TensorRT

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TensorRT Skill Overview

Welcome to the TensorRT Skill page. You can use this skill
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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.

Micro Skills

Fundamental Awareness7 micro skills • 0 months work experience

Understanding the definition of TensorRT

Recognizing the main features of TensorRT

Understanding how TensorRT optimizes deep learning models

Knowing why optimization is important for deep learning models

Recognizing the role of the TensorRT builder

Understanding the function of the TensorRT runtime

Knowing what the TensorRT parser does

Novice12 micro skills • 3 months work experience

Downloading the correct version of TensorRT

Installing dependencies for TensorRT

Verifying the installation

Understanding the role of each API component

Knowing how to use basic API functions

Recognizing common API patterns in TensorRT

Defining the network architecture

Loading weights into the network

Setting up the network for inference

Recognizing how TensorRT accelerates inference

Understanding the difference between training and inference

Knowing when to use TensorRT in a deep learning pipeline

Intermediate15 micro skills • 6 months work experience

Understanding the process of model conversion

Using UFF or ONNX for model conversion

Handling unsupported operations during conversion

Understanding precision modes in TensorRT

Applying layer fusion and kernel auto-tuning

Implementing dynamic shapes for optimization

Defining the interface for a custom layer

Implementing the forward function for a custom layer

Registering the custom layer with the network

Understanding the structure of TensorRT's Python API

Creating and manipulating TensorRT networks using Python

Performing inference with TensorRT's Python API

Understanding how TensorRT optimizes inference

Knowing the different optimization profiles

Applying optimization strategies to specific use cases

Advanced15 micro skills • 12 months work experience

Understanding the concept of mixed precision inference

Implementing mixed precision inference in TensorRT

Evaluating the performance of mixed precision inference

Understanding the requirements for integrating TensorRT

Modifying existing code to incorporate TensorRT

Testing and debugging the integrated application

Understanding the concept of dynamic shapes

Implementing dynamic shapes in TensorRT

Optimizing the use of dynamic shapes in TensorRT

Understanding the optimization techniques used by TensorRT

Applying these techniques to complex neural networks

Evaluating the performance improvements from these optimizations

Understanding common issues in TensorRT applications

Using debugging tools to identify issues

Implementing solutions to fix these issues

Expert20 micro skills • 24 months work experience

Understanding the impact of different optimization settings on performance

Profiling and benchmarking TensorRT applications

Optimizing memory usage in TensorRT

Applying advanced techniques for layer fusion and kernel auto-tuning

Understanding the intricacies of TensorRT's layer API

Designing custom layers for complex operations

Implementing and testing custom layers in both C++ and Python

Optimizing custom layers for performance

Understanding the role and usage of TensorRT plugins

Creating custom plugins for non-standard operations

Integrating custom plugins into TensorRT networks

Optimizing and testing custom plugins

Architecting large-scale applications using TensorRT

Integrating TensorRT with other libraries and frameworks

Managing memory and resources in complex TensorRT applications

Debugging and troubleshooting complex TensorRT applications

Understanding the TensorRT codebase and architecture

Identifying areas for improvement or new features in TensorRT

Writing high-quality, efficient, and maintainable code

Testing and documenting contributions to TensorRT

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