awslabs/aws-ai-bootcamp-labs

Name: aws-ai-bootcamp-labs

Owner: Amazon Web Services - Labs

Owner: AWS Samples

Description: This library holds a collection of Notebooks and code examples for AWS AI Bootcamps.

Created: 2017-09-15 17:30:25.0

Updated: 2018-01-07 05:53:47.0

Pushed: 2017-12-20 10:47:11.0

Homepage: null

Size: 546

Language: Jupyter Notebook

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README

AWS-AI-Bootcamp-Labs

This library has a collection of Notebooks and code examples for AWS AI Bootcamps.

Content

MNIST MXNet Demo Notebook

Amazon Lex Demo Notebook

Amazon Polly Demo Notebook

Amazon Rekognition Demo Notebook

Amazon Comprehend Demo Notebook

Amazon Machine Learning Demo Notebook

Serverless Predictions at Scale

Launch EC2 instance using the deep learning AMI and open fashion MNIST MXNet demo
  1. Create EC2 IAM role for the workshop as described here. We will apply permission policies as documented in each notebook
  2. Launch EC2 Instance using the Ubuntu deep learning AMI in eu-west-1, Ireland (p2.xlarge - $0.972/hour) http://amzn.to/2j3FdOZ
  3. Connect via SSH and tunnel port 8888:
    • Linux, Mac:
      • ssh -i user.pem -L 8888:localhost:8888 ubuntu@ec2-ip-ip-ip-ip.region.compute.amazonaws.com
    • Windows:
      • Follow the instructions here to download PuTTY and to convert your private key
      • Host Name: ubuntu@ec2-ip-ip-ip-ip.region.compute.amazonaws.com
      • Expand Connection and choose Auth, select your .ppk file
      • Expand Connection > SSH, choose Tunnels, specify Source Port: 8888, Destination: localhost:8888
      • Choose Add and Open
  4. Clone aws-ai-bootcamp-labs github repository git clone https://github.com/awslabs/aws-ai-bootcamp-labs
  5. Start jupyter notebook: nohup jupyter notebook &
  6. tail nohup.out to get the login token
    • look for http://localhost:8888/?token=<your_login_token>
  7. Open demo notebook (FashionMNIST_MXNet_Demo.ipynb)
    • Select Kernel > Change kernel > Python 2
  8. Follow steps in notebook
This work is supported by the National Institutes of Health's National Center for Advancing Translational Sciences, Grant Number U24TR002306. This work is solely the responsibility of the creators and does not necessarily represent the official views of the National Institutes of Health.