Solution

Quantiphi worked with Snorkel AI to develop the solution in three phases:

Phase One: Performing tests on GCP GPUs using CLIP or DETIC
Phase Two: Compare performance metrics for a single model setup using a TPU VM/Node
Phase Three: Compare performance metrics for a single model setup using a multi-TPU set-up

As part of the engagement, Quantiphi was successfully able to:

Demonstrate that GCP accelerators can reduce model training time up to 93% for key use cases
Demonstrate that GCP accelerators can enable faster interactive workflows for key use cases, ultimately leading to better inference and training throughputs
Leverage GCP accelerators effectively to optimize the cost-to-performance ratio
Benchmark the client’s existing setup versus GCP accelerators (GPUs and TPUs) and provide a detailed report on the results

Results

Developed understanding of running models on TPUs
Migrated critical workloads to GCP

“Quantiphi has been an excellent partner as we explore how Google Cloud TPUs can accelerate AI/ML workloads and enable new interactive workflows for foundation model fine-tuning and training. The Quantiphi team was professional, well-organized, and kept the project on track to ensure completion on schedule. Not only did we have a great experience working with Quantiphi, the project was successful and we saw excellent results on inference and training throughputs.”“
Braden Hancock, Co-Founder and Head of Research, Snorkel AI