테크놀로지 리더를 위한 전문 도서관
PrintBI/분석 / 머신러닝 / 클라우드2019.12.02
금융회사의 디지털화, 성공적인 여정을 위한 전략
IBM
금융 디지털 전환 전략의 성패는 고객이 원하는 뱅킹서비스를 디지털 환경에서 신속하게 제공할 수 있느냐의 문제로 종결된다. 이는 결국 금융회사가 가진 ‘데이터전략’과 인공지능(AI)기반 금융서비스의 내재화 성숙도의 문제이다. 금융회사들이 ‘데이터 중심 정보회사’ 전략에 사활을 거는 이유다.
문제는 금융회사가 이러한 ‘디지털전환’전략을 성공적으로 구현하기가 생각보다 매우 어렵다는 점이다. 그렇다면 금융회사가 ‘AI기반의 데이터 중심 정보회사’로 거듭나기 위한 빠르고 효과적인 전략은 없을까. 이에 IBM은 4단계로 구성된 ‘AI 사다리(Ladder)’ 전략과 이를 클라우드로 구현하기위한 ‘IBM Cloud Pak for Data’ 플랫폼을 제시하고 있다. 

주요 내용
-    금융서비스의 디지털화란 무엇인가?
-    “AI기반 데이터 중심 기업으로 성장”… IBM의 ‘AI 사다리’
-    AI 사다리’전략을 구현하는 플랫폼, ‘IBM Cloud Pak for Data’
-    IBM ‘왓슨’의 놀라운 역할… AI 편향성 위험 제거한 ‘왓슨 오픈스케일’
-    ‘데이터 중심 기업’에 도전하는 기업에게 드리는 4가지 조언
 AI사다리 / 금융서비스 / 데이터전략 / 인공지능
     다운로드     
추천 테크라이브러리

혁신 해법 ‘오픈소스’, 지원 해법 IBM 오픈소스서비스 - IDG Tech Dossier
“클라우드에서도 보안 책임은 기업 스스로” 클라우드 보안 과제와 기업의 대응 전략 - IDG DeepDive
소프트웨어 정의 스토리지의 총아, 오브젝트 스토리지의 이해와 도입시 고려해야 할 4가지 - IDG Summary
정보 아키텍처를 단순화하여 데이터 처리 : Cloud Pak for Data
금융회사의 디지털화, 성공적인 여정을 위한 전략
롯데백화점, 인공 지능 기술로 온·오프라인 고객 경험 시너지와 개인화 서비스 증대
 
 백업 전략으로 데이터 보호하기

추천기사
'홀린듯 당한다'··· 최신 모바일 공격 기법 7가지'흔들리며 피는 꽃'··· 프로젝트 반발에 대한 8가지 조언김진철의 How-to-Big Data | 빅데이터의 미래 (1)협업 앱, 기싸움이 시작됐다··· '마이크로소프트 vs. 슬랙'디지털 변혁 시대의 임직원 교육 '핵심은 경험이다'

ITworld Korea

News
뉴스
인사이트
슬라이드쇼
How To
테크비디오
오피니언
Topics
클라우드
빅데이터
보안
사물인터넷
iOS
안드로이드
개발자
Business
테크라이브러리
테크서베이
테크퀴즈
컨퍼런스
마케팅 서비스
리서치 서비스
연락/문의
Join Us On
 Twitter
 Facebook
 RSS
IDG Network
Question 1 
가용한 모든 데이터의 효율적인 수집 및 관리
고품질의 데이터의 용이한 셀프 분석
머신러닝 모델의 개발 / 테스트 / 배포의 효율적 관리
신뢰성과 투명성을 갖춘 Al의 개발 / 운영 / 확산

보이스봇
챗봇
VOC 분석
상당 유형 분류
자동 문의 답변
상담원 지원 (답변 추천)


어느 정도 대응 가능하나 증설 혹은 교체를 고려중임
대응이 어려워 새로운 시스템／솔루션을 도입해야 할 상황임


IBM Analytics 브리핑－ 머신러닝 및 인공지능 도입을 위한 Analytics 소개 세션 (1시간）
IBM Watson Studio 세션－ 효과적인 데이터 사이언스 프로젝트 수행 방안 세션 ((1일, 무료）
Cloud Pak for Data 찾아가는 세미나 - 빠르고 안전하게 비즈니스에 활용할 수 있도록 직접 고객을 찾아가서 플랫폼 및 Use Case에 대한 상세 브리핑 제공 (2시간
전문가 상담 신청 （전화 상담）


이메일로
전화로
우편으로


P.S 건물 1Cm만 옮겨주세요. 어려운거 아니잖아요. 아니예요. 다시 원복해 주세요.



BEST Image Picker

https://github.com/topics/imagepicker?l=objective-c

https://github.com/RITL/RITLImagePickerDemo
https://github.com/lincf0912/ImagePickerSheetViewController
https://github.com/zhangao0086/DKImagePickerController
https://github.com/Yummypets/YPImagePicker



 [[APDocument alloc] initWithString:anXMLString];



Existing instance variable 'parent' for property 'parent' with assign attribute must be __unsafe_unretained

optimization Level
Debug Fastest, Smallest [-Os]
Distribution
Debug Fastest, Smallest [-Os]
Release
Debug Fastest, Smallest [-Os]


티스토리 ioswift
 
직접 대면
전화
이메일
메신저
회식, 티타임 등 사석에서 이야기
게시판 
열린 조직 문화
나의 원만한 커뮤니케이션 능력
다른 사람의 이야기에 귀 기울이고, 수용할 줄 아는 동료, 상사, 후배
공식적인 커뮤니케이션 시스템의 체계적 확립
경영진과의 열린 소통
스킨십 프로그램
본부 간, 직급 간, 열린 소통
직원 사기진작 프로그램
커뮤니케이션 Tool (SNS 등)
본부 간, 팀 간 이기주의 타파
젊은 직원들의 소통채널
동영상 기반의 방송
모바일 서비스 (스마트폰에서 시청이 가능한)
블로그 (사내블로그 활용한 사내커뮤니케이션)


- (IBAction)onClickCheckingCategory:(id)sender {
       self.title = @"이전";

saparator none

1) 올플래시

2) 미드레인지 스토리지 (스토와이즈)

3) 하이브리드 플래시

4) 테이프, 가상화 테이프

5) 소프트웨어 정의 스토리지

6) 오브젝트 스토리지

https://www.google.com/search?q=%22Failed+to+invoke+the+interpreter+with+error%22&oq=%22Failed+to+invoke+the+interpreter+with+error%22&aqs=chrome.0.69i59j69i60.4768j0j7&sourceid=chrome&ie=UTF-8

https://books.google.co.kr/books?id=e--oDwAAQBAJ&pg=PA145&lpg=PA145&dq=%22Failed+to+invoke+the+interpreter+with+error%22&source=bl&ots=CqkTVIC9Gb&sig=ACfU3U1g0P6zHckN-f8lYSDoypASxlZMrw&hl=ko&sa=X&ved=2ahUKEwi0wpGpv7vmAhUBMN4KHWDdAG4Q6AEwAHoECAkQAQ#v=onepage&q=%22Failed%20to%20invoke%20the%20interpreter%20with%20error%22&f=false


func runModel(onFrame pixelBuffer: CVPixelBuffer) -> Result? {
    let sourcePixelFormat = CVPixelBufferGetPixelFormatType(pixelBuffer)
    assert(sourcePixelFormat == kCVPixelFormatType_32ARGB ||
             sourcePixelFormat == kCVPixelFormatType_32BGRA ||
               sourcePixelFormat == kCVPixelFormatType_32RGBA)


    let imageChannels = 4
    assert(imageChannels >= inputChannels)

    // Crops the image to the biggest square in the center and scales it down to model dimensions.
    let scaledSize = CGSize(width: inputWidth, height: inputHeight)
    guard let thumbnailPixelBuffer = pixelBuffer.centerThumbnail(ofSize: scaledSize) else {
      return nil
    }

    let interval: TimeInterval
    let outputTensor: Tensor
    do {
      let inputTensor = try interpreter.input(at: 0)

      // Remove the alpha component from the image buffer to get the RGB data.
      guard let rgbData = rgbDataFromBuffer(
        thumbnailPixelBuffer,
        byteCount: batchSize * inputWidth * inputHeight * inputChannels,
        isModelQuantized: inputTensor.dataType == .uInt8
      ) else {
        print("Failed to convert the image buffer to RGB data.")
        return nil
      }

      // Copy the RGB data to the input `Tensor`.
      try interpreter.copy(rgbData, toInputAt: 0)

      // Run inference by invoking the `Interpreter`.
      let startDate = Date()
      try interpreter.invoke()
      interval = Date().timeIntervalSince(startDate) * 1000

      // Get the output `Tensor` to process the inference results.
      outputTensor = try interpreter.output(at: 0)
    } catch let error {
      print("Failed to invoke the interpreter with error: \(error.localizedDescription)")
      return nil
    }

    let results: [Float]
    switch outputTensor.dataType {
    case .uInt8:
      guard let quantization = outputTensor.quantizationParameters else {
        print("No results returned because the quantization values for the output tensor are nil.")
        return nil
      }
      let quantizedResults = [UInt8](outputTensor.data)
      results = quantizedResults.map {
        quantization.scale * Float(Int($0) - quantization.zeroPoint)
      }
    case .float32:
      results = [Float32](unsafeData: outputTensor.data) ?? []
    default:
      print("Output tensor data type \(outputTensor.dataType) is unsupported for this example app.")
      return nil
    }

    // Process the results.
    let topNInferences = getTopN(results: results)

    // Return the inference time and inference results.
    return Result(inferenceTime: interval, inferences: topNInferences)
  }


Xcode 11
/Applications/Xcode.app/Contents/Developer/Platforms/iPhoneOS.platform/Library/Developer/CoreSimulator/Profiles/Runtimes/iOS.simruntime/Contents/Resources/RuntimeRoot/usr/lib/
put tbd copy to the path:

/Applications/Xcode.app/Contents/Developer/Platforms/iPhoneSimulator.platform/Developer/SDKs/iPhoneSimulator.sdk/usr/lib/

architecture x86_64 in file 


I've faced the same issue after updating to xcode 10. I've resolved this issue by downloading libstdc++.6.0.9.tbd and libstdc++.6.tbd from https://github.com/Kila2/libstdc-.6.0.9.tbd.

then i copied these files to my project folder. After that I added these libs in Linked Frameworks and Libraries by following these steps: Project > Target > General > Linked Frameworks and Libraries click (+) > Click 'Add Other' > Navigat to your project folder in 'Open File' and select these libraries and add.

Now run the project it will run perfectly..


변환한 텐서 모델은 안드로이드에서 잘 되는데 iOS 지원 바로 안되게 되어 있는 듯.
libc 6.0은 dylib가 아니라 tbd만 올려서 해결. 시뮬레이터에서는 안됨. 제길슨.

 3  sudo apt install python3-pip3 -y
    4  pip33 install --user --upgrade tensorflow
    5  history
    6  python -c "import tensorflow as tf;print(tf.reduce_sum(tf.random.normal([1000, 1000])))"



    5  sudo apt install git -y
    6  git clone https://github.com/tensorflow/models.git


joe@ubuntu:~/ai/models/research$ export PYTHONPATH=$(pwd):$(pwd)/slim
joe@ubuntu:~/ai/models/research$ echo $PYTHONPATH
/home/joe/ai/models/research:/home/joe/ai/models/research/slim


import os 
import sys 
args = sys.argv 
directory = args[1] 
protoc_path = args[2] 
for file in os.listdir(directory):
if file.endswith(".proto"):
os.system(protoc_path+" "+directory+"/"+file+" --python_out=.")


     pip3 install protobuf
       pip3 install pillow
       pip3 install lxml
       pip3 install Cython
       pip3 install jupyter
       pip3 install matplotlib
       pip3 install pandas
       pip3 install opencv-python 


joe@ubuntu:~/ai/models/research$ sudo apt install protobuf -y
Reading package lists... Done
Building dependency tree       
Reading state information... Done

No apt package "protobuf", but there is a snap with that name.
Try "snap install protobuf"

E: Unable to locate package protobuf
joe@ubuntu:~/ai/models/research$ snap install protobuf
error: This revision of snap "protobuf" was published using classic confinement and
       thus may perform arbitrary system changes outside of the security sandbox that
       snaps are usually confined to, which may put your system at risk.

       If you understand and want to proceed repeat the command including --classic.
joe@ubuntu:~/ai/models/research$ snap install protobuf --classic


models/research$ python3 test.py ./object_detection/protos/ /snap/bin/protoc
joe@ubuntu:~/ai/models/research$ 


   43  python3 setup.py build
   44  python3 setup.py install
   45  sudo python3 setup.py install


....

i     echo $PYTHONPATH
     sudo apt install vim -y
     python3 test.py ./object_detection/protos/ ./bin/protoc
     whereis protoc
     sudo updatedb
     locate protoc | grep bin
     sudo apt install protobuf -y
     snap install protobuf
     snap install protobuf --classic
     python3 test.py ./object_detection/protos/ /snap/bin/protoc
     python3 setup.py build
     sudo python3 setup.py install
     jupyter notebook object_detection_tutorial.ipynb
     sudo snap install jupyter
     sudo apt install jupyter-core -y
     ls
     jupyter notebook object_detection_tutorial.ipynb
     ls
     find ./ -type f -iname "object*.ipynb"
     cd research/object_detection
     sudo -H pip3 install --upgrade pip
     sudo -H pip3 install jupyter
     python3 -m notebook ./object_detection_tutorial.ipynb 


https://teachablemachine.withgoogle.com/



Failed to invoke the interpreter with error: Provided data count 1080000 must match the required count 602112.

 3  sudo apt install python3-pip3 -y
    4  pip33 install --user --upgrade tensorflow
    5  history
    6  python -c "import tensorflow as tf;print(tf.reduce_sum(tf.random.normal([1000, 1000])))"



    5  sudo apt install git -y
    6  git clone https://github.com/tensorflow/models.git


joe@ubuntu:~/ai/models/research$ export PYTHONPATH=$(pwd):$(pwd)/slim
joe@ubuntu:~/ai/models/research$ echo $PYTHONPATH
/home/joe/ai/models/research:/home/joe/ai/models/research/slim


import os 
import sys 
args = sys.argv 
directory = args[1] 
protoc_path = args[2] 
for file in os.listdir(directory):
if file.endswith(".proto"):
os.system(protoc_path+" "+directory+"/"+file+" --python_out=.")


     pip3 install protobuf
       pip3 install pillow
       pip3 install lxml
       pip3 install Cython
       pip3 install jupyter
       pip3 install matplotlib
       pip3 install pandas
       pip3 install opencv-python 


joe@ubuntu:~/ai/models/research$ sudo apt install protobuf -y
Reading package lists... Done
Building dependency tree       
Reading state information... Done

No apt package "protobuf", but there is a snap with that name.
Try "snap install protobuf"

E: Unable to locate package protobuf
joe@ubuntu:~/ai/models/research$ snap install protobuf
error: This revision of snap "protobuf" was published using classic confinement and
       thus may perform arbitrary system changes outside of the security sandbox that
       snaps are usually confined to, which may put your system at risk.

       If you understand and want to proceed repeat the command including --classic.
joe@ubuntu:~/ai/models/research$ snap install protobuf --classic


models/research$ python3 test.py ./object_detection/protos/ /snap/bin/protoc
joe@ubuntu:~/ai/models/research$ 


   43  python3 setup.py build
   44  python3 setup.py install
   45  sudo python3 setup.py install


....

i     echo $PYTHONPATH
     sudo apt install vim -y
     python3 test.py ./object_detection/protos/ ./bin/protoc
     whereis protoc
     sudo updatedb
     locate protoc | grep bin
     sudo apt install protobuf -y
     snap install protobuf
     snap install protobuf --classic
     python3 test.py ./object_detection/protos/ /snap/bin/protoc
     python3 setup.py build
     sudo python3 setup.py install
     jupyter notebook object_detection_tutorial.ipynb
     sudo snap install jupyter
     sudo apt install jupyter-core -y
     ls
     jupyter notebook object_detection_tutorial.ipynb
     ls
     find ./ -type f -iname "object*.ipynb"
     cd research/object_detection
     sudo -H pip3 install --upgrade pip
     sudo -H pip3 install jupyter
     python3 -m notebook ./object_detection_tutorial.ipynb 

Version:1.0 StartHTML:0000000100 EndHTML:0003685829 StartFragment:0000000100 EndFragment:0003685829

object_detection_tutorial Last Checkpoint: a few seconds ago (autosaved)

Python 3

• File
• Edit
• View
• Insert
• Cell
• Kernel
• Help

Object Detection API Demo

Welcome to the Object Detection API. This notebook will walk you step by step through the process of using a pre-trained model to detect objects in an image.

Important: This tutorial is to help you through the first step towards using Object Detection API to build models. If you just just need an off the shelf model that does the job, see the TFHub object detection example.

Setup

Important: If you're running on a local machine, be sure to follow the installation instructions. This notebook includes only what's necessary to run in Colab.

