NVIDIA深度解析: Triton和TensorRT的DeepStream推論選項
Table of Contents
- Introduction
- Tissue Inference Options with Triton and 1030
- Egyptian Inference Pipeline
- Triton Inference Property
- Pre-processing and Post-processing Plugins
- SDK Overview
- AI Inference Based on TPU and DSP
- Launching Different Pipelines
- Deep Stream App with DS Triton
- Triton Model Ripple Tree
- Using Different Batching Policies
- Deep Stream Application Data Flow
- Tissue Triton Inference Plugin Features
- Differences in Triton Plugin Libraries
- Triton Server App Launch
- Highlights
- FAQ
Introduction
In this article, we will delve into the details of tissue inference options with Triton and 1030. We will explore the Egyptian inference pipeline, different inference patching policies, and the Triton inference property for both CPR and DRPC. Additionally, we will discuss the Deep Stream technology influence plugins, pre-processing, and post-processing plugins.
Tissue Inference Options with Triton and 1030
Tissue inference refers to the process of analyzing tissue samples using machine learning models. Triton and 1030 offer various options for conducting tissue inference. These options include the Egyptian inference pipeline, different inference patching policies, and the use of Triton for both CPR and DRPC.
Egyptian Inference Pipeline
The Egyptian inference pipeline is a comprehensive approach to tissue inference that involves multiple stages of processing. It includes the capture, decode, preprocessing, batching, AI inference, tracking, business analytics, and display and competition stages. Each stage plays a crucial role in the overall pipeline.
Different Inference Patching Policies
Inference patching refers to the technique of combining multiple input streams together for batch processing. The Egyptian inference pipeline supports different inference patching policies, such as temporal patching and Spatial patching. These policies determine how the input data is organized and processed during the inference stage.
Tipstream Inference App Sample
The tipstream inference app sample is a useful tool for conducting tissue inference. This app provides pre-built functionality for both C and Python users. The C purpose and Python samples offer different approaches to implementing tissue inference. Users can choose the option that best suits their needs.
C Purpose
The C purpose sample provides a C-based implementation of the tipstream inference app. It allows users to process tissue samples and conduct inference using the C programming language. This sample is ideal for users who prefer a lower-level approach to tissue inference.
Python
The Python sample, on the other HAND, offers a Python-based implementation of the tipstream inference app. It leverages the power and simplicity of Python to provide a higher-level interface for conducting tissue inference. This sample is perfect for users who are more comfortable with Python programming.
Triton Inference Property
Triton is a powerful tool that offers various inference properties for conducting tissue inference. These properties include support for both CPR and DRPC, deep stream technology influence plugins, and pre-processing and post-processing plugins.
For both CPR and DRPC
Triton provides support for both CPR (Cascaded Pose Regression) and DRPC (Decentralized Random Process Clustering) techniques. These techniques enable accurate and efficient tissue inference by leveraging advanced algorithms and deep learning models.
Deep Stream Technology Influence Plugins
Deep stream technology influence plugins play a crucial role in the tissue inference process. These plugins are responsible for performing specific tasks, such as object detection, classification, segmentation, and road tensor data analysis. They integrate seamlessly with Triton to deliver accurate and reliable results.
Pre-processing and Post-processing Plugins
Pre-processing and post-processing plugins are essential components of the tissue inference pipeline. They help prepare the input data for inference and refine the output data for further analysis.
Pre-processing Plugins
Pre-processing plugins handle tasks such as color conversion, core view normalization, and data delivery. These plugins ensure that the input data is in the correct format and ready for inference. They support various input tensor data types, such as Internet Union 8, lp72, lp16, 16 into 32, and 32 into 32. They also offer support for different input tensor orders, such as nthw and hwc.
Post-processing Plugins
Post-processing plugins, on the other hand, focus on refining the output data generated during inference. They perform tasks such as classification, detection, segmentation, and drawing output tensor data. These plugins enable users to extract valuable insights from the tissue samples and Visualize the results effectively.
SDK Overview
The tissue inference SDK provides developers with a comprehensive set of tools and libraries for conducting tissue inference. It covers various stages of the tissue inference pipeline, including capture, decode, preprocessing, batching, AI inference, tracking, business analytics, display, and competition.
Capture Decode
The capture decode stage is responsible for capturing and decoding the input data, such as tissue images or videos. It prepares the data for further processing and analysis.
Preprocessing Batching AI Inference
The preprocessing batching AI inference stage involves applying preprocessing techniques to the input data and conducting AI inference using deep learning models. It plays a crucial role in extracting valuable information from the tissue samples.
Tracking Business Analytics
The tracking business analytics stage focuses on tracking objects and analyzing business-related data. It enables users to gain insights into the behavior and characteristics of the tissue samples.
Display and Competition
The display and competition stage involves visualizing the results of the tissue inference process and competing in various metrics and challenges. It aims to showcase the effectiveness and accuracy of the tissue inference system.
AI Inference Based on TPU and DSP
AI inference based on TPU (Tensor Processing Unit) and DSP (Digital Signal Processor) offers significant advantages in terms of performance and accuracy. The combination of these technologies ensures efficient and reliable tissue inference results.
Snapshot of Tissue Inference Sample App
The snapshot of the tissue inference sample app showcases the wide range of capabilities offered by AI inference based on TPU and DSP. It includes features such as primary detection, secondary classification, and support for multiple types of tissue samples.
Primary Detection
The primary detection feature focuses on detecting and identifying objects in tissue samples. It employs advanced algorithms and deep learning models to accurately analyze the samples and provide valuable insights.
Secondary Classification
The secondary classification feature goes beyond primary detection and provides additional information about the detected objects. It classifies objects based on specific attributes, such as their type, make, color, etc. This feature enhances the overall accuracy and usefulness of the tissue inference system.
Launching Different Pipelines
Launching different pipelines is a crucial aspect of conducting tissue inference. It involves configuring the system to handle different types of tissue samples and optimizing the workflow for maximum efficiency.
Launching Density Power Plant
Launching the density power plant pipeline entails configuring the system to handle high-density tissue samples. It involves setting up the necessary infrastructure, such as the Android 10 power plant with CAPI mode and GRPC mode.
Launching Android 10 Power Plant
The Android 10 power plant provides an efficient and reliable platform for conducting tissue inference. It leverages the power of Android 10 and supports various modes, such as CAPI mode and GRPC mode, to meet the diverse needs of users.
Deep Stream App with DS Triton
The deep stream app is a powerful tool for conducting tissue inference with DS Triton. It offers a seamless integration of deep stream technology influence plugins and Triton for accurate and efficient inference.
Installing the API
To begin using the deep stream app with DS Triton, you need to install the necessary API. The installation process is straightforward and well-documented, ensuring a smooth setup and configuration.
DS Triton Config File
The DS Triton config file is a crucial component of the deep stream app. It contains all the necessary configurations and settings for seamless integration between deep stream technology influence plugins and Triton. The config file allows users to customize the behavior and performance of the system.
Triton Model Ripple Tree
The Triton model ripple tree is a comprehensive framework for managing and organizing deep learning models within the Triton ecosystem. It provides a hierarchical structure that ensures efficient and reliable model deployment and execution.
Model Repo
The model repo is a top-level directory within the Triton model ripple tree. It serves as a centralized repository for storing and managing all the deep learning models used in the tissue inference process. The model repo allows users to easily access and deploy the models on-demand.
Model Config File
The model config file is an essential component of the Triton model ripple tree. It contains detailed information about each model, such as its name, version, platform support, input tensor types, output tensor types, etc. The model config file ensures seamless integration and compatibility between the models and the Triton ecosystem.
Input Tensor Data Types
The input tensor data types define the format and encoding of the input data used in the tissue inference process. Triton supports various input tensor data types, such as Internet Union 8, lp72, lp16, 16 into 32, and 32 into 32. This flexibility allows users to handle different types of input data effectively.
Input Tensor Orders
The input tensor orders determine the layout and organization of the input data within the tensor. Triton supports various input tensor orders, such as nthw and hwc. These orders ensure compatibility and interoperability with different deep learning frameworks and models.
Using Different Batching Policies
Different batching policies offer users flexibility and control over the batching process during tissue inference. Triton supports both general batching and Triton-specific batching policies, allowing users to optimize the inference process according to their requirements.
General Batching Before Inference
In general batching, all the input streams are batched together before passing them to the inference plugins. This approach improves efficiency and throughput by processing multiple input streams simultaneously. Users can control the batch size by specifying it in the application config file.
Triton Batching Policy
Triton offers a specific batching policy known as Triton batching. In this approach, the input streams are passed to Triton for batching and inference. Triton dynamically manages the batching process based on the specified policies, such as dynamic batching for improved performance. Users can configure the preferred batch size and timeouts to fine-tune the batching process.
Deep Stream Application Data Flow
Understanding the data flow within the deep stream application is essential for conducting efficient and accurate tissue inference. It involves capturing, decoding, preprocessing, inference, and post-processing stages.
Inference Data Passage
The inference data passes through various stages, such as capturing, decoding, preprocessing, and inference. The deep stream application handles and manages the data flow between these stages to ensure smooth and accurate tissue inference. The data format and structure are maintained throughout the process to enable seamless analysis and visualization.
Tensority Low-Level Library
The Tensority low-level library is a critical component of the deep stream application. It provides various functions and APIs for handling tensor operations, such as detection, classification, segmentation, etc. The library ensures efficient and reliable processing of the tissue samples and enhances the overall performance of the application.
Triton Server App
The Triton server app is responsible for managing the deep learning models and conducting the inference process. It receives the input data from the low-level library, performs the necessary computations using the models, and generates the output data. The Triton server app ensures accurate and reliable tissue inference results.
Highlights
- Tissue inference options with Triton and 1030.
- Egyptian inference pipeline and patching policies.
- DS Triton and deep stream technology influence plugins.
- Pre-processing and post-processing plugins.
- Overview of the tissue inference SDK and its components.
- AI inference based on TPU and DSP.
- Launching different pipelines for tissue inference.
- Deep stream app with DS Triton and its configuration.
- Triton model ripple tree and model configuration.
- Different batching policies and their implications.
- Data flow in the deep stream application.
FAQ
Q: Can I run Triton inference on the host directly?
Yes, Triton supports running inference directly on the host machine without the need for separate containers or processes. This allows for seamless integration and efficient inference.
Q: Does Triton support multiple deep learning frameworks?
Yes, Triton supports multiple deep learning frameworks, including TensorFlow, PyTorch, and custom C programs. This flexibility allows users to leverage their preferred frameworks for tissue inference.
Q: Can Triton manage all the dragon models together?
Yes, Triton can manage all the dragon models together using its powerful model management capabilities. It can handle multiple models simultaneously and ensure accurate and efficient inference.
Q: Does Triton support grpc inference?
Yes, Triton supports grpc inference, which enables remote inference across containers, processes, or machines. This feature enhances the scalability and flexibility of the tissue inference system.
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