tsbk 1.8.1

Provides a buildkit for constructing Triton Inference Server model repositories

tritonserver-buildkit

A powerful Python toolkit for building, deploying, and testing NVIDIA Triton Inference Server model repositories with ease.

Overview

tsbk (Triton Server Build Kit) simplifies the process of creating and managing Triton Inference Server deployments. It provides both a declarative YAML-based configuration system and a programmatic Python SDK, making it easy to:

• Build Triton-compatible model repositories from simple configurations
• Deploy and run Triton servers in Docker containers
• Test model deployments with built-in validation framework
• Integrate with mlflow_backend for deploying models registered with MLFlow

Whether you're developing ML models locally or deploying them to production, tsbk streamlines the entire workflow.

Features

• Declarative Configuration: Define model repositories using simple YAML files
• Programmatic SDK: Build model repositories programmatically using Python
• Automatic Testing: Built-in test framework with input/output validation
• MLflow Integration: Seamlessly load models from MLflow registry
• S3 Support: Fetch model artifacts from S3-compatible storage
• Multi-Backend Support: ONNX, TensorRT, Python, ensemble models, and more
• Docker Integration: Automatic Triton server deployment in containers
• Test Plan Serialization: Create reusable test plans for CI/CD pipelines
• HTTP & gRPC Support: Test models via both protocols

Installation

Using pip

pip install tsbk

Requirements

• Python 3.11+
• Docker (for running Triton servers)
• AWS credentials (for S3 model storage, optional)
• Databricks credentials (for MLflow model access, optional)

Quick Start

CLI: YAML Configuration

1. Create a model configuration (model-config.yaml):

name: quickstart
models:
  your-model-name:
    backend: onnxruntime
    versions:
      - artifact_uri: s3://your-bucket/model.onnx

2. Build and run with a single command:

tsbk run model-config.yaml ./model-repo

This will:

• Build the tritonserver model repository at ./model-repo
• Launch Triton server in a Docker container

SDK: Python API

import tsbk

# Define your model repository
repo = tsbk.TritonModelRepo(
    name='quickstart',
    path='./model-repo',
    models={
        'your-model-name': tsbk.TritonModel(
            backend='onnxruntime',
            versions=[
                tsbk.TritonModelVersion(
                    artifact_uri='models:/your-onnx-model/1'  # MLflow model URI or S3 URI supported
                )
            ]
        )
    }
)

# Build the repository
repo.build()

# Run Triton server
repo.run()

CLI Commands

tsbk build

Build a Triton model repository from a configuration file.

tsbk build model-config.yaml ./model-repo

tsbk run

Build and run a Triton server from a configuration file.

# Run with defaults
tsbk run model-config.yaml ./model-repo

Optionally detach from the server to run in the background:

# Detach mode
tsbk run model-config.yaml ./model-repo --detach

Optionally run tests after starting the server:

# Run and test
tsbk run model-config.yaml ./model-repo --test

tsbk test

Test a running Triton server against your configuration. This requires providing test cases for your models. They can be specified via YAML or programmatically in the SDK. tsbk also supports taking test data from MLFlow models with defined example_inputs.

# Test HTTP endpoint
tsbk test model-config.yaml ./model-repo --url http://localhost:8000

# Test gRPC endpoint
tsbk test model-config.yaml ./model-repo --url localhost:8001 --grpc

tsbk create-test-plan

Create a serialized test plan for CI/CD pipelines.

tsbk create-test-plan model-config.yaml ./model-repo test-plan.msgpack

tsbk run-test-plan

Execute a serialized test plan against a running server.

tsbk run-test-plan test-plan.msgpack --url http://localhost:8000

Examples

Explore the examples/ directory for complete working examples:

• Config-based deployment: Using YAML configuration files
• SDK-based deployment: Using the Python SDK programmatically

Environment Variables

• TSBK_DIR: Working directory for tsbk operations (default: ./.tsbk)
• TSBK_S3_PREFIX: S3 prefix for temporary shared model artifacts
• TSBK_K8S_SERVICE_ACCOUNT: Kubernetes service account used when running build jobs (default: default)

Troubleshooting

Docker Issues

If Triton server fails to start:

• Ensure Docker is running
• Check port availability (8000, 8001, 8002)
• Verify Docker has sufficient resources (memory, disk space)

Model Loading Errors

If models fail to load:

• Verify artifact URIs are correct
• Ensure model format matches backend (e.g., .onnx for onnxruntime)
• Review Triton server logs: docker logs <container-id>

Test Failures

If tests fail unexpectedly:

• Check tolerance settings (rtol, atol)
• Verify input/output tensor shapes match model expectations
• Review expected output values

Acknowledgments

• Built on NVIDIA Triton Inference Server
• Integrates with MLflow for model management

Roadmap

Future enhancements we're considering:

• Kubernetes deployment support
• TensorRT model optimization utilities
• Performance benchmarking tools
• Direct Hugging Face model support

Suggestions and contributions are welcome!