1}{$#2$}}%
\newcommand*{\resize}[2]{\resizebox{#1}{!}{$#2$}}%
\newcommand\ko[1]{
\begin{CJK}{UTF8}{} % require \usepackage{CJKutf8}
 \CJKfamily{mj}
 #1
\end{CJK}
}

dongkwan-kim

from typing import Dict, Any, List, Tuple

import torch
from torch import Tensor


def auto_index_select(value_tensor: Tensor, index_tensor: Tensor):
    index_tensor = index_tensor.squeeze()
    sizes = value_tensor.size()
    for dim, dim_size in enumerate(sizes):

dongkwan-kim

%%%%% NEW MATH DEFINITIONS %%%%%

\usepackage{amsmath,amsfonts,bm}

% Mark sections of captions for referring to divisions of figures
\newcommand{\figleft}{{\em (Left)}}
\newcommand{\figcenter}{{\em (Center)}}
\newcommand{\figright}{{\em (Right)}}
\newcommand{\figtop}{{\em (Top)}}
\newcommand{\figbottom}{{\em (Bottom)}}

dongkwan-kim

nvidia-smi --query-compute-apps=pid,process_name,used_memory --format=csv

ps all --sort=-time | grep python

python -u analysis.py | tee log.txt

ps aux | grep -ie "query" | awk '{print $2}' | xargs kill -9

dongkwan-kim

/* get_link_for_letter(2019xxxx, YYMMDD, "이름석자"); */
function get_link_for_letter(date_to_enter, date_of_birth, name) {
  date_to_enter = Base64.encode(date_to_enter + "");
  date_of_birth = Base64.encode(date_of_birth + "");
  return `http://www.katc.mil.kr/katc/community/children.jsp?search:search_key1:child_search=etc_char8&search:search_key2:child_search=etc_char9&search:search_key3:child_search=etc_char1&search_val1=${date_to_enter}&search_val2=${date_of_birth}&search_val3=${name}`
}

// Base64 from cms.js from http://www.katc.mil.kr
var Base64 = {
  _keyStr: "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/=",

dongkwan-kim

# https://github.com/golbin/TensorFlow-Tutorials/blob/master/06%20-%20MNIST/01%20-%20MNIST.py

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("./mnist/data/", one_hot=True)

X = tf.placeholder(tf.float32, [None, 784])
Y = tf.placeholder(tf.float32, [None, 10])

dongkwan-kim

from utils import *

params = {}


def create_variable(scope, name, shape, trainable=True, on_cpu=True, **kwargs) -> tf.Variable:
    def _create_variable():
        with tf.variable_scope(scope):
            _w = tf.get_variable(name, shape, trainable=trainable, **kwargs)
            params[_w.name] = _w

dongkwan-kim

# In the server, (images what I mostly used)
docker pull nvidia/cuda:11.7.1-cudnn8-devel-ubuntu20.04
docker pull nvidia/cuda:11.0.3-cudnn8-devel-ubuntu18.04
docker pull nvidia/cuda:10.2-cudnn8-devel-ubuntu18.04

# cuda 10.0 multiple ports volume binding
docker run -ti --runtime=nvidia --name dongkwan -p 8082:22 -p 8083:6006 --ipc=host -v /mnt/nas2:/mnt/nas2 -d nvidia/cuda:10.2-cudnn8-devel-ubuntu18.04 /bin/bash
# Unknown runtime specified nvidia
docker run -ti --gpus '"device=0,1,2,3"' --name dongkwan -p 8070:22 --ipc=host -d nvidia/cuda:10.0-cudnn7-devel-ubuntu16.04 /bin/bash