MInner

find . -name '*.png' | while read line; do echo ${line}; convert -resize 70% ${line} ${line}.small.png; pngquant --speed 1 ${line}.small.png -o ${line}.quant.png; done
find . -name '*.small.png' | while read line; do rm $line; done
read -p "Press [Enter] if you are happy with *.quant.png images, otherwise press Ctrl-C."
find . -name '*.quant.png' | while read line; do mv ${line} ${line:0:-10}; done

for EXT in png pdf; do find -name "*.${EXT}" | while read line; do echo $line; done; done | while read line; do if ! grep -q -r --include \*.tex ${line#*/} .; then echo ${line}; rm ${line}; fi; done

MInner

use std::error::Error;

enum TaskError<'a, C> {
    CustomError(C),
    StdError(Box<dyn Error + 'a>)
}

type TaskResult<'a, R, E> = std::result::Result<R, TaskError<'a, E>>;

impl<'a, E: Error + 'a, C> From<E> for TaskError<'a, C> {

MInner

a=(x y z); b=(q w e); for i in ${!a[@]}; do echo ${a[i]}-${b[i]}; done

MInner

import sys
import glob

import tqdm
import imageio
import matplotlib.pyplot as plt

import numpy as np
import tensorflow as tf

MInner

import tensorflow as tf
import tensorflow_datasets as tfds

spec = tfds.features.FeaturesDict({
    'image': tfds.features.Tensor(shape=(28, 28, 1), dtype=tf.float32),
    'label': tfds.features.ClassLabel(names=['no', 'yes']),
    'metadata': {
        'id': tf.int64,
        'language': tf.string,
    },

MInner

def run_release_gpu(func):
  def parallel_wrapper(output_dict, *argv, **kwargs):
    ret = func(*argv, **kwargs)
    if ret is not None:
      output_dict['ret'] = ret
  
  def outer_wrapper(*argv, **kwargs):
    same_process = kwargs.pop('same_process', False)
    if same_process:
      return func(*argv, **kwargs)

MInner

#!/usr/bin/bash
watch "nvidia-smi | tail -n +7; nvidia-smi | nvidia-smi | sed -n '/| Processes:/,\$p' | tail -n +4 | head -n -1 | awk '{print \$3}' | while read line; do ps a -o pid,user | grep \$line | grep -v grep; done
"

MInner

import datetime
import linecache
import os

import pynvml3
import torch

print_tensor_sizes = True
last_tensor_sizes = set()
gpu_profile_fn = f'{datetime.datetime.now():%d-%b-%y-%H:%M:%S}-gpu_mem_prof.txt'

MInner

backtick 0 0 0 whoami
caption always "%{-b ..}%-w%{+b ..}[[%n%f*%t]]%{-}%+w %= %0`@%H | %l"

bind ` focus prev

MInner

# based on https://github.com/google/seq2seq/blob/master/bin/tools/generate_beam_viz.py

# extracts probabilities and sequences from .npz file generated during beam search. 
# and pickles a list of the length n_samples that has beam_width most probable tuples 
# (path, logprob, prob) 
# where probs are scaled to 1. 

import numpy as np
import networkx as nx
import pickle