Install

!pip install -U --pre --user tensorflow=="2.*"

Collecting tensorflow==2.* Using cached https://files.pythonhosted.org/packages/d5/97/fbec42dfdb93a37ec971ca0996ff70b8eb5817789a9c1880aafd4684c9af/tensorflow-2.1.0rc1-cp36-cp36m-manylinux2010_x86_64.whl Requirement already satisfied, skipping upgrade: numpy<2.0,>=1.16.0 in /home/joe/.local/lib/python3.6/site-packages (from tensorflow==2.*) (1.17.4) Collecting tensorflow-estimator<2.2.0,>=2.1.0rc0 Using cached https://files.pythonhosted.org/packages/54/3a/c3e5e835a1e0afd8106a06fdfdba1441915cc7b6e3852c7612f437f2887e/tensorflow_estimator-2.1.0rc0-py2.py3-none-any.whl Requirement already satisfied, skipping upgrade: wrapt>=1.11.1 in /home/joe/.local/lib/python3.6/site-packages (from tensorflow==2.*) (1.11.2) Requirement already satisfied, skipping upgrade: grpcio>=1.8.6 in /home/joe/.local/lib/python3.6/site-packages (from tensorflow==2.*) (1.25.0) Requirement already satisfied, skipping upgrade: six>=1.12.0 in /home/joe/.local/lib/python3.6/site-packages (from tensorflow==2.*) (1.13.0) Requirement already satisfied, skipping upgrade: absl-py>=0.7.0 in /home/joe/.local/lib/python3.6/site-packages (from tensorflow==2.*) (0.8.1) Processing /home/joe/.cache/pip/wheels/2c/b1/94/43d03e130b929aae7ba3f8d15cbd7bc0d1cb5bb38a5c721833/opt_einsum-3.1.0-cp36-none-any.whl Requirement already satisfied, skipping upgrade: protobuf>=3.8.0 in /home/joe/.local/lib/python3.6/site-packages (from tensorflow==2.*) (3.11.1) Requirement already satisfied, skipping upgrade: termcolor>=1.1.0 in /home/joe/.local/lib/python3.6/site-packages (from tensorflow==2.*) (1.1.0) Requirement already satisfied, skipping upgrade: wheel>=0.26; python_version >= "3" in /home/joe/.local/lib/python3.6/site-packages (from tensorflow==2.*) (0.33.6) Requirement already satisfied, skipping upgrade: google-pasta>=0.1.6 in /home/joe/.local/lib/python3.6/site-packages (from tensorflow==2.*) (0.1.8) Requirement already satisfied, skipping upgrade: astor>=0.6.0 in /home/joe/.local/lib/python3.6/site-packages (from tensorflow==2.*) (0.8.1) Collecting tensorboard<2.2.0,>=2.1.0 Using cached https://files.pythonhosted.org/packages/40/23/53ffe290341cd0855d595b0a2e7485932f473798af173bbe3a584b99bb06/tensorboard-2.1.0-py3-none-any.whl Requirement already satisfied, skipping upgrade: keras-preprocessing>=1.1.0 in /home/joe/.local/lib/python3.6/site-packages (from tensorflow==2.*) (1.1.0) Requirement already satisfied, skipping upgrade: keras-applications>=1.0.8 in /home/joe/.local/lib/python3.6/site-packages (from tensorflow==2.*) (1.0.8) Processing /home/joe/.cache/pip/wheels/5c/2e/7e/a1d4d4fcebe6c381f378ce7743a3ced3699feb89bcfbdadadd/gast-0.2.2-cp36-none-any.whl Requirement already satisfied, skipping upgrade: setuptools in /home/joe/.local/lib/python3.6/site-packages (from protobuf>=3.8.0->tensorflow==2.*) (42.0.2) Requirement already satisfied, skipping upgrade: markdown>=2.6.8 in /home/joe/.local/lib/python3.6/site-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow==2.*) (3.1.1) Collecting google-auth<2,>=1.6.3 Using cached https://files.pythonhosted.org/packages/54/31/f944cbd5bdbcc90d5b36f0615036308c8ec1e41b4788da5b55d4900f6803/google_auth-1.8.2-py2.py3-none-any.whl Requirement already satisfied, skipping upgrade: werkzeug>=0.11.15 in /home/joe/.local/lib/python3.6/site-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow==2.*) (0.16.0) Collecting google-auth-oauthlib<0.5,>=0.4.1 Using cached https://files.pythonhosted.org/packages/7b/b8/88def36e74bee9fce511c9519571f4e485e890093ab7442284f4ffaef60b/google_auth_oauthlib-0.4.1-py2.py3-none-any.whl Collecting requests<3,>=2.21.0 Using cached https://files.pythonhosted.org/packages/51/bd/23c926cd341ea6b7dd0b2a00aba99ae0f828be89d72b2190f27c11d4b7fb/requests-2.22.0-py2.py3-none-any.whl Requirement already satisfied, skipping upgrade: h5py in /home/joe/.local/lib/python3.6/site-packages (from keras-applications>=1.0.8->tensorflow==2.*) (2.10.0) Collecting rsa<4.1,>=3.1.4 Using cached https://files.pythonhosted.org/packages/02/e5/38518af393f7c214357079ce67a317307936896e961e35450b70fad2a9cf/rsa-4.0-py2.py3-none-any.whl Collecting cachetools<3.2,>=2.0.0 Using cached https://files.pythonhosted.org/packages/2f/a6/30b0a0bef12283e83e58c1d6e7b5aabc7acfc4110df81a4471655d33e704/cachetools-3.1.1-py2.py3-none-any.whl Collecting pyasn1-modules>=0.2.1 Using cached https://files.pythonhosted.org/packages/52/50/bb4cefca37da63a0c52218ba2cb1b1c36110d84dcbae8aa48cd67c5e95c2/pyasn1_modules-0.2.7-py2.py3-none-any.whl Collecting requests-oauthlib>=0.7.0 Using cached https://files.pythonhosted.org/packages/a3/12/b92740d845ab62ea4edf04d2f4164d82532b5a0b03836d4d4e71c6f3d379/requests_oauthlib-1.3.0-py2.py3-none-any.whl Requirement already satisfied, skipping upgrade: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/lib/python3/dist-packages (from requests<3,>=2.21.0->tensorboard<2.2.0,>=2.1.0->tensorflow==2.*) (1.22) Requirement already satisfied, skipping upgrade: idna<2.9,>=2.5 in /usr/lib/python3/dist-packages (from requests<3,>=2.21.0->tensorboard<2.2.0,>=2.1.0->tensorflow==2.*) (2.6) Requirement already satisfied, skipping upgrade: chardet<3.1.0,>=3.0.2 in /usr/lib/python3/dist-packages (from requests<3,>=2.21.0->tensorboard<2.2.0,>=2.1.0->tensorflow==2.*) (3.0.4) Requirement already satisfied, skipping upgrade: certifi>=2017.4.17 in /usr/lib/python3/dist-packages (from requests<3,>=2.21.0->tensorboard<2.2.0,>=2.1.0->tensorflow==2.*) (2018.1.18) Collecting pyasn1>=0.1.3 Using cached https://files.pythonhosted.org/packages/62/1e/a94a8d635fa3ce4cfc7f506003548d0a2447ae76fd5ca53932970fe3053f/pyasn1-0.4.8-py2.py3-none-any.whl Collecting oauthlib>=3.0.0 Using cached https://files.pythonhosted.org/packages/05/57/ce2e7a8fa7c0afb54a0581b14a65b56e62b5759dbc98e80627142b8a3704/oauthlib-3.1.0-py2.py3-none-any.whl Installing collected packages: tensorflow-estimator, opt-einsum, pyasn1, rsa, cachetools, pyasn1-modules, google-auth, oauthlib, requests, requests-oauthlib, google-auth-oauthlib, tensorboard, gast, tensorflow Found existing installation: tensorflow-estimator 1.14.0 Uninstalling tensorflow-estimator-1.14.0: Successfully uninstalled tensorflow-estimator-1.14.0 WARNING: The scripts pyrsa-decrypt, pyrsa-encrypt, pyrsa-keygen, pyrsa-priv2pub, pyrsa-sign and pyrsa-verify are installed in '/home/joe/.local/bin' which is not on PATH. Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. WARNING: The script google-oauthlib-tool is installed in '/home/joe/.local/bin' which is not on PATH. Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. Found existing installation: tensorboard 1.14.0 Uninstalling tensorboard-1.14.0: Successfully uninstalled tensorboard-1.14.0 WARNING: The script tensorboard is installed in '/home/joe/.local/bin' which is not on PATH. Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. Found existing installation: gast 0.3.2 Uninstalling gast-0.3.2: Successfully uninstalled gast-0.3.2 Found existing installation: tensorflow 1.14.0 Uninstalling tensorflow-1.14.0: Successfully uninstalled tensorflow-1.14.0 WARNING: The scripts estimator_ckpt_converter, saved_model_cli, tensorboard, tf_upgrade_v2, tflite_convert, toco and toco_from_protos are installed in '/home/joe/.local/bin' which is not on PATH. Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. Successfully installed cachetools-3.1.1 gast-0.2.2 google-auth-1.8.2 google-auth-oauthlib-0.4.1 oauthlib-3.1.0 opt-einsum-3.1.0 pyasn1-0.4.8 pyasn1-modules-0.2.7 requests-2.22.0 requests-oauthlib-1.3.0 rsa-4.0 tensorboard-2.1.0 tensorflow-2.1.0rc1 tensorflow-estimator-2.1.0rc0

Make sure you have pycocotools installed

!pip install --user pycocotools

Processing /home/joe/.cache/pip/wheels/dc/e6/36/0e1ae88c868eb42d3f92181b1c9bbd0b217a7ec3da6bd62e55/pycocotools-2.0.0-cp36-cp36m-linux_x86_64.whl Installing collected packages: pycocotools Successfully installed pycocotools-2.0.0

Get tensorflow/models or cd to parent directory of the repository.

import osimport pathlib​​if "models" in pathlib.Path.cwd().parts: while "models" in pathlib.Path.cwd().parts: os.chdir('..')elif not pathlib.Path('models').exists(): !git clone --depth 1 https://github.com/tensorflow/models

Compile protobufs and install the object_detection package

%%bashcd models/research/protoc object_detection/protos/*.proto --python_out=.

%%bash cd models/researchpip install --user .

Processing /home/joe/ai/models/research Requirement already satisfied: Pillow>=1.0 in /home/joe/.local/lib/python3.6/site-packages (from object-detection==0.1) (6.2.1) Requirement already satisfied: Matplotlib>=2.1 in /home/joe/.local/lib/python3.6/site-packages (from object-detection==0.1) (3.1.2) Requirement already satisfied: Cython>=0.28.1 in /home/joe/.local/lib/python3.6/site-packages (from object-detection==0.1) (0.29.14) Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /home/joe/.local/lib/python3.6/site-packages (from Matplotlib>=2.1->object-detection==0.1) (2.4.5) Requirement already satisfied: python-dateutil>=2.1 in /home/joe/.local/lib/python3.6/site-packages (from Matplotlib>=2.1->object-detection==0.1) (2.8.1) Requirement already satisfied: numpy>=1.11 in /home/joe/.local/lib/python3.6/site-packages (from Matplotlib>=2.1->object-detection==0.1) (1.17.4) Requirement already satisfied: cycler>=0.10 in /home/joe/.local/lib/python3.6/site-packages (from Matplotlib>=2.1->object-detection==0.1) (0.10.0) Requirement already satisfied: kiwisolver>=1.0.1 in /home/joe/.local/lib/python3.6/site-packages (from Matplotlib>=2.1->object-detection==0.1) (1.1.0) Requirement already satisfied: six>=1.5 in /home/joe/.local/lib/python3.6/site-packages (from python-dateutil>=2.1->Matplotlib>=2.1->object-detection==0.1) (1.13.0) Requirement already satisfied: setuptools in /home/joe/.local/lib/python3.6/site-packages (from kiwisolver>=1.0.1->Matplotlib>=2.1->object-detection==0.1) (42.0.2) Building wheels for collected packages: object-detection Building wheel for object-detection (setup.py): started Building wheel for object-detection (setup.py): finished with status 'done' Created wheel for object-detection: filename=object_detection-0.1-cp36-none-any.whl size=1018408 sha256=b7b6c58ff616468e23fbc4235cec9f629c4d20bbe64e97aa141002aabf96fcee Stored in directory: /tmp/pip-ephem-wheel-cache-27tuvthk/wheels/66/74/0f/57548ad5db5a02c1e963e016e857cda21c07d4640bd920543c Successfully built object-detection Installing collected packages: object-detection Successfully installed object-detection-0.1

Imports

import numpy as npimport osimport six.moves.urllib as urllibimport sysimport tarfileimport tensorflow as tfimport zipfile​from collections import defaultdictfrom io import StringIOfrom matplotlib import pyplot as pltfrom PIL import Imagefrom IPython.display import display

Import the object detection module.

from object_detection.utils import ops as utils_opsfrom object_detection.utils import label_map_utilfrom object_detection.utils import visualization_utils as vis_util

Patches:

# patch tf1 into `utils.ops`utils_ops.tf = tf.compat.v1​# Patch the location of gfiletf.gfile = tf.io.gfile

Model preparation

Variables

Any model exported using the export_inference_graph.py tool can be loaded here simply by changing the path.

By default we use an "SSD with Mobilenet" model here. See the detection model zoo for a list of other models that can be run out-of-the-box with varying speeds and accuracies.

Loader

def load_model(model_name): base_url = 'http://download.tensorflow.org/models/object_detection/' model_file = model_name + '.tar.gz' model_dir = tf.keras.utils.get_file( fname=model_name, origin=base_url + model_file, untar=True)​ model_dir = pathlib.Path(model_dir)/"saved_model"​ model = tf.saved_model.load(str(model_dir)) model = model.signatures['serving_default']​ return model

Loading label map

Label maps map indices to category names, so that when our convolution network predicts 5, we know that this corresponds to airplane. Here we use internal utility functions, but anything that returns a dictionary mapping integers to appropriate string labels would be fine

# List of the strings that is used to add correct label for each box.PATH_TO_LABELS = 'models/research/object_detection/data/mscoco_label_map.pbtxt'category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)

For the sake of simplicity we will test on 2 images:

# If you want to test the code with your images, just add path to the images to the TEST_IMAGE_PATHS.PATH_TO_TEST_IMAGES_DIR = pathlib.Path('models/research/object_detection/test_images')TEST_IMAGE_PATHS = sorted(list(PATH_TO_TEST_IMAGES_DIR.glob("*.jpg")))TEST_IMAGE_PATHS

[PosixPath('models/research/object_detection/test_images/image1.jpg'), PosixPath('models/research/object_detection/test_images/image2.jpg')]

Detection

Load an object detection model:

model_name = 'ssd_mobilenet_v1_coco_2017_11_17'detection_model = load_model(model_name)

Downloading data from http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_2017_11_17.tar.gz 76537856/76534733 [==============================] - 19s 0us/step INFO:tensorflow:Saver not created because there are no variables in the graph to restore

Check the model's input signature, it expects a batch of 3-color images of type uint8:

print(detection_model.inputs)

[<tf.Tensor 'image_tensor:0' shape=(None, None, None, 3) dtype=uint8>]

And retuns several outputs:

detection_model.output_dtypes

{'detection_scores': tf.float32, 'detection_classes': tf.float32, 'num_detections': tf.float32, 'detection_boxes': tf.float32}

detection_model.output_shapes

{'detection_scores': TensorShape([None, 100]), 'detection_classes': TensorShape([None, 100]), 'num_detections': TensorShape([None]), 'detection_boxes': TensorShape([None, 100, 4])}

Add a wrapper function to call the model, and cleanup the outputs:

def run_inference_for_single_image(model, image): image = np.asarray(image) # The input needs to be a tensor, convert it using `tf.convert_to_tensor`. input_tensor = tf.convert_to_tensor(image) # The model expects a batch of images, so add an axis with `tf.newaxis`. input_tensor = input_tensor[tf.newaxis,...]​ # Run inference output_dict = model(input_tensor)​ # All outputs are batches tensors. # Convert to numpy arrays, and take index [0] to remove the batch dimension. # We're only interested in the first num_detections. num_detections = int(output_dict.pop('num_detections')) output_dict = {key:value[0, :num_detections].numpy() for key,value in output_dict.items()} output_dict['num_detections'] = num_detections​ # detection_classes should be ints. output_dict['detection_classes'] = output_dict['detection_classes'].astype(np.int64) # Handle models with masks: if 'detection_masks' in output_dict: # Reframe the the bbox mask to the image size. detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks( output_dict['detection_masks'], output_dict['detection_boxes'], image.shape[0], image.shape[1]) detection_masks_reframed = tf.cast(detection_masks_reframed > 0.5, tf.uint8) output_dict['detection_masks_reframed'] = detection_masks_reframed.numpy() return output_dict

