VinACE

############################################################################
#                                                                          #
#               ------- Useful Docker Aliases --------                     #
#                                                                          #
#     # Installation :                                                     #
#     copy/paste these lines into your .bashrc or .zshrc file or just      #
#     type the following in your current shell to try it out:              #
#     wget -O - https://gist.githubusercontent.com/jgrodziski/9ed4a17709baad10dbcd4530b60dfcbb/raw/d84ef1741c59e7ab07fb055a70df1830584c6c18/docker-aliases.sh | bash
#                                                                          #
#     # Usage:                                                             #

VinACE

# Install tmux on rhel/centos 7

# install deps
yum install gcc kernel-devel make ncurses-devel

# DOWNLOAD SOURCES FOR LIBEVENT AND MAKE AND INSTALL
curl -OL https://github.com/libevent/libevent/releases/download/release-2.0.22-stable/libevent-2.0.22-stable.tar.gz
tar -xvzf libevent-2.0.22-stable.tar.gz
cd libevent-2.0.22-stable
./configure --prefix=/usr/local

VinACE

import nltk

text = """The Buddha, the Godhead, resides quite as comfortably in the circuits of a digital
computer or the gears of a cycle transmission as he does at the top of a mountain
or in the petals of a flower. To think otherwise is to demean the Buddha...which is
to demean oneself."""

# Used when tokenizing words
sentence_re = r'''(?x)      # set flag to allow verbose regexps
      ([A-Z])(\.[A-Z])+\.?  # abbreviations, e.g. U.S.A.

VinACE

VinACE

#################################################################################
# Authors: Keiller Nogueira, Victor de Melo
#################################################################################

# nvidia-docker seems more stable now and several images are starting to adopt it (including 
# [kaixhin](https://github.com/NVIDIA/nvidia-docker/issues/85).
# hence, we suggest to use it instead, as it solves a few issues with gpu passthrough.
# usage: it is only required when creating a container, i.e, with `run` and related arguments
# you're not required to provide $DOCKER_NVIDIA_DEVICES as it will find the devices itself

VinACE

'''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

VinACE

'''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

VinACE

'''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

VinACE

# multiply/divide
df['quantity'] = df['quantity'].apply(lambda x: x*-1)

# round function on dataframe 
df['quantity'] = df['quantity'].apply(lambda x: round(x, 2))

# List unique values in a DataFrame column
# h/t @makmanalp for the updated syntax!
df['Column Name'].unique()

VinACE

from pyelasticsearch import ElasticSearch
import pandas as pd
from time import time

root_path="/home/clemsos/Dev/mitras/"
raw_data_path=root_path+"data/"
csv_filename="week10.csv"

t0=time()