Explore the world of model conversion and inference optimization in this 40-minute video tutorial. Learn how to convert a machine learning model to ONNX, OpenVino, and Tensor-RT formats, and compare their inference performance on both CPU and GPU against native PyTorch inference. Gain practical insights into LLMOps techniques for data science and machine learning applications. Access the accompanying notebook on GitHub to follow along and experiment with the demonstrated concepts.
LLMOps: Comparison of OpenVino, ONNX, TensorRT, and PyTorch Inference
The Machine Learning Engineer via YouTube
Overview
Syllabus
LLMOps: Comparison Openvino, ONNX, TensorRT and Pytorch Inference #datascience #machinelearning
Taught by
The Machine Learning Engineer
Related Courses
-
LLMOps: Comparación de Openvino, ONNX, TensorRT y Pytorch para Inferencia
-
LLMOps: Using Nvidia TensorRT SDK for GPU Inference
-
LLMOps: Converting Video Classifier (ViViT) to ONNX and Inference on CPU
-
LLMOps: Quantization Models and Inference with ONNX Generative Runtime
-
Rust for Large Language Model Operations (LLMOps)
-
LLMOps: Converting Fine-Tuned ViT Classifier to ONNX for CPU Inference
