wonglkd

=MID(
    IF(OR(LEFT(C2,1)="S", LEFT(C2,1)="T"),
       "JZIHGFEDCBA",
       "XWUTRQPNMLK"),
   MOD(
       MID(C2,2,1)*2+
       MID(C2,3,1)*7+
       MID(C2,4,1)*6+
       MID(C2,5,1)*5+
       MID(C2,6,1)*4+

wonglkd

#!/usr/bin/python
import argparse
import subprocess


def pssh(servers, cmd):
	ps = [subprocess.Popen(['ssh', srv, cmd], stdout=subprocess.PIPE)
	      for srv in servers]
	outputs = []
	for srv, p in zip(servers, ps):

wonglkd

• Brooce for managing jobs
• Secure Pipes for managing SSH tunnels (great when you have Bastions)
• ?? for monitoring
• tmux on bastion for session management

wonglkd

#!/bin/bash
# usage: bash ../utils/spell-check.sh
# setup:
# brew install hunspell
# wget https://raw.githubusercontent.com/wooorm/dictionaries/master/dictionaries/en-US/index.dic -O ~/Library/Spelling/en_US.dic
# wget https://raw.githubusercontent.com/wooorm/dictionaries/master/dictionaries/en-US/index.aff -O ~/Library/Spelling/en_US.aff
#
hunspell -l -t *.tex | sort | uniq > ../utils/misspelled.tmp
sort ../utils/dictionary -o ../utils/dictionary
comm -23 ../utils/misspelled.tmp ../utils/dictionary

wonglkd

scikit-learn==0.18.1
pandas==0.20.1
Keras>=2.0.4
numpy>=1.12.1
scipy>=0.19.0
tensorflow>=1.1.0
spacy>=1.8.2
# sacred==0.7.0
git+https://github.com/IDSIA/sacred.git@5a823657d824103cbbe09177a3aaf706c557ce55#egg=sacred
pymongo>=3.4.0

wonglkd

import pdb, sys, traceback
def info(type, value, tb):
    traceback.print_exception(type, value, tb)
    pdb.pm()
sys.excepthook = info

wonglkd

import pdb, sys, traceback
def info(type, value, tb):
    traceback.print_exception(type, value, tb)
    pdb.pm()
sys.excepthook = info

wonglkd

• Git Bash + MinGW: https://nuwen.net/files/mingw/mingw-14.1.exe from https://nuwen.net/mingw.html

Python

• Sublime Text
• conda
  • https://conda.io/miniconda.html

Things to check

• Make sure MinGW is in your path
• Checking your path: Windows+Pause (or right-click Computer -> Properties) -> Environment variables

wonglkd

        # Implement REINFORCE rule by using crossentropy
        # + (reward - baseline) as sample_weight.
        self.model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=self.lr))

        # Adapted from model.compile: redo the computation of loss.
        loss_functions = [objectives.get('categorical_crossentropy')]

        total_loss = None

        for i in range(len(self.model.outputs)):

wonglkd

# on server
# tips: -u for UDP,
nc -l <port>
# on client
nc <ip> <port>