NVIDIA Triton Inference Server Boosts Deep Learning Inference

The NVIDIA Triton Inference Server, previously known as TensorRT Inference Server, is now available from NVIDIA NGC or via GitHub.

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NVIDIA
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Nefi Alarcon
•2 min read•beginner•
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BERTDeep LearningPyTorchTensorFlow

Overview

The NVIDIA Triton Inference Server, formerly known as TensorRT Inference Server, facilitates the deployment of high-performance inference services for deep learning models. It supports various frameworks and allows seamless updates of models without downtime, making it ideal for developers and AI companies.

What You'll Learn

1

How to deploy models from different framework backends using NVIDIA Triton Inference Server

2

Why NVIDIA Triton Inference Server is beneficial for real-time inference in retail applications

3

How to leverage NVIDIA Triton Inference Server for seamless model updates without disruptions

Prerequisites & Requirements

  • Understanding of deep learning frameworks such as TensorFlow, TensorRT, and PyTorch
  • Familiarity with NVIDIA Triton Inference Server and its deployment(optional)

Key Questions Answered

What is NVIDIA Triton Inference Server and how is it used?
NVIDIA Triton Inference Server is a high-performance inference server that allows developers to deploy models from various frameworks like TensorFlow, TensorRT, and PyTorch. It provides inference services via HTTP/REST or GRPC endpoints, making it suitable for cloud, on-premises, or edge deployments.
How does Tracxpoint utilize NVIDIA Triton Inference Server?
Tracxpoint uses NVIDIA Triton Inference Server to deploy and serve multiple models for tasks such as object detection and personalized offers in retail environments. This flexibility allows them to update models without application restarts, enhancing user experience.
What are the benefits of using NVIDIA Triton Inference Server for model deployment?
The benefits include the ability to serve models from multiple frameworks, seamless updates of retrained models without downtime, and support for high-performance inference in various environments, including cloud and edge.
What role does NVIDIA Triton Inference Server play in Kubeflow and KFServing?
NVIDIA Triton Inference Server is part of the open inference platforms Kubeflow and KFServing, and it will be one of the first to adopt the new KFServing V2 API, enhancing its integration with these platforms.

Technologies & Tools

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Inference Server
Nvidia Triton Inference Server
Used for deploying and serving deep learning models from various frameworks.
Deep Learning Framework
Tensorflow
One of the frameworks supported by NVIDIA Triton Inference Server.
Deep Learning Framework
Tensorrt
Another framework supported by NVIDIA Triton Inference Server.
Deep Learning Framework
Pytorch
Supported framework for model deployment in NVIDIA Triton Inference Server.
Deep Learning Framework
Onnx Runtime
Framework supported by NVIDIA Triton Inference Server for model inference.

Key Actionable Insights

1
Utilize NVIDIA Triton Inference Server to streamline model deployment across various frameworks, enhancing flexibility.
This approach is particularly beneficial for organizations that need to manage multiple models from different frameworks, allowing for a more cohesive deployment strategy.
2
Leverage the seamless model update capability of NVIDIA Triton Inference Server to minimize downtime during deployments.
This is crucial for applications requiring continuous availability, such as retail environments where user experience should not be disrupted.
3
Consider integrating NVIDIA Triton Inference Server with Kubeflow for improved model management and orchestration.
This integration can simplify the deployment process and enhance the scalability of AI applications.