Hosting a private PyPI server for Amazon SageMaker Studio notebooks in a VPC

 Hosting a private PyPI server for Amazon SageMaker Studio notebooks in a VPC

Amazon SageMaker Studio notebooks provide a full-featured integrated development environment (IDE) for flexible machine learning (ML) experimentation and development. Security measures secure and support a versatile and collaborative environment. In some cases, such as to protect sensitive data or meet regulatory requirements, security protocols require that public internet access be disabled in the development environment.

Typically, developers have access to the public internet and can install any new libraries you want to import. You can install Python packages from the public Python Package Index (PyPI), a Python software repository, using standard tools such as pip. You can find hundreds of thousands of packages, including common packages such as NumPy, Pandas, Matplotlib, Pytest, Requests, Django, and BeautifulSoup.

In a development environment with internet access disabled, you can instead mirror packages and host your own PyPI server hosted in your own Amazon Virtual Private Cloud (Amazon VPC). A VPC is a logically isolated virtual network into which you can launch resources, such as Amazon Elastic Compute Cloud (Amazon EC2) instances and SageMaker Studio domains. You have fine-grained access control over its network connectivity. You can specify an IP address range for the VPC and associate security groups to control its inbound and outbound traffic. You can also add subnets that use a subset of IP addresses within the VPC, and choose whether each subnet is open to the public internet or is private.

When you use a local PyPI server with this architecture and install Python libraries from your SageMaker Studio notebook, you connect to your private server instead of a public package index, and all traffic remains within a single secured VPC and private subnet.

SageMaker Studio recently launched VPC integration to meet these security needs. You can now launch Studio notebooks within a private VPC, disabling internet access. To install Python packages within this secure environment, you can configure an EC2 instance in your VPC that acts as a PyPI server for your notebooks. This enables you to maintain productivity and ease of package installation while working within a private environment that isn’t accessible from the public internet.

Solution overview

This solution creates a private PyPI server on an EC2 instance, and connects it to a SageMaker Studio notebook through network configuration including a VPC, private subnet, security group, and elastic network interface. The following diagram illustrates this architecture.

The following diagram illustrates this architecture.

You complete the following steps to implement this solution:

  1. Launch an EC2 instance within a VPC, subnet, and security group.
  2. Configure the instance to function as a private PyPI server.
  3. Create a VPC endpoint a
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Source - Continue Reading: https://aws.amazon.com/blogs/machine-learning/hosting-a-private-pypi-server-for-amazon-sagemaker-studio-notebooks-in-a-vpc/

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