GPU Training & Inference with Snowflake Notebooks

Predictive machine learning continues to be a cornerstone of data-driven decision-making. However, as organizations accumulate more data in a wide variety of forms, and as modeling techniques continue to advance, the tasks of a data scientist and ML engineer are becoming increasingly complex. Oftentimes, more effort is spent on managing infrastructure, jumping through package management hurdles, and dealing with scalability issues than on actual model development.

Today, we’re excited to expand the functionality of Snowflake ML with the new Container Runtime for Snowflake Notebooks, available in public preview across all AWS commercial regions. This fully-managed, container-based runtime comes preconfigured with the most popular Python libraries and frameworks, with the flexibility to extend from open source hubs such as PyPi and HuggingFace. Container Runtime includes APIs that automatically parallelize data loading and model training. This delivers 3-7x execution speed improvement – based on internal benchmarking over running the same workload with OSS libraries outside of the runtime — making it easy to efficiently scale your ML workflows. By using Snowflake Notebooks on Container Runtime, data scientists and ML engineers spend significantly less time on infrastructure and scalability, and can spend more time developing and optimizing their ML models, and focusing on rapid business impact.