igie

What problem does it solve?

This Skill streamlines the deployment and optimization of AI inference workloads on Iluvatar GPUs, enabling efficient model conversion, quantization, and deployment.

Core Features & Use Cases

• Model Import and Conversion: Supports importing models from ONNX, PyTorch, TensorFlow, and others, simplifying the deployment pipeline.
• Quantization and Optimization: Facilitates INT8 and FP16 quantization for faster inference and reduced memory usage, applicable to vision and NLP models.
• Dynamic and Static Deployment: Implements static shape compilation and dynamic shape support for flexible inference scenarios.
• Use Case: Deploy a ResNet model in FP16 precision on Iluvatar GPU, achieving accelerated inference for real-time applications.

Quick Start

Load an ONNX model, import it with relay.frontend, build for Iluvatar hardware, and run inference using TVM's graph executor.