Run it on each test image and show the results:

def show_inference(model, image_path): # the array based representation of the image will be used later in order to prepare the # result image with boxes and labels on it. image_np = np.array(Image.open(image_path)) # Actual detection. output_dict = run_inference_for_single_image(model, image_np) # Visualization of the results of a detection. vis_util.visualize_boxes_and_labels_on_image_array( image_np, output_dict['detection_boxes'], output_dict['detection_classes'], output_dict['detection_scores'], category_index, instance_masks=output_dict.get('detection_masks_reframed', None), use_normalized_coordinates=True, line_thickness=8)​ display(Image.fromarray(image_np))

for image_path in TEST_IMAGE_PATHS: show_inference(detection_model, image_path)​

Instance Segmentation

model_name = "mask_rcnn_inception_resnet_v2_atrous_coco_2018_01_28"masking_model = load_model("mask_rcnn_inception_resnet_v2_atrous_coco_2018_01_28")

INFO:tensorflow:Saver not created because there are no variables in the graph to restore

The instance segmentation model includes a detection_masks output:

masking_model.output_shapes

for image_path in TEST_IMAGE_PATHS: show_inference(masking_model, image_path)

​

​

​

/AI/models-master/research  sudo chown -R $(whoami):admin /Library/Python/

python setup.py install


  File "/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/setuptools/sandbox.py", line 250, in run_setup
    _execfile(setup_script, ns)
  File "/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/setuptools/sandbox.py", line 44, in _execfile
    code = compile(script, filename, 'exec')
  File "/var/folders/tl/fj2bb8c16_j3wp9r8770v8380000gn/T/easy_install-NWJPJ0/matplotlib-3.2.0rc1/setup.py", line 136
    raise IOError(f"Failed to download jquery-ui.  Please download "
                                                                   ^

models-master/research  sudo easy_install pip
Searching for pip
Reading https://pypi.org/simple/pip/
Downloading https://files.pythonhosted.org/packages/00/b6/9cfa56b4081ad13874b0c6f96af8ce16cfbc1cb06bedf8e9164ce5551ec1/pip-19.3.1-py2.py3-none-any.whl#sha256=6917c65fc3769ecdc61405d3dfd97afdedd75808d200b2838d7d961cebc0c2c7
Best match: pip 19.3.1
Processing pip-19.3.1-py2.py3-none-any.whl
Installing pip-19.3.1-py2.py3-none-any.whl to /Library/Python/2.7/site-packages
Adding pip 19.3.1 to easy-install.pth file
Installing pip script to /usr/local/bin
Installing pip3.7 script to /usr/local/bin
Installing pip3 script to /usr/local/bin

Installed /Library/Python/2.7/site-packages/pip-19.3.1-py2.7.egg
Processing dependencies for pip
Finished processing dependencies for pip

AI/models-master/research  pip install --verbose --no-binary :all: 'matplotlib==2.2.4'

Removed build tracker '/private/var/folders/tl/fj2bb8c16_j3wp9r8770v8380000gn/T/pip-req-tracker-r2EEVh'
ERROR: Command errored out with exit status 1: python setup.py egg_info Check the logs for full command output.

Downloading https://files.pythonhosted.org/packages/07/32/78676432117f2e0a40daa5f676233aa4914da5cc36126826a49f3c77b02b/matplotlib-3.2.0rc1.tar.gz#sha256=077b17f4bd73d322ee7322f6f029f5805e8a8db3ad972cb4a875ac40300ee842
Best match: matplotlib 3.2.0rc1
Processing matplotlib-3.2.0rc1.tar.gz
Writing /var/folders/tl/fj2bb8c16_j3wp9r8770v8380000gn/T/easy_install-85gP6h/matplotlib-3.2.0rc1/setup.cfg
Running matplotlib-3.2.0rc1/setup.py -q bdist_egg --dist-dir /var/folders/tl/fj2bb8c16_j3wp9r8770v8380000gn/T/easy_install-85gP6h/matplotlib-3.2.0rc1/egg-dist-tmp-sATGuT
Traceback (most recent call last):
  File "setup.py", line 15, in 
    description='Tensorflow Object Detection Library',
  File "/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/setuptools/__init__.py", line 145, in setup
    return distutils.core.setup(**attrs)
  File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/distutils/core.py", line 151, in setup
    dist.run_commands()



REQUIRED_PACKAGES = ['Pillow>=1.0', 'Matplotlib>=2.1', 'Cython>=0.28.1']

deleting Matolotlib 




Installed /Library/Python/2.7/site-packages/Pillow-6.2.1-py2.7-macosx-10.15-x86_64.egg
Searching for Cython==0.29.14
Best match: Cython 0.29.14
Processing Cython-0.29.14-py2.7-macosx-10.15-x86_64.egg
Cython 0.29.14 is already the active version in easy-install.pth
Installing cython script to /usr/local/bin
Installing cygdb script to /usr/local/bin
Installing cythonize script to /usr/local/bin

Using /Library/Python/2.7/site-packages/Cython-0.29.14-py2.7-macosx-10.15-x86_64.egg
Finished processing dependencies for object-detection==0.1



from absl.testing import parameterized
ImportError: No module named absl.testing
 ✘ junhoha  AI/models-master/research  sudo pip install absl-py


AI/models-master/research  pip install --upgrade tensorflow


Building wheels for collected packages: gast, wrapt, keras-applications, functools32, opt-einsum, termcolor
  WARNING: Building wheel for gast failed: [Errno 13] Permission denied: '/Users/junhoha/Library/Caches/pip/wheels/5c'
  WARNING: Building wheel for wrapt failed: [Errno 13] Permission denied: '/Users/junhoha/Library/Caches/pip/wheels/d7'
  WARNING: Building wheel for keras-applications failed: [Errno 13] Permission denied: '/Users/junhoha/Library/Caches/pip/wheels/dd'
  WARNING: Building wheel for functools32 failed: [Errno 13] Permission denied: '/Users/junhoha/Library/Caches/pip/wheels/b5'
  WARNING: Building wheel for opt-einsum failed: [Errno 13] Permission denied: '/Users/junhoha/Library/Caches/pip/wheels/51'
  WARNING: Building wheel for termcolor failed: [Errno 13] Permission denied: '/Users/junhoha/Library/Caches/pip/wheels/7c'
Failed to build gast wrapt keras-applications functools32 opt-einsum termcolor
ERROR: matplotlib 1.3.1 requires nose, which is not installed.
ERROR: matplotlib 1.3.1 requires tornado, which is not installed.
Installing collected packages: numpy, funcsigs, mock, gast, futures, grpcio, keras-preprocessing, wrapt, protobuf, h5py, keras-applications, tensorflow-estimator, functools32, opt-einsum, backports.weakref, google-pasta, oauthlib, chardet, idna, urllib3, certifi, requests, requests-oauthlib, pyasn1, pyasn1-modules, rsa, cachetools, google-auth, google-auth-oauthlib, werkzeug, markdown, tensorboard, termcolor, astor, tensorflow
  Found existing installation: numpy 1.8.0rc1
ERROR: Cannot uninstall 'numpy'. It is a distutils installed project and thus we cannot accurately determine which files belong to it which would lead to only a partial uninstall.
 ✘ junhoha  ~/Desktop/AI/models-master/research  history | grep chown
 1141  sudo chown -R $USER:admin /usr/local /Library/Caches/Homebrew
 1142  sudo chown -R junhoha:admin /usr/local /Library/Caches/Homebrew
 1143  sudo chown -R junhoha:junhoha /usr/local /Library/Caches/Homebrew
 1144  sudo chown -R junhoha:junhohOBa /usr/local /Library/Caches/Homebrew
 1147  sudo chown -R root:root /usr/local /Library/Caches/Homebrew
 1148  sudo chown -R root:admin /usr/local /Library/Caches/Homebrew
 1166  sudo chown -R $(whoami):admin /usr/local/Library/Taps/*/**/.git
 1167  sudo chown -R $(whoami):admin /usr/local/Homebrew/.git
 1169  sudo chown -R $(whoami):admin /usr/local/Homebrew/
 1176  sudo chown -R $(whoami):admin /usr/local/etc
 1177  sudo chown -R $(whoami):admin /usr/local/share
 1197  sudo chown -R $(whoami):admin /Library/Python/
 junhoha  ~/Desktop/AI/models-master/research  !1148
 junhoha  ~/Desktop/AI/models-master/research  sudo chown -R $(whoami):admin /Users/junhoha/Library/


pip install nose
pip install tornado


AI/models-master/research  python object_detection/builders/model_builder_test.py
Traceback (most recent call last):
  File "object_detection/builders/model_builder_test.py", line 20, in 
    import tensorflow as tf
ImportError: No module named tensorflow

AI/models-master/research  python -c "import tensorflow as tf;print(tf.reduce_sum(tf.random.normal([1000, 1000])))"
Traceback (most recent call last):
  File "", line 1, in 
ImportError: No module named tensorflow

export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/tensorflow-0.9.0-py2-none-any.whl

sudo pip install --upgrade $TF_BINARY_URL

AI/models-master/research  sudo pip install --upgrade $TF_BINARY_URL
DEPRECATION: Python 2.7 will reach the end of its life on January 1st, 2020. Please upgrade your Python as Python 2.7 won't be maintained after that date. A future version of pip will drop support for Python 2.7. More details about Python 2 support in pip, can be found at https://pip.pypa.io/en/latest/development/release-process/#python-2-support
WARNING: The directory '/Users/junhoha/Library/Caches/pip/http' or its parent directory is not owned by the current user and the cache has been disabled. Please check the permissions and owner of that directory. If executing pip with sudo, you may want sudo's -H flag.
WARNING: The directory '/Users/junhoha/Library/Caches/pip' or its parent directory is not owned by the current user and caching wheels has been disabled. check the permissions and owner of that directory. If executing pip with sudo, you may want sudo's -H flag.
Collecting tensorflow==0.9.0
  Downloading https://storage.googleapis.com/tensorflow/mac/tensorflow-0.9.0-py2-none-any.whl (25.8MB)
     |████████████████████████████████| 25.8MB 8.7MB/s
Requirement already satisfied, skipping upgrade: six>=1.10.0 in /System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python (from tensorflow==0.9.0) (1.12.0)
Collecting protobuf==3.0.0b2
  Downloading https://files.pythonhosted.org/packages/00/8e/9a3feb39d464eb7aacc108e6e6e1f2368ec741821486964c4cd0f41baabb/protobuf-3.0.0b2-py2.py3-none-any.whl (326kB)
     |████████████████████████████████| 327kB 149kB/s
Collecting numpy>=1.10.1
  Downloading https://files.pythonhosted.org/packages/51/67/8907005262f493e356195bcbd61b41988eecf63cb1d97ea2f6e55fe24205/numpy-1.16.5-cp27-cp27m-macosx_10_9_x86_64.whl (13.9MB)
     |████████████████████████████████| 13.9MB 1.5MB/s
Requirement already satisfied, skipping upgrade: wheel in /System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python (from tensorflow==0.9.0) (0.33.1)
Requirement already satisfied, skipping upgrade: setuptools in /System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python (from protobuf==3.0.0b2->tensorflow==0.9.0) (41.0.1)
Installing collected packages: protobuf, numpy, tensorflow
  Found existing installation: numpy 1.8.0rc1
ERROR: Cannot uninstall 'numpy'. It is a distutils installed project and thus we cannot accurately determine which files belong to it which would lead to only a partial uninstall.

AI/models-master/research  python -c "import tensorflow as tf;print(tf.reduce_sum(tf.random.normal([1000, 1000])))"
Traceback (most recent call last):
  File "", line 1, in 
ImportError: No module named tensorflow

AI/models-master/research  python --version
Python 2.7.16

AI/models-master/research  export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-2.0.0-py2-none-any.whl
 junhoha  ~/Desktop/AI/models-master/research  sudo pip install --upgrade $TF_BINARY_URL
DEPRECATION: Python 2.7 will reach the end of its life on January 1st, 2020. Please upgrade your Python as Python 2.7 won't be maintained after that date. A future version of pip will drop support for Python 2.7. More details about Python 2 support in pip, can be found at https://pip.pypa.io/en/latest/development/release-process/#python-2-support
WARNING: The directory '/Users/junhoha/Library/Caches/pip/http' or its parent directory is not owned by the current user and the cache has been disabled. Please check the permissions and owner of that directory. If executing pip with sudo, you may want sudo's -H flag.
WARNING: The directory '/Users/junhoha/Library/Caches/pip' or its parent directory is not owned by the current user and caching wheels has been disabled. check the permissions and owner of that directory. If executing pip with sudo, you may want sudo's -H flag.
Collecting tensorflow==2.0.0
  ERROR: HTTP error 404 while getting https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-2.0.0-py2-none-any.whl
  ERROR: Could not install requirement tensorflow==2.0.0 from https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-2.0.0-py2-none-any.whl because of error 404 Client Error: Not Found for url: https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-2.0.0-py2-none-any.whl
ERROR: Could not install requirement tensorflow==2.0.0 from https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-2.0.0-py2-none-any.whl because of HTTP error 404 Client Error: Not Found for url: https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-2.0.0-py2-none-any.whl for URL https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-2.0.0-py2-none-any.whl


 pip install tensorflow-gpu
DEPRECATION: Python 2.7 will reach the end of its life on January 1st, 2020. Please upgrade your Python as Python 2.7 won't be maintained after that date. A future version of pip will drop support for Python 2.7. More details about Python 2 support in pip, can be found at https://pip.pypa.io/en/latest/development/release-process/#python-2-support
Collecting tensorflow-gpu
  Downloading https://files.pythonhosted.org/packages/d3/84/87cd052ffdb04c50985cdf23ac18fa6d479b916bc7048a05096eb2c522a0/tensorflow_gpu-1.1.0-cp27-cp27m-macosx_10_11_x86_64.whl (80.5MB)
     |████████████▎                   | 30.9MB 1.6MB/s eta 0:00:32


junhoha  ~/Desktop/hajunho/AI/models-master/research  python -c "import tensorflow as tf;print(tf.reduce_sum(tf.random.normal([1000, 1000])))"
dyld: warning, LC_RPATH $ORIGIN/../../_solib_darwin/_U@local_Uconfig_Ucuda_S_Scuda_Ccublas___Uexternal_Slocal_Uconfig_Ucuda_Scuda_Slib in /Users/junhoha/Library/Python/2.7/lib/python/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so being ignored in restricted program because it is a relative path
dyld: warning, LC_RPATH $ORIGIN/../../_solib_darwin/_U@local_Uconfig_Ucuda_S_Scuda_Ccuda_Udriver___Uexternal_Slocal_Uconfig_Ucuda_Scuda_Slib in /Users/junhoha/Library/Python/2.7/lib/python/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so being ignored in restricted program because it is a relative path
dyld: warning, LC_RPATH $ORIGIN/../../_solib_darwin/_U@local_Uconfig_Ucuda_S_Scuda_Ccudnn___Uexternal_Slocal_Uconfig_Ucuda_Scuda_Slib in /Users/junhoha/Library/Python/2.7/lib/python/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so being ignored in restricted program because it is a relative path
dyld: warning, LC_RPATH $ORIGIN/../../_solib_darwin/_U@local_Uconfig_Ucuda_S_Scuda_Ccufft___Uexternal_Slocal_Uconfig_Ucuda_Scuda_Slib in /Users/junhoha/Library/Python/2.7/lib/python/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so being ignored in restricted program because it is a relative path
dyld: warning, LC_RPATH $ORIGIN/../../_solib_darwin/_U@local_Uconfig_Ucuda_S_Scuda_Ccurand___Uexternal_Slocal_Uconfig_Ucuda_Scuda_Slib in /Users/junhoha/Library/Python/2.7/lib/python/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so being ignored in restricted program because it is a relative path
dyld: warning, LC_RPATH $ORIGIN/../../_solib_darwin/_U@local_Uconfig_Ucuda_S_Scuda_Ccudart___Uexternal_Slocal_Uconfig_Ucuda_Scuda_Slib in /Users/junhoha/Library/Python/2.7/lib/python/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so being ignored in restricted program because it is a relative path
dyld: warning, LC_RPATH ../local_config_cuda/cuda/lib in /Users/junhoha/Library/Python/2.7/lib/python/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so being ignored in restricted program because it is a relative path
dyld: warning, LC_RPATH ../local_config_cuda/cuda/extras/CUPTI/lib in /Users/junhoha/Library/Python/2.7/lib/python/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so being ignored in restricted program because it is a relative path
Traceback (most recent call last):
  File "", line 1, in 
  File "/Users/junhoha/Library/Python/2.7/lib/python/site-packages/tensorflow/__init__.py", line 24, in 
    from tensorflow.python import *
  File "/Users/junhoha/Library/Python/2.7/lib/python/site-packages/tensorflow/python/__init__.py", line 51, in 
    from tensorflow.python import pywrap_tensorflow
  File "/Users/junhoha/Library/Python/2.7/lib/python/site-packages/tensorflow/python/pywrap_tensorflow.py", line 52, in 
    raise ImportError(msg)
ImportError: Traceback (most recent call last):
  File "/Users/junhoha/Library/Python/2.7/lib/python/site-packages/tensorflow/python/pywrap_tensorflow.py", line 41, in 
    from tensorflow.python.pywrap_tensorflow_internal import *
  File "/Users/junhoha/Library/Python/2.7/lib/python/site-packages/tensorflow/python/pywrap_tensorflow_internal.py", line 28, in 
    _pywrap_tensorflow_internal = swig_import_helper()
  File "/Users/junhoha/Library/Python/2.7/lib/python/site-packages/tensorflow/python/pywrap_tensorflow_internal.py", line 24, in swig_import_helper
    _mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description)
ImportError: dlopen(/Users/junhoha/Library/Python/2.7/lib/python/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so, 10): Library not loaded: @rpath/libcublas.8.0.dylib
  Referenced from: /Users/junhoha/Library/Python/2.7/lib/python/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so
  Reason: image not found


Failed to load the native TensorFlow runtime.


brew install python


python3
Python 3.7.3 (default, Nov 15 2019, 04:04:52)
[Clang 11.0.0 (clang-1100.0.33.16)] on darwin
Type "help", "copyright", "credits" or "license" for more information.


sudo ln -s /usr/libexec/locate.updatedb /usr/local/bin/updatedb


buy paralles
install ubuntu 18.04.3

.
.
.
quit doing on mac

18.04.3 깔고...
sudo apt install python3-pip
pip3 install --user --upgrade tensorflow

단 2줄에 설치 끝.

python3 -c "import tensorflow as tf;print(tf.reduce_sum(tf.random.normal([1000, 1000])))"
/home/joe/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.

https://github.com/tensorflow/models

AI  brew install protoc

Cellar is not writable. You should change the
ownership and permissions of

sudo chown -R $USER:admin /usr/local /Library/Caches/Homebrew

AI/models-master/research

brew tap homebrew/versions
brew install protobuf241

usr/local/Homebrew/.git/FETCH_HEAD: Permission denied


sudo chown -R $(whoami):admin /usr/local/Homebrew/.git

ermission denied
fatal: cannot create directory at 'Library/Homebrew/vendor/bundle/ruby/2.6.0/gems/rubocop-rspec-1.37.0': Permission denied
error: cannot rebase: You have unstaged changes.
error: Please commit or stash them.
To restore the stashed changes to /usr/local/Homebrew run:
  'cd /usr/local/Homebrew && git stash pop'
Error: homebrew/versions was deprecated. This tap is now empty as all its formulae were migrated.
 ✘ junhoha  ~  sudo chown -R $(whoami):admin /usr/local/Homebrew/

Error: homebrew/versions was deprecated. This tap is now empty as all its formulae were migrated.

brew install protobuf241


==> Searching taps...
==> Searching taps on GitHub...
Error: No formulae found in taps.


