cozek

def get_gpu_with_highest_free_memory():
    """
    Returns the index of the GPU with the highest free memory.

    This function sorts the GPUs by PCI bus ID and then returns the index of the GPU with the highest free memory.

    Returns:
        list: A list containing the index and free memory of the GPU with the highest free memory.

    Notes:

cozek

\usepackage{scalerel,xparse}
\NewDocumentCommand\emojismile{}{
    \scalerel*{
        \includegraphics{EmojiFolder/u1F60A.png}
    }{X}
}

% Alternative
\NewDocumentCommand\emojismiley{}{
    \includegraphics[scale=0.05]{EmojiFolder/u1F60A.png}

cozek

def split_dataframe(df:pd.DataFrame, train_frac:float, shuffle: bool ):
    """
    Splits DataFrame into train and val 
    Args:
        df: DataFrame to split, note: indexes will be reset
        train_frac: fraction to use for training 
        shuffle: Shuffles df if true
    Returns:
        split_df: DataFrame with splits mentioned in 'split' column
    """

cozek

# https://stackoverflow.com/questions/44544766/how-do-i-check-if-keras-is-using-gpu-version-of-tensorflow/44547144

# confirm TensorFlow sees the GPU
from tensorflow.python.client import device_lib
assert 'GPU' in str(device_lib.list_local_devices())
# print(device_lib.list_local_devices())

# confirm Keras sees the GPU
from keras import backend
assert len(backend.tensorflow_backend._get_available_gpus()) > 0

cozek

"""
Tweet tokenizing script based on the one provided by pennington GloVe coupled with NLTK tweettokenizer

Date: 4th October 2019
"""

import pickle
import sys
import re
import csv

cozek

"""
preprocess-twitter.py

python preprocess-twitter.py "Some random text with #hashtags, @mentions and http://t.co/kdjfkdjf (links). :)"

Script for preprocessing tweets by Romain Paulus
with small modifications by Jeffrey Pennington
with translation to Python by Motoki Wu

Translation of Ruby script to create features for GloVe vectors for Twitter data.

cozek

def sent_to_embedding(embedding, data, max_len=None):
    '''
    Creates fasttext embedding of given list of sentences
    input:
        embedding: fasttext.model
        data: list of sentences
        max_len: maximum number of words to consider
      
    returns:
        emb_matrix: numpy matrix containing word level embeddings

cozek

import numpy as np
import matplotlib.pyplot as plt
from pprint import pprint
from tqdm import tqdm

class myNN(object):
    
    def __init__(self):
        self.params = None
        self.layer_dims = None

cozek

#!/usr/bin/env python3

#DISCLAMER: Provided with no warranty, you are responsible for whatever may befall you
# or your property as a result of using this script.
#You implicitly agree to this by running this script.

#Feel free to improve, modify and distribute

import os
import sys

cozek

#!/bin/bash

mkdir wordpress-site && cd wordpress-site

touch docker-compose.yml

cat > docker-compose.yml <<EOL
version: "2"
services:
  my-wpdb: