Introduction to Containers with Mark III Systems
Hallenbeck's presentation was designed for accessibility, catering to both newcomers and seasoned data science enthusiasts. It commenced with a comprehensive lexicon, laying a strong foundation of useful vocabulary for the insights that would be discussed later. This session specifically focused on containers, practical software stacks, and basic setups for AI/ML.
He introduced Docker, a container operation system (OS), and NGC (NVIDIA GPU Cloud) containers – highlighting their advantages, such as efficiency, portability, and scalability. Unlike virtual machines that virtualize the entire operating system, containers utilize the existing kernel of the host system and provide their own isolated file system and networking through namespaces. This approach streamlines resource usage and enhances adaptability, making it a compelling choice for managing software configurations and different library requirements.
The highlight of Hallenbeck's presentation was later however, when he demonstrated the training of a computer vision model to classify clothing articles, exemplifying the potential of deep learning in real-world scenarios. He began by running a Jupyter notebook inside a Docker container and using TensorFlow and Keras (both high-level APIs developed by Google) as frameworks. The audience gained firsthand experience in running deep learning workloads within containers, all while enjoying the benefits of GPU acceleration.
Thanks to Hallenbeck’s accessible terminology and thorough exploration of containerization and AI, workshop attendees were able to gain valuable insight into deploying deep learning models within NGC containers for efficient data processing. This webinar served as an educational resource for anyone eager to delve into the transformative realm of containerized solutions within data science.