New Implementation of PyTorch Training for Mac
After a new PyTorch v1.12 release, creators and researchers can now benefit from Apple’s silicon GPUs to enable an essentially faster model training. They gain the ability to implement machine learning workflows like modeling and fine-tuning locally, right on their Macs. The MPS backend develops the PyTorch framework, providing scripts and capabilities to arrange and run operations on Mac. MPS improves compute performance with kernels that are fine-tuned for the unique characteristics of every
After a new PyTorch v1.12 release, creators and researchers can now benefit from Apple’s silicon GPUs to enable an essentially faster model training. They gain the ability to implement machine learning workflows like modeling and fine-tuning locally, right on their Macs.
The MPS backend develops the PyTorch framework, providing scripts and capabilities to arrange and run operations on Mac. MPS improves compute performance with kernels that are fine-tuned for the unique characteristics of every Metal GPU family.
In addition, this reduces costs associated with cloud-based development or the requirements for additional local GPUs. The Unified Memory architecture also reduces data retrieval latency, thus radically improving end-to-end performance.
An entrepreneur with over a decade of experience in AI, Cloud, and HPC. He is currently a DevOps Architect and the founder of Data Phoenix, an influential media voice for the AI industry, with a strong focus on community building and open source.
More news
Study: Some personal AI agents steer wealthier personas toward pricier options
Databricks adds Workday Data Connect federation to Unity Catalog
Google Cloud introduces a universal Gemini agent for enterprise work
