triton-model-navigator 0.5.5

Triton Model Navigator provides tools supporting to create Deep Learning production ready inference models

Model conversion plays a crucial role in unlocking the maximum performance capabilities of the underlying hardware. By applying these transformation techniques, models can be optimized to fully utilize the specific features and optimizations offered by the hardware architecture. Furthermore, conversions allow for serialization of models, separating them from the source code. This serialization process enhances portability, allowing the models to be seamlessly deployed in production environments. The decoupling of models from the source code also facilitates maintenance, updates, and collaboration among developers.

However, this process comprises multiple steps and offers various potential paths, making manual execution complicated and time-consuming. The Triton Model Navigator offers a user-friendly and automatic solution for converting, optimizing and deploying machine learning models. It offers a single entry point for various supported frameworks, allowing users to start the process of searching for the best deployment option with a single call to the dedicated optimize function. Model Navigator handles model export, conversion, correctness testing, and performance profiling, saving all generated artifacts.

Throughout the optimize process, the Model Navigator considers multiple factors, including different precisions, runtimes, and data shapes. This careful consideration allows for the adjustment of the model’s configuration to achieve improved performance, effectively minimizing the costs associated with serving the model.

Converted models can be easily deployed on the PyTriton or Triton Inference Server .

The Model Navigator generates multiple optimized and production-ready models. The table below illustrates the model formats that can be obtained by using the Model Navigator with various frameworks.

Table: Supported conversion target formats per each supported Python framework or file.

PyTorch	TensorFlow 2	JAX	ONNX
Torch 2 Compile TorchScript Trace TorchScript Script TorchTensorRT ONNX TensorRT	SavedModel TensorFlowTensorRT ONNX TensorRT	SavedModel TensorFlowTensorRT ONNX TensorRT	TensorRT

Note: The Model Navigator has the capability to support any Python function as input. However, in this particular case, its role is limited to profiling the function without generating any serialized models.

The Model Navigator stores all artifacts within the navigator_workspace. Additionally, it provides the option to save a portable and transferable Navigator Package that includes only the models with minimal latency and maximal throughput. This package also includes base formats that can be used to regenerate the TensorRT plan on the target hardware.

Table: Model formats that can be generated from saved Navigator Package and from model sources.

From model source	From Navigator Package
SavedModel TensorFlowTensorRT TorchScript Trace TorchScript Script Torch 2 Compile TorchTensorRT ONNX TensoRT	TorchTensorRT TensorFlowTensorRT ONNX TensorRT

Installation

The package can be installed using extra index url:

pip install -U --extra-index-url https://pypi.ngc.nvidia.com triton-model-navigator[<extras,>]

or with nvidia-pyindex:

pip install nvidia-pyindex
pip install -U triton-model-navigator[<extras,>]

Extras:

• tensorflow - Model Navigator with dependencies for TensorFlow2

• jax - Model Navigator with dependencies for JAX

For using with PyTorch no extras are needed.

Quick Start

Using Model Navigator is as simply as calling optimize with model and dataloader: The optimize function will save all the artifacts it generates in the navigator_workspace.

Note: The dataloader is utilized to determine the maximum and minimum shapes of the inputs utilized during model conversions. The Model Navigator employs a single sample from the dataloader, which is then repeated to generate synthetic batches for profiling purposes. Correctness tests are conducted on a subset of the dataloader samples, while verification tests are executed on the entire dataloader.

import logging

import torch
import model_navigator as nav

nav.torch.optimize(
    model=torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_resnet50', pretrained=True).eval(),
    dataloader=[torch.randn(1, 3, 256, 256) for _ in range(10)],
)

The code snippet below demonstrates the usage of the PyTritonAdapter to retrieve the runner and other necessary information. The runner serves as an abstraction that connects the model checkpoint with its runtime, making the inference process more accessible and straightforward. Following that, it initiates the PyTriton server using the provided parameters.

pytriton_adapter = nav.pytriton.PyTritonAdapter(package=package, strategy=nav.MaxThroughputStrategy())
runner = pytriton_adapter.runner

runner.activate()

@batch
def infer_func(**inputs):
    return runner.infer(inputs)

with Triton() as triton:
    triton.bind(
        model_name="resnet50",
        infer_func=infer_func,
        inputs=pytriton_adapter.inputs,
        outputs=pytriton_adapter.outputs,
        config=pytriton_adapter.config,
    )
    triton.serve()

Alternatively, Model Navigator can generate model_repository that can be served on the Triton Inference Server:

nav.triton.model_repository.add_model_from_package(
    model_repository_path=pathlib.Path("model_repository"),
    model_name="resnet50",
    package=package,
    strategy=nav.MaxThroughputStrategy(),
)

Examples

We provide step-by-step examples that demonstrate how to use various features of Model Navigator. For the sake of readability and accessibility, we use a simple torch.nn.Linear model as an example. These examples illustrate how to optimize, test and deploy the model on the PyTriton and Triton Inference Server.

https://github.com/triton-inference-server/model_navigator/tree/main/examples.

Links

• Documentation: https://triton-inference-server.github.io/model_navigator

• Source: https://github.com/triton-inference-server/model_navigator

• Issues: https://github.com/triton-inference-server/model_navigator/issues

• Changelog: https://github.com/triton-inference-server/model_navigator/blob/main/CHANGELOG.md

• Known Issues: https://github.com/triton-inference-server/model_navigator/blob/main/docs/known_issues.md

• Contributing: https://github.com/triton-inference-server/model_navigator/blob/main/CONTRIBUTING.md