✘ junhoha  ~  rm -rf /usr/local/Homebrew/Library/Taps/homebrew/homebrew-core; brew update
Already up-to-date.
 junhoha  ~  brew install protobuf241

==> Tapping homebrew/core
Cloning into '/usr/local/Homebrew/Library/Taps/homebrew/homebrew-core'...
remote: Enumerating objects: 5092, done.
remote: Counting objects: 100% (5092/5092), done.
remote: Compressing objects: 100% (4888/4888), done.
remote: Total 5092 (delta 50), reused 314 (delta 8), pack-reused 0
Receiving objects: 100% (5092/5092), 4.13 MiB | 2.90 MiB/s, done.
Resolving deltas: 100% (50/50), done.

 ✘ junhoha  ~  brew install protobuf

Error: The following directories are not writable by your user:
/usr/local/etc/bash_completion.d
/usr/local/share/doc
/usr/local/share/man
/usr/local/share/man/man1
/usr/local/share/zsh
/usr/local/share/zsh/site-functions

You should change the ownership of these directories to your user.
  sudo chown -R $(whoami) /usr/local/etc/bash_completion.d /usr/local/share/doc /usr/local/share/man /usr/local/share/man/man1 /usr/local/share/zsh /usr/local/share/zsh/site-functions

And make sure that your user has write permission.
  chmod u+w /usr/local/etc/bash_completion.d /usr/local/share/doc /usr/local/share/man /usr/local/share/man/man1 /usr/local/share/zsh /usr/local/share/zsh/site-functions
 ✘ junhoha  ~  !chown
zsh: event not found: chown
 ✘ junhoha  ~  sudo chown -R $(whoami):admin /usr/local/etc
Password:
 junhoha  ~  sudo chown -R $(whoami):admin /usr/local/share
 junhoha  ~  brew install protobuf


==> Downloading https://homebrew.bintray.com/bottles/protobuf-3.11.1.catalina.bo
==> Downloading from https://akamai.bintray.com/34/34e7c02caf05fc97635455eee47e4
######################################################################## 100.0%
==> Pouring protobuf-3.11.1.catalina.bottle.tar.gz
🍺  /usr/local/Cellar/protobuf/3.11.1: 268 files, 19.6MB



