Learn how to convert a fine-tuned Video Vision Transformer (ViViT) to ONNX format and perform inference on a CPU in this 32-minute tutorial. Explore the process of transforming a previously fine-tuned video classifier model, making it compatible with various hardware platforms. Gain hands-on experience in model conversion and optimization techniques essential for efficient deployment in production environments. Access the accompanying code repository for practical implementation and further experimentation with LLMOps workflows in machine learning and data science projects.
LLMOps: Converting Video Classifier (ViViT) to ONNX and Inference on CPU
The Machine Learning Engineer via YouTube
Overview
Syllabus
LLMOps: Convert Video Classifier (ViViT ) to ONNX, Inference on a CPU #machinelearning #datascience
Taught by
The Machine Learning Engineer
Related Courses
-
LLMOps: Converting Video Classifier (ViViT) to ONNX for CPU Inference
-
LLMOps: Converting Fine-Tuned ViT Classifier to ONNX for CPU Inference
-
LLMOps: Inference of Fine-Tuned ViT Classifier on CPU with C#
-
LLMOps: Converting Fine-Tuned ViT Classifier to ONNX for CPU Inference
-
LLMOps: Fine-Tuning Video Classifier (ViViT) with Custom Data
-
LLMOps: Comparison of OpenVino, ONNX, TensorRT, and PyTorch Inference
