How to save models trained locally in Amazon SageMaker?

Question

I'm trying to use a local training job in SageMaker.

Following this AWS notebook (https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker-python-sdk/mxnet_gluon_mnist/mxnet_mnist_with_gluon_local_mode.ipynb) I was able to train and predict locally.

There is any way to train locally and save the trained model in the Amazon SageMaker Training Job section? Otherwise, how can I properly save trained models I trained using local mode?

Answer 1

There is no way to have your local mode training jobs appear in the AWS console. The intent of local mode is to allow for faster iteration/debugging before using SageMaker for training your model.

You can create SageMaker Models from local model artifacts. Compress your model artifacts into a .tar.gz file, upload that file to S3, and then create the Model (with the SDK or in the console).

Documentation:

• https://sagemaker.readthedocs.io/en/stable/overview.html#using-models-trained-outside-of-amazon-sagemaker
• https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateModel.html

Answer 2

As @lauren said, just compress it and creates your model. Once you local trained it, you don’t have to save it as a training job since you already have the artifacts for a model.

Training jobs are a combination of input_location, output_location, chosen algorithm, and hyperparameters. That’s what is saved on a training job and not a trained model. When a training job completes, it actually compress the artifacts and save your model in Amazon S3 so you can create a Model out of it.

So, since you trained locally (instead of decoupling the training step), create a model with the compressed artifacts, then create an endpoint, and do some inferences.