AI/models-master/research  python setup.py build

running build
running build_py
creating build
creating build/lib
creating build/lib/object_detection
copying object_detection/exporter_test.py -> build/lib/object_detection
copying object_detection/export_tflite_ssd_graph_lib.py -> build/lib/object_detection
copying object_detection/model_lib.py -> build/lib/object_detection
copying object_detection/model_lib_test.py -> build/lib/object_detection
copying object_detection/exporter.py -> build/lib/object_detection
copying object_detection/model_lib_v2_test.py -> build/lib/object_detection
copying object_detection/__init__.py -> build/lib/object_detection
copying object_detection/eval_util_test.py -> build/lib/object_detection
copying object_detection/export_tflite_ssd_graph_lib_test.py -> build/lib/object_detection
copying object_detection/eval_util.py -> build/lib/object_detection
copying object_detection/inputs.py -> build/lib/object_detection
copying object_detection/export_tflite_ssd_graph.py -> build/lib/object_detection
copying object_detection/model_hparams.py -> build/lib/object_detection
copying object_detection/inputs_test.py -> build/lib/object_detection
copying object_detection/model_main.py -> build/lib/object_detection
copying object_detection/model_tpu_main.py -> build/lib/object_detection
copying object_detection/model_lib_v2.py -> build/lib/object_detection
copying object_detection/export_inference_graph.py -> build/lib/object_detection
creating build/lib/object_detection/metrics
copying object_detection/metrics/coco_tools.py -> build/lib/object_detection/metrics
copying object_detection/metrics/oid_vrd_challenge_evaluation_utils.py -> build/lib/object_detection/metrics
copying object_detection/metrics/oid_vrd_challenge_evaluation.py -> build/lib/object_detection/metrics
copying object_detection/metrics/calibration_metrics_test.py -> build/lib/object_detection/metrics
copying object_detection/metrics/calibration_evaluation_test.py -> build/lib/object_detection/metrics
copying object_detection/metrics/tf_example_parser_test.py -> build/lib/object_detection/metrics
copying object_detection/metrics/offline_eval_map_corloc_test.py -> build/lib/object_detection/metrics
copying object_detection/metrics/calibration_evaluation.py -> build/lib/object_detection/metrics
copying object_detection/metrics/tf_example_parser.py -> build/lib/object_detection/metrics
copying object_detection/metrics/__init__.py -> build/lib/object_detection/metrics
copying object_detection/metrics/oid_challenge_evaluation_utils_test.py -> build/lib/object_detection/metrics
copying object_detection/metrics/coco_evaluation.py -> build/lib/object_detection/metrics
copying object_detection/metrics/oid_vrd_challenge_evaluation_utils_test.py -> build/lib/object_detection/metrics
copying object_detection/metrics/coco_tools_test.py -> build/lib/object_detection/metrics
copying object_detection/metrics/offline_eval_map_corloc.py -> build/lib/object_detection/metrics
copying object_detection/metrics/io_utils.py -> build/lib/object_detection/metrics
copying object_detection/metrics/oid_challenge_evaluation_utils.py -> build/lib/object_detection/metrics
copying object_detection/metrics/coco_evaluation_test.py -> build/lib/object_detection/metrics
copying object_detection/metrics/calibration_metrics.py -> build/lib/object_detection/metrics
copying object_detection/metrics/oid_challenge_evaluation.py -> build/lib/object_detection/metrics
creating build/lib/object_detection/meta_architectures
copying object_detection/meta_architectures/faster_rcnn_meta_arch.py -> build/lib/object_detection/meta_architectures
copying object_detection/meta_architectures/rfcn_meta_arch_test.py -> build/lib/object_detection/meta_architectures
copying object_detection/meta_architectures/ssd_meta_arch_test_lib.py -> build/lib/object_detection/meta_architectures
copying object_detection/meta_architectures/__init__.py -> build/lib/object_detection/meta_architectures
copying object_detection/meta_architectures/faster_rcnn_meta_arch_test_lib.py -> build/lib/object_detection/meta_architectures
copying object_detection/meta_architectures/ssd_meta_arch_test.py -> build/lib/object_detection/meta_architectures
copying object_detection/meta_architectures/faster_rcnn_meta_arch_test.py -> build/lib/object_detection/meta_architectures
copying object_detection/meta_architectures/rfcn_meta_arch.py -> build/lib/object_detection/meta_architectures
copying object_detection/meta_architectures/ssd_meta_arch.py -> build/lib/object_detection/meta_architectures
creating build/lib/object_detection/tpu_exporters
copying object_detection/tpu_exporters/export_saved_model_tpu_lib_test.py -> build/lib/object_detection/tpu_exporters
copying object_detection/tpu_exporters/utils_test.py -> build/lib/object_detection/tpu_exporters
copying object_detection/tpu_exporters/ssd.py -> build/lib/object_detection/tpu_exporters
copying object_detection/tpu_exporters/faster_rcnn.py -> build/lib/object_detection/tpu_exporters
copying object_detection/tpu_exporters/__init__.py -> build/lib/object_detection/tpu_exporters
copying object_detection/tpu_exporters/export_saved_model_tpu.py -> build/lib/object_detection/tpu_exporters
copying object_detection/tpu_exporters/utils.py -> build/lib/object_detection/tpu_exporters
copying object_detection/tpu_exporters/export_saved_model_tpu_lib.py -> build/lib/object_detection/tpu_exporters
creating build/lib/object_detection/protos
copying object_detection/protos/pipeline_pb2.py -> build/lib/object_detection/protos
copying object_detection/protos/string_int_label_map_pb2.py -> build/lib/object_detection/protos
copying object_detection/protos/keypoint_box_coder_pb2.py -> build/lib/object_detection/protos
copying object_detection/protos/train_pb2.py -> build/lib/object_detection/protos
copying object_detection/protos/square_box_coder_pb2.py -> build/lib/object_detection/protos
copying object_detection/protos/argmax_matcher_pb2.py -> build/lib/object_detection/protos
copying object_detection/protos/region_similarity_calculator_pb2.py -> build/lib/object_detection/protos
copying object_detection/protos/box_predictor_pb2.py -> build/lib/object_detection/protos
copying object_detection/protos/faster_rcnn_pb2.py -> build/lib/object_detection/protos
copying object_detection/protos/grid_anchor_generator_pb2.py -> build/lib/object_detection/protos
copying object_detection/protos/matcher_pb2.py -> build/lib/object_detection/protos
copying object_detection/protos/__init__.py -> build/lib/object_detection/protos
copying object_detection/protos/bipartite_matcher_pb2.py -> build/lib/object_detection/protos
copying object_detection/protos/image_resizer_pb2.py -> build/lib/object_detection/protos
copying object_detection/protos/calibration_pb2.py -> build/lib/object_detection/protos
copying object_detection/protos/target_assigner_pb2.py -> build/lib/object_detection/protos
copying object_detection/protos/ssd_anchor_generator_pb2.py -> build/lib/object_detection/protos
copying object_detection/protos/preprocessor_pb2.py -> build/lib/object_detection/protos
copying object_detection/protos/eval_pb2.py -> build/lib/object_detection/protos
copying object_detection/protos/graph_rewriter_pb2.py -> build/lib/object_detection/protos
copying object_detection/protos/flexible_grid_anchor_generator_pb2.py -> build/lib/object_detection/protos
copying object_detection/protos/faster_rcnn_box_coder_pb2.py -> build/lib/object_detection/protos
copying object_detection/protos/mean_stddev_box_coder_pb2.py -> build/lib/object_detection/protos
copying object_detection/protos/anchor_generator_pb2.py -> build/lib/object_detection/protos
copying object_detection/protos/box_coder_pb2.py -> build/lib/object_detection/protos
copying object_detection/protos/model_pb2.py -> build/lib/object_detection/protos
copying object_detection/protos/optimizer_pb2.py -> build/lib/object_detection/protos
copying object_detection/protos/hyperparams_pb2.py -> build/lib/object_detection/protos
copying object_detection/protos/losses_pb2.py -> build/lib/object_detection/protos
copying object_detection/protos/input_reader_pb2.py -> build/lib/object_detection/protos
copying object_detection/protos/multiscale_anchor_generator_pb2.py -> build/lib/object_detection/protos
copying object_detection/protos/post_processing_pb2.py -> build/lib/object_detection/protos
copying object_detection/protos/ssd_pb2.py -> build/lib/object_detection/protos
creating build/lib/object_detection/core
copying object_detection/core/target_assigner.py -> build/lib/object_detection/core
copying object_detection/core/prefetcher_test.py -> build/lib/object_detection/core
copying object_detection/core/data_decoder.py -> build/lib/object_detection/core
copying object_detection/core/matcher.py -> build/lib/object_detection/core
copying object_detection/core/freezable_batch_norm_test.py -> build/lib/object_detection/core
copying object_detection/core/box_list_ops_test.py -> build/lib/object_detection/core
copying object_detection/core/balanced_positive_negative_sampler_test.py -> build/lib/object_detection/core
copying object_detection/core/preprocessor_cache.py -> build/lib/object_detection/core
copying object_detection/core/batch_multiclass_nms_test.py -> build/lib/object_detection/core
copying object_detection/core/box_coder.py -> build/lib/object_detection/core
copying object_detection/core/batcher_test.py -> build/lib/object_detection/core
copying object_detection/core/__init__.py -> build/lib/object_detection/core
copying object_detection/core/standard_fields.py -> build/lib/object_detection/core
copying object_detection/core/box_list_test.py -> build/lib/object_detection/core
copying object_detection/core/minibatch_sampler.py -> build/lib/object_detection/core
copying object_detection/core/preprocessor.py -> build/lib/object_detection/core
copying object_detection/core/data_parser.py -> build/lib/object_detection/core
copying object_detection/core/prefetcher.py -> build/lib/object_detection/core
copying object_detection/core/box_list_ops.py -> build/lib/object_detection/core
copying object_detection/core/model.py -> build/lib/object_detection/core
copying object_detection/core/anchor_generator.py -> build/lib/object_detection/core
copying object_detection/core/minibatch_sampler_test.py -> build/lib/object_detection/core
copying object_detection/core/preprocessor_test.py -> build/lib/object_detection/core
copying object_detection/core/keypoint_ops_test.py -> build/lib/object_detection/core
copying object_detection/core/box_predictor.py -> build/lib/object_detection/core
copying object_detection/core/balanced_positive_negative_sampler.py -> build/lib/object_detection/core
copying object_detection/core/freezable_batch_norm.py -> build/lib/object_detection/core
copying object_detection/core/target_assigner_test.py -> build/lib/object_detection/core
copying object_detection/core/box_list.py -> build/lib/object_detection/core
copying object_detection/core/post_processing.py -> build/lib/object_detection/core
copying object_detection/core/losses.py -> build/lib/object_detection/core
copying object_detection/core/region_similarity_calculator.py -> build/lib/object_detection/core
copying object_detection/core/box_coder_test.py -> build/lib/object_detection/core
copying object_detection/core/class_agnostic_nms_test.py -> build/lib/object_detection/core
copying object_detection/core/losses_test.py -> build/lib/object_detection/core
copying object_detection/core/keypoint_ops.py -> build/lib/object_detection/core
copying object_detection/core/region_similarity_calculator_test.py -> build/lib/object_detection/core
copying object_detection/core/batcher.py -> build/lib/object_detection/core
copying object_detection/core/multiclass_nms_test.py -> build/lib/object_detection/core
copying object_detection/core/matcher_test.py -> build/lib/object_detection/core
creating build/lib/object_detection/builders
copying object_detection/builders/optimizer_builder.py -> build/lib/object_detection/builders
copying object_detection/builders/graph_rewriter_builder_test.py -> build/lib/object_detection/builders
copying object_detection/builders/input_reader_builder.py -> build/lib/object_detection/builders
copying object_detection/builders/optimizer_builder_test.py -> build/lib/object_detection/builders
copying object_detection/builders/graph_rewriter_builder.py -> build/lib/object_detection/builders
copying object_detection/builders/model_builder.py -> build/lib/object_detection/builders
copying object_detection/builders/box_predictor_builder_test.py -> build/lib/object_detection/builders
copying object_detection/builders/matcher_builder.py -> build/lib/object_detection/builders
copying object_detection/builders/model_builder_test.py -> build/lib/object_detection/builders
copying object_detection/builders/box_coder_builder_test.py -> build/lib/object_detection/builders
copying object_detection/builders/preprocessor_builder.py -> build/lib/object_detection/builders
copying object_detection/builders/post_processing_builder.py -> build/lib/object_detection/builders
copying object_detection/builders/dataset_builder_test.py -> build/lib/object_detection/builders
copying object_detection/builders/matcher_builder_test.py -> build/lib/object_detection/builders
copying object_detection/builders/preprocessor_builder_test.py -> build/lib/object_detection/builders
copying object_detection/builders/region_similarity_calculator_builder_test.py -> build/lib/object_detection/builders
copying object_detection/builders/post_processing_builder_test.py -> build/lib/object_detection/builders
copying object_detection/builders/calibration_builder.py -> build/lib/object_detection/builders
copying object_detection/builders/__init__.py -> build/lib/object_detection/builders
copying object_detection/builders/hyperparams_builder.py -> build/lib/object_detection/builders
copying object_detection/builders/input_reader_builder_test.py -> build/lib/object_detection/builders
copying object_detection/builders/dataset_builder.py -> build/lib/object_detection/builders
copying object_detection/builders/image_resizer_builder_test.py -> build/lib/object_detection/builders
copying object_detection/builders/anchor_generator_builder_test.py -> build/lib/object_detection/builders
copying object_detection/builders/anchor_generator_builder.py -> build/lib/object_detection/builders
copying object_detection/builders/box_predictor_builder.py -> build/lib/object_detection/builders
copying object_detection/builders/region_similarity_calculator_builder.py -> build/lib/object_detection/builders
copying object_detection/builders/image_resizer_builder.py -> build/lib/object_detection/builders
copying object_detection/builders/target_assigner_builder_test.py -> build/lib/object_detection/builders
copying object_detection/builders/hyperparams_builder_test.py -> build/lib/object_detection/builders
copying object_detection/builders/losses_builder_test.py -> build/lib/object_detection/builders
copying object_detection/builders/losses_builder.py -> build/lib/object_detection/builders
copying object_detection/builders/calibration_builder_test.py -> build/lib/object_detection/builders
copying object_detection/builders/box_coder_builder.py -> build/lib/object_detection/builders
copying object_detection/builders/target_assigner_builder.py -> build/lib/object_detection/builders
creating build/lib/object_detection/legacy
copying object_detection/legacy/trainer_test.py -> build/lib/object_detection/legacy
copying object_detection/legacy/__init__.py -> build/lib/object_detection/legacy
copying object_detection/legacy/train.py -> build/lib/object_detection/legacy
copying object_detection/legacy/trainer.py -> build/lib/object_detection/legacy
copying object_detection/legacy/eval.py -> build/lib/object_detection/legacy
copying object_detection/legacy/evaluator.py -> build/lib/object_detection/legacy
creating build/lib/object_detection/predictors
copying object_detection/predictors/convolutional_box_predictor_test.py -> build/lib/object_detection/predictors
copying object_detection/predictors/mask_rcnn_box_predictor.py -> build/lib/object_detection/predictors
copying object_detection/predictors/rfcn_box_predictor.py -> build/lib/object_detection/predictors
copying object_detection/predictors/convolutional_keras_box_predictor_test.py -> build/lib/object_detection/predictors
copying object_detection/predictors/__init__.py -> build/lib/object_detection/predictors
copying object_detection/predictors/mask_rcnn_keras_box_predictor.py -> build/lib/object_detection/predictors
copying object_detection/predictors/rfcn_keras_box_predictor_test.py -> build/lib/object_detection/predictors
copying object_detection/predictors/rfcn_keras_box_predictor.py -> build/lib/object_detection/predictors
copying object_detection/predictors/convolutional_keras_box_predictor.py -> build/lib/object_detection/predictors
copying object_detection/predictors/rfcn_box_predictor_test.py -> build/lib/object_detection/predictors
copying object_detection/predictors/mask_rcnn_keras_box_predictor_test.py -> build/lib/object_detection/predictors
copying object_detection/predictors/mask_rcnn_box_predictor_test.py -> build/lib/object_detection/predictors
copying object_detection/predictors/convolutional_box_predictor.py -> build/lib/object_detection/predictors
creating build/lib/object_detection/utils
copying object_detection/utils/visualization_utils.py -> build/lib/object_detection/utils
copying object_detection/utils/autoaugment_utils.py -> build/lib/object_detection/utils
copying object_detection/utils/label_map_util_test.py -> build/lib/object_detection/utils
copying object_detection/utils/metrics.py -> build/lib/object_detection/utils
copying object_detection/utils/np_mask_ops_test.py -> build/lib/object_detection/utils
copying object_detection/utils/test_utils.py -> build/lib/object_detection/utils
copying object_detection/utils/np_box_list_test.py -> build/lib/object_detection/utils
copying object_detection/utils/learning_schedules.py -> build/lib/object_detection/utils
copying object_detection/utils/context_manager.py -> build/lib/object_detection/utils
copying object_detection/utils/np_box_list_ops.py -> build/lib/object_detection/utils
copying object_detection/utils/spatial_transform_ops.py -> build/lib/object_detection/utils
copying object_detection/utils/learning_schedules_test.py -> build/lib/object_detection/utils
copying object_detection/utils/np_box_mask_list_test.py -> build/lib/object_detection/utils
copying object_detection/utils/per_image_evaluation_test.py -> build/lib/object_detection/utils
copying object_detection/utils/model_util.py -> build/lib/object_detection/utils
copying object_detection/utils/variables_helper.py -> build/lib/object_detection/utils
copying object_detection/utils/category_util_test.py -> build/lib/object_detection/utils
copying object_detection/utils/np_box_list_ops_test.py -> build/lib/object_detection/utils
copying object_detection/utils/np_box_ops_test.py -> build/lib/object_detection/utils
copying object_detection/utils/config_util_test.py -> build/lib/object_detection/utils
copying object_detection/utils/np_box_list.py -> build/lib/object_detection/utils
copying object_detection/utils/patch_ops_test.py -> build/lib/object_detection/utils
copying object_detection/utils/object_detection_evaluation_test.py -> build/lib/object_detection/utils
copying object_detection/utils/dataset_util_test.py -> build/lib/object_detection/utils
copying object_detection/utils/shape_utils.py -> build/lib/object_detection/utils
copying object_detection/utils/variables_helper_test.py -> build/lib/object_detection/utils
copying object_detection/utils/np_box_mask_list.py -> build/lib/object_detection/utils
copying object_detection/utils/static_shape_test.py -> build/lib/object_detection/utils
copying object_detection/utils/__init__.py -> build/lib/object_detection/utils
copying object_detection/utils/metrics_test.py -> build/lib/object_detection/utils
copying object_detection/utils/json_utils_test.py -> build/lib/object_detection/utils
copying object_detection/utils/per_image_evaluation.py -> build/lib/object_detection/utils
copying object_detection/utils/np_mask_ops.py -> build/lib/object_detection/utils
copying object_detection/utils/np_box_ops.py -> build/lib/object_detection/utils
copying object_detection/utils/test_case.py -> build/lib/object_detection/utils
copying object_detection/utils/ops.py -> build/lib/object_detection/utils
copying object_detection/utils/patch_ops.py -> build/lib/object_detection/utils
copying object_detection/utils/label_map_util.py -> build/lib/object_detection/utils
copying object_detection/utils/spatial_transform_ops_test.py -> build/lib/object_detection/utils
copying object_detection/utils/np_box_mask_list_ops_test.py -> build/lib/object_detection/utils
copying object_detection/utils/per_image_vrd_evaluation_test.py -> build/lib/object_detection/utils
copying object_detection/utils/np_box_mask_list_ops.py -> build/lib/object_detection/utils
copying object_detection/utils/dataset_util.py -> build/lib/object_detection/utils
copying object_detection/utils/ops_test.py -> build/lib/object_detection/utils
copying object_detection/utils/visualization_utils_test.py -> build/lib/object_detection/utils
copying object_detection/utils/per_image_vrd_evaluation.py -> build/lib/object_detection/utils
copying object_detection/utils/object_detection_evaluation.py -> build/lib/object_detection/utils
copying object_detection/utils/json_utils.py -> build/lib/object_detection/utils
copying object_detection/utils/static_shape.py -> build/lib/object_detection/utils
copying object_detection/utils/context_manager_test.py -> build/lib/object_detection/utils
copying object_detection/utils/config_util.py -> build/lib/object_detection/utils
copying object_detection/utils/category_util.py -> build/lib/object_detection/utils
copying object_detection/utils/model_util_test.py -> build/lib/object_detection/utils
copying object_detection/utils/vrd_evaluation_test.py -> build/lib/object_detection/utils
copying object_detection/utils/vrd_evaluation.py -> build/lib/object_detection/utils
copying object_detection/utils/test_utils_test.py -> build/lib/object_detection/utils
copying object_detection/utils/shape_utils_test.py -> build/lib/object_detection/utils
creating build/lib/object_detection/models
copying object_detection/models/ssd_mobilenet_v1_ppn_feature_extractor.py -> build/lib/object_detection/models
copying object_detection/models/ssd_mobilenet_v2_keras_feature_extractor.py -> build/lib/object_detection/models
copying object_detection/models/faster_rcnn_mobilenet_v1_feature_extractor_test.py -> build/lib/object_detection/models
copying object_detection/models/ssd_pnasnet_feature_extractor_test.py -> build/lib/object_detection/models
copying object_detection/models/ssd_mobilenet_v2_fpn_feature_extractor_test.py -> build/lib/object_detection/models
copying object_detection/models/ssd_mobilenet_v3_feature_extractor_test.py -> build/lib/object_detection/models
copying object_detection/models/faster_rcnn_pnas_feature_extractor_test.py -> build/lib/object_detection/models
copying object_detection/models/ssd_inception_v3_feature_extractor.py -> build/lib/object_detection/models
copying object_detection/models/faster_rcnn_resnet_v1_feature_extractor.py -> build/lib/object_detection/models
copying object_detection/models/ssd_mobilenet_v1_fpn_keras_feature_extractor.py -> build/lib/object_detection/models
copying object_detection/models/ssd_mobilenet_v3_feature_extractor_testbase.py -> build/lib/object_detection/models
copying object_detection/models/faster_rcnn_inception_resnet_v2_keras_feature_extractor.py -> build/lib/object_detection/models
copying object_detection/models/ssd_inception_v3_feature_extractor_test.py -> build/lib/object_detection/models
copying object_detection/models/embedded_ssd_mobilenet_v1_feature_extractor.py -> build/lib/object_detection/models
copying object_detection/models/ssd_mobilenet_v2_fpn_feature_extractor.py -> build/lib/object_detection/models
copying object_detection/models/ssd_mobilenet_v1_feature_extractor.py -> build/lib/object_detection/models
copying object_detection/models/ssd_mobilenet_v1_feature_extractor_test.py -> build/lib/object_detection/models
copying object_detection/models/ssd_pnasnet_feature_extractor.py -> build/lib/object_detection/models
copying object_detection/models/feature_map_generators_test.py -> build/lib/object_detection/models
copying object_detection/models/ssd_inception_v2_feature_extractor.py -> build/lib/object_detection/models
copying object_detection/models/ssd_resnet_v1_fpn_feature_extractor.py -> build/lib/object_detection/models
copying object_detection/models/faster_rcnn_inception_resnet_v2_feature_extractor_test.py -> build/lib/object_detection/models
copying object_detection/models/ssd_mobilenet_edgetpu_feature_extractor.py -> build/lib/object_detection/models
copying object_detection/models/faster_rcnn_pnas_feature_extractor.py -> build/lib/object_detection/models
copying object_detection/models/faster_rcnn_inception_v2_feature_extractor.py -> build/lib/object_detection/models
copying object_detection/models/__init__.py -> build/lib/object_detection/models
copying object_detection/models/faster_rcnn_mobilenet_v1_feature_extractor.py -> build/lib/object_detection/models
copying object_detection/models/faster_rcnn_inception_resnet_v2_feature_extractor.py -> build/lib/object_detection/models
copying object_detection/models/faster_rcnn_inception_v2_feature_extractor_test.py -> build/lib/object_detection/models
copying object_detection/models/faster_rcnn_inception_resnet_v2_keras_feature_extractor_test.py -> build/lib/object_detection/models
copying object_detection/models/embedded_ssd_mobilenet_v1_feature_extractor_test.py -> build/lib/object_detection/models
copying object_detection/models/ssd_resnet_v1_fpn_feature_extractor_testbase.py -> build/lib/object_detection/models
copying object_detection/models/faster_rcnn_nas_feature_extractor.py -> build/lib/object_detection/models
copying object_detection/models/ssd_mobilenet_edgetpu_feature_extractor_testbase.py -> build/lib/object_detection/models
copying object_detection/models/ssd_mobilenet_v1_ppn_feature_extractor_test.py -> build/lib/object_detection/models
copying object_detection/models/ssd_resnet_v1_ppn_feature_extractor.py -> build/lib/object_detection/models
copying object_detection/models/ssd_resnet_v1_ppn_feature_extractor_testbase.py -> build/lib/object_detection/models
copying object_detection/models/ssd_mobilenet_v1_keras_feature_extractor.py -> build/lib/object_detection/models
copying object_detection/models/ssd_resnet_v1_ppn_feature_extractor_test.py -> build/lib/object_detection/models
copying object_detection/models/ssd_mobilenet_v2_feature_extractor.py -> build/lib/object_detection/models
copying object_detection/models/ssd_resnet_v1_fpn_keras_feature_extractor.py -> build/lib/object_detection/models
copying object_detection/models/ssd_inception_v2_feature_extractor_test.py -> build/lib/object_detection/models
copying object_detection/models/ssd_mobilenet_edgetpu_feature_extractor_test.py -> build/lib/object_detection/models
copying object_detection/models/feature_map_generators.py -> build/lib/object_detection/models
copying object_detection/models/ssd_feature_extractor_test.py -> build/lib/object_detection/models
copying object_detection/models/ssd_mobilenet_v1_fpn_feature_extractor_test.py -> build/lib/object_detection/models
copying object_detection/models/ssd_mobilenet_v3_feature_extractor.py -> build/lib/object_detection/models
copying object_detection/models/ssd_mobilenet_v2_feature_extractor_test.py -> build/lib/object_detection/models
copying object_detection/models/faster_rcnn_resnet_v1_feature_extractor_test.py -> build/lib/object_detection/models
copying object_detection/models/ssd_mobilenet_v1_fpn_feature_extractor.py -> build/lib/object_detection/models
copying object_detection/models/faster_rcnn_nas_feature_extractor_test.py -> build/lib/object_detection/models
copying object_detection/models/ssd_resnet_v1_fpn_feature_extractor_test.py -> build/lib/object_detection/models
copying object_detection/models/ssd_mobilenet_v2_fpn_keras_feature_extractor.py -> build/lib/object_detection/models
creating build/lib/object_detection/anchor_generators
copying object_detection/anchor_generators/flexible_grid_anchor_generator_test.py -> build/lib/object_detection/anchor_generators
copying object_detection/anchor_generators/multiscale_grid_anchor_generator_test.py -> build/lib/object_detection/anchor_generators
copying object_detection/anchor_generators/grid_anchor_generator_test.py -> build/lib/object_detection/anchor_generators
copying object_detection/anchor_generators/multiscale_grid_anchor_generator.py -> build/lib/object_detection/anchor_generators
copying object_detection/anchor_generators/__init__.py -> build/lib/object_detection/anchor_generators
copying object_detection/anchor_generators/flexible_grid_anchor_generator.py -> build/lib/object_detection/anchor_generators
copying object_detection/anchor_generators/multiple_grid_anchor_generator_test.py -> build/lib/object_detection/anchor_generators
copying object_detection/anchor_generators/multiple_grid_anchor_generator.py -> build/lib/object_detection/anchor_generators
copying object_detection/anchor_generators/grid_anchor_generator.py -> build/lib/object_detection/anchor_generators
creating build/lib/object_detection/dataset_tools
copying object_detection/dataset_tools/oid_hierarchical_labels_expansion_test.py -> build/lib/object_detection/dataset_tools
copying object_detection/dataset_tools/create_pascal_tf_record.py -> build/lib/object_detection/dataset_tools
copying object_detection/dataset_tools/oid_tfrecord_creation.py -> build/lib/object_detection/dataset_tools
copying object_detection/dataset_tools/create_kitti_tf_record.py -> build/lib/object_detection/dataset_tools
copying object_detection/dataset_tools/oid_hierarchical_labels_expansion.py -> build/lib/object_detection/dataset_tools
copying object_detection/dataset_tools/tf_record_creation_util.py -> build/lib/object_detection/dataset_tools
copying object_detection/dataset_tools/create_pascal_tf_record_test.py -> build/lib/object_detection/dataset_tools
copying object_detection/dataset_tools/create_oid_tf_record.py -> build/lib/object_detection/dataset_tools
copying object_detection/dataset_tools/__init__.py -> build/lib/object_detection/dataset_tools
copying object_detection/dataset_tools/create_coco_tf_record_test.py -> build/lib/object_detection/dataset_tools
copying object_detection/dataset_tools/tf_record_creation_util_test.py -> build/lib/object_detection/dataset_tools
copying object_detection/dataset_tools/oid_tfrecord_creation_test.py -> build/lib/object_detection/dataset_tools
copying object_detection/dataset_tools/create_pet_tf_record.py -> build/lib/object_detection/dataset_tools
copying object_detection/dataset_tools/create_coco_tf_record.py -> build/lib/object_detection/dataset_tools
copying object_detection/dataset_tools/create_kitti_tf_record_test.py -> build/lib/object_detection/dataset_tools
creating build/lib/object_detection/inference
copying object_detection/inference/detection_inference_test.py -> build/lib/object_detection/inference
copying object_detection/inference/detection_inference.py -> build/lib/object_detection/inference
copying object_detection/inference/__init__.py -> build/lib/object_detection/inference
copying object_detection/inference/infer_detections.py -> build/lib/object_detection/inference
creating build/lib/object_detection/data_decoders
copying object_detection/data_decoders/__init__.py -> build/lib/object_detection/data_decoders
copying object_detection/data_decoders/tf_example_decoder_test.py -> build/lib/object_detection/data_decoders
copying object_detection/data_decoders/tf_example_decoder.py -> build/lib/object_detection/data_decoders
creating build/lib/object_detection/matchers
copying object_detection/matchers/bipartite_matcher_test.py -> build/lib/object_detection/matchers
copying object_detection/matchers/__init__.py -> build/lib/object_detection/matchers
copying object_detection/matchers/argmax_matcher_test.py -> build/lib/object_detection/matchers
copying object_detection/matchers/bipartite_matcher.py -> build/lib/object_detection/matchers
copying object_detection/matchers/argmax_matcher.py -> build/lib/object_detection/matchers
creating build/lib/object_detection/box_coders
copying object_detection/box_coders/faster_rcnn_box_coder.py -> build/lib/object_detection/box_coders
copying object_detection/box_coders/keypoint_box_coder_test.py -> build/lib/object_detection/box_coders
copying object_detection/box_coders/square_box_coder.py -> build/lib/object_detection/box_coders
copying object_detection/box_coders/__init__.py -> build/lib/object_detection/box_coders
copying object_detection/box_coders/square_box_coder_test.py -> build/lib/object_detection/box_coders
copying object_detection/box_coders/keypoint_box_coder.py -> build/lib/object_detection/box_coders
copying object_detection/box_coders/faster_rcnn_box_coder_test.py -> build/lib/object_detection/box_coders
copying object_detection/box_coders/mean_stddev_box_coder.py -> build/lib/object_detection/box_coders
copying object_detection/box_coders/mean_stddev_box_coder_test.py -> build/lib/object_detection/box_coders
creating build/lib/object_detection/tpu_exporters/testdata
copying object_detection/tpu_exporters/testdata/__init__.py -> build/lib/object_detection/tpu_exporters/testdata
creating build/lib/object_detection/predictors/heads
copying object_detection/predictors/heads/keras_class_head.py -> build/lib/object_detection/predictors/heads
copying object_detection/predictors/heads/keras_box_head_test.py -> build/lib/object_detection/predictors/heads
copying object_detection/predictors/heads/mask_head.py -> build/lib/object_detection/predictors/heads
copying object_detection/predictors/heads/box_head_test.py -> build/lib/object_detection/predictors/heads
copying object_detection/predictors/heads/box_head.py -> build/lib/object_detection/predictors/heads
copying object_detection/predictors/heads/keras_mask_head.py -> build/lib/object_detection/predictors/heads
copying object_detection/predictors/heads/keypoint_head.py -> build/lib/object_detection/predictors/heads
copying object_detection/predictors/heads/keras_box_head.py -> build/lib/object_detection/predictors/heads
copying object_detection/predictors/heads/__init__.py -> build/lib/object_detection/predictors/heads
copying object_detection/predictors/heads/keypoint_head_test.py -> build/lib/object_detection/predictors/heads
copying object_detection/predictors/heads/keras_class_head_test.py -> build/lib/object_detection/predictors/heads
copying object_detection/predictors/heads/head.py -> build/lib/object_detection/predictors/heads
copying object_detection/predictors/heads/class_head_test.py -> build/lib/object_detection/predictors/heads
copying object_detection/predictors/heads/keras_mask_head_test.py -> build/lib/object_detection/predictors/heads
copying object_detection/predictors/heads/mask_head_test.py -> build/lib/object_detection/predictors/heads
copying object_detection/predictors/heads/class_head.py -> build/lib/object_detection/predictors/heads
creating build/lib/object_detection/models/keras_models
copying object_detection/models/keras_models/model_utils.py -> build/lib/object_detection/models/keras_models
copying object_detection/models/keras_models/test_utils.py -> build/lib/object_detection/models/keras_models
copying object_detection/models/keras_models/resnet_v1.py -> build/lib/object_detection/models/keras_models
copying object_detection/models/keras_models/inception_resnet_v2.py -> build/lib/object_detection/models/keras_models
copying object_detection/models/keras_models/inception_resnet_v2_test.py -> build/lib/object_detection/models/keras_models
copying object_detection/models/keras_models/resnet_v1_test.py -> build/lib/object_detection/models/keras_models
copying object_detection/models/keras_models/mobilenet_v2.py -> build/lib/object_detection/models/keras_models
copying object_detection/models/keras_models/__init__.py -> build/lib/object_detection/models/keras_models
copying object_detection/models/keras_models/mobilenet_v1_test.py -> build/lib/object_detection/models/keras_models
copying object_detection/models/keras_models/mobilenet_v1.py -> build/lib/object_detection/models/keras_models
copying object_detection/models/keras_models/mobilenet_v2_test.py -> build/lib/object_detection/models/keras_models
running egg_info
creating object_detection.egg-info
writing requirements to object_detection.egg-info/requires.txt
writing object_detection.egg-info/PKG-INFO
writing top-level names to object_detection.egg-info/top_level.txt
writing dependency_links to object_detection.egg-info/dependency_links.txt
writing manifest file 'object_detection.egg-info/SOURCES.txt'
reading manifest file 'object_detection.egg-info/SOURCES.txt'
writing manifest file 'object_detection.egg-info/SOURCES.txt'
 junhoha  ~/Desktop/AI/models-master/research  python setup.py install
running install
error: can't create or remove files in install directory

