Get started on SageMaker

Everything you need before deploying your first endpoint. You do this setup once per AWS account, then reuse it for every Saaras v3, Bulbul v3, and Sarvam Vision deployment.

Prerequisites

• An AWS account with permission to subscribe on AWS Marketplace and create SageMaker resources.
• Amazon SageMaker Studio (or a local environment with the AWS CLI and boto3).
• GPU instance quota in your region for the instance types you plan to use (see Request quota).
• The SageMaker Python SDK and boto3 if you’re deploying from code.

$
pip install "sagemaker>=2.220" boto3

1. Create a SageMaker execution role (once)

Every endpoint runs under an IAM execution role. Create it once and reuse it. The managed policy AmazonSageMakerFullAccess covers most needs; add S3 access if you’ll use batch or async (so SageMaker can read inputs and write outputs).

The role needs a trust policy allowing SageMaker to assume it:

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{
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  "Version": "2012-10-17",
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  "Statement": [
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    {
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      "Effect": "Allow",
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      "Principal": { "Service": "sagemaker.amazonaws.com" },
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      "Action": "sts:AssumeRole"
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    }
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  ]
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}

Your own user or role also needs permission to subscribe to Marketplace products and manage SageMaker:

2. Subscribe on AWS Marketplace

3. Request service quotas

GPU instances often need a quota increase before first use. In the Service Quotas console, search for Amazon SageMaker and request the endpoint-usage quota for the instance types you’ll run.

Recommended instances

Both models run best on the ml.g6e family (NVIDIA L40S). Start with the recommended instance and scale up for more throughput or concurrency.

For Sarvam Vision, the concurrent documents an instance handles scales with its size:

You’re set