Programming

WSL2에서의 Spark 설치

Programming, Settings, WSL2, Spark

개요

  • 간단하게 PySpark를 설치해보는 과정을 작성한다.
  • WSL2 설치 방법은 다루지 않는다.

필수 파일 설치

  • 자바 및 Spark 파일을 설치하도록 한다.
$ sudo apt-get install openjdk-8-jdk
$ sudo wget https://archive.apache.org/dist/spark/spark-3.2.0/spark-3.2.0-bin-hadoop3.2.tgz
$ sudo tar -xvzf spark-3.2.0-bin-hadoop3.2.tgz

.bashrc 파일 수정

  • 필자의 현재 경로는 다음과 같다.
evan@evan:/mnt/c/hadoop$ pwd
/mnt/c/hadoop
  • 설치한 파일은 다음과 같다.
evan@evan:/mnt/c/hadoop$ ls
spark-3.2.0-bin-hadoop3.2  spark-3.2.0-bin-hadoop3.2.tgz
  • vi ~/.bashrc 파일을 열고 다음과 같이 코드를 작성한다.
    • 다른 코드는 만지지 않는다.
    • 가장 맨 마지막으로 내려온다.
export JAVA_HOME=/usr/lib/jvm/java-8-openjdk-amd64
export SPARK_HOME=/mnt/c/hadoop/spark-3.2.0-bin-hadoop3.2
export PATH=$JAVA_HOME/bin:$PATH
export PATH=$SPARK_HOME/sbin:$PATH
export PYSPARK_PYTHON=/usr/bin/python3

테스트

  • pyspark를 실행한다. (경로에 주의한다)
evan@evan:/mnt/c/hadoop/spark-3.2.0-bin-hadoop3.2$ pwd
/mnt/c/hadoop/spark-3.2.0-bin-hadoop3.2
evan@evan:/mnt/c/hadoop/spark-3.2.0-bin-hadoop3.2$ pysaprk
Python 3.8.10 (default, Mar 15 2022, 12:22:08)
[GCC 9.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
22/04/19 19:39:57 WARN Utils: Your hostname, evan resolves to a loopback address: 127.0.1.1; using 172.17.233.202 instead (on interface eth0)
22/04/19 19:39:57 WARN Utils: Set SPARK_LOCAL_IP if you need to bind to another address
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
22/04/19 19:39:59 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /__ / .__/\_,_/_/ /_/\_\   version 3.2.0
      /_/

Using Python version 3.8.10 (default, Mar 15 2022 12:22:08)
Spark context Web UI available at http://172.17.233.202:4040
Spark context available as 'sc' (master = local[*], app id = local-1650364801421).
SparkSession available as 'spark'.
>>> rd = sc.textFile("README.md")
>>> rd.count()
109

참조

PyCaret Kaggle Notebook (Since April 10, 2022)

Programming, Kaggle, PyCaret, Python

개요

  • PyCaret이 최근 업데이트 되면서 Kaggle에서 설치 오류가 뜨기 시작함.
  • 해결책은 몇가지 있으나, 그 중 Downgrade 해서 설치 할 예정

캐글 대회 시작

  • 캐글 노트북 시작을 하면 다음 코드가 나타난다. 다음 Cell부터 진행한다.
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session

/kaggle/input/tabular-playground-series-apr-2022/sample_submission.csv
/kaggle/input/tabular-playground-series-apr-2022/train_labels.csv
/kaggle/input/tabular-playground-series-apr-2022/train.csv
/kaggle/input/tabular-playground-series-apr-2022/test.csv

라이브러리 Downgrade

  • 설치하려고 하는 Library는 PyCaret 2.3.5 버전임 (4월 10일 기준 2.3.10 버전)
  • Scikit-Learn 최신 버전은 1.0대 버전임
  • 이를 낮춰서 진행할 것임
!pip install numpy==1.19.5
!pip install matplotlib==3.4.0
!pip install scikit-learn==0.23.2
!pip install pycaret==2.3.5

