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#!/bin/bash

apt update
apt install -y make build-essential libssl-dev zlib1g-dev libbz2-dev \
  libreadline-dev libsqlite3-dev wget curl llvm libncurses5-dev libncursesw5-dev \
  xz-utils tk-dev libffi-dev liblzma-dev python-openssl git

curl https://pyenv.run | bash

# Load pyenv automatically by adding

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with open('/tmp/sylviabeach-card-images.txt') as found_images:
    urls = found_images.read().split('\n')
    identifiers = {}
    for u in urls:
        identifier = '/'.join(u.split('/')[-3:])
        identifiers[identifier] = ''


with open('/tmp/pudl0123-825298-noboxes-sorted.txt') as image_list:

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# use env to set django settings module
# env DJANGO_SETTINGS_MODULE=mep.settings python mep_cardholders.py

import csv
import codecs


import django
from django.db import models

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library(tidyr)
library(dplyr)
library(UpSetR)

# read in the data
ppa <- read.csv("ppa-digitizedworks-20190419T18_24_42.csv")

# subset to only needed rows and split Collection into multiple rows
new_df <- ppa %>% select("Title", "Source.ID", "Collection") %>% separate_rows("Collection", sep=";")
# give a truth column value to map on spread

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// path and webpack
const path = require('path')
const webpack = require('webpack')

// webpack plugins
const BundleTracker = require('webpack-bundle-tracker')
const VueLoaderPlugin = require('vue-loader/lib/plugin')
const MiniCssExtractPlugin = require('mini-css-extract-plugin')

// configure dev mode because of absurdly misleading webpack documentation

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const webpackConf = require('./webpack.config.js')
const merge = require('webpack-merge')

module.exports = function (config) {
  config.set({
    browsers: [ 'ChromeHeadless' ], //run in Chrome
    browserNoActivityTimeout: 60000,
    frameworks: [ 'mocha', 'sinon-chai' ], //use the mocha test framework
    files: [
      //'src/**/*.{jsx,js}',

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#!/bin/bash
#SBATCH --gres=gpu:1 
#one GPU, asking for only one CPU core since we're offloading to GPU
#SBATCH -t 00:05:00
#SBATCH --mail-type=begin
#SBATCH --mail-type=end
#SBATCH [email protected]

module load anaconda3
# assumes you have a conda environment named tf with tensorflow and matplotlib, i.e.

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import pickle
from tensorflow import keras

# Compute nodes don't have internet, so download in advance and pickle the
# image training data

# adapted from https://www.tensorflow.org/tutorials/keras/basic_classification
# see notes there for how to actually work things

fashion_mnist = keras.datasets.fashion_mnist

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import pickle

import tensorflow as tf
from tensorflow import keras

import numpy as np

import matplotlib.pyplot as plt

# reload pickled data