amazon-sagemaker

With AWS SageMaker, is it possible to deploy a pre-trained model using the sagemaker SDK?

喜夏-厌秋 提交于 2020-12-29 07:37:09
问题 I'm trying to avoid migrating an existing model training process to SageMaker and avoid creating a custom Docker container to host our trained model. My hope was to inject our existing, trained model into the pre-built scikit learn container that AWS provides via the sagemaker-python-sdk. All of the examples that I have found require training the model first which creates the model/model configuration in SageMaker. This is then deployed with the deploy method. Is it possible to provide a

How to create ner pipelines using aws [closed]

筅森魡賤 提交于 2020-12-27 07:30:33
问题 Closed . This question needs to be more focused. It is not currently accepting answers. Want to improve this question? Update the question so it focuses on one problem only by editing this post. Closed 5 days ago . Improve this question I am working on creating ner pipelines for different use-cases. I am planning to use aws for ner pipelines. I have three questions. 1.) Different Options for creating ner pipelines other than aws. 2.) is there any cost-efficient approach other than aws one to

Deployment Error (Reason) : Please make sure all images included in the model for the production variant AllTraffic exist

ⅰ亾dé卋堺 提交于 2020-12-06 15:59:24
问题 I am Able to train my modelusing Sagemaker TensorFlow container. Below is the code model_dir = '/opt/ml/model' train_instance_type = 'ml.c4.xlarge' hyperparameters = {'epochs': 10, 'batch_size': 256, 'learning_rate': 0.001} script_mode_estimator = TensorFlow( entry_point='model.py', train_instance_type=train_instance_type, train_instance_count=1, model_dir=model_dir, hyperparameters=hyperparameters, role=sagemaker.get_execution_role(), base_job_name='tf-fashion-mnist', framework_version='1.12