스타벅스 아이스 아메리카노를 선물로 보내드리겠습니다.
(GCP) GKE를 활용하여 nginx를 실행해보자.네비게이션 메뉴에서 Kubernetes Engine > Clusters를 클릭합니다.
위 화면에서 Create를 클릭합니다.
그 이후에, Cluster 이름은 standard-cluster-1으로 바꾸고, Zone은 us-central1-a로 바꿉니다.
나머지는 모두 Default로 그냥 놔둡니다.
스타벅스 아이스 아메리카노를 선물로 보내드리겠습니다.(GCP) Cloud Build를 활용하여 Docker를 활용해보자.APIs & Services를 클릭한다.Enable APIs and Services를 클릭한다.Search for APIs & Services에서 Cloud Build를 입력한다.Cloud Build API를 클릭한 후, Enable 버튼을 클릭한다.Google Container Registry API 버튼을 클릭한다.아래 그림처럼 Activate Cloud Shell를 클릭한다.
!pip install kaggle
Requirement already satisfied: kaggle in /usr/local/lib/python3.7/dist-packages (1.5.10)
Requirement already satisfied: python-dateutil in /usr/local/lib/python3.7/dist-packages (from kaggle) (2.8.1)
Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.7/dist-packages (from kaggle) (1.15.0)
Requirement already satisfied: urllib3 in /usr/local/lib/python3.7/dist-packages (from kaggle) (1.24.3)
Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from kaggle) (2.23.0)
Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from kaggle) (4.41.1)
Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from kaggle) (2020.12.5)
Requirement already satisfied: python-slugify in /usr/local/lib/python3.7/dist-packages (from kaggle) (4.0.1)
Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle) (3.0.4)
Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle) (2.10)
Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.7/dist-packages (from python-slugify->kaggle) (1.3)
from google.colab import files
uploaded = files.upload()
for fn in uploaded.keys():
print('User uploaded file "{name}" with length {length} bytes'.format(
name=fn, length=len(uploaded[fn])))
# Then move kaggle.json into the folder where the API expects to find it.
!mkdir -p ~/.kaggle/ && mv kaggle.json ~/.kaggle/ && chmod 600 ~/.kaggle/kaggle.json
!mkdir ~/.kaggle
!echo '{"username":"your_name","key":"your_key"}' > ~/.kaggle/kaggle.json
!chmod 600 ~/.kaggle/kaggle.json
mkdir: cannot create directory ‘/root/.kaggle’: File exists
!kaggle competitions list
Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.12 / client 1.5.4)
ref deadline category reward teamCount userHasEntered
--------------------------------------------- ------------------- --------------- --------- --------- --------------
contradictory-my-dear-watson 2030-07-01 23:59:00 Getting Started Prizes 102 False
gan-getting-started 2030-07-01 23:59:00 Getting Started Prizes 209 False
tpu-getting-started 2030-06-03 23:59:00 Getting Started Knowledge 493 False
digit-recognizer 2030-01-01 00:00:00 Getting Started Knowledge 3394 False
titanic 2030-01-01 00:00:00 Getting Started Knowledge 27213 True
house-prices-advanced-regression-techniques 2030-01-01 00:00:00 Getting Started Knowledge 6963 True
connectx 2030-01-01 00:00:00 Getting Started Knowledge 595 False
nlp-getting-started 2030-01-01 00:00:00 Getting Started Knowledge 1723 True
competitive-data-science-predict-future-sales 2022-12-31 23:59:00 Playground Kudos 10797 True
jane-street-market-prediction 2021-08-23 23:59:00 Featured $100,000 4245 True
hungry-geese 2021-07-26 23:59:00 Playground Prizes 423 False
coleridgeinitiative-show-us-the-data 2021-06-22 23:59:00 Featured $90,000 188 False
bms-molecular-translation 2021-06-02 23:59:00 Featured $50,000 350 False
iwildcam2021-fgvc8 2021-05-26 23:59:00 Research Knowledge 11 False
herbarium-2021-fgvc8 2021-05-26 23:59:00 Research Knowledge 30 False
plant-pathology-2021-fgvc8 2021-05-26 23:59:00 Research Knowledge 121 False
hotel-id-2021-fgvc8 2021-05-26 23:59:00 Research Knowledge 26 False
hashcode-2021-oqr-extension 2021-05-25 23:59:00 Playground Knowledge 101 False
indoor-location-navigation 2021-05-17 23:59:00 Research $10,000 734 False
hpa-single-cell-image-classification 2021-05-11 23:59:00 Featured $25,000 432 False
key값도 새로 입력해줘야 합니다.
귀찮지만, 두개의
Cell은 실행합시다. 작은 도움이 되기를 바랍니다.Happy to Code
Why are my contributions not showing up on my profile? 이런 글이 있습니다.
그러나, 제 경우에는 제 개인 PC는 반영이 잘 되고, 강사 PC에는 안되는 상황이어서, 맞지 않은 케이스였습니다.
Your local Git commit email isn’t connected to your account Commits must be made with an email address that is connected to your account on GitHub, or the GitHub-provided noreply email address provided to you in your email settings, in order to appear on your contributions graph. For more information about noreply email addresses, see “Setting your commit email address.”
