jorgemf

# docker build --pull -t tf/tensorflow-serving --label 1.6 -f Dockerfile .
# export TF_SERVING_PORT=9000
# export TF_SERVING_MODEL_PATH=/tf_models/mymodel
# export CONTAINER_NAME=tf_serving_1_6
# CUDA_VISIBLE_DEVICES=0 docker run --runtime=nvidia -it -p $TF_SERVING_PORT:$TF_SERVING_PORT -v $TF_SERVING_MODEL_PATH:/root/tf_model --name $CONTAINER_NAME tf/tensorflow-serving /usr/local/bin/tensorflow_model_server --port=$TF_SERVING_PORT --enable_batching=true --model_base_path=/root/tf_model/
# docker start -ai $CONTAINER_NAME

FROM nvidia/cuda:9.0-cudnn7-devel-ubuntu16.04

hyukkwonepic

# -*- coding: utf-8 -*- 
from flask import Flask, request, jsonify # flask 사용
import json
import requests # requests 사용
app = Flask(__name__)

def validate(imp_uid):
    # 1. 인증 토큰 발급 받기
    access_token = ""
    try:

kkweon

나만의 Docker Reference

• Docker 타겟유저
  • 빌드 문제
  • DB사용
  • 배포
  • Scalability
• Docker와 함께하는 개발 workflow
• Docker 연습 환경

goodvc78

jbochi

avloss

TensorFlow Serving in 10 minutes!

TensorFlow SERVING is Googles' recommended way to deploy TensorFlow models. Without proper computer engineering background, it can be quite intimidating, even for people who feel comfortable with TensorFlow itself. Few things that I've found particularly hard were:

• Tutorial examples have C++ code (which I don't know)
• Tutorials have Kubernetes, gRPG, Bezel (some of which I saw for the first time)
• It needs to be compiled. That process takes forever!

After all, it worked just fine. Here I present an easiest possible way to deploy your models with TensorFlow Serving. You will have your self-built model running inside TF-Serving by the end of this tutorial. It will be scalable, and you will be able to query it via REST.

savelee

  #standardSQL
  #Top two Hacker News articles by day
  WITH TitlesAndScores AS (
  SELECT
    ARRAY_AGG(STRUCT(title,
        score)) AS titles,
    EXTRACT(DATE
    FROM
      time_ts) AS date
  FROM

bernhardschaefer

#!/bin/bash

# Minimum TODOs on a per job basis:
# 1. define name, application jar path, main class, queue and log4j-yarn.properties path
# 2. remove properties not applicable to your Spark version (Spark 1.x vs. Spark 2.x)
# 3. tweak num_executors, executor_memory (+ overhead), and backpressure settings

# the two most important settings:
num_executors=6
executor_memory=3g

rex-lin

SELECT
  a.date,
  'iOS' AS platform,
  COUNT(DISTINCT a.fullVisitorId) AS day0,
  COUNT(DISTINCT CASE WHEN a.date = date_add(b.date, INTERVAL -1 DAY) THEN b.fullVisitorId ELSE NULL END) AS day1,
  COUNT(DISTINCT CASE WHEN a.date = date_add(b.date, INTERVAL -7 DAY) THEN b.fullVisitorId ELSE NULL END) AS day7,
  COUNT(DISTINCT CASE WHEN a.date = date_add(b.date, INTERVAL -14 DAY) THEN b.fullVisitorId ELSE NULL END) AS day14,
  COUNT(DISTINCT CASE WHEN a.date = date_add(b.date, INTERVAL -30 DAY) THEN b.fullVisitorId ELSE NULL END) AS day30,
  COUNT(DISTINCT CASE WHEN a.date = date_add(b.date, INTERVAL -60 DAY) THEN b.fullVisitorId ELSE NULL END) AS day60,
  COUNT(DISTINCT CASE WHEN a.date = date_add(b.date, INTERVAL -90 DAY) THEN b.fullVisitorId ELSE NULL END) AS day90

armgilles