pmbaumgartner

from contextlib import closing
from io import StringIO
from time import time

import pandas as pd
from django.core.management.base import BaseCommand
from django.db import transaction
from faker import Faker

from core.models import Thing

pmbaumgartner

#!/bin/sh

# use this to get predictions on a test.csv located in BERT_DATA_DIR

export OUTCOME={classification_task_name}
export NOTEBOOK=/notebooks # don't change me

docker run --runtime=nvidia -it --rm \
  -v $(pwd):$NOTEBOOK/ \
  -e "BERT_BASE_DIR=$NOTEBOOK/uncased_L-12_H-768_A-12" \

pmbaumgartner

pmbaumgartner

pmbaumgartner

import gensim.downloader as gensim_api
import umap
import requests
import pandas as pd
from numpy import log10

import plotly
import plotly.graph_objs as go

w2v_model = gensim_api.load('word2vec-google-news-300')

pmbaumgartner

redis: redis-server
celery_worker: celery -A tasks worker -l info

pmbaumgartner

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Github Commits - Forecast Components\n",
    "\n",
    "Data from [BigQuery Github Dataset](https://cloud.google.com/bigquery/public-data/github) using the following query:\n",
    "\n",

pmbaumgartner

pmbaumgartner

pmbaumgartner

import random
from string import ascii_uppercase

def get_difficulty():

    difficulty_length = {'easy': (4, 6),
                         'medium': (6, 8),
                         'hard': (8, 20)
                         }