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$ curl -X PUT http://XXX.XXX.XXX.XXX:XXXX/ \
     -H 'Content-Type: application/json' \
     -d '{"prompt": "this movie was sucks!"}' | jq

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; ~/.config/nvtop/interface.ini
; Please do not edit this file.
; The file is automatically generated and modified by nvtop by pressing F12.
; If you wish to modify an option, use nvtop's setup window (F2) and follow up by saving the preference (F12).
[GeneralOption]
UseColor = true
UpdateInterval = 1000
ShowInfoMessages = true

[HeaderOption]

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CONTAINER_ID=$(docker ps --format '{{.Names}}')
docker exec $CONTAINER_ID /usr/sbin/service ssh start

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import { serve } from "https://deno.land/std@0.95.0/http/server.ts";

const s = serve({ port: 8000 });
console.log("http://localhost:8000/");

for await (const req of s) {
  req.respond({ body: "Hello World\n" });
}

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// References: https://cloud.google.com/vision/docs/detecting-faces#vision_face_detection_gcs-drest
package main

import (
	vision "cloud.google.com/go/vision/apiv1"
	"fmt"
	"golang.org/x/net/context"
	"google.golang.org/api/option"
	pb "google.golang.org/genproto/googleapis/cloud/vision/v1"
	"io"

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FROM ubuntu:18.04

# Install dependencies
RUN apt-get update && \
 apt-get -y install apache2

# Install apache and write hello world message
RUN echo 'Hello World!' > /var/www/html/index.html

# Configure apache

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import google.auth
from google.cloud import bigquery
from google.cloud import bigquery_storage_v1beta1


# Explicitly create a credentials object. This allows you to use the same
# credentials for both the BigQuery and BigQuery Storage clients, avoiding
# unnecessary API calls to fetch duplicate authentication tokens.
def bigquery_auth(project_id: str = 'edith-xxx') -> None:
    logging.info('[AUTH] Create a credentials.')

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declare TARGET="ec2-XXXX.ap-northeast-2.compute.amazonaws.com"

readonly VERSION=$(date '+%y.%m.%d')  # Today's yy.mm.dd ubuntu version style.
readonly IMAGE_NAME=edith/edith-XXXX
readonly ECR_REGISTRY=09960362XXXX.dkr.ecr.ap-northeast-2.amazonaws.com/edith/edith-XXXX
readonly AWS_OTP=$(aws ecr get-login-password --region ap-northeast-2)

# Build
docker build -t ${IMAGE_NAME} .

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# %%
import json

datas = []

# Process KorQuAD's training data.
# Counts: 60,407
for i in range(0, 1):
    in_file = '/Users/likejazz/Desktop/korquad'
    # in_file += '/korquad2.0_train_0' + str(i) + '.json'

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import time
import redis

for i in range(10):
    start_time = time.time()
    try:
        conn = redis.StrictRedis(
            host='xxx',
            port=6379,
            db=0)