Cloudian engineers have added Nvidia GPUDirect support to a PyTorch connector to accelerate AI and machine learning workloads.
In the machine learning world, the Torch open source software library interfaces with deep learning algorithms. It was started in 2001 and became PyTorch in 2018. The PyTorch ecosystem serves millions of developers worldwide, from individual researchers to enterprise operations. Meta is a major contributor to PyTorch, and companies like Tesla, Hugging Face, and Catalyst also use PyTorch for developing AI and deep learning models. Cloudian added GPUDirect support for objects to its HyperStore scale-out storage last year, and Milvus vector database functionality earlier this month. Now it has built a connector linking its HyperStore object storage to PyTorch libraries. This connector supports GPUDirect and RDMA, and is optimized for Nvidia Spectrum-X Ethernet networking and ConnectX SuperNICs.
Cloudian CMO Jon Toor tells us: “This development represents a major advancement for organizations running artificial intelligence and machine learning workloads, delivering a remarkable 74 percent improvement in data processing performance while simultaneously reducing CPU utilization by 43 percent.”
This connector eliminates traditional network bottlenecks through direct memory-to-memory data transfers between Cloudian storage systems and GPU-accelerated AI frameworks. It processes 52,000 images per second versus 30,000 with a standard S3 connector, based on TorchBench testing.
Toor says it helps provide a more efficient AI infrastructure architecture. “This efficiency gain becomes increasingly valuable as AI models grow larger and more complex, requiring faster data access patterns to maintain training productivity.”
The Cloudian PyTorch connector is now available for evaluation by PyTorch users, AI researchers, and users involved in enterprise ML operations, computer vision, and other AI/ML applications.
Bootnote
Benchmark testing was conducted using Cloudian HyperStore 8.2.2 software running on six Supermicro servers equipped with Nvidia networking platforms in an all-flash media configuration, representing enterprise-grade storage infrastructure commonly deployed for GPU-accelerated AI workloads.
