NVIDIA TensorRT Inference Server

The NVIDIA TensorRT Inference Server provides a cloud inferencing solution optimized for NVIDIA GPUs. The server provides an inference service via an HTTP or GRPC endpoint, allowing remote clients to request inferencing for any model being managed by the server.

What’s New In 1.8.0

• Shared-memory support is expanded to include CUDA shared memory.

• Improve efficiency of pinned-memory used for ensemble models.

• The perf_client application has been improved with easier-to-use command-line arguments (which maintaining compatibility with existing arguments).

• Support for string tensors added to perf_client.

• Documentation contains a new “Optimization” section discussing some common optimization strategies and how to use perf_client to explore these strategies.

Features

• Multiple framework support. The server can manage any number and mix of models (limited by system disk and memory resources). Supports TensorRT, TensorFlow GraphDef, TensorFlow SavedModel, ONNX, PyTorch, and Caffe2 NetDef model formats. Also supports TensorFlow-TensorRT integrated models. Variable-size input and output tensors are allowed if supported by the framework. See Capabilities for detailed support information for each framework.

• Concurrent model execution support. Multiple models (or multiple instances of the same model) can run simultaneously on the same GPU.

• Batching support. For models that support batching, the server can accept requests for a batch of inputs and respond with the corresponding batch of outputs. The inference server also supports multiple scheduling and batching algorithms that combine individual inference requests together to improve inference throughput. These scheduling and batching decisions are transparent to the client requesting inference.

• Custom backend support. The inference server allows individual models to be implemented with custom backends instead of by a deep-learning framework. With a custom backend a model can implement any logic desired, while still benefiting from the GPU support, concurrent execution, dynamic batching and other features provided by the server.

• Ensemble support. An ensemble represents a pipeline of one or more models and the connection of input and output tensors between those models. A single inference request to an ensemble will trigger the execution of the entire pipeline.

• Multi-GPU support. The server can distribute inferencing across all system GPUs.

• The inference server provides multiple modes for model management. These model management modes allow for both implicit and explicit loading and unloading of models without requiring a server restart.

• Model repositories may reside on a locally accessible file system (e.g. NFS), in Google Cloud Storage or in Amazon S3.

• Readiness and liveness health endpoints suitable for any orchestration or deployment framework, such as Kubernetes.

• Metrics indicating GPU utilization, server throughput, and server latency.

• C library inferface allows the full functionality of the inference server to be included directly in an application.

User Guide

• Quickstart
  • Prerequisites
  • Using A Prebuilt Docker Container
  • Building With Docker
  • Building With CMake
  • Run TensorRT Inference Server
  • Verify Inference Server Is Running Correctly
  • Getting The Client Examples
  • Running The Image Classification Example
• Installing the Server
  • Installing Prebuilt Containers
• Running the Server
  • Example Model Repository
  • Running The Inference Server
  • Running The Inference Server On A System Without A GPU
  • Running The Inference Server Without Docker
  • Checking Inference Server Status
• Client Libraries
  • Getting the Client Libraries
  • Building Your Own Client
  • Client API
• Client Examples
  • Getting the Client Examples
  • Image Classification Example Application
  • Ensemble Image Classification Example Application
  • Performance Measurement Application
• Models And Schedulers
  • Stateless Models
  • Stateful Models
  • Ensemble Models
• Model Repository
  • Modifying the Model Repository
  • Model Versions
  • Framework Model Definition
  • Custom Backends
  • Ensemble Backends
• Model Configuration
  • Generated Model Configuration
  • Datatypes
  • Reshape
  • Version Policy
  • Instance Groups
  • Scheduling And Batching
  • Optimization Policy
• Model Management
  • Model Control Mode NONE
  • Model Control Mode POLL
  • Model Control Mode EXPLICIT
• Optimization
  • Optimization Settings
  • perf_client
• Metrics

Developer Guide

• Architecture
  • Concurrent Model Execution
• Custom Operations
  • TensorRT
  • TensorFlow
• HTTP and GRPC API
  • Health
  • Status
  • Model Control
  • Inference
  • Stream Inference
• Library API
• Building
  • Building the Server
  • Building A Custom Backend
  • Building the Client Libraries and Examples
  • Building the Documentation
• Testing
  • Generate QA Model Repositories
  • Build QA Container
  • Run QA Container
• Contributing
  • Coding Convention

Reference

• FAQ
  • What are the advantages of running a model with TensorRT Inference Server compared to running directly using the model’s framework API?
  • Can TensorRT Inference Server run on systems that don’t have GPUs?
  • Can TensorRT Inference Server be used in non-Docker environments?
  • How would you use TensorRT Inference Server within the AWS environment?
  • How do I measure the performance of my model running in the TensorRT Inference Server?
  • How can I fully utilize the GPU with TensorRT Inference Server?
  • If I have a server with multiple GPUs should I use one TensorRT Inference Server to manage all GPUs or should I use multiple inference servers, one for each GPU?
• Capabilities
• Protobuf API
  • HTTP/GRPC API
  • Model Configuration
  • Status
• C++ API
  • Class Hierarchy
  • File Hierarchy
  • Full API
• Python API
  • Client

Indices and tables

• Index