The following error occurred while trying to add or remove files in the
installation directory:

    [Errno 13] Permission denied: '/Library/Python/2.7/site-packages/test-easy-install-34984.write-test'

https://ko.wikipedia.org/wiki/%ED%9E%88%ED%94%BC


히피(영어: hippie 또는 hippy)는 1960년대 미국 샌프란시스코, LA 등지 청년층에서부터 시작된, 기성의 사회 통념, 제도, 가치관을 부정하고 인간성의 회복, 자연으로의 귀의 등을 주장하며 탈사회적으로 행동하는 사람들을 가리키는 말이다. 어원으로는 happy(행복한), hipped한, 화가 단단히 난), hip(재즈용어로 가락을 맞추다), hip(허벅지), "hip,hip"(갈채를 보낼 때의 소리) 등에서 나왔다는 설이 있다.


초기 히피문화는 틀에 박힌 가치가 아니라 자기 자신의 가치와 의미에 따라 개성의 표현을 추구하고, 기성사회의 성적 억압과 관습적 도덕을 해체함으로써 개방적인 성의 표현을 통해 친밀성과 이를 통한 새로운 공동체의 건설을 성취하려고 했다. 흔히 일어나는 반전운동이나 민권운동과는 달리 히피문화는 기존질서 체제를 정면으로 거부하기보다는 기성사회의 새로운 가치질서를 만들고자 하였다. 

히피문화의 상징은 꽃으로 되어있는데, 이는 폭력과 억압에 저항하고, 부드럽고 비폭력적인 자연에 대한 사랑으로 모든 것은 대치한다는 의미가 내포되어 있다. 특히 샌프란시스코의 꽃은 '꽃의 아이들(flower children)'로 불리는 히피(hippie)에서 비롯한다. 

!-- "This app is not allowed to query for scheme url " -> plist problem


!-- When virtual keyboard show/hide, Screen is up and down

    CGFloat temp;

- (void)viewDidLoad
{
    self->temp = self.view.frame.origin.y;


#pragma mark -
#pragma mark TextField Delegate
-(BOOL)textFieldShouldReturn:(UITextField *)textField {
    [textField resignFirstResponder];
    return NO;
}


#pragma mark -
#pragma mark TextField Delegate
-(BOOL)textFieldShouldReturn:(UITextField *)textField {
    [textField resignFirstResponder];
    return NO;
}

-(BOOL)textFieldShouldBeginEditing:(UITextField *)textField {
    [[NSNotificationCenter defaultCenter] addObserver:self selector:@selector(keyboardDidShow:) name:UIKeyboardDidShowNotification object:nil];
    return YES;
}

- (BOOL)textFieldShouldEndEditing:(UITextField *)textField {
    [[NSNotificationCenter defaultCenter] addObserver:self selector:@selector(keyboardDidHide:) name:UIKeyboardDidHideNotification object:nil];

    [self.view endEditing:YES];
    return YES;
}

- (void)keyboardDidShow:(NSNotification *)notification
{
    [UIView beginAnimations:nil context:NULL];
    [UIView setAnimationDelegate:self];
    [UIView setAnimationDuration:0.1];
    [UIView setAnimationBeginsFromCurrentState:YES];
    
    self.view.frame = CGRectMake(self.view.frame.origin.x, (self->temp  - 220), self.view.frame.size.width, self.view.frame.size.height);
    [UIView commitAnimations];
    
}

-(void)keyboardDidHide:(NSNotification *)notification
{
        [UIView beginAnimations:nil context:NULL];
        [UIView setAnimationDelegate:self];
        [UIView setAnimationDuration:0.1];
        [UIView setAnimationBeginsFromCurrentState:YES];
        
        self.view.frame = CGRectMake(self.view.frame.origin.x, (self->temp), self.view.frame.size.width, self.view.frame.size.height);
        [UIView commitAnimations];
    
}



!-- bonus

- (BOOL) isKeyboardOnScreen
{
    BOOL isKeyboardShown = NO;

    NSArray *windows = [UIApplication sharedApplication].windows;
    if (windows.count > 1) {
        NSArray *wSubviews =  [windows[1]  subviews];
        if (wSubviews.count) {
            CGRect keyboardFrame = [wSubviews[0] frame];
            CGRect screenFrame = [windows[1] frame];
            if (keyboardFrame.origin.y+keyboardFrame.size.height == screenFrame.size.height) {
                isKeyboardShown = YES;
            }
        }
    }

    return isKeyboardShown;
}



!- swift 소스로...
Always Embed Swift Standard Libraries
https://objectivec2swift.com/#/xcode-extension/api-key?ref=xcode-extension
이용해서 objective-C 코드가 어떻게 swift로 바뀌는지 형태를 좀 익혀두면 좋음.
(이미 알고 있겠지만 사실, 변환 연습을 며칠해 보니 코드 리딩하면서 바로 스위프트
코드가 떠오름. 물론, 결론은 변환은 무슨... 다 새로 짜는게 나음)

Here are the iPhones that will receive iOS 13:
iPhone 11, iPhone 11 Pro, and iPhone 11 Pro Max.
iPhone XS and iPhone XS Max.
iPhone XR.
iPhone X.
iPhone 8 and 8 Plus.
iPhone 7 and 7 Plus.
iPhone 6S and 6S Plus.
iPhone SE.
2019. 9. 24.

      queryDelete = [NSString stringWithFormat:@"delete from tableB;"];
        delete_stmt = [queryDelete UTF8String];
        sqlite3_prepare_v2(database, delete_stmt, -1, &statement, NULL);
        sqlite3_step(statement);
        sqlite3_reset(statement);

번호 후보기술 및 기술설명 평가 (점수)
낮음 <<     >> 높음
1 (후보기술) 프라임 에디팅(Prime editing) 기술적
혁신성 ① ② ③ ④ ⑤


(기술설명) 살아있는 세포 내에서 유전자서열을 목표에 따라 고효율로 첨가,
삭제, 치환하는 기술 산업적
혁신성 ① ② ③ ④ ⑤


2 (후보기술) 고분해능 생체분자 구조분석기술(High-resolution cryo-EM
/cryo-ET bio-imaging) 필수 항목입니다. 기술적
혁신성 ① ② ③ ④ ⑤


(기술설명) 거대 단백질 복합체, 막단백질, 세포의 입체구조를
초저온-전자현미경을 통해 분석하는 기술 필수 항목입니다. 산업적
혁신성 ① ② ③ ④ ⑤


3 (후보기술) 키메라 바이오인공장기(Artificial bio-organs by chimerism)
필수 항목입니다. 기술적
혁신성 ① ② ③ ④ ⑤


(기술설명) 환자 맞춤형 이식 및 치료를 목적으로 동물에서 사람의 세포,
조직, 장기 등을 키워내는 기술 필수 항목입니다. 산업적
혁신성 ① ② ③ ④ ⑤


4 (후보기술) 단일 신경세포 트래킹(Tracking single neurons)
필수 항목입니다. 기술적
혁신성 ① ② ③ ④ ⑤


(기술설명) 뇌 특정 부위에 전극을 주입 후 각 신경세포의 전기신호를
측정하고 분류하여 신경회로 구성 및 뇌기능 제어기전을
규명하는 기술 필수 항목입니다. 산업적
혁신성 ① ② ③ ④ ⑤


5 (후보기술) 장기관류시스템(Organ perfusion system)
필수 항목입니다. 기술적
혁신성 ① ② ③ ④ ⑤


(기술설명) 체내 환경(혈류, 산소, 온도 등)을 모사하여 체외에서 장기의
세포 괴사나 손상 없이 기능을 유지하는 기술 필수 항목입니다. 산업적
혁신성 ① ② ③ ④ ⑤


6 (후보기술) 단일세포 이미징 추적기술(Single cell biology tracing
visible tech) 필수 항목입니다. 기술적
혁신성 ① ② ③ ④ ⑤


(기술설명) 대량 프로브(probe) 등을 활용한 단일세포 이미징을 통해 세포
위치 정보 및 조직, 장기 발달에 관한 정보를 추적하는 기술
필수 항목입니다. 산업적
혁신성 ① ② ③ ④ ⑤


번호 후보기술 및 기술설명 평가 (점수)
낮음 <<     >> 높음
1 (후보기술) 소포체 기반 약물 전달 기술(Drug delivery with extracellular
 vesicles) 기술적
혁신성 ① ② ③ ④ ⑤


(기술설명) 세포 소기관인 소포체에 약물을 탑재하여 목적 세포까지
정확하게 전달하는 기술 산업적
혁신성 ① ② ③ ④ ⑤


2 (후보기술) 면역세포치료제 활성화 백신 (Boosting vaccine for
CAR-T/NK cells) 기술적
혁신성 ① ② ③ ④ ⑤


(기술설명) CAR-T, NK 등 면역세포의 활성화를 유도하는 물질(백신)을
투입하여 고형암을 치료하는 기술 산업적
혁신성 ① ② ③ ④ ⑤


3 (후보기술) 실시간 액체생검 분석기술(Real-time liquid biopsies)
기술적
혁신성 ① ② ③ ④ ⑤


(기술설명) 혈액, 소변 등에 존재하는 핵산 조각들을 분석하여 실시간 질병의
진행을 추적하는 기술 산업적
혁신성 ① ② ③ ④ ⑤


4 (후보기술) 알츠하이머 치매를 위한 세포치료(Cell therapy for
Alzheimer’s disease) 기술적
혁신성 ① ② ③ ④ ⑤


(기술설명) 대표적인 퇴행성 뇌질환인 알츠하이머성 치매를 치료하기 위한
세포 혹은 줄기세포를 이용한 치료법 산업적
혁신성 ① ② ③ ④ ⑤


5 (후보기술) 전자의약(Digital therapeutics) 기술적
혁신성 ① ② ③ ④ ⑤


(기술설명) 기존의 먹는 알약이나 주사제가 아닌 디지털기술(소프트웨어)을
기반으로 질병예방, 관리 및 치료하는 신개념 의약품
산업적
혁신성 ① ② ③ ④ ⑤


6 (후보기술) 조직 내 노화세포 제거기술(Senolytics) 기술적
혁신성 ① ② ③ ④ ⑤


(기술설명) 조직의 노화 및 기능 이상을 유발하는 노화세포를 선택적으로
제거하여 조직 항상성을 유지하고 질병을 치료하는 기술
산업적
혁신성 ① ② ③ ④ ⑤


번호후보기술 및 기술설명평가 (점수)
낮음 <<     >> 높음1(후보기술) 엽록체 바이오공장(Chloroplast biofactory for high-level
production of biomolecules)기술적
혁신성
① ② ③ ④ ⑤

(기술설명) 식물 세포 소기관인 엽록체를 이용하여 유용 외래 단백질 등을
대량 생산하는 기술산업적
혁신성
① ② ③ ④ ⑤

2(후보기술) 식물 종간 장벽제거기술(Removing interspecific
incompatibility for cross-species hybridization)
기술적
혁신성
① ② ③ ④ ⑤

(기술설명) 이종 교배의 장벽 역할을 하는 단백질 엔지니어링을 통해
이종 교배를 가능하게 하는 기술산업적
혁신성
① ② ③ ④ ⑤

3(후보기술) 친환경 에너지 발전과 농업의 결합(Agrivoltaics for efficient
food and energy production)기술적
혁신성
① ② ③ ④ ⑤

(기술설명) 식물 재배시설에 친환경 에너지 발전 시설을 결합하여 농업
효율과 에너지 생산 효율을 동반 상승시키는 기술
산업적
혁신성
① ② ③ ④ ⑤

4(후보기술) 애완동물 유전체의약(Pet genomics medicine)
기술적
혁신성
① ② ③ ④ ⑤

(기술설명) 애완동물의 유전체 염기서열 분석을 통해 확보된 유전정보를
바탕으로 각 애완동물의 특성에 맞게 치료하는 기술
산업적
혁신성
① ② ③ ④ ⑤

번호 후보기술 및 기술설명 평가 (점수)
낮음 <<     >> 높음
1 (후보기술) 빅데이터 기반 생태건강성 평가 (Ecosystem health
assessment using bio-big data) 기술적
혁신성 ① ② ③ ④ ⑤


(기술설명) 바이오 빅데이터(특히, 미생물 군집구조)를 포함한 환경,
생태 데이터를 통합하여 생태계의 건강성을 평가하는 기술
산업적
혁신성 ① ② ③ ④ ⑤


2 (후보기술) 바이오파운드리(Biofoundry) 기술적
혁신성 ① ② ③ ④ ⑤


(기술설명) Bio+AI+Robot 기반의 융합기술로 생산성과 예측성을
개선함으로써 다양한 바이오 소재를 제조하는 기술
산업적
혁신성 ① ② ③ ④ ⑤


3 (후보기술) 인공생태계(Manipulation of artificial ecosystem)
기술적
혁신성 ① ② ③ ④ ⑤


(기술설명) 최소한의 요소로 실제 생태계를 최대한 모사하는 기술로
다양한 생태계 교란의 영향을 현장처럼 시뮬레이션하는 기술
산업적
혁신성 ① ② ③ ④ ⑤


4 (후보기술) 무세포 합성생물학(Cell-free synthetic biology)
기술적
혁신성 ① ② ③ ④ ⑤


(기술설명) 세포 없이 필요한 요소(단백질, 효소 등)로 단순하게 구성하여
자연적인 생물 시스템을 엔지니어링하는 기술
산업적
혁신성 ① ② ③ ④ ⑤

바이오 분야는 정말 생소한 기술이 많네.

