Introduction to JumpStart - Machine Translation

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.

Welcome to Amazon SageMaker JumpStart! You can use JumpStart to solve many Machine Learning tasks through one-click in SageMaker Studio, or through SageMaker JumpStart API.

In this demo notebook, we demonstrate how to use the JumpStart API for Machine Translation. Machine Translation is the task of translating text from one language to another. Here, we show how to use state-of-the-art pre-trained T5 models for translating text from English to German.

1. Set Up
2. Select a model
3. Retrieve JumpStart Artifacts & Deploy an Endpoint
4. Query endpoint and parse response
5. Clean up the endpoint

Note: This notebook was tested on ml.t3.medium instance in Amazon SageMaker Studio with Python 3 (Data Science) kernel and in Amazon SageMaker Notebook instance with conda_python3 kernel.

1. Set Up

Before executing the notebook, there are some initial steps required for set up. This notebook requires latest version of sagemaker and ipywidgets

Permissions and environment variables

To host on Amazon SageMaker, we need to set up and authenticate the use of AWS services. Here, we use the execution role associated with the current notebook as the AWS account role with SageMaker access.

2. Select a model

Here, we download jumpstart model_manifest file from the jumpstart s3 bucket, filter-out all the Machine Translation models and select a model for inference.

Chose a model for Inference

Different models are trained on different input and output languages. Default huggingface-translation-t5-base model translates text from English to German.

3. Retrieve JumpStart Artifacts & Deploy an Endpoint

Using JumpStart, we can perform inference on the pre-trained model, even without fine-tuning it first on a new dataset. We start by retrieving the deploy_image_uri, deploy_source_uri, and model_uri for the pre-trained model. To host the pre-trained model, we create an instance of sagemaker.model.Model and deploy it. This may take a few minutes.

4. Query endpoint and parse response

Input to the endpoint is any string of text dumped in json and encoded in utf-8 format. Output of the endpoint is a json with translated text.

Below, we put in some example input text. You can put in any text and the model will translate the text.

5. Clean up the endpoint

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.