Streamlining Data Science & ML: AWS Deep Learning Containers Unify SageMaker Experience

Streamlining Data Science & ML: AWS Deep Learning Containers Unify SageMaker Experience

Streamlining Data Science & ML: AWS Deep Learning Containers Unify SageMaker Experience

As Seen On

As data scientists’ toolsets continue to expand, the need for a consistent, reproducible environment to manage dependencies, ensure security, and promote collaboration becomes paramount. Amazon Web Services (AWS) Deep Learning Containers facilitate this need by offering pre-built Docker images for various frameworks. However, the focus has shifted toward creating a unified experience for developers. The newly introduced SageMaker open-source distribution aims to meet this growing demand by providing a seamless experience for machine learning (ML) practitioners of all expertise levels.

Announced at the 2023 JupyterCon event, the SageMaker open-source distribution caters to popular data science, ML, and visualization packages and libraries like TensorFlow, PyTorch, Scikit-learn, Pandas, and Matplotlib. The distribution is conveniently available for download from the Amazon ECR Public Gallery.

The power of the SageMaker open-source distribution is best showcased through an example. Imagine training an image classification model utilizing PyTorch on the KMNIST dataset. With SageMaker, the process of transitioning from local experimentation to production jobs becomes a breeze.

Before diving in, certain prerequisites need to be met, including a Docker installation, an active AWS account with administrator permissions, an environment with AWS CLI and Docker installed, and an existing SageMaker domain.

To set up the local environment, developers can employ the open-source distribution directly on their local machines. By running specific commands in the terminal, JupyterLab springs to life, ready for experimentation. Depending on the type of machine used, developers can select the latest-gpu tag for GPU-supported devices or replace the ECRIMAGEID if necessary.

After setting up the local environment, it’s time to embrace the full power of SageMaker Studio – an end-to-end integrated development environment designed specifically for ML projects. Launching a compute instance within SageMaker Studio is a walk in the park, thanks to the user-friendly Studio Launcher. With the example repository cloned, developers can open the notebook and begin transferring their work from the local environment to SageMaker Studio.

Once the notebook environment is set up, developers can dive into data preparation, move on to defining and training their neural network, and finally, test their model’s performance by examining training and test loss.

One of the standout features of the SageMaker open-source distribution is its ability to schedule notebooks as jobs on the platform. This allows developers to train models at specific intervals or in response to particular events. Observing the progress of these jobs is simple through the use of job statuses and logs.

In summary, the introduction of the SageMaker open-source distribution empowers data scientists and ML developers to experiment in local environments while seamlessly promoting jobs on the robust SageMaker platform. By adopting this unified experience, developers benefit from a consistent and reproducible platform, perfect for crafting innovative solutions in the ever-evolving realm of data science and machine learning.

Casey Jones Avatar
Casey Jones
12 months ago

Why Us?

  • Award-Winning Results

  • Team of 11+ Experts

  • 10,000+ Page #1 Rankings on Google

  • Dedicated to SMBs

  • $175,000,000 in Reported Client

Contact Us

Up until working with Casey, we had only had poor to mediocre experiences outsourcing work to agencies. Casey & the team at CJ&CO are the exception to the rule.

Communication was beyond great, his understanding of our vision was phenomenal, and instead of needing babysitting like the other agencies we worked with, he was not only completely dependable but also gave us sound suggestions on how to get better results, at the risk of us not needing him for the initial job we requested (absolute gem).

This has truly been the first time we worked with someone outside of our business that quickly grasped our vision, and that I could completely forget about and would still deliver above expectations.

I honestly can't wait to work in many more projects together!

Contact Us


*The information this blog provides is for general informational purposes only and is not intended as financial or professional advice. The information may not reflect current developments and may be changed or updated without notice. Any opinions expressed on this blog are the author’s own and do not necessarily reflect the views of the author’s employer or any other organization. You should not act or rely on any information contained in this blog without first seeking the advice of a professional. No representation or warranty, express or implied, is made as to the accuracy or completeness of the information contained in this blog. The author and affiliated parties assume no liability for any errors or omissions.