VinACE

## docker-aliases.sh
############################################################################
#                                                                          #
#               ------- 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:                                                             #

## install-tmux
# 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

## nltk-intro.py
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.

## from_path_and_array.ipynb

      
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                VinACE
                / from_path_and_array.ipynb
            
            
              Created
              November 16, 2018 12:45
                — forked from elmarculino/from_path_and_array.ipynb
            
              
                ImageClassifierData.from_path_and_array
              
          
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## docker_cheat_sheet.sh
#################################################################################
# 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

## classifier_from_little_data_script_3.py
'''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

## classifier_from_little_data_script_2.py
'''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

## classifier_from_little_data_script_1.py
'''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

## useful_pandas_snippets.py
# 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()

## csv_to_elastic_search_bulk_insert.py
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()
	############################################################################
	# #
	# ------- 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: #
	# 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
	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.
	#################################################################################
	# 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
	'''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
	# 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()
	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()