Train an TensorFlow model with a SageMaker Training Job and track it using SageMaker Experiments

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.

This notebook shows how you can use the SageMaker SDK to track a Machine Learning experiment.

We introduce two concepts in this notebook -

• Experiment: An experiment is a collection of runs. When you initialize a run in your training loop, you include the name of the experiment that the run belongs to. Experiment names must be unique within your AWS account.
• Run: A run consists of all the inputs, parameters, configurations, and results for one iteration of model training. Initialize an experiment run for tracking a training job with Run().

In this notebook we train a Keras model using the MNIST dataset on a remote SageMaker instance using a training job.

Prepare the training script

Here we use a SageMaker Training job to train the model on a remote instance.

Create an Experiment and launch a training job

Register the trained model in the Model Registry

This is an optional step users can take if they want to keep track of their models in a central model catalog.

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.