l1m2p3

import csv
import sys
from dynamo_access import put_words, get_words

def get_frequent_words(dataset , numOfWords):
	pairs = []
	with open(dataset, 'rb') as csvfile:
		reader = csv.reader(csvfile, delimiter=',')
		next(reader, None)
		for row in reader:

l1m2p3

import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F

words_dim = 300
input_channel = 1
output_channel = 100
dropout_rate = 0.5

l1m2p3

import boto3
import json
from datetime import datetime
from pytz import timezone


lambda_client = boto3.client('lambda')

print('invoking')

l1m2p3

import os
import boto3
import json
import base64
from datetime import datetime
from pytz import timezone
import sys


lambda_client = boto3.client('lambda')

l1m2p3

import boto3
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import json

# change this to use other models
from SimpleModel import SimpleModel

l1m2p3

sudo yum -y update
sudo yum install -y gcc zlib zlib-devel openssl openssl-devel git make automake gcc-c++ kernel-devel

# cmake 3.6.2
cd
wget https://cmake.org/files/v3.6/cmake-3.6.2.tar.gz
tar -zxvf cmake-3.6.2.tar.gz
cd cmake-3.6.2
sudo ./bootstrap --prefix=/usr/local
sudo make

l1m2p3

import boto3
import numpy
import pickle
import spacy

table_name = 'wordvec' # table name on DynamoDB

# batch size specified by DynamoDB. See DynamoDB's doc for more details
write_batch_size = 25
read_batch_size = 100

l1m2p3

import torch
import numpy
import pickle

# change this to your own files' name
dataset_file = 'word2vec.sst-1.pt' # file to load from
wordindex_file = 'wordindex.pkl' # file to save to
indexvec_file = 'indexvec.npy' # file to save to