h2oai/h2o3-sagemaker

Name: h2o3-sagemaker

Owner: H2O.ai

Description: Integrating H2O-3 AutoML with Amazon Sagemaker

Created: 2018-03-13 01:56:59.0

Updated: 2018-05-18 18:13:06.0

Pushed: 2018-05-18 18:13:05.0

Homepage: null

Size: 56

Language: Jupyter Notebook

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README

H2O 3 and AWS SageMaker Integration

Proof-of-Concept for integrating H2O-3 AutoML with Amazon SageMaker

Content of Repo, See below for explanation of files:

• README.md
• Dockerfile
• hyperparameters.json
• sample_sagemaker_notebook.ipynb
• automl_scripts
• train
• predictor.py
• nginx.conf
• serve
• wsgi.py

Dockerfile

Used to build the docker image that AWS SageMaker will use for model training purposes

hyperparameters.json

Editable. Contains three nested dictionaries which will be ingested and used during training

1. “training” –> will be used to pass along settings such as whether or not to train as a classification problem
2. “h2o” –> dictionary of all keyword arguments for h2o.init()
3. “aml” –> dictionary of all keyword arguments for H2OAutoML()

sample_sagemaker_notebook

Example of what a jupyter notebook might look like within the AWS SageMaker notebook instance

automl_scripts

The backend code that tells AWS SageMaker what it is expected to do.

files:

• nginx.conf, serve, and wsgi.py do not need to be changed. They are backend code required by AWS SageMaker to build properly
• train and predictor.py likely do not need to be edited. Any arguments you would want to pass are available in hyperparameters.json

To Deploy:

1. Create an S3 Bucket with “sagemaker” somewhere in the name. SageMaker will be able to access it in order to pull train/test data
2. Create an image repository with AWS ECS
3. Build a docker image using `docker build -t <image name here> -f Dockerfile .`
4. Tag the image appropriately for your AWS ECS repo `docker tag <image> <new image tag>`
5. Push the image to your AWS ECS repo `docker push <image>`
6. Follow steps in sample_sagemaker_notebook.ipynb