Скачать или смотреть Understanding Data Uploads in Amazon SageMaker: Accessing Your Files in the SageMaker Directory

Learn how to effectively upload and access data in your Amazon SageMaker notebook. Explore directory locations, command line access, and the persistence of your files.
---
This video is based on the question https://stackoverflow.com/q/63179080/ asked by the user 'Jesse Downing' ( https://stackoverflow.com/u/5530627/ ) and on the answer https://stackoverflow.com/a/63182068/ provided by the user 'Olivier Cruchant' ( https://stackoverflow.com/u/5331834/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: When I upload data into an Sagemaker Notebook instance, in which directory does the data live and how do I access it?

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Understanding Data Uploads in Amazon SageMaker

When working in Amazon SageMaker, one of the most common tasks is uploading data into your notebook instance. This leads to a fundamental question: When I upload data into a SageMaker Notebook instance, in which directory does the data live, and how do I access it? If you’ve ever been unsure where your files go or how to access them in the command line, then this guide is for you! Let’s explore how file uploads work in SageMaker and how you can efficiently manage your data.

Where Do Uploaded Files Go?

The SageMaker Home Directory

When you upload data files to a SageMaker notebook instance, they are stored in the following directory:

[[See Video to Reveal this Text or Code Snippet]]

This is the primary directory where all your uploaded files will reside. Here’s how it works:

Visibility in Jupyter: Anything you send to /home/ec2-user/SageMaker will be visible on the Jupyter home page of your notebook. This means that you can easily navigate and see the files you’ve uploaded directly within the Jupyter interface.

Visibility in Terminal: If you prefer using the command line, you can access the contents of this directory by typing the following command in the terminal:

[[See Video to Reveal this Text or Code Snippet]]

This command will list all the files and directories inside the SageMaker folder.

Persistence of Your Data

Understanding Storage Volumes

One of the advantages of using SageMaker is the inclusion of a storage volume known as the ML Storage Volume. Here’s what you need to know about it:

Size and Cost: By default, this volume is 5GB, and you can scale it up to a maximum of 16TB. It's important to keep in mind that this is charged in addition to the instance compute pricing.

Data Persistence: The great thing about this storage volume is that it retains the data even when you turn off your notebook instance. This means any files you save in /home/ec2-user/SageMaker will stay there until you delete them intentionally.

Important Note: If you save files anywhere other than this designated directory, be aware that they will be lost when you switch off your instance. Always ensure your important files are saved in the /home/ec2-user/SageMaker directory for safe keeping.

Final Thoughts

Understanding where your uploaded data lives in Amazon SageMaker and how to access it is crucial for efficient workflow and data management. By utilizing the /home/ec2-user/SageMaker directory and the ML Storage Volume, you can easily manage your data while ensuring it remains persistent even after shutting down your notebook instance.

This knowledge not only enhances your utilization of SageMaker but also instills confidence in managing your machine learning projects.

Happy coding, and may your data handling be as smooth as your model training!