스타벅스 아이스 아메리카노를 선물로 보내드리겠습니다.최근 기술 트렌드 중의 Hot한 주제는 DevOps이다.
Dev는 Development의 약어이며, Ops는 Operation의 약자이다.Communication 문제! 이러한 문제점을 해결하기 위해 나온 방법론이 DevOps이다.이러한 부분은 머신러닝과 딥러닝도 동일함. 즉, 개발자를 위한 배포와 운영이 DevOps라면, 머신러닝 엔지니어를 위해 나온 기술은 MLOps로 볼 수 있다. 자세한 것은 유투브 영상을 통해서 기본적인 개념을 배웠으면 한다.
주의: 윈도우 10 64비트
여러 좋은 자료들이 많아서 같이 참고하기를 바란다.
https://apps.twitter.com에 접속한다.
create an app 버튼을 클릭한다.
필자는 Hobbysit-Exploring the API를 선택했다.
인증 메일이 오기전까지는 조금 시일이 걸린다.
별도의 인증 절차 없이 사용 가능한 패키지
우선 설치 후, 사용해보도록 한다.
# install.packages("rtweet")
library(rtweet)
library(dplyr)
library(ggplot2)
search_tweets() 함수를 활용하면 매우 쉽게 데이터를 가져올 수 있다.rstats <- search_tweets("#테슬라", n = 1000, include_rts = FALSE) %>%
select(name, location, description)
glimpse(rstats)
## Rows: 132
## Columns: 3
## $ name <chr> "얼리어답터", "얼리어답터", "허프포스트코리아", "뉴스핌", "뉴스핌", "disclosure", ~
## $ location <chr> "", "", "SEOUL", "Korea", "Korea", "", "", "", "", "", "",~
## $ description <chr> "2001년부터 전세계의 테크 트랜드를 한국에 소개했던 얼리어답터가 완전히 새롭게 다시 시작합니다. 더 ~
rtweet 패키지가 어떤 Task를 수행하는 비교하는 표가 있다.
install.packages("disk.frame")
suppressPackageStartupMessages(library(disk.frame))
library(nycflights13)
chunks로 분해하여 하나의 폴더 안에 저장한다. (HDD 디스크 사용)setup_disk.frame()
## The number of workers available for disk.frame is 1
# this allows large datasets to be transferred between sessions
options(future.globals.maxSize = Inf)
# convert the flights data.frame to a disk.frame
# optionally, you may specify an outdir, otherwise, the
flights.df <- as.disk.frame(nycflights13::flights, overwrite = TRUE)
class(flights.df)
## [1] "disk.frame" "disk.frame.folder"
flights.df
## path: "/var/folders/zq/ch7gky6n3rzgjf1pd0w2l35w0000gn/T//Rtmp3ymGwx/file61302e8c8834.df"
## nchunks: 1
## nrow (at source): 336776
## ncol (at source): 19
## nrow (post operations): ???
## ncol (post operations): ???
객체가 disk.frame으로 변환된 것을 확인할 수 있다.
zip 파일을 받는다면 어떻게 해야할까?$ unzip a.zip b.zip c.zip d.zip
$ kaggle competitions download -c sf-crime
Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.10 / client 1.5.4)
Downloading test.csv.zip to /content
48% 9.00M/18.7M [00:00<00:00, 52.3MB/s]
100% 18.7M/18.7M [00:00<00:00, 74.0MB/s]
Downloading sampleSubmission.csv.zip to /content
0% 0.00/2.35M [00:00<?, ?B/s]
100% 2.35M/2.35M [00:00<00:00, 157MB/s]
Downloading train.csv.zip to /content
41% 9.00M/22.1M [00:00<00:00, 33.6MB/s]
100% 22.1M/22.1M [00:00<00:00, 63.4MB/s]
$ ls
sampleSubmission.csv.zip test.csv.zip train.csv.zip
.zip 파일을 해제하도록 한다.$ unzip '*.zip'
Archive: train.csv.zip
inflating: train.csv
Archive: test.csv.zip
inflating: test.csv
Archive: sampleSubmission.csv.zip
inflating: sampleSubmission.csv
3 archives were successfully processed.
import os
DATA_PATH = "./"
for file in os.listdir(DATA_PATH):
if 'csv' in file and 'zip' not in file:
print(file.ljust(30) + str(round(os.path.getsize(file) / 1000000, 2)) + 'MB')
test.csv 91.0MB
sampleSubmission.csv 75.05MB
train.csv 127.43MB
$ pwd
/Users/username
Chris Jean, Unzip Multiple Files from Linux Command Line Retrieved from https://chrisjean.com/unzip-multiple-files-from-linux-command-line/
Travis에 깃허브로 로그인 한다. 아래와 같은 화면이 나오면 로그인이 잘 된 것이다.
영문 내용을 잘 읽어본다.
Once you’re signed in, go to your profile page where you’ll see all the organizations you’re a member of. You can install the GitHub App integration by clicking the Activate button for each organization you would like to use with Travis CI.