AWS SageMaker

Route to your AWS Sagemaker models through Portkey

Sagemaker allows users to host any ML model on their own AWS infrastructure.

With portkey you can manage/restrict access, log requests, and more.

Provider Slug. sagemaker

Portkey SDK Integration with AWS Sagemaker

1. Install the Portkey SDK

Add the Portkey SDK to your application to interact with Sagemaker’s API through Portkey’s gateway.

NodeJS

npm install --save portkey-ai

npm install --save portkey-ai

pip install portkey-ai

2. Initialize Portkey with a Virtual Key

There are multiple ways to integrate Sagemaker with Portkey. You can use your AWS credentials, or use an assumed role.

In this example we will create a virtual key and use it to interact with Sagemaker. This helps you restrict access (specific models, few endpoints, etc).

Here’s how to find your AWS credentials:

AWS Access Key

Use your AWS Secret Access Key, AWS Access Key Id, and AWS Region to create your Virtual key.

Integration Guide

AWS Assumed Role

Take your AWS Assumed Role ARN and AWS Region to create the virtaul key.

Integration Guide

Create a virtual key in the Portkey dashboard in the virtual keys section. You can select sagemaker as the provider, and fill in deployment details.

Initialize the Portkey SDK with the virtual key. (If you are using the REST API, skip to next step)

NodeJS SDK

import Portkey from 'portkey-ai'

const portkey = new Portkey({
    apiKey: "PORTKEY_API_KEY", // Replace with your Portkey API key
    virtualKey: "VIRTUAL_KEY" // Replace with your Sagemaker Virtual Key
})

import Portkey from 'portkey-ai'

const portkey = new Portkey({
    apiKey: "PORTKEY_API_KEY", // Replace with your Portkey API key
    virtualKey: "VIRTUAL_KEY" // Replace with your Sagemaker Virtual Key
})

from portkey_ai import Portkey

portkey = Portkey(
    api_key="PORTKEY_API_KEY",  # Replace with your Portkey API key
    virtual_key="VIRTUAL_KEY" # Replace with your Sagemaker Virtual Key
)

3. Invoke the Sagemaker model

Python SDK

response = portkey.post(
    url="endpoints/{endpoint_name}/invocations",
    # You can pass any key value pair required by the model, apart from `url`, they are passed as kwargs to the Sagemaker endpoint
    inputs="my_custom_value",
    my_custom_key="my_custom_value",
)

print(response)

response = portkey.post(
    url="endpoints/{endpoint_name}/invocations",
    # You can pass any key value pair required by the model, apart from `url`, they are passed as kwargs to the Sagemaker endpoint
    inputs="my_custom_value",
    my_custom_key="my_custom_value",
)

print(response)

const response = await portkey.post(
    url="endpoints/{endpoint_name}/invocations",
    // You can pass any key value pair required by the model, apart from `url`, they are passed as kwargs to the Sagemaker endpoint
    inputs="my_custom_value",
    my_custom_key="my_custom_value",
)


console.log(response);

curl --location 'https://api.portkey.ai/v1/endpoints/{endpoint_name}/invocations' \
--header 'x-portkey-virtual-key: {VIRTUAL_KEY}' \
--header 'x-portkey-api-key: {PORTKEY_API_KEY}' \
--header 'Content-Type: application/json' \
--data '{
    # You can pass any key value pair required by the model, they are passed as kwargs to the Sagemaker endpoint
    "inputs": "my_custom_value",
    "my_custom_key": "my_custom_value"
}'

Making Requests without Virtual Keys

If you do not want to add your AWS details to Portkey vault, you can also directly pass them while instantiating the Portkey client.

These are the supported headers/parameters for Sagemaker (Not required if you’re using a virtual key):

