Description
TensorRT is a high-performance deep learning inference optimizer and runtime library developed by NVIDIA. It's used to optimize, validate, and deploy trained neural network models in production environments, enabling applications to run faster. TensorRT can import trained models from every major deep learning framework, convert them into an optimized format, and then use its powerful optimizations to maximize inference speed while maintaining accuracy. Skills in TensorRT range from understanding its basic concept and benefits, installing and setting up the software, converting and optimizing trained models, to advanced performance tuning and implementing complex applications.
Expected Behaviors
Fundamental Awareness
At the fundamental awareness level, an individual is expected to understand the basic concept of TensorRT and its benefits. They should be able to recognize where TensorRT can be applied in the field of deep learning.
Novice
A novice is expected to install and set up TensorRT successfully. They should have a basic understanding of the TensorRT API and be able to create a simple TensorRT network. They should also understand how TensorRT fits into the broader context of deep learning.
Intermediate
An intermediate user should be proficient in converting trained models to TensorRT and optimizing them. They should be capable of implementing custom layers in TensorRT and using its Python API. They should also have a good understanding of how TensorRT optimizes inference.
Advanced
Advanced users are expected to perform mixed precision inference with TensorRT and integrate TensorRT into existing applications. They should be comfortable using dynamic shapes in TensorRT and applying its optimizations to complex networks. Debugging TensorRT applications should also be within their skillset.
Expert
Experts should be adept at advanced performance tuning with TensorRT and implementing advanced custom layers. They should be able to use TensorRT plugins for custom operations and design and implement complex TensorRT applications. Contributing to TensorRT development is also expected at this level.