Collecting numpy==1.19.5
  Downloading numpy-1.19.5-cp37-cp37m-manylinux2010_x86_64.whl (14.8 MB)
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[?25hInstalling collected packages: numpy
  Attempting uninstall: numpy
    Found existing installation: numpy 1.21.5
    Uninstalling numpy-1.21.5:
      Successfully uninstalled numpy-1.21.5
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
tensorflow-io 0.21.0 requires tensorflow-io-gcs-filesystem==0.21.0, which is not installed.
beatrix-jupyterlab 3.1.7 requires google-cloud-bigquery-storage, which is not installed.
thinc 8.0.15 requires typing-extensions<4.0.0.0,>=3.7.4.1; python_version < "3.8", but you have typing-extensions 4.1.1 which is incompatible.
tfx-bsl 1.7.0 requires pyarrow<6,>=1, but you have pyarrow 7.0.0 which is incompatible.
tfx-bsl 1.7.0 requires tensorflow!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,<3,>=1.15.5, but you have tensorflow 2.6.3 which is incompatible.
tensorflow 2.6.3 requires absl-py~=0.10, but you have absl-py 1.0.0 which is incompatible.
tensorflow 2.6.3 requires six~=1.15.0, but you have six 1.16.0 which is incompatible.
tensorflow 2.6.3 requires typing-extensions<3.11,>=3.7, but you have typing-extensions 4.1.1 which is incompatible.
tensorflow 2.6.3 requires wrapt~=1.12.1, but you have wrapt 1.14.0 which is incompatible.
tensorflow-transform 1.7.0 requires pyarrow<6,>=1, but you have pyarrow 7.0.0 which is incompatible.
tensorflow-transform 1.7.0 requires tensorflow!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,<2.9,>=1.15.5, but you have tensorflow 2.6.3 which is incompatible.
tensorflow-serving-api 2.8.0 requires tensorflow<3,>=2.8.0, but you have tensorflow 2.6.3 which is incompatible.
spacy 3.2.4 requires typing-extensions<4.0.0.0,>=3.7.4; python_version < "3.8", but you have typing-extensions 4.1.1 which is incompatible.
pdpbox 0.2.1 requires matplotlib==3.1.1, but you have matplotlib 3.5.1 which is incompatible.
imageio 2.16.1 requires numpy>=1.20.0, but you have numpy 1.19.5 which is incompatible.
featuretools 1.8.0 requires numpy>=1.21.0, but you have numpy 1.19.5 which is incompatible.
apache-beam 2.37.0 requires dill<0.3.2,>=0.3.1.1, but you have dill 0.3.4 which is incompatible.
apache-beam 2.37.0 requires httplib2<0.20.0,>=0.8, but you have httplib2 0.20.4 which is incompatible.
apache-beam 2.37.0 requires pyarrow<7.0.0,>=0.15.1, but you have pyarrow 7.0.0 which is incompatible.
Successfully installed numpy-1.19.5
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
Collecting matplotlib==3.4.0
  Downloading matplotlib-3.4.0-cp37-cp37m-manylinux1_x86_64.whl (10.3 MB)
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[?25hRequirement already satisfied: pyparsing>=2.2.1 in /opt/conda/lib/python3.7/site-packages (from matplotlib==3.4.0) (3.0.7)
Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.7/site-packages (from matplotlib==3.4.0) (1.4.0)
Requirement already satisfied: numpy>=1.16 in /opt/conda/lib/python3.7/site-packages (from matplotlib==3.4.0) (1.19.5)
Requirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.7/site-packages (from matplotlib==3.4.0) (2.8.2)
Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.7/site-packages (from matplotlib==3.4.0) (0.11.0)
Requirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.7/site-packages (from matplotlib==3.4.0) (9.0.1)
Requirement already satisfied: typing-extensions in /opt/conda/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib==3.4.0) (4.1.1)
Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.7/site-packages (from python-dateutil>=2.7->matplotlib==3.4.0) (1.16.0)
Installing collected packages: matplotlib
  Attempting uninstall: matplotlib
    Found existing installation: matplotlib 3.5.1
    Uninstalling matplotlib-3.5.1:
      Successfully uninstalled matplotlib-3.5.1
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
beatrix-jupyterlab 3.1.7 requires google-cloud-bigquery-storage, which is not installed.
pdpbox 0.2.1 requires matplotlib==3.1.1, but you have matplotlib 3.4.0 which is incompatible.
Successfully installed matplotlib-3.4.0
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
Collecting scikit-learn==0.23.2
  Downloading scikit_learn-0.23.2-cp37-cp37m-manylinux1_x86_64.whl (6.8 MB)
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[?25hRequirement already satisfied: scipy>=0.19.1 in /opt/conda/lib/python3.7/site-packages (from scikit-learn==0.23.2) (1.7.3)
Requirement already satisfied: joblib>=0.11 in /opt/conda/lib/python3.7/site-packages (from scikit-learn==0.23.2) (1.0.1)
Requirement already satisfied: numpy>=1.13.3 in /opt/conda/lib/python3.7/site-packages (from scikit-learn==0.23.2) (1.19.5)
Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.7/site-packages (from scikit-learn==0.23.2) (3.1.0)
Installing collected packages: scikit-learn
  Attempting uninstall: scikit-learn
    Found existing installation: scikit-learn 1.0.2
    Uninstalling scikit-learn-1.0.2:
      Successfully uninstalled scikit-learn-1.0.2
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
yellowbrick 1.4 requires scikit-learn>=1.0.0, but you have scikit-learn 0.23.2 which is incompatible.
pdpbox 0.2.1 requires matplotlib==3.1.1, but you have matplotlib 3.4.0 which is incompatible.
imbalanced-learn 0.9.0 requires scikit-learn>=1.0.1, but you have scikit-learn 0.23.2 which is incompatible.
hypertools 0.8.0 requires scikit-learn>=0.24, but you have scikit-learn 0.23.2 which is incompatible.
featuretools 1.8.0 requires numpy>=1.21.0, but you have numpy 1.19.5 which is incompatible.
Successfully installed scikit-learn-0.23.2
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
Collecting pycaret==2.3.5
  Downloading pycaret-2.3.5-py3-none-any.whl (288 kB)
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[?25hRequirement already satisfied: ipywidgets in /opt/conda/lib/python3.7/site-packages (from pycaret==2.3.5) (7.6.5)
Requirement already satisfied: pyLDAvis in /opt/conda/lib/python3.7/site-packages (from pycaret==2.3.5) (3.2.2)
Requirement already satisfied: pandas in /opt/conda/lib/python3.7/site-packages (from pycaret==2.3.5) (1.3.5)
Collecting scipy<=1.5.4
  Downloading scipy-1.5.4-cp37-cp37m-manylinux1_x86_64.whl (25.9 MB)
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[?25hRequirement already satisfied: nltk in /opt/conda/lib/python3.7/site-packages (from pycaret==2.3.5) (3.2.4)
Requirement already satisfied: IPython in /opt/conda/lib/python3.7/site-packages (from pycaret==2.3.5) (7.32.0)
