tensorrt-optimization
NVIDIA TensorRT model optimization and deployment. Convert models to TensorRT engines, configure optimization profiles and precision modes, apply INT8 calibration, analyze kernel fusion, generate custom plugins, and profile inference performance.
npx skills add a5c-ai/babysitter --skill tensorrt-optimization --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# tensorrt-optimization You are **tensorrt-optimization** - a specialized skill for NVIDIA TensorRT model optimization and deployment. This skill provides expert capabilities for optimizing deep learning models for inference. ## Overview This skill enables AI-powered TensorRT optimization including: - Convert models to TensorRT engines - Configure optimization profiles and precision modes - Apply INT8 calibration and quantization - Analyze kernel fusion opportunities - Generate custom TensorRT plugins - Profile inference latency and throughput - Handle dynamic shapes and batch sizes - Compare TensorRT vs framework inference ## Prerequisites - TensorRT 8.5+ - CUDA Toolkit 11.0+ - ONNX Runtime (for ONNX models) - Python TensorRT package ## Capabilities ### 1. Model Conversion to TensorRT Convert models from various frameworks: ```python import tensorrt as trt # Create builder and network logger = trt.Logger(trt.Logger.WARNING) builder = trt.Builder(logger) network = builder.create_network( 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)) # Parse ONNX model parser = trt.OnnxParser(network, logger) with open("model.onnx", "rb") as f: parser.parse(f.read()) # Configure builde
- Overview
- Prerequisites
- Capabilities
- 1. Model Conversion to TensorRT
- 2. Precision Configuration
- 3. INT8 Calibration
- 4. Dynamic Shapes
- 5. Inference Execution
- 6. Plugin Development
- 7. Performance Profiling
- 8. Kernel Fusion Analysis
- Command Line Tools
- Process Integration
- Output Format
Use trtexec for analysis trtexec --onnx=model.onnx \ Profile with Nsight Systems nsys profile -o trt_profile \ trtexec --loadEngine=model.engine --iterations=100 View layer timing trtexec --loadEngine=model.engine \ Convert ONNX to TensorRT trtexec --onnx=model.onnx --saveEngine=model.engine With FP16
What does the tensorrt-optimization skill do?
NVIDIA TensorRT model optimization and deployment. Convert models to TensorRT engines, configure optimization profiles and precision modes, apply INT8 calibration, analyze kernel fusion, generate custom plugins, and profile inference performance.
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill tensorrt-optimization --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.