schmmd

#!/usr/bin/env python

import os
import re
import subprocess
import socket
import sys

def pids_of_jid(jid):
    result = subprocess.run(["sstat", "-p", "--format=PID", "-j", jid, "--noheader"], stdout=subprocess.PIPE)

soumith

import numpy as np
import os
import time
import warnings
import pickle
# from accimage import Image
from PIL import Image
import io

try:

asroy

I've tested it on Fedora 23 and Ubuntu 16.04. I'm using gcc-5.3.1, python-3.4, VS Code-1.14.0
You can debug mixed Python/C++ in the same GUI. It also works for MPI applications. You can switch between the debuggers and corresponding call stacks.

1. Packages needed
  1) Visual Studio Code

  2) Extensions for VS Code:
      "Python" from Don Jayamanne (I'm using 0.6.7)
        This allows VS Code act as the front end to debug python.
        This gives VS Code ability to attach to a python script that uses module "ptvsd". 

csswizardry

Vim without NERD tree or CtrlP

I used to use NERD tree for quite a while, then switched to CtrlP for something a little more lightweight. My setup now includes zero file browser or tree view, and instead uses native Vim fuzzy search and auto-directory switching.

Fuzzy Search

There is a super sweet feature in Vim whereby you can fuzzy find your files using **/*, e.g.:

:vs **/*<partial file name><Tab>

JosephRedfern

import requests
from collections import defaultdict

csv_prefix = "https://research.google.com/youtube8m/csv"

r = requests.get("{0}/verticals.json".format(csv_prefix))
verticals = r.json()

block_urls = defaultdict(list)
count = 0

harpone

import tensorflow as tf
from tensorflow.python.framework import ops
import numpy as np

# Define custom py_func which takes also a grad op as argument:
def py_func(func, inp, Tout, stateful=True, name=None, grad=None):
    
    # Need to generate a unique name to avoid duplicates:
    rnd_name = 'PyFuncGrad' + str(np.random.randint(0, 1E+8))
    

udibr

import numpy as np
from keras.layers import GRU, initializations, K
from collections import OrderedDict

class GRULN(GRU):
    '''Gated Recurrent Unit with Layer Normalization
    Current impelemtation only works with consume_less = 'gpu' which is already
    set.

    # Arguments

mstankie

# 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.

epixoip

8x Nvidia GTX 1080 Hashcat Benchmarks

Product: Sagitta Brutalis 1080 (PN S3480-GTX-1080-2697-128)

Software: Hashcat v3.00-beta-145-g069634a, Nvidia driver 367.18

Accelerator: 8x Nvidia GTX 1080 Founders Edition

Highlights

fchollet

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats