| from pyspark import SparkContext | |
| import numpy as np | |
| from sklearn.cross_validation import train_test_split, Bootstrap | |
| from sklearn.datasets import make_classification | |
| from sklearn.metrics import accuracy_score | |
| from sklearn.tree import DecisionTreeClassifier | |
| def run(sc): |
| #!/bin/sh | |
| set -e | |
| HOST=localhost | |
| DB=test-entd-products | |
| COL=asimproducts | |
| S3PATH="s3://mongodb-backups-test1-entd/$DB/$COL/" | |
| S3BACKUP=$S3PATH`date +"%Y%m%d_%H%M%S"`.dump.gz | |
| S3LATEST=$S3PATH"latest".dump.gz | |
| /usr/bin/aws s3 mb $S3PATH |
The root filesystem is rootfs, which is stored in memory and therefore wiped on reboot. The Micro SD card is mounted at /tmp/fuse_d/. Something is also mounted at /tmp/fuse_a and /tmp/fuse_z.
| #!/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 |
