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 1.5.0

• Added a new execution mode allows the inference server to start without loading any models from the model repository. Model loading and unloading is then controlled by a new GRPC/HTTP model control API.

• Added a new instance-group mode allows TensorFlow models that explicitly distribute inferencing across multiple GPUs to run in that manner in the inference server.

• Improved input/output tensor reshape to allow variable-sized dimensions in tensors being reshaped.

• Added a C++ wrapper around the custom backend C API to simplify the creation of custom backends. This wrapper is included in the custom backend SDK.

• Improved the accuracy of the compute statistic reported for inference requests. Previously the compute statistic included some additional time beyond the actual compute time.

• The performance client, perf_client, now reports more information for ensemble models, including statistics for all contained models and the entire ensemble.

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) or in Google Cloud Storage.

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

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

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
  • Checking Inference Server Status
• Client Libraries and Examples
  • Getting the Client Libraries and Examples
  • Image Classification Example Application
  • Ensemble Image Classification Example Application
  • Performance Example Application
  • Client API
• 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
• Inference Server API
  • Health
  • Status
  • Model Control
  • Inference
  • Stream Inference
• Metrics

Developer Guide

• Architecture
  • Concurrent Model Execution
• Custom Operations
  • TensorRT
  • TensorFlow
• Building
  • Building the Server with Docker
  • Building the Server with CMake
  • 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

• Capabilities
• Protobuf API
  • HTTP/GRPC API
  • Model Configuration
  • Status
• C++ API
  • Class Hierarchy
  • File Hierarchy
  • Full API
• Python API
  • Client

Indices and tables

• Index