AutoGluon-Tabular in AWS Marketplace

This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.

AutoGluon automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy deep learning models on tabular, image, and text data. This notebook shows how to use AutoGluon-Tabular in AWS Marketplace.

Contents:

• Step 1: Subscribe to AutoML algorithm from AWS Marketplace
• Step 2: Set up environment
• Step 3: Prepare and upload data
• Step 4: Train a model
• Step 5: Deploy the model and perform a real-time inference
• Step 6: Use Batch Transform
• Step 7: Clean-up

Step 1: Subscribe to AutoML algorithm from AWS Marketplace

1. Open AutoGluon-Tabular listing from AWS Marketplace
2. Read the Highlights section and then product overview section of the listing.
3. View usage information and then additional resources.
4. Note the supported instance types and specify the same in the following cell.
5. Next, click on Continue to subscribe.
6. Review End user license agreement, support terms, as well as pricing information.
7. Next, "Accept Offer" button needs to be clicked only if your organization agrees with EULA, pricing information as well as support terms. Once Accept offer button has been clicked, specify compatible training and inference types you wish to use.

Notes:

1. If Continue to configuration button is active, it means your account already has a subscription to this listing.
2. Once you click on Continue to configuration button and then choose region, you will see that a product ARN will appear. This is the algorithm ARN that you need to specify in your training job. However, for this notebook, the algorithm ARN has been specified in src/algorithm_arns.py file and you do not need to specify the same explicitly.

Step 2 : Set up environment

Step 3: Get the data

In this example we'll use the [1] UCI Machine Learning Repository: Adult Data Set to build a binary classification model that predicts whether customers will accept or decline a marketing offer.
First we'll download the data and split it into train and test sets. AutoGluon does not require a separate validation set (it uses bagged k-fold cross-validation).

[1] Dua, D. and Graff, C. (2019). UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science.

Check the data

Upload the data to s3

Step 4: Train a model

Next, let us train a model.

Note: Depending on how many underlying models are trained, train_volume_size may need to be increased so that they all fit on disk.

Step 5: Deploy the model and perform a real-time inference

Deploy a remote endpoint

Predict on unlabeled test data

Predict on data that includes label column

Prediction performance metrics will be printed to endpoint logs.

Check that classification performance metrics match evaluation printed to endpoint logs as expected

Step 6: Use Batch Transform

By including the label column in the test data, you can also evaluate prediction performance (In this case, passing test_s3_path instead of X_test_s3_path).

Step 7: Clean-up

Once you have finished performing predictions, you can delete the endpoint to avoid getting charged for the same.

Finally, if the AWS Marketplace subscription was created just for the experiment and you would like to unsubscribe to the product, here are the steps that can be followed. Before you cancel the subscription, ensure that you do not have any deployable model created from the model-package or using the algorithm. Note - You can find this by looking at container associated with the model.

Steps to un-subscribe to product from AWS Marketplace:

1. Navigate to Machine Learning tab on Your Software subscriptions page
2. Locate the listing that you would need to cancel subscription for, and then Cancel Subscription can be clicked to cancel the subscription.

Notebook CI Test Results

This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.