pacarvalho

from gcapi import GCapiClient # From https://github.com/rickykim93/gcapi-python
from gcapi.gcapi_tools import format_date # From https://github.com/rickykim93/gcapi-python
from datetime import datetime
from time import sleep
import numpy

def check_sell_criteria(prices):
  # TODO: Write your algorithm to detect sell condition here
  return False

pacarvalho

name: SLURM Enqueue Workflow

on: 
  push: 
    branches:
      - 'master'

jobs: 
  enqueue:
    runs-on: ubuntu-latest

pacarvalho

# Library used for training of the model
# NOTE: We require the environment variable SURPRISE_DATA_FOLDER to designate the
#       the directory used by Surprise. Else it will err due to the default Lambda home
#       directory being read only.
from surprise import SVD
from surprise import Dataset
from surprise import Reader
import os  # Get environment variables
import psycopg2  # PSQL connector library
import csv  # Format we will export ratings as

pacarvalho

AWSTemplateFormatVersion: "2010-09-09"
Transform: AWS::Serverless-2016-10-31
Description: >
  collaborative-filtering

  SAM project for collaborative-filtering
  Imports data from SQL and exports results to S3

Parameters:
  RdsPassword:

pacarvalho

(...)
    print('Making predictions for an unseen user...')
    items_to_rate = [trainset.to_raw_iid(i) for i in trainset.all_items()]

    # We will consider this user to be ID 0 since that is not a valid ID for our real users
    user_id = 0

    # Make predictions
    predictions = []
    for iid in items_to_rate:

pacarvalho

const childProcess = require('child_process'),
	spawnPromise = function (command, argsarray, envOptions) {
		return new Promise((resolve, reject) => {
			console.log('executing', command, argsarray.join(' '));
      
			const childProc = childProcess.spawn(command, argsarray, envOptions || {env: process.env, cwd: process.cwd()})
      const resultBuffers = [];
      
			childProc.stdout.on('data', buffer => {
				console.log(buffer.toString());

pacarvalho

/*global module, require, Promise, console */

const aws = require("aws-sdk");
const fs = require("fs");
const s3 = new aws.S3();

const downloadFileFromS3 = function (bucket, fileKey, filePath) {
  "use strict";
  console.log("Downloading", bucket, fileKey, filePath);
  return new Promise(function (resolve, reject) {

pacarvalho

const s3Util = require("./s3-util");
const childProcessPromise = require("./child-process-promise");
const path = require("path");
const os = require("os");
const fs = require("fs");
const https = require("https");

const OUTPUT_BUCKET = process.env.OUTPUT_BUCKET;
const VIDEO_MIME_TYPE = process.env.VIDEO_MIME_TYPE;
const LAMBDA_SHARED_SECRET = process.env.LAMBDA_SHARED_SECRET;

pacarvalho

AWSTemplateFormatVersion: "2010-09-09"
Transform: AWS::Serverless-2016-10-31
Description: >
  SAM project for Medium article about uploading and processing videos

Parameters:
  EnvironmentValue:
    AllowedValues:
      - "staging"
      - "production"

pacarvalho

class Api::VideosController < Api::ApplicationController
  skip_before_action :authenticate_user, only: %i[derivative]
  # POST /api/videos
  def create
    @video = current_user.videos.new
    if @video.save
      render json: VideoSerializer.render(@video, view: :with_upload_details), status: :ok
    else
      render json: @video.errors, status: :bad_request
    end