ANALYST, BUSINESS AND GAMEPLAY INSIGHTS - HEARTHSTONE

Global Insights•Irvine, California

Blizzard Entertainment games don’t just begin with game ideas or end once those games are released. A lot more goes into the creation of a Blizzard product than the work of developers—and we support our games for years after they’re in the hands of gamers worldwide.  Blizzard Entertainment is seeking an Analyst to lead strategic analysis focused on identification, evaluation, and execution of opportunities to drive growth and operational excellence for the Hearthstone franchise.

QUEST DESCRIPTION

• Slice through petabytes of data to find compelling and actionable product insights
• Apply vision and confidence to design and direct qualitative and quantitative analysis
• Stand before stakeholders, including senior leaders, to clearly communicate your strategic findings and recommendations
• Translate recommendations into efficient action plans for senior management and department leaders
• Stay abreast of industry trends and innovative developments

REQUIRED TALENT BUILD

• Arcane Brilliance: Analytical orientation and strong problem-solving skills, demonstrated by the ability to structure an issue and analyze alternative solutions. Tireless advocate for the use of data to solve problems.
• Break it down!: Superior communications, presentation, and facilitation skills: must have the ability to structure a concise, clear presentation of findings, based on large sets of complex data, and effectively present this to any level in the organization.
• Power Word: Curiosity: Curious, internally-motivated self-starter with ability to plan, organize, and establish priorities to manage multiple projects and achieve results.
• Co-op Commander: Collaborate effectively across multiple departments with a proven ability to go deep in the trenches or 80/20 when required to get to insights quickly.
• Chart the Course: Capable of building highly functional and insightful dashboards.
• Confident Aim: Strong quantitative skills with a penchant for analytical query writing. Experience working with data using SQL.
• Stay Awhile and Listen: Excellent interpersonal skills. Effective with storytelling using data and appropriate visualizations.

RECOMMENDED GEAR

• A minimum of 3+ years’ experience in Gaming, Consulting, Data Analytics, Product Management, or Finance
• Passionate gamer with working knowledge of Blizzard games, products, and services (particularly Hearthstone)
• MBA or Masters’ degree in an analytical field a plus
• Experience with specialized visualization tools (Tableau)

Required Application Materials

• Resume
• Cover Letter which should include:
  • Why you are interested in working at Blizzard
  • What games you are currently playing
• **Only Resumes with Cover Letters will be considered.

Blizzard Entertainment is a global company committed to growing our employees. We offer generous benefits and perks with an eye on providing true work / life balance. We’ve worked hard to champion an intensely collaborative and creative environment, a diverse and inclusive employee culture, and training and opportunity for professional growth. Our people are everything. Our core values are real, and our mission has never changed.

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We are dedicated to creating the most epic entertainment experiences…ever. Join us!

CLOUD SYSTEMS ENGINEER INTERN

IT - Infrastructure Engineering•Irvine, California

2020 SUMMER INTERNSHIPS

CLOUD SYSTEMS ENGINEER INTERN

Greetings, students! We’re geared up here to offer another round of epic internship experiences in 2020. The internship program offers the opportunity to pause your studies for one summer and work side-by-side with Blizzard Entertainment employees in your area of interest, doing real work that oftentimes ends up in production. As such, we hold you to the same high standards as anybody else in the company.

Just like our gameplay experiences, Blizzard internships are a fully immersive experience. We want interns who are enthusiastic about what they want to do for a living and excited to come in every day to learn and contribute in this hyper-creative atmosphere. Being fully engaged with our team also means communicating effectively and maintaining an open mind - one that is receptive to feedback and able to adapt and grow. Your hobbies and interests can also play in your favor. Passion for technology, gaming, game development, and in-depth knowledge of Blizzard games, products, and services will always be a plus with us, regardless of the position you’re applying for. Be passionate about who you are, be into our industry, and be willing to go all-in and embrace this opportunity to the fullest!

QUEST OBJECTIVES

Blizzard Cloud is dedicated to powering the world's most epic entertainment experiences. To achieve this, we understand that epic entertainment demands epic infrastructure.  Our mission is to provide a world-class multi-cloud with global reach. We focus on technologies that make Blizzard efficient, consistent, agile and portable across the private and public clouds. We empower rapid iteration and rapid delivery - everywhere our game developers and player community need it.

In pursuit of our mission, we run one of the largest private OpenStack clouds in the world across seven geographical sites. Operating at this scale comes with challenges. A core value of Blizzard is to learn and grow, so as we solve and overcome these challenges, we are compelled to share our solutions. To that end, we contribute to many Openstack projects including Magnum, Senlin and Designate. While we continue to scale and grow an awesome private cloud, our strategy for the future is hinged upon multi-cloud. We value public cloud for functionality, reach and scale when it makes sense. The agility offered by multi-cloud has helped realize many successes.

We are looking to hire an intern Cloud Software Engineer. This role will be focused on delivering a fixed-length project that will solve a real-world problem for Blizzard Cloud. This is a great opportunity to learn and grow with a strong engineering team, getting experience and exposure to a large scale, complex and challenging set of technologies.

ELIGIBILITY REQUIREMENTS

• Currently enrolled in a college or university
• Returning for at least one more semester or quarter following the summer 2020 internship
• Able to work fulltime hours for 11 – 12 weeks at the Blizzard office indicated in this job posting between May 2020 – August 2020 or June 2020 – September 2020 (group dates depend on when you get out of school)
• Authorized to work in the US

RESPONSIBILITIES

As an intern, you will be paired up with a mentor who will help onboard you to how we build and operate Blizzard Cloud. Your mentor will also help to get you started on your assigned project and meet frequently to assess and provide feedback and guidance.  Your assigned project will be a real-world problem that we need to be solved. You will make a lasting difference in how we power Blizzards epic entertainment experiences!

LEVEL REQUIREMENTS

• Good understanding of Linux and network fundamentals
• Basic knowledge of Linux virtualization, e.g. KVM, Xen
• Ability to write software in Python or another language commonly used in systems administration

RECOMMENDED TALENTS

• Understanding of Infrastructure as Code concepts and related tools. e.g. Git, Terraform, Ansible, Puppet
• Experience/Knowledge of the offerings of multiple large public clouds. g. AWS, GCP, Azure, and/or infrastructure-as-a-service offerings (e.g. Openstack, OpenNebula, VMWare)

QUEST ITEMS

• Resume
• Cover Letter

Blizzard Entertainment is a global company committed to growing our employees. We offer generous benefits and perks with an eye on providing true work/life balance. We’ve worked hard to champion an intensely collaborative and creative environment, a diverse and inclusive employee culture, and training and opportunity for professional growth. Our people are everything. Our core values are real, and our mission has never changed.

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We are dedicated to creating the most epic entertainment experiences...ever. Join us!

Please submit your application no later than January 10, 2020.

DEVOPS ENGINEER INTERN, MONITORING & AUTOMATION

IT - GNOC•Irvine, California

2020 SUMMER INTERNSHIPS

DevOps Engineer Intern, Monitoring & Automation

Greetings, students! We’re geared up here to offer another round of epic internship experiences in 2020. The internship program offers the opportunity to pause your studies for one summer and work side-by-side with Blizzard Entertainment employees in your area of interest, doing real work that oftentimes ends up in production. As such, we hold you to the same high standards as anybody else in the company.

Just like our gameplay experiences, Blizzard internships are a fully immersive experience. We want interns who are enthusiastic about what they want to do for a living and excited to come in every day to learn and contribute in this hyper-creative atmosphere. Being fully engaged with our team also means communicating effectively and maintaining an open mind - one that is receptive to feedback and able to adapt and grow. Your hobbies and interests can also play in your favor. Passion for technology, gaming, game development, and in-depth knowledge of Blizzard games, products, and services will always be a plus with us, regardless of the position you’re applying for. Be passionate about who you are, be into our industry, and be willing to go all-in and embrace this opportunity to the fullest!

QUEST OBJECTIVES

Blizzard Entertainment's operations center is looking for a DevOps engineer intern to help maintain Blizzard’s worldwide IT services. The operations center serves as the focal point for all Blizzard IT Service Operations, including the core functions of Monitoring, Request Fulfillment, and Incident, Event, and Change Management, with devops engineers responsible for the architecture/design, management, and integrations with Blizzard's monitoring and event management services. The core skillsets for a devops engineer intern include software engineering and integrations, systems engineering, and a desire to understand event/alert management, monitoring tools, and automation engines. The devops engineer intern position also relies on knowledge management skill sets to develop documentation and training guides. The operations center is a dynamic, fast-paced, high-reliability, 24 / 7 facility supporting one of the most complex and prolific gaming infrastructures on the planet. If you excited about full-stack engineering (hardware, containers, and application development), read on.

RESPONSIBILITES

• Work with service operations managers, operations center leadership, and internal stakeholders to design and implement solutions that improve and expand Blizzard's Event Management tooling and capabilities.
• Work with service owners to engineer and on-board new monitoring solutions to the operations center.
• Work with operations center administrators and engineers to continuously improve event quality (signal/noise) and process/tooling performance.
• Develop API-driven software solutions to integrate diverse commercial or internally developed tools.
• Collaborate with management in defining metrics and KPIs critical to the analysis of event management performance.
• Collaborate with the service operations team to develop reports to highlight monitoring/alert coverage and effectiveness for service owners and senior leadership.
• Perform detailed research, analysis, and provide recommendations on proofs-of-concept and build vs. buy scenarios.
• Create technical and end-user documentation for integrations and tooling managed by the Monitoring and Automation team.
• Participate in production support and scheduled on-call responsibilities.

ELIGIBILITY REQUIREMENTS

• Currently enrolled in a college or university
• Returning for at least one more semester or quarter following the summer 2020 internship
• Able to work fulltime hours for 11 – 12 weeks at the Blizzard office indicated in this job posting between May 2020 – August 2020 or June 2020 – September 2020 (group dates depend on when you get out of school)
• Authorized to work in the US

LEVEL REQUIREMENTS

• Experience working with Unix/Linux systems from kernel to shell and beyond, with experience working with system libraries, file systems, and client-server protocols.
• Awareness of monitoring technology and protocols (Nagios, SolarWinds, Telegraf (TICK), Sensu, AppDynamix/NewRelic, NMS)
• Familiarity with the ELK stack and metrics time-series databases (InfluxDB, Graphite, OpenTSDB)
• Experience developing and writing code in at least 1 modern scripted/interpreted language (Python, C#, Ruby, Groovy, Node.js, Go)
• Experience with reading and understanding Python, Javascript, CSS
• Functional knowledge developing microservice-based HTTP applications and servers, including JMX, Flask, Apache/NGINX
• Understanding of the Software Development Life Cycle; including CI and CD pipeline architecture.
• Understanding of cloud orchestration frameworks, enterprise IT service provisioning tools, and their role in IT transformation.
• Familiarity with service configuration and deployments tools, such as Ansible, Consul, Jenkins, Puppet, Terraform, and Vault.
• Strong interpersonal and communication skills, able to train users on topics, provide presentations to an internal audience, and interact positively with and receive direction.

RECOMMENDED TALENTS

• Experience with developing and delivering presentations with Microsoft PowerPoint
• Passion for video games and technology!

QUEST ITEMS

• Resume
• Cover Letter
  • Why you are interested in working at Blizzard Entertainment
  • What technology or games you are currently playing with

Blizzard Entertainment is a global company committed to growing our employees. We offer generous benefits and perks with an eye on providing true work/life balance. We’ve worked hard to champion an intensely collaborative and creative environment, a diverse and inclusive employee culture, and training and opportunity for professional growth. Our people are everything. Our core values are real, and our mission has never changed.

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We are dedicated to creating the most epic entertainment experiences...ever. Join us!

Please submit your application no later than January 10, 2020.

ASSOCIATE SOFTWARE ENGINEER, E-COMMERCE - PURCHASE

Battle.net & Online Products•Irvine, California

The Battle.net & Online Products organization is home to 300+ superpowered engineers, program managers, and designers focused on the technology that powers Blizzard Entertainment’s games. Whether you’re playing one of our titles, chatting with friends, or just shopping online, B&OP ensures that our players are immersed in engaging, exciting, and secure experiences.

Our E-commerce team handles millions of transactions spanning the globe for the StarCraft, Diablo, World of Warcraft, Hearthstone, and Overwatch franchises. Have you ever wondered what happens when you make a purchase in Hearthstone? Are you curious about what happens during a game launch where millions of customers unlock access to games, features, upgrades, and more? We're the team that provides the platform that powers all those features and more.

As a software engineer on the E-commerce Payments team, you will a join group of engineers dedicated to building a global platform allowing our fans worldwide to experience Blizzard content. You will be a part of our continuing efforts to evolve our order acquisition and workflow orchestration systems to keep up with an ever-changing commerce landscape. You will also be exposed to the inner-workings of the online and mobile payment spaces. In this role, you will not only be writing code but be a part of projects across all phases of our SDLC.

Responsibilities

• Design and implement new features for purchasing, inventory management, and payment processing on our cross-platform e-commerce system, including integrations with second and third parties
• Collaborate with multiple development teams ensuring proper designs and integrations
• Be a part of the implementation and maintenance of our infrastructure
• Monitor, optimize, and troubleshoot performance across all tiers
• Support and improve live production service
• Perform research to acquire new knowledge necessary to perform tasks and maintain a process of technological evolution

Requirements

• A minimum of 2 years of professional software engineering experience, with a solid foundation in Java, C#, or another object-oriented language
• Proven knowledge of CS fundamentals, including algorithms, data structures, and design patterns
• Understanding of web services, messaging middleware, security, microservices, and n-tier architectures
• Previous experience developing, delivering, and maintaining APIs
• Familiarity with testing practices, including regression, acceptance, functional, and load testing
• Familiarity in data-modeling and RDBMS development practices
• Bachelor's degree in Computer Science, Mathematics, or equivalent experience

Pluses

• Experience developing for and operating large distributed systems at high scale (hundreds of servers, global deployments, tens of thousands of req/sec)
• Familiarity with the Spring Framework
• Familiarity with Docker or other container platforms
• Familiarity with AWS or other cloud platforms
• Experience with billing and payment systems
• Passion for technology and video games

ASSOCIATE SOFTWARE WEB ENGINEER, E-COMMERCE - ONLINE SHOP

Battle.net & Online Products•Irvine, California

The Battle.net & Online Products organization is home to 300+ superpowered engineers, program managers, and designers focused on the technology that powers Blizzard Entertainment’s games. Whether you’re playing one of our titles, chatting with friends, or just shopping online, B&OP ensures that our players are immersed in engaging, exciting, and secure experiences.

The Shop team is part of the Battle.net & Online Products organization and is responsible for Blizzard Entertainment’s digital shop web site and the tooling that powers it.  Our sites handle millions of transactions spanning the globe for the StarCraft, Diablo, World of Warcraft, Hearthstone, and Overwatch franchises.

Responsibilities 

• Develop new features for catalog display, purchasing, and gifting, integrating with the rest of our e-commerce team and game teams. 
• Collaborate with fellow engineers, designers, and numerous other partners to deliver quality products. 

• Learn and grow with the team, adapting to new technologies. 
• See projects through from requirement gathering to development, testing support, launch, and live coverage. 

Requirements 

• Knowledge of computer science fundamentals, including algorithms, data structures, and design patterns. 
• Familiarity with common web security practices and the reasons to use them. 

• Experience developing with object-oriented programming and client-side javascript. 
• Understanding of the browser DOM. 
• Deep understanding of web tech (HTTP, WebSockets, REST, etc.) 
• Bachelor’s Degree in Computer Science or equivalent experience 

Pluses 

• Working knowledge of Spring framework 
• Familiarity with the Angular framework or similar (Vue.js, React) 
• Experience writing unit tests and automation tests 

ASSOCIATE SOFTWARE ENGINEER

Service Technologies•Irvine, California

Blizzard Entertainment is looking for a talented software engineer to enhance our customer support applications and services.  You will collaborate with data scientists, designers, product managers, software engineers, and other partners across Blizzard to improve our customer experience both in and out of our games.

The software we build together ensures that players can enjoy our games in a positive and friendly environment and resolve any issue they encounter.  Our team loves Blizzard games and we care deeply about ensuring everyone can fully enjoy our games; if you do too, then this is the team for you!

REQUIREMENTS

• Bachelor’s degree in Computer Science or related discipline; or equivalent experience
• Passion to learn and grow as a software engineer
• Excellent verbal and written communication skills

PLUSES

• Experience developing applications or services using C#
• Experience developing applications using the Angular framework
• Experience developing and integrating with relational and document database solutions
• Experience authoring, executing and analyzing the results of unit, integration, and regression tests
• Experience with public cloud providers and private cloud solutions
• Experience with container development and orchestration solutions
• Contributions to open-source or personal projects
• Passion for video games

REQUIRED APPLICATION MATERIALS

• Resume
• Cover letter

Blizzard Entertainment is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, gender expression, national origin, protected veteran status, or any other basis protected by applicable law, and will not be discriminated against on the basis of disability.

Blizzard Entertainment is a global company committed to growing our employees along with the business. We offer generous benefits and perks with an eye on providing true work/life balance. We’ve worked hard to foster an intensely collaborative and creative environment, a diverse and inclusive employee culture, and training and opportunity for professional growth. Our people are everything. Our core values are real, and our mission has never changed. We are dedicated to creating the most epic entertainment experiences…ever. Join us!

SENIOR SOFTWARE ENGINEER (C++), GAME SERVICES - CORE

Battle.net & Online Products•Irvine, California

The Battle.net & Online Products organization is home to 300+ superpowered engineers, program managers, and designers focused on the technology that powers Blizzard Entertainment’s games. Whether you’re playing one of our titles, chatting with friends, or just shopping online, B&OP ensures that our players are immersed in engaging, exciting, and secure experiences.

Blizzard Entertainment has an opening for a talented and enthusiastic Senior Software Server Engineer to join our Battle.net Game Services Group

The Game Services Group develops the software, services and infrastructure that keeps millions of players online simultaneously worldwide, 24 hours a day, 365 days a year.  When a player logs in, sends a friend request, a whisper, or a chat within one of our rich virtual worlds, Game Services powers these capabilities.  When you use voice chat, check your profile statistics, or create a new social group, we are the team that makes those things possible.  From Overwatch to Hearthstone, StarCraft 2 to Diablo 3, World of Warcraft to Heroes, regardless of the game, time zone, or scale, Game Services is ready to answer the call with effectiveness and professionalism, acting as the central pillar to supercharge all player engagement.

Responsibilities

• Work with a small and talented team to develop scalable, highly performant platform services
• Implement new features and services to support the needs of multiple teams
• Coordinate with embedded and external teams, create relationships and assist with shared initiatives
• Participate in the ongoing effort to improve our platform infrastructure, with the goal of achieving ever-increasing service availability
• Perform research to acquire new knowledge necessary to perform assigned tasks and maintain a process of technological evolution
• Develop unit and integration test code to validate service reliability
• Mentor and educate more junior engineers on the team

Requirements

• A degree in computer science, or a related field
• A minimum of 5 years of relevant work experience
• Ability to work in a collaborative environment
• Excellent communication skills
• Advanced understanding of C++
• Prior development work on distributed systems and client/server architectures
• Strong data-structure, logic, and algorithm skills
• Knowledge of network and server security issues
• Experience with performance analysis and code optimization
• Experience with protocol and API design
• Database development experience (MySQL, Oracle, Cassandra, etc.)
• Self-motivated and able to break down complex tasks
• A desire to help make the service the best that it can be for our players

Pluses

• Proficient in at least one scripting language such as Python
• Linux development experience (server applications, gdb debugging, etc.)
• Experience with cloud/virtualization/containerization technologies and infrastructure
• Enthusiastic about supporting a live service
• Familiarity with Blizzard Entertainment games and features, at least at a casual player level

SOFTWARE ENGINEER, SERVER - MOBILE

Mobile•Irvine, California

We are looking for a talented server engineer, who is excited to architect and implement server technology at a global scale in service of amazing experiences for our players.  If you have a passion for game development and want to participate in the future of Blizzard, we would love to hear from you!  Our team values creativity, collaboration and a commitment to quality and we are searching for like-minded developers to add to our team of talented game makers.

You will help develop new systems and services for our game, coordinating across disciplines with designers and client engineers.  In addition to having the usual technical chops, we want to hear from qualified engineers who have an absolute passion for gaming, programming, and finding simple solutions to complex problems. The ideal candidate should be excited by the idea of working closely with designers to create compelling and epic features that will keep players engaged for years. We love engineering, geeking out over new technology and helping move the industry forward. Most importantly, we learn, grow, and ultimately enjoy success as a team, overcoming new challenges together.

Responsibilities

• Collaborate with other engineers to design and implement server architecture
• Design and implement server-side components in support of gameplay features
• Coordinate with client engineers on gameplay simulation and network efficiency
• Design and deliver code that is robust, extensible, flexible and simple to use
• Champion Blizzard’s core values, especially “Gameplay First”

Requirements

• Strong, proven programming skills (C#, C++, Python, .NET Core, Mono)
• Minimum of 2+ years of relevant professional experience in software development
• Expertise in relevant technologies such as SQL, Cassandra, Docker, Kubernetes, etc.
• Networking experience in TCP, UDP, and higher-level protocols
• Ability to communicate effectively with designers and client engineers
• Self-motivated and willing to participate in all areas of engineering

Pluses

• Bachelor or Master degree in Computer Science, related field or equivalent experience
• Previous experience shipping and supporting a Live product at a global scale
• Familiar with security considerations (encryption, denial of service)
• Familiar with cloud technologies (virtualization, containers)
• Developed software in a cloud-hosted environment (AWS, GCP)
• iOS and/or Android software development expertise
• Linux development experience
• Avid mobile gamer

Required Application Materials

• Resume
• Cover Letter which should include:
  • Why you are interested in working at Blizzard
  • What games you are currently playing

Blizzard Entertainment is a global company committed to growing our employees along with the business. We offer generous benefits and perks with an eye on providing true work/life balance. We’ve worked hard to foster an intensely collaborative and creative environment, a diverse and inclusive employee culture, and training and opportunity for professional growth. Our people are everything. Our core values are real, and our mission has never changed. We are dedicated to creating the most epic entertainment experiences…ever. Join us! 

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

SOFTWARE ENGINEER, MOBILE

Media Network•Irvine, California

Blizzard Entertainment is looking for a ​rockstar mobile engineer who has a passion for building epic video experiences. The ideal candidate would relish in the details with an affinity for Blizzard games and Esports. The Blizzard Media Network team is responsible for building and supporting the video experience for Android and iOS media apps across Activision/Blizzard/King. 

Responsibilities

• Focus on building front end experience supporting the standard media mobile playback experience company-wide.  
• Collaborate with product, design and dev ops in delivering epic video experiences 
• Contribute to the architecture, design, implementation, deployment, and support of mobile video experiences 
• Instrument applications to monitor for engagement, user pathing, quality of service, and performance 
• Communicate estimations/timelines/progress of individual contribution, raise risks and ensure deliverables are documented
• Contribute to app architecture/design reviews, code reviews, writing unit tests, performance tuning
• Help ideate and prototype new features 

Requirements

• Bachelor's degree in Computer Science, ​Engineering​, or equivalent experience 
• 3+ years of relevant native mobile development experience
• Demonstrated skills in Android development (Java, Kotlin)
• One or more applications successfully deployed to the Google Play Store
• Understanding of Android design principles, guidelines, and best practices
• Deep understanding of CI workflow that leverages industry-standard Android testing  and builds tools needed to deliver quality apps
• Knowledge of Exoplayer framework
• Experience working/integrating with video player solutions such as Bitmovin, Brightcove, JWPlayer,
• Drive quality and stability by writing unit, functional and performance tests with Test  Driven Development (TDD)
• Experience with debugging and profiling native applications on major devices
• Excellent analytical, problem solving and debugging skills
• Attention to detail and quality
• Effective communication skills with developers of all technical skill levels 

Pluses

• Experience in iOS development including Objective-C, Swift, Cocoa Touch, Xcode,  CocoaPods, etc. 
• Experience working with content (live, vod) encoding and transcoding
• Server-Side Ad Insertion (SSAI) technology stacks including MediaTailor, DFP, SpotX
• Understanding of video codecs and compression (H.264/AVC, H.265/HEVC) and streaming technologies (HLS, MPEG-DASH)
• Experience in Broadcast/Broadband Television, Video-On-Demand
• Experience with digital rights management
• Deep understanding of all aspects of content clipping, live video encoding, and DVR
• Experience building video experiences for OTT devices (Roku, Apple TV, Google Chromecast, Amazon Fire, etc.)
• Familiarity with HTTP/S and basic networking concepts
• Familiarity with content protection, and native security standards
• Experience with identity management (OAuth, Open ID Connect, SAML, etc.) 

Required Application Materials

• Resume
• Cover Letter

Blizzard Entertainment is a global company committed to growing our employees. We offer generous benefits and perks with an eye on providing true work/life balance. We’ve worked hard to champion an intensely collaborative and creative environment, a diverse and inclusive employee culture, and training and opportunity for professional growth. Our people are everything. Our core values are real, and our mission has never changed.

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We are dedicated to creating the most epic entertainment experiences…ever. Join us!

SENIOR SOFTWARE ENGINEER (IOS), APPLICATIONS - MOBILE

Battle.net & Online Products•Irvine, California

The Battle.net & Online Products organization is home to 300+ superpowered engineers, program managers, and designers focused on the technology that powers Blizzard Entertainment’s games. Whether you’re playing one of our titles, chatting with friends, or just shopping online, B&OP ensures that our players are immersed in engaging, exciting, and secure experiences.

Are you passionate about iOS development? Would connecting millions of the world’s most passionate gamers together fill you with pride? Would you like to provide the social gateway to a vibrant and thriving online gaming community?

If your brain is screaming “yes!” to these questions, then the Batttle.net and Online Products Applications group is calling for you to join our Mobile team.

Our Mobile team develops Blizzard quality mobile applications for both iOS and Android.  The primary mission of our Batttle.net and Online Products Mobile team is to allow players to enjoy the Blizzard community wherever they may be. We develop the Blizzard Mobile Authenticator, BlizzCon and Battle.net Mobile App. Our flagship app (Battle.net Mobile) presents a place where millions of players worldwide can socialize with their friends and fellow gamers.

As one of our Senior Software Engineers, you must be able to work both independently and with team members across engineering, program management, and design. Enthusiasm and a drive for crafting epic social and content experiences is a must. If the idea of reaching millions of players across the globe appeals to your inner geek, then our team may just be the place for you.

Requirements

• Mastery of Swift development with a minimum of 5 years’ professional experience
• Experience using iOS UI frameworks and building custom UI
• Excellent verbal and written communication skills
• Bachelor’s Degree in Computer Science or equivalent experience

Pluses

• Fluency with RxSwift and Reactive patterns strongly desired
• Objective-C programming experience
• Familiarity with networking and high levels protocols such as HTTP or XMPP
• Experience working with 3rd party libraries (RxSwift, Alamofire, Protobuf, etc…)
• Experience with shipping and maintaining mobile applications that support a global user base
• Experience supporting a live product (ship planning, risk mitigation, etc..)
• Agile development experience
• Passion for video games or social networks

SOUND EDITOR - TEMP

Sound•Irvine, California

Blizzard Entertainment's Sound Department is looking for an exceptional Sound Editor to work on various Blizzard Entertainment properties. You’ll need to have extensive experience in linear post-production editorial as well as some mixing and live sound recording background. You will work closely with various departments and with the Lead Sound Supervisor on various video and cinematic projects, performing dialog, foley, and FX editing, assisting with live sound recording on video shoots, and occasional mixing.  You should also have a firm grasp on troubleshooting signal flow, creating and modifying Pro Tools templates and operating digital mixing consoles. You should be comfortable working under tight deadlines, have excellent organizational and time management skills, readily accept direction and criticism, adapt easily to shifting priorities, and be capable of working with equal effectiveness, both solo and as part of a project team. Requirements

• Experience editing and conforming sound-to-picture
• Proficient in the editing dialog
• Experience working with commercial audio tools on multiple platforms and workstations, including Pro Tools
• Able to work with equal effectiveness, both solo and as part of a project team
• Experience using and troubleshooting Windows and Macintosh operating systems
• Proven skills with live audio mixing and microphone placement
• Experience with digital mixing consoles
• Experience with the live recording (with emphasis on dialog recording)
• Experience with post-production audio mixing and mastering techniques

 Pluses

• Mixing/recording experience in a broadcast environment
• Experience with field recording equipment and techniques

Required Application Materials: Resume (PDF Preferred) Job Status: Temporary Primary Location: United States-CA-Irvine Blizzard Entertainment is a global company committed to growing our employees along with the business. We offer generous benefits and perks with an eye on providing true work/life balance. We’ve worked hard to foster an intensely collaborative and creative environment, a diverse and inclusive employee culture, and training and opportunity for professional growth. Our people are everything. Our core values are real, and our mission has never changed. We are dedicated to creating the most epic entertainment experiences…ever. Join us!

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

SENIOR SOFTWARE ENGINEER, APPLICATION SECURITY

Security•Austin, Texas

We are looking for a talented and motivated Senior Software Engineer specializing in Application Security to join its ranks in Austin, TX.

If this is you, you are a Software Engineer with a deep passion for security. This person should have experience with a variety of systems, languages, tools, and architectures.

About Our Team

The Security Development (SecDev) organization owns all security-related software development efforts across Blizzard. We are a mix of hardcore software engineers, server developers and security-minded individuals who work together to ensure that our company and players are safe from Evil.

We work with all games and software platforms produced by Blizzard, so we get exposed to a mind-boggling array of technologies – not only security technologies but graphic engines, machine learning systems, web servers, databases, etc. It’s an incredible place for passionate software developers to get acquainted with all aspects of developing world-class videogames.

You will be part of SecDev’s Application Security team. You will embed yourself in one or more of Blizzard’s teams to help them build secure software. Not only you will help find and fix existing vulnerabilities, but also actively participate in preventing new vulnerabilities from being introduced. You will also have the opportunity to work with elite software engineers to solve specific hard security problems, “tiger team” style.

Responsibilities

• Perform security reviews and provide insights throughout all phases of software development with our partner teams.
• Develop and enhance new and existing security-focused tools, systems and services.
• Evaluate the impact on the organization of current security advisories, publications, and trends.
• Develop new security solutions/tools to prevent security vulnerabilities and assist in addressing existing security problems.
• Help detect, highlight, and close security vulnerabilities that surface during the software development lifecycle.

Requirements

• Bachelor’s Degree in Computer Science or equivalent experience.
• A minimum of 5 years of experience in secure Web Application development.
• Expert knowledge of security problems associated with modern web languages and frameworks, including but not limited to Javascript (front and backend), Java and C#.
• Knowledge of penetration testing techniques, application security vulnerabilities, OWASP Top 10, SANS 25, CWE, and Whitebox exploitation via Burpsuite/Fiddler or similar.
• Excellent ability to discover and demonstrate flaws such as SQL injection, XSS, and CSRF.
• Solid understanding of database (SQL, NoSQL) security.
• Knowledge of encryption theory and practice (e.g. TLS, HMAC, RSA, AES, PKI) .
• Be able to adapt and be entrepreneurial and solve problems quickly, creatively, and collaboratively.

Pluses

• Aptitude for doing threat modeling on complex systems.
• Experience in Mobile software development and security.
• Experience with application security testing tools (static and dynamic code analyzers, fuzzing tools).
• Enjoys tasty beverages – we work hard and play hard.

Blizzard Entertainment is a global company committed to growing our employees along with the business. We offer generous benefits and perks with an eye on providing true work/life balance. We’ve worked hard to foster an intensely collaborative and creative environment, a diverse and inclusive employee culture, and training and opportunity for professional growth. Our people are everything. Our core values are real, and our mission has never changed. We are dedicated to creating the most epic entertainment experiences…ever. Join us!