Hey all, we are launching Terraform Provider Iterative (TPI).
It was designed for machine learning (ML/AI) teams and optimizes CPU/GPU expenses:
1. Spot instances auto-recovery (if an instance was evicted/terminated) with data and checkpoint synchronization
2. Auto-terminate instances when ML training is finished - you won't forget to terminate your expensive GPU instance for a week :)
3. Familiar Terraform commands and config (HCL)
The secret sauce is auto-recovery logic that is based on cloud auto-scaling groups and does not require any monitoring service to run (another cost-saving!). Cloud providers recover it for you. TPI just unifies auto-scaling groups for all the major cloud providers: AWS, Azure, GCP and Kubernetes. Yeah, it was tricky to unify all clouds :)
Yes, it' from DVC/CML team! We started TPI as a "computational backend" for CML project (CI/CD for ML). But then we realized that it can be useful as an independent tool.
Comments
Hey all, we are launching Terraform Provider Iterative (TPI).
It was designed for machine learning (ML/AI) teams and optimizes CPU/GPU expenses:
1. Spot instances auto-recovery (if an instance was evicted/terminated) with data and checkpoint synchronization
2. Auto-terminate instances when ML training is finished - you won't forget to terminate your expensive GPU instance for a week :)
3. Familiar Terraform commands and config (HCL)
The secret sauce is auto-recovery logic that is based on cloud auto-scaling groups and does not require any monitoring service to run (another cost-saving!). Cloud providers recover it for you. TPI just unifies auto-scaling groups for all the major cloud providers: AWS, Azure, GCP and Kubernetes. Yeah, it was tricky to unify all clouds :)
We'd love to hear your feedback!
Awesome, this project is from the team behind data version control (dvc) and CML, I’ll give it a try!
Yes, it' from DVC/CML team! We started TPI as a "computational backend" for CML project (CI/CD for ML). But then we realized that it can be useful as an independent tool.
Auto-scaling for ML workloads, integrated with the TF workflow - very cool!