Collecting spacy<2.4.0
  Downloading spacy-2.3.7-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (10.4 MB)
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[?25hRequirement already satisfied: lightgbm>=2.3.1 in /opt/conda/lib/python3.7/site-packages (from pycaret==2.3.5) (3.3.1)
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Requirement already satisfied: textblob in /opt/conda/lib/python3.7/site-packages (from pycaret==2.3.5) (0.17.1)
Requirement already satisfied: plotly>=4.4.1 in /opt/conda/lib/python3.7/site-packages (from pycaret==2.3.5) (5.7.0)
Requirement already satisfied: scikit-plot in /opt/conda/lib/python3.7/site-packages (from pycaret==2.3.5) (0.3.7)
Requirement already satisfied: umap-learn in /opt/conda/lib/python3.7/site-packages (from pycaret==2.3.5) (0.5.2)
Collecting gensim<4.0.0
  Downloading gensim-3.8.3-cp37-cp37m-manylinux1_x86_64.whl (24.2 MB)
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[?25hRequirement already satisfied: Boruta in /opt/conda/lib/python3.7/site-packages (from pycaret==2.3.5) (0.3)
Requirement already satisfied: matplotlib in /opt/conda/lib/python3.7/site-packages (from pycaret==2.3.5) (3.4.0)
Requirement already satisfied: pandas-profiling>=2.8.0 in /opt/conda/lib/python3.7/site-packages (from pycaret==2.3.5) (3.1.0)
Collecting imbalanced-learn==0.7.0
  Downloading imbalanced_learn-0.7.0-py3-none-any.whl (167 kB)
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[?25hCollecting mlflow
  Downloading mlflow-1.25.1-py3-none-any.whl (16.8 MB)
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[?25hCollecting pyod
  Downloading pyod-0.9.9.tar.gz (116 kB)
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[?25h  Preparing metadata (setup.py) ... [?25ldone
[?25hRequirement already satisfied: kmodes>=0.10.1 in /opt/conda/lib/python3.7/site-packages (from pycaret==2.3.5) (0.12.0)
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Requirement already satisfied: numpy==1.19.5 in /opt/conda/lib/python3.7/site-packages (from pycaret==2.3.5) (1.19.5)
Requirement already satisfied: seaborn in /opt/conda/lib/python3.7/site-packages (from pycaret==2.3.5) (0.11.2)
Requirement already satisfied: mlxtend>=0.17.0 in /opt/conda/lib/python3.7/site-packages (from pycaret==2.3.5) (0.19.0)
Requirement already satisfied: scikit-learn==0.23.2 in /opt/conda/lib/python3.7/site-packages (from pycaret==2.3.5) (0.23.2)
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Collecting catalogue<1.1.0,>=0.0.7
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Collecting thinc<7.5.0,>=7.4.1
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Collecting srsly<1.1.0,>=1.0.2
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Collecting yellowbrick>=1.0.1
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Collecting prometheus-flask-exporter
  Downloading prometheus_flask_exporter-0.20.1-py3-none-any.whl (18 kB)
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Collecting databricks-cli>=0.8.7
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[?25h  Preparing metadata (setup.py) ... [?25ldone
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Building wheels for collected packages: pyod, databricks-cli
  Building wheel for pyod (setup.py) ... [?25ldone
[?25h  Created wheel for pyod: filename=pyod-0.9.9-py3-none-any.whl size=139325 sha256=d0a36e8fd0573bc188a9e8f06c62d07c3ca5c12d5e2466310139677ba7731700
  Stored in directory: /root/.cache/pip/wheels/68/32/f0/0dc3050775e77b6661a116b70817b02b4305fa253269d6d998
  Building wheel for databricks-cli (setup.py) ... [?25ldone
[?25h  Created wheel for databricks-cli: filename=databricks_cli-0.16.6-py3-none-any.whl size=112631 sha256=3ae75cdf238ec349a4cf775a5fd33709acb1b17e8f1c1265eb3de4fe7c88fa22
  Stored in directory: /root/.cache/pip/wheels/96/c1/f8/d75a22e789ab6a4dff11f18338c3af4360189aa371295cc934
Successfully built pyod databricks-cli
Installing collected packages: srsly, plac, scipy, querystring-parser, gunicorn, gensim, catalogue, yellowbrick, thinc, imbalanced-learn, databricks-cli, spacy, pyod, prometheus-flask-exporter, mlflow, pycaret
  Attempting uninstall: srsly
    Found existing installation: srsly 2.4.2
    Uninstalling srsly-2.4.2:
      Successfully uninstalled srsly-2.4.2
  Attempting uninstall: scipy
    Found existing installation: scipy 1.7.3
    Uninstalling scipy-1.7.3:
      Successfully uninstalled scipy-1.7.3
  Attempting uninstall: gensim
    Found existing installation: gensim 4.0.1
    Uninstalling gensim-4.0.1:
      Successfully uninstalled gensim-4.0.1
  Attempting uninstall: catalogue
    Found existing installation: catalogue 2.0.7
    Uninstalling catalogue-2.0.7:
      Successfully uninstalled catalogue-2.0.7
  Attempting uninstall: yellowbrick
    Found existing installation: yellowbrick 1.4
    Uninstalling yellowbrick-1.4:
      Successfully uninstalled yellowbrick-1.4
  Attempting uninstall: thinc
    Found existing installation: thinc 8.0.15
    Uninstalling thinc-8.0.15:
      Successfully uninstalled thinc-8.0.15
  Attempting uninstall: imbalanced-learn
    Found existing installation: imbalanced-learn 0.9.0
    Uninstalling imbalanced-learn-0.9.0:
      Successfully uninstalled imbalanced-learn-0.9.0
  Attempting uninstall: spacy
    Found existing installation: spacy 3.2.4
    Uninstalling spacy-3.2.4:
      Successfully uninstalled spacy-3.2.4
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
scattertext 0.1.6 requires gensim>=4.0.0, but you have gensim 3.8.3 which is incompatible.
pymc3 3.11.5 requires scipy<1.8.0,>=1.7.3, but you have scipy 1.5.4 which is incompatible.
pdpbox 0.2.1 requires matplotlib==3.1.1, but you have matplotlib 3.4.0 which is incompatible.
hypertools 0.8.0 requires scikit-learn>=0.24, but you have scikit-learn 0.23.2 which is incompatible.
featuretools 1.8.0 requires numpy>=1.21.0, but you have numpy 1.19.5 which is incompatible.
en-core-web-sm 3.2.0 requires spacy<3.3.0,>=3.2.0, but you have spacy 2.3.7 which is incompatible.
en-core-web-lg 3.2.0 requires spacy<3.3.0,>=3.2.0, but you have spacy 2.3.7 which is incompatible.
Successfully installed catalogue-1.0.0 databricks-cli-0.16.6 gensim-3.8.3 gunicorn-20.1.0 imbalanced-learn-0.7.0 mlflow-1.25.1 plac-1.1.3 prometheus-flask-exporter-0.20.1 pycaret-2.3.5 pyod-0.9.9 querystring-parser-1.2.4 scipy-1.5.4 spacy-2.3.7 srsly-1.0.5 thinc-7.4.5 yellowbrick-1.3.post1
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv


테스트

  • 분류를 위한 필수 라이브러리를 가져왔다.
from pycaret.classification import *
import warnings
warnings.filterwarnings("ignore")
  • 데이터를 불러오는 코드다.
from pycaret.datasets import get_data
data = get_data('diabetes')

png

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Hugo 깃허브 블로그 - Windows (2022)

Programming, Hugo, Settings, Git

패키지 관리자 설치

Untitled

Chocolatey (Windows)

  • If you are on a Windows machine and use Chocolatey for package management, you can install Hugo with the following one-liner:
  • 우선 PowerShell을 관리자로 실행 후, 아래와 같이 Chocolately를 설치한다.
    • 명령어 : Set-ExecutionPolicy Bypass -Scope Process -Force; [System.Net.ServicePointManager]::SecurityProtocol = [System.Net.ServicePointManager]::SecurityProtocol -bor 3072; iex ((New-Object System.Net.WebClient).DownloadString('[https://community.chocolatey.org/install.ps1](https://community.chocolatey.org/install.ps1)'))
Windows PowerShell
Copyright (C) Microsoft Corporation. All rights reserved.
                                                                                                                        
새로운 크로스 플랫폼 PowerShell 사용 https://aka.ms/pscore6                                                                                                                                                                                     
PS C:\WINDOWS\system32> Set-ExecutionPolicy Bypass -Scope Process -Force; [System.Net.ServicePointManager]::SecurityProtocol = [System.Net.ServicePointManager]::SecurityProtocol -bor 3072; iex ((New-Object System.Net.WebClient).DownloadString('https://community.chocolatey.org/install.ps1'))
Forcing web requests to allow TLS v1.2 (Required for requests to Chocolatey.org)
Getting latest version of the Chocolatey package for download.
Not using proxy.
Getting Chocolatey from https://community.chocolatey.org/api/v2/package/chocolatey/1.1.0.
Downloading https://community.chocolatey.org/api/v2/package/chocolatey/1.1.0 to C:\Users\512-24\AppData\Local\Temp\chocolatey\chocoInstall\chocolatey.zip
Not using proxy.
Extracting C:\Users\512-24\AppData\Local\Temp\chocolatey\chocoInstall\chocolatey.zip to C:\Users\512-24\AppData\Local\Temp\chocolatey\chocoInstall
Installing Chocolatey on the local machine
Creating ChocolateyInstall as an environment variable (targeting 'Machine')
  Setting ChocolateyInstall to 'C:\ProgramData\chocolatey'
WARNING: It's very likely you will need to close and reopen your shell
  before you can use choco.
Restricting write permissions to Administrators
We are setting up the Chocolatey package repository.
The packages themselves go to 'C:\ProgramData\chocolatey\lib'
  (i.e. C:\ProgramData\chocolatey\lib\yourPackageName).
A shim file for the command line goes to 'C:\ProgramData\chocolatey\bin'
  and points to an executable in 'C:\ProgramData\chocolatey\lib\yourPackageName'.

Creating Chocolatey folders if they do not already exist.

WARNING: You can safely ignore errors related to missing log files when
  upgrading from a version of Chocolatey less than 0.9.9.
  'Batch file could not be found' is also safe to ignore.
  'The system cannot find the file specified' - also safe.
chocolatey.nupkg file not installed in lib.
 Attempting to locate it from bootstrapper.
PATH environment variable does not have C:\ProgramData\chocolatey\bin in it. Adding...
경고: Not setting tab completion: Profile file does not exist at
'C:\Users\512-24\Documents\WindowsPowerShell\Microsoft.PowerShell_profile.ps1'.
Chocolatey (choco.exe) is now ready.
You can call choco from anywhere, command line or powershell by typing choco.
Run choco /? for a list of functions.
You may need to shut down and restart powershell and/or consoles
 first prior to using choco.
Ensuring Chocolatey commands are on the path
Ensuring chocolatey.nupkg is in the lib folder
PS C:\WINDOWS\system32>

choco를 활용한 Hugo 설치

  • 설치 시, 관리자로 PowerShell을 실행한다.
$ choco install hugo-extended -confirm
  • hugo version을 확인한다.
$ hugo version
hugo v0.97.0-c07f3626e7c8160943591f4d209977efa02c3dca windows/amd64 BuildDate=2022-04-14T08:45:07Z VendorInfo=gohugoio
PS C:\WINDOWS\system32>

Git & Github 설치

  • 이제 Git을 설치하도록 한다. Git이 있어야 배포를 할 수 있기 때문에 반드시 설치한다.
  • Github 회원가입도 안했다면 회원가입을 진행하도록 한다.

Hugo 명령어를 활용한 첫번째 싸이트 만들기

  • 간단한 명령어를 활용하여 싸이트를 생성한다.
    • 명령어 : hugo new site your_blog_name
  • 필자는 폴더명을 myblog로 지정했다.
PS C:\Users\your_name\Desktop> hugo new site myblog
Congratulations! Your new Hugo site is created in C:\Users\512-24\Desktop\myblog.

Just a few more steps and you're ready to go:

1. Download a theme into the same-named folder.
   Choose a theme from https://themes.gohugo.io/ or
   create your own with the "hugo new theme <THEMENAME>" command.
2. Perhaps you want to add some content. You can add single files
   with "hugo new <SECTIONNAME>\<FILENAME>.<FORMAT>".
3. Start the built-in live server via "hugo server".

Visit https://gohugo.io/ for quickstart guide and full documentation.

Github & Submodule 설정

  • 우선 Github 세팅을 먼저 할 것이다.

깃허브 Repo 작성

  • myblog & username.github.io 두 개의 repo를 작성한다.
  • 깃허브와 현재 작성중인 폴더(myblog)를 연결한다.
    • 모든 명령어는 myblog/ 에서 적용한다.
    • 이 때 public 폴더가 있으면 지워준다.
cd myblog
rm -rf public
echo "# myblog" >> README.md
git init
git add README.md
git commit -m "first commit"
git branch -M main
git remote add origin https://github.com/rain0430/myblog.git
git push -u origin main
  • 처음 깃헙을 하시는 분들은 주로 하단에서 에러가 나타날 것이다.
    • 이는 로그인을 하라는 뜻이다. (RUN 아래 코드를 순차적으로 실행)
$ git commit -m "first commit"
Author identity unknown

*** Please tell me who you are.

Run

  git config --global user.email "you@example.com"
  git config --global user.name "Your Name"
  • git commit 부터 다시 순차적으로 실행한다. git push 할 때, sign in 팝업창이 뜰 수도 있다.
  • 이제 서브모듈(Submodule)을 통해서 배포를 진행하려고 한다.
  • git 서브모듈을 추가한다.
    • 이 때, .gitmodules가 뜨는지 확인한다.
    • 또한 rain0430.github.io Repo는 새로 만들어준다.
    • 그리고 다음 명령어을 추가한다.
$ git submodule add -b main https://github.com/rain0430/rain0430.github.io.git public
  • 만약 에러가 난다면, 강제로 실행을 한다.
$ git submodule add -f https://github.com/rain0430/rain0430.github.io.git public

Hugo 기본 싸이트 확인

  • 우선 기본 테마를 설정해본다.
  • 필자는 다음 테마를 설정할 것이다. Codex | Hugo Themes (gohugo.io)
    • 테마 설정 시, 약간의 팁을 드리면, Updated가 최근 날짜인지 확인한다. 최근 날짜가 아니라면 관리가 잘 안되어 테마일수도 있다.
    • 각 테마마다 구체적인 설정 방법이 있기 때문에 각 설치 문서를 참조한다.

Untitled

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Airflow 데이터 파이프라인 구축 예제

Programming, Airflow, Data Engineering, Python

개요

  • 이번에는 CSV-JSON으로 데이터를 변환하는 파이프라인을 구축하도록 한다.

Step 01. Dags 폴더 생성

  • 프로젝트 Root 하단에 Dags 폴더를 만든다.
    • dags 폴더를 확인한다.
$ ls
airflow.cfg  airflow.db  dags  logs  venv  webserver_config.py

Step 02. 가상의 데이터 생성

  • 이번 테스트에서 사용할 라이브러리가 없다면 우선 설치한다.
$ pip3 install faker pandas
  • faker 라이브러리를 활용하여 가상의 데이터를 생성한다. (파일 경로 : data/step01_writecsv.py)
from faker import Faker
import csv
output=open('data.csv','w')
fake=Faker()
header=['name','age','street','city','state','zip','lng','lat']
mywriter=csv.writer(output)
mywriter.writerow(header)
for r in range(1000):
    mywriter.writerow([fake.name(),fake.random_int(min=18, max=80, step=1), fake.street_address(), fake.city(),fake.state(),fake.zipcode(),fake.longitude(),fake.latitude()])
output.close()
  • 생성된 후, 파일을 확인하도록 한다.
evan@evan:/mnt/c/airflow-test/data$ ls
data.csv  step01_writecsv.py

Step 03. csv2json 파일 구축

  • 이번에는 CSV와 JSON 변환 파일을 구축하는 코드를 작성한다. (파일 경로 : dags/csv2json.py)\
  • 주요 목적 함수 csvToJson()의 역할은 data/data.csv 파일을 불러와서 fromAirflow.json 파일로 변경하는 것이다.
  • DAG는 csvToJson 함수를 하나의 작업으로 등록하는 과정을 담는다. 작업의 소유자, 시작일시, 실패 시 재시도 횟수, 재시도 지연시 시간을 지정한다.
  • print_starting >> csvJson 에서 >> 는 하류 설정 연산자라고 부른다. (동의어 비트 자리이동 연산자)
import datetime as dt
from datetime import timedelta

from airflow import DAG
from airflow.operators.bash import BashOperator
from airflow.operators.python import PythonOperator

import pandas as pd

def csvToJson():
    df=pd.read_csv('data/data.csv')
    for i,r in df.iterrows():
        print(r['name'])
    df.to_json('fromAirflow.json',orient='records')

default_args = {
    'owner': 'evan',
    'start_date': dt.datetime(2020, 3, 18),
    'retries': 1,
    'retry_delay': dt.timedelta(minutes=5),
}

with DAG('MyCSVDAG',
         default_args=default_args,
         schedule_interval=timedelta(minutes=5),      # '0 * * * *',
         ) as dag:

    print_starting = BashOperator(task_id='starting',
                               bash_command='echo "I am reading the CSV now....."')
    
    csvJson = PythonOperator(task_id='convertCSVtoJson',
                                 python_callable=csvToJson)

print_starting >> csvJson

Step 04. Airflow Webserver 및 Scheduler 동시 실행

  • 이제 웹서버와 스케쥴러를 동시에 실행한다. (터미널을 2개 열어야 함에 주의한다.)
$ airflow webserver -p 8080
$ airflow scheduler
  • 이제 WebUI를 확인하면 정상적으로 작동하는 것을 확인할 수 있다.

Untitled

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R Selenium 설치 가이드 (Windows)

Programming, Selenium, Crawling, R

개요

  • R에서 Selenium을 설치하는 과정을 보여준다.
  • 우선 자바가 설치되어 있는지 확인한다.
  • 유투브에서 제목만 가져오는 Demo 코드를 작성한다.

자바를 모르시는 분

  • 만약 자바 설치를 해본적이 없다면 아래 코드를 순차적으로 입력한다.
install.packages("multilinguer")
library(multilinguer)
install_jdk()

# Rtools 설치 필요 
# https://cran.r-project.org/bin/windows/Rtools
write('PATH="${RTOOLS40_HOME}\\usr\\bin;${PATH}"', file = "~/.Renviron", append = TRUE)
Sys.which("make")

install.packages(c("stringr", "hash", "tau", "Sejong", "RSQLite", "devtools"),
                 type = "binary")

install.packages("remotes")
remotes::install_github("haven-jeon/KoNLP",
                        upgrade = "never",
                        INSTALL_opts = c("--no-multiarch"))

library(KoNLP)
useNIADic()
  • 마지막 코드에서 콘솔창이 보인다면 정상적으로 설치가 완료가 된 것이다.
> library(KoNLP)
Checking user defined dictionary!

> useNIADic()
Backup was just finished!
1213109 words dictionary was built.
  • 검색창에서 시스템 환경 변수 편집을 검색 후, 실제 자바가 설치가 되어 있는지 직접 확인한다.
    • 환경변수 - 시스템 변수 탭 내 Path 클릭 후 확인 버튼 - 환경 변수 편집 순차적으로 탭을 확인한다.

Untitled

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Apache NiFi 설치와 설정 in WSL2

Programming, Windows, WSL2

설치

  1. wsl2에서 JAVA 설치 한다.
$ sudo apt-get update && sudo apt-get upgrade
$ sudo apt install openjdk-11-jre-headless
$ vi ~/.bash_profile
export JAVA_HOME=/usr/lib/jvm/java-11-openjdk-amd64
  1. curl을 이용해서 NiFi를 현재 경로에 내려받는다.
$ sudo wget https://downloads.apache.org/nifi/1.16.0/nifi-1.16.0-bin.tar.gz
  1. .tar.gz 파일의 압축을 푼다.
$ sudo tar xvzf nifi-1.16.0-bin.tar.gz
  1. 압축파일을 푼 다음에는 cd nifi-1.16.0 폴더에 접속을 한다.
$ cd nifi-1.16.0/bin
  1. ls를 실행해서 nifi-env.sh 파일이 있는지 확인하고 있다면, vi 에디터로 연다.
    1. .bash_profile에서 한 것처럼 동일하게 자바 환경변수를 잡아준다.
$ sudo vi nifi-env.sh
export JAVA_HOME="/usr/lib/jvm/java-11-openjdk-amd64"
  1. 그리고, nifi-env.sh 파일을 실행한다.
$ sudo ./nifi.sh start

Java home: /usr/lib/jvm/java-11-openjdk-amd64
NiFi home: /nifi-1.16.0

Bootstrap Config File: /nifi-1.16.0/conf/bootstrap.conf
  1. webserver 주소를 확인한다.
/nifi-1.16.0/conf$ cd .. 
/nifi-1.16.0/conf$ vi nifi.properties

Untitled

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Convert Plotly Jupyterlab to HTML

Programming, Data Visualisation, Plotly, HTML, Kaggle

개요

  • jupyter notebook에서 plotly 기반의 시각화를 작성한다.
  • jupyter notebook에서 html로 변환 시, plotly로 작성된 코드는 나타나지 않았다.
  • 이 때 필수적으로 입력해야 할 코드를 작성한다.

필수 코드 적용 전 변환 시

  • 간단한 시각화 코드를 작성 후, html로 변환한다.
import plotly.express as px

fig = px.line(x=["a","b","c"], y=[1,3,2], title="sample figure")
fig.show()
  • 아래 그림은 일반적으로 JupyterLab 에디터에서 HTML로 변환하는 과정이다.
    • File - Save and Export Notebook As… - HTML 순차적으로 클릭한다.

Screen Shot 2022-04-11 at 10.56.22 PM.png

Read more…

ElasticSearch & Kibana 설치 in WSL2

Programming, Windows, VC Code, WSL2

Step 1. 사전 필수 패키지 설치

  1. 우선 시스템 패키지를 업데이트 하고, HTTPS와 관련된 패키지를 설치한다.
$ sudo apt update
$ sudo apt install apt-transport-https
  1. 자바를 설치한다.
  • 이미 설치가 되어 있다면 버전만 확인한다.
$ sudo apt install openjdk-11-jdk
  • 설치한 버전을 확인한다.
$ java -version
openjdk version "11.0.14.1" 2022-02-08
OpenJDK Runtime Environment (build 11.0.14.1+1-Ubuntu-0ubuntu1.20.04)
OpenJDK 64-Bit Server VM (build 11.0.14.1+1-Ubuntu-0ubuntu1.20.04, mixed mode, sharing)
  1. 자바 환경 변수를 설정하기 위해 아래와 같이 에디터를 입력한다.
$ sudo vi /etc/environment
  • 그리고 아래와 같이 추가한다.
JAVA_HOME="/usr/lib/jvm/java-11-openjdk-amd64"
  • 환경변수를 업데이트 한다.
$ source /etc/environment
  • 실제 경로가 나오는지 확인한다.
$ echo $JAVA_HOME
/usr/lib/jvm/java-11-openjdk-amd64

Step 2. ElasticSearch 설치

  • GPG Keys를 확인하여 설치를 진행한다.
$ wget -qO - https://artifacts.elastic.co/GPG-KEY-elasticsearch | sudo apt-key add -
OK
  • 라이브러리를 아래와 같이 추가한다.
$ sudo sh -c 'echo "deb https://artifacts.elastic.co/packages/7.x/apt stable main" > /etc/apt/sources.list.d/elastic-7.x.list'
  • 이제 elasticsearch를 설치한다.
$ sudo apt-get update
Hit:1 https://artifacts.elastic.co/packages/7.x/apt stable InRelease
Hit:2 http://security.ubuntu.com/ubuntu focal-security InRelease
Hit:3 http://archive.ubuntu.com/ubuntu focal InRelease
Hit:4 http://archive.ubuntu.com/ubuntu focal-updates InRelease
Hit:5 http://archive.ubuntu.com/ubuntu focal-backports InRelease
Reading package lists... Done
$ sudo apt-get install elasticsearch
Reading package lists... Done
Building dependency tree
Reading state information... Done
The following NEW packages will be installed:
  elasticsearch
0 upgraded, 1 newly installed, 0 to remove and 0 not upgraded.
Need to get 312 MB of archives.
After this operation, 517 MB of additional disk space will be used.
Get:1 https://artifacts.elastic.co/packages/7.x/apt stable/main amd64 elasticsearch amd64 7.17.2 [312 MB]
Fetched 312 MB in 8s (40.9 MB/s)
Selecting previously unselected package elasticsearch.
(Reading database ... 32942 files and directories currently installed.)
Preparing to unpack .../elasticsearch_7.17.2_amd64.deb ...
Creating elasticsearch group... OK
Creating elasticsearch user... OK
Unpacking elasticsearch (7.17.2) ...
Setting up elasticsearch (7.17.2) ...
### NOT starting on installation, please execute the following statements to configure elasticsearch service to start automatically using systemd
 sudo systemctl daemon-reload
 sudo systemctl enable elasticsearch.service
### You can start elasticsearch service by executing
 sudo systemctl start elasticsearch.service
warning: usage of JAVA_HOME is deprecated, use ES_JAVA_HOME
Created elasticsearch keystore in /etc/elasticsearch/elasticsearch.keystore
Processing triggers for systemd (245.4-4ubuntu3.15) ...

Step 3. Elasticsearch 서비스 시작

  • 이번에는 elasticsearch 서비스를 시작한다.
$ sudo systemctl start elasticsearch

System has not been booted with systemd as init system (PID 1). 
Can't operate. Failed to connect to bus: Host is down
  • 다음 명령어를 추가한다.
$ sudo -b unshare --pid --fork --mount-proc /lib/systemd/systemd --system-unit=basic.target
$ sudo -E nsenter --all -t $(pgrep -xo systemd) runuser -P -l $USER -c "exec $SHELL"
  • 서비스가 가능하도록 한다.
$ sudo systemctl enable elasticsearch
Synchronizing state of elasticsearch.service with SysV service script with /lib/systemd/systemd-sysv-install.
Executing: /lib/systemd/systemd-sysv-install enable elasticsearch
Created symlink /etc/systemd/system/multi-user.target.wants/elasticsearch.service → /lib/systemd/system/elasticsearch.service.
  • 서비스를 시작한다.
$ sudo systemctl start elasticsearch
  • 실제 서비스가 작동하는지 확인한다.
$ curl -X GET "localhost:9200/"
{
  "name" : "evan",
  "cluster_name" : "elasticsearch",
  "cluster_uuid" : "vATWEOO1T9yOFGLc7G3L4w",
  "version" : {
    "number" : "7.17.2",
    "build_flavor" : "default",
    "build_type" : "deb",
    "build_hash" : "de7261de50d90919ae53b0eff9413fd7e5307301",
    "build_date" : "2022-03-28T15:12:21.446567561Z",
    "build_snapshot" : false,
    "lucene_version" : "8.11.1",
    "minimum_wire_compatibility_version" : "6.8.0",
    "minimum_index_compatibility_version" : "6.0.0-beta1"
  },
  "tagline" : "You Know, for Search"
}
  • 만약 위와 같은 메시지가 뜨면, 윈도우 화면에서 재 확인해본다.

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PostgreSQL Installation on WSL2 and Windows

Programming, PostgreSQL, Settings, WSL2, PgAdmin

개요

  • WSL2에서 PostgreSQL을 설치한다.
  • pgAdmin은 Windows에 설치한다.

터미널 업그레이드

  • 먼저 WSL 터미널을 열고, Ubuntu 패키지를 모두 업데이트 및 업그레이드를 한다.
$ sudo apt update
[sudo] password for evan:
Hit:1 https://artifacts.elastic.co/packages/7.x/apt stable InRelease
Get:2 http://security.ubuntu.com/ubuntu focal-security InRelease [114 kB]
Hit:3 http://archive.ubuntu.com/ubuntu focal InRelease
Get:4 http://archive.ubuntu.com/ubuntu focal-updates InRelease [114 kB]
Get:5 http://archive.ubuntu.com/ubuntu focal-backports InRelease [108 kB]
Get:6 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 Packages [1712 kB]
Get:7 http://archive.ubuntu.com/ubuntu focal-updates/universe amd64 Packages [916 kB]
Fetched 2963 kB in 5s (600 kB/s)
Reading package lists... Done
Building dependency tree
Reading state information... Done
All packages are up to date.
  • 이번에는 업그레이드를 해본다.
$ sudo apt-get upgrade
Reading package lists... Done
Building dependency tree
Reading state information... Done
Calculating upgrade... Done
0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded.

PostgreSQL Installation in WSL2

  • 이번에는 WSL2에서 PostgreSQL을 설치한다. 설치가 종료되면, 반드시 버전을 확인한다.
$ sudo apt install postgresql postgresql-contrib
$ psql --version
psql (PostgreSQL) 12.9 (Ubuntu 12.9-0ubuntu0.20.04.1)
  • 설치 이후에는 Database를 접근가능하도록 활성화해야 한다.
    • 포트가 활성화 되어 있지 않다면 아래와 같은 메시지가 나타날 것이다.
$ sudo service postgresql status
12/main (port 5432): down
  • 이번에는 활성화를 해보도록 한다. 온라인이라는 메시지가 나타난다면 활성화가 되었다는 것을 의미한다.
$ sudo service postgresql start
 * Starting PostgreSQL 12 database server
$ sudo service postgresql status
12/main (port 5432): online
  • 이번에는 활성화된 데이터베이스를 종료시킨다.
$ sudo service postgresql stop
 * Stopping PostgreSQL 12 database server                                                                        [ OK ]
$ sudo service postgresql status
12/main (port 5432): down

사용자 계정 Password 설정

  • 기본적으로 admin 사용자로 등록이 되어 있다. 보통 DB 초기 세팅 시에는 패스워드를 입력받아야 한다. (예: evan)
$ sudo passwd postgres
New password:
Retype new password:
passwd: password updated successfully
  • 여기까지 했다면, WSL2에서 추가로 설정할 것은 더 없다.

pgAdmin Installation on Windows

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VSCode Remote WLS 연동

Programming, Windows, VC Code, WSL2

VSCode 설치

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  • 설치 시, 환경변수 체크란만 잘 확인한다.

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  • 설치가 다 끝난 후에는 재부팅을 실시한다.

Remote WSL 연동

  • Extension 버튼을 클릭한다.

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  • 검색창에서 Remote WSL을 검색 후, 설치를 진행한다.

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  • 모두 클릭 후, Mark Done을 선택한다.

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  • Open Folder를 클릭한다.

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  • WSL에서 설치했던 airflow-test 폴더를 선택한다.

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  • 해당 프로젝트를 열도록 한다.

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  • 메뉴바에 Terminal을 선택 후, 화면 하단에서 WSL이 있는지 확인한다.

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