Artifactory Setup

ARTIFACTORY: How to Use JFrog Artifactory with AWS Sagemaker

AuthorFullName__c
Melissa McKay
articleNumber
000005986
ft:sourceType
Salesforce
FirstPublishedDate
2024-01-17T12:49:57Z
lastModifiedDate
2024-01-17
VersionNumber
2
Python packages and model hubs like Hugging Face are commonly used in ML application and model development. To resolve dependencies from a public external repository (via proxy) or to store your own binaries produced during development, set up the appropriate repositories in Artifactory.

Create the following in Artifactory:

1.  Remote PyPi Repository: to proxy all PyPi packages that will be needed from pypi.org
Set Up Remote PyPI Repositories

2.  Virtual PyPi Repository: to aggregate requests for remote PyPi packages and any locally hosted or proprietary PyPi packages in a local PyPi repository in Artifactory 
Set Up Virtual PyPI Repositories

NOTE: Although this tutorial will not require the creation of a Local PyPi Repository, it is best practice to reference the Virtual PyPi Repo when configuring pip.

3.  Local Hugging Face Repository: to store the custom ML Model version created by the SageMaker training job 
Set Up Local Hugging Face Repositories

4.  Remote Docker Repository: to proxy requests for Docker images needed from Docker Hub
Remote Docker Repositories

5.  Local Docker Repository: to store a custom Docker image to be used for training
Local Docker Repositories
 
6.  Virtual Docker Repository: to aggregate requests for both the Remote and Local Docker Repositories
Virtual Docker Repositories

7.  Scoped Identity Token: to provide limited and scoped permission to access Artifactory from the SageMaker environment
Generating Scoped Tokens