Node SDK	Python SDK	REST Headers
awsAccessKeyId	aws_access_key_id	x-portkey-aws-access-key-id
awsSecretAccessKey	aws_secret_access_key	x-portkey-aws-secret-access-key
awsRegion	aws_region	x-portkey-aws-region
awsSessionToken	aws_session_token	x-portkey-aws-session-token
sagemakerCustomAttributes	sagemaker_custom_attributes	x-portkey-amzn-sagemaker-custom-attributes
sagemakerTargetModel	sagemaker_target_model	x-portkey-amzn-sagemaker-target-model
sagemakerTargetVariant	sagemaker_target_variant	x-portkey-amzn-sagemaker-target-variant
sagemakerTargetContainerHostname	sagemaker_target_container_hostname	x-portkey-amzn-sagemaker-target-container-hostname
sagemakerInferenceId	sagemaker_inference_id	x-portkey-amzn-sagemaker-inference-id
sagemakerEnableExplanations	sagemaker_enable_explanations	x-portkey-amzn-sagemaker-enable-explanations
sagemakerInferenceComponent	sagemaker_inference_component	x-portkey-amzn-sagemaker-inference-component
sagemakerSessionId	sagemaker_session_id	x-portkey-amzn-sagemaker-session-id
sagemakerModelName	sagemaker_model_name	x-portkey-amzn-sagemaker-model-name

Example

Python

from portkey_ai import Portkey

portkey = Portkey(
    api_key="PORTKEY_API_KEY",  # Replace with your Portkey API key
    provider="sagemaker",
    aws_region="us-east-1", # Replace with your AWS region  
    aws_access_key_id="AWS_ACCESS_KEY_ID", # Replace with your AWS access key id
    aws_secret_access_key="AWS_SECRET_ACCESS_KEY", # Replace with your AWS secret access key
    amzn_sagemaker_inference_component="SAGEMAKER_INFERENCE_COMPONENT" # Replace with your Sagemaker inference component
)

response = portkey.post(
    url="endpoints/{endpoint_name}/invocations",
    # You can pass any key value pair required by the model, apart from `url`, they are passed as kwargs to the Sagemaker endpoint
    inputs="my_custom_value",
    my_custom_key="my_custom_value"
)

print(response)

from portkey_ai import Portkey

portkey = Portkey(
    api_key="PORTKEY_API_KEY",  # Replace with your Portkey API key
    provider="sagemaker",
    aws_region="us-east-1", # Replace with your AWS region  
    aws_access_key_id="AWS_ACCESS_KEY_ID", # Replace with your AWS access key id
    aws_secret_access_key="AWS_SECRET_ACCESS_KEY", # Replace with your AWS secret access key
    amzn_sagemaker_inference_component="SAGEMAKER_INFERENCE_COMPONENT" # Replace with your Sagemaker inference component
)

response = portkey.post(
    url="endpoints/{endpoint_name}/invocations",
    # You can pass any key value pair required by the model, apart from `url`, they are passed as kwargs to the Sagemaker endpoint
    inputs="my_custom_value",
    my_custom_key="my_custom_value"
)

print(response)

import Portkey from 'portkey-ai'

const portkey = new Portkey({
    api_key:"PORTKEY_API_KEY",
    provider:"sagemaker",
    aws_access_key_id:"AWS_ACCESS_KEY_ID",
    aws_secret_access_key:"AWS_SECRET_ACCESS_KEY",
    aws_region:"us-east-1",
    amzn_sagemaker_inference_component:"SAGEMAKER_INFERENCE_COMPONENT"
})

const response = await portkey.post(
    url="endpoints/{endpoint_name}/invocations",
    // You can pass any key value pair required by the model, apart from `url`, they are passed as kwargs to the Sagemaker endpoint
    inputs="my_custom_value",
    my_custom_key="my_custom_value"
)

console.log(response)

curl https://api.portkey.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "x-portkey-api-key: $PORTKEY_API_KEY" \
  -H "x-portkey-provider: sagemaker" \
  -H "x-portkey-aws-access-key-id: $AWS_ACCESS_KEY_ID" \
  -H "x-portkey-aws-secret-access-key: $AWS_SECRET_ACCESS_KEY" \
  -H "x-portkey-aws-region: $AWS_REGION" \
  -H "x-portkey-amzn-sagemaker-inference-component: $SAGEMAKER_INFERENCE_COMPONENT" \
  -d '{
    # You can pass any key value pair apart from `url` required by the model, they are passed as kwargs to the Sagemaker endpoint
    "inputs": "my_custom_value",
    "my_custom_key": "my_custom_value"
  }'

Next Steps

The complete list of features supported in the SDK are available on the link below.

SDK

You’ll find more information in the relevant sections:

1. Add metadata to your requests
2. Add gateway configs to your Sagemaker requests
3. Tracing Sagemaker requests