The CUDA Abstraction Layer for GPU Workload Execution

Decoupling CUDA Execution from GPUs for Unbounded AI Infrastructure Management

Unprecedented Efficiency

Reimagined Consumption

Diverse GPU Support

Seamless Integration

More efficient parallel GPU usage than MPS

GPU resource management happens at the Kernel execution level

New Wooly Instruction Set for multi-GPU vendor support

Users can work in GPU-less Pytorch client containers

Your GPU-less Client ML Environment

Run your Pytorch apps in Linux containers with the Wooly Runtime Library and CPU only infrastructure

docker pull woolyai/client:latest Status: Downloaded newer image for wooly docker run --name wooly-container woolyai/client:latest docker exec -it wooly-container wooly login ****** success docker exec wooly-container wooly credits 994239242 docker exec wooly-container python3 pytorch-project.py torch.cuda.get_device_name(0): WoolyAI . . .

CUDA Abstraction for Pytorch

Compiling Shaders into Wooly Instruction Set (IS)