# SageMaker @remote decorator with private package repository

This repository is part of AWS blog post [Easily access private repos using the remote decorator mode for SageMaker training workloads](https://aws.amazon.com/blogs/machine-learning/access-private-repos-using-the-remote-decorator-for-amazon-sagemaker-training-workloads/) which demonstrates how to use [@remote decorator](https://docs.aws.amazon.com/sagemaker/latest/dg/train-remote-decorator.html) from [Amazon SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable/) while running the SageMaker training job in a VPC with no internet access. To enable download of dependencies for the training job, it uses a privte PyPI AWS CodeArtifact repository instead.



## Contents in the repository

- [cloudformation](/cloudformation) folder: This folder contains the cloudformation scripts needed to setup the VPC, CodeArtifact Repository and SageMaker Studio. It contains two files:
    - vpc.yaml: Contains the code for setting up the VPC and VPC endpoints for all supported services.
    - sagemaker_studio_codeartifact.yaml: Contains the code for setting up SageMaker Studio with VPC and Code Artifact Domain, private repository and upstream repository    
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- [config](/config) folder: This folder contains:
    - `config.yaml`: Contains all the configurations needed to run the SageMaker training job while connecting to private PyPI repository.
    - `requirements.txt`: Contains all the dependencies needed to run the training script.
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- `remote-decorator-private-repo.ipynb`: Contains the code for running the training job.

## Instruction to run the code
- Execute the cloudformtion template `vpc.yaml`. Provide a name to the stack(example: No-Internet). Wait for it to get completed.
- Execute the cloudformation template `sagemaker_studio_codeartifact.yaml`. Provide the VPC stack name(No-Internet). You may leave everything else as default or change the value of any other parameter if you like. Wait for the stack to be completed
- Open `remote-decorator-private-repo.ipynb`. 
    - Select Python 3(Data Science) as Kernel and image. Wait for the kernel session to begin.
    - Read through the instructions of the notebook and execute cells. 
    - Update the content of `config.yaml` based on your VPC and CodeArtifact details(detailed instructions in the notebook). Leave everything else in the yaml as-is. 
    - Continue the exection of cells after updating `config.yaml` and run the training job.


## Clean up

You will be creating several billable resources in your AWS account as part of this setup. After experimentation, make sure you follow the steps in [CLEANUP.md](CLEANUP.md) to remove all resources created. If you encounter any issues when deleting the resources, troubleshoot the issues by following the [AWS CloudFormation troubleshooting guide](https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/troubleshooting.html)


## Support
Open a bug in case you find anything not working.

## Contributing
See [CONTRIBUTING.md](CONTRIBUTING.md) for details.

## License
See [LICENSE](LICENSE)