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SageMaker Endpoint

Amazon SageMaker is a system that can build, train, and deploy machine learning (ML) models with fully managed infrastructure, tools, and workflows.

We use SageMaker to host our model and expose it as the SageMaker Endpoint.

Installation and Setup​

pip install boto3

For instructions on how to expose model as a SageMaker Endpoint, please see here.

Note: In order to handle batched requests, we need to adjust the return line in the predict_fn() function within the custom inference.py script:

Change from

return {"vectors": sentence_embeddings[0].tolist()}

to:

return {"vectors": sentence_embeddings.tolist()}

We have to set up following required parameters of the SagemakerEndpoint call:

  • endpoint_name: The name of the endpoint from the deployed Sagemaker model. Must be unique within an AWS Region.
  • credentials_profile_name: The name of the profile in the ~/.aws/credentials or ~/.aws/config files, which has either access keys or role information specified. If not specified, the default credential profile or, if on an EC2 instance, credentials from IMDS will be used. See this guide.

LLM​

See a usage example.

from langchain import SagemakerEndpoint
from langchain.llms.sagemaker_endpoint import LLMContentHandler

API Reference:

Text Embedding Models​

See a usage example.

from langchain.embeddings import SagemakerEndpointEmbeddings
from langchain.llms.sagemaker_endpoint import ContentHandlerBase

API Reference: