LocalStack Pro provides a local version of the SageMaker API, which allows running jobs to create machine learning models (e.g., using TensorFlow).
A basic example using the
sagemaker.tensorflow.TensorFlow class is provided in this Github repository. Essentially, the code boils down to these core lines:
inputs = ... # load training data files mnist_estimator = TensorFlow(entry_point='mnist.py', role='arn:aws:...', framework_version='1.12.0', sagemaker_session=sagemaker_session, train_instance_count=1, training_steps=10, evaluation_steps=10) mnist_estimator.fit(inputs, logs=False)
The code snippet above uploads the model training code to local S3, submits a new training job to the local SageMaker API, and finally puts the trained model back to an output S3 bucket. Please refer to the sample repo for more details.
Note: SageMaker is a fairly comprehensive API - for now, only a subset of the functionality is provided locally, but new features are being added on a regular basis.