This gist contains lists of modules available in
in AWS Lambda.
It also contains the code to run in Lambda to generate these lists. In addition there
is a less_versbose module in the code that you can call to get a list of the top
level modules installed and the version of those modules (if they contain a version
| '''This script goes along the blog post | |
| "Building powerful image classification models using very little data" | |
| from blog.keras.io. | |
| It uses data that can be downloaded at: | |
| https://www.kaggle.com/c/dogs-vs-cats/data | |
| In our setup, we: | |
| - created a data/ folder | |
| - created train/ and validation/ subfolders inside data/ | |
| - created cats/ and dogs/ subfolders inside train/ and validation/ | |
| - put the cat pictures index 0-999 in data/train/cats |
| import cPickle as c | |
| import os | |
| from sklearn import * | |
| from collections import Counter | |
| def load(clf_file): | |
| with open(clf_file) as fp: | |
| clf = c.load(fp) | |
| return clf |
| #!/bin/sh | |
| # Make sure to: | |
| # 1) Name this file `backup.sh` and place it in /home/ubuntu | |
| # 2) Run sudo apt-get install awscli to install the AWSCLI | |
| # 3) Run aws configure (enter s3-authorized IAM user and specify region) | |
| # 4) Fill in DB host + name | |
| # 5) Create S3 bucket for the backups and fill it in below (set a lifecycle rule to expire files older than X days in the bucket) | |
| # 6) Run chmod +x backup.sh | |
| # 7) Test it out via ./backup.sh |
| <?php | |
| $attributeValues = array( | |
| 'color' => array('Red', 'White', 'Blue'), | |
| 'size' => array(1, 2, 3, 4), | |
| 'fabric' => array('Cloth', 'Silk') | |
| ); | |
| class Cartesian | |
| { |
| #!/usr/bin/env python | |
| import sys | |
| import pandas as pd | |
| import pymongo | |
| import json | |
| def import_content(filepath): | |
| mng_client = pymongo.MongoClient('localhost', 27017) |
| # List unique values in a DataFrame column | |
| # h/t @makmanalp for the updated syntax! | |
| df['Column Name'].unique() | |
| # Convert Series datatype to numeric (will error if column has non-numeric values) | |
| # h/t @makmanalp | |
| pd.to_numeric(df['Column Name']) | |
| # Convert Series datatype to numeric, changing non-numeric values to NaN | |
| # h/t @makmanalp for the updated syntax! |
| # This is just a cheat sheet: | |
| # On production | |
| sudo -u postgres pg_dump database | gzip -9 > database.sql.gz | |
| # On local | |
| scp -C production:~/database.sql.gz | |
| dropdb database && createdb database | |
| gunzip < database.sql.gz | psql database |