AWS SageMaker-在本地培训，但要部署到AWS？

Question

I have a the following challenge with SageMaker:

• I've downloaded one of the tutorial notebooks ( https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker-python-sdk/tensorflow_abalone_age_predictor_using_keras/tensorflow_abalone_age_predictor_using_keras.ipynb )

• I ran the training locally (successfully) with the modifying the following line:

   abalone_estimator = TensorFlow(entry_point='abalone.py', role=role, training_steps= 100, evaluation_steps= 100, hyperparameters={'learning_rate': 0.001}, train_instance_count=1, **train_instance_type='local'**) abalone_estimator.fit(inputs) 

• I then wanted to deploy my model to AWS with the following line but it seems the SDK deploys it locally (it doesn't fail, I just see it running on my machine)

  abalone_predictor = abalone_estimator.deploy(initial_instance_count=1, instance_type='ml.m4.xlarge')

Any tips on how to either fix it so it gets deployed to AWS or alternatively re-load my training model and deploy it to AWS from scratch?

Many thanks, Stefan

Answer 1

Its easier to run the training again on SageMaker. Otherwise, here are the steps that you would have to do.

1. Take the checkpoint file generated during the training and convert them into tensorflow serving models.
2. Zip them in a specific format and upload to S3
3. Then create estimator as you have done above and do the inference.

If you want details on each of the specific steps above do let me know, but if your dataset is not too big, I would say just retrain on SageMaker.