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

Triton Model Navigator is an inference toolkit designed for optimizing and deploying Deep Learning models with a focus on NVIDIA GPUs.
https://github.com/triton-inference-server/model_navigator

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Triton Model Navigator is an inference toolkit designed for optimizing and deploying Deep Learning models with a focus on NVIDIA GPUs.

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README 

          



# Triton Model Navigator



Welcome to [Triton Model Navigator](https://github.com/triton-inference-server/model_navigator), an inference toolkit designed

for optimizing and deploying Deep Learning models with a focus on NVIDIA GPUs. The Triton Model Navigator streamlines the

process of moving models and pipelines implemented in [PyTorch](https://pytorch.org),

[TensorFlow](https://www.tensorflow.org), and/or [ONNX](https://onnx.ai)

to [TensorRT](https://github.com/NVIDIA/TensorRT).



The Triton Model Navigator automates several critical steps, including model export, conversion, correctness testing, and

profiling. By providing a single entry point for various supported frameworks, users can efficiently search for the best

deployment option using the per-framework optimize function. The resulting optimized models are ready for deployment on

either [PyTriton](https://github.com/triton-inference-server/pytriton)

or [Triton Inference Server](https://github.com/triton-inference-server/server).



## Features at Glance



The distinct capabilities of Triton Model Navigator are summarized in the feature matrix:



| Feature                     | Description                                                                                                                                      |

|-----------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------|

| Ease-of-use                 | Single line of code to run all possible optimization paths directly from your source code                                                        |

| Wide Framework Support      | Compatible with various machine learning frameworks including PyTorch, TensorFlow, and ONNX                                                      |

| Models Optimization         | Enhance the performance of models such as ResNET and BERT for efficient inference deployment                                                     |

| Pipelines Optimization      | Streamline Python code pipelines for models such as Stable Diffusion and Whisper using Inplace Optimization, exclusive to PyTorch                |

| Model Export and Conversion | Automate the process of exporting and converting models between various formats with focus on TensorRT and Torch-TensorRT                        |

| Correctness Testing         | Ensures the converted model produce correct outputs validating against the original model                                                        |

| Performance Profiling       | Profiles models to select the optimal format based on performance metrics such as latency and throughput to optimize target hardware utilization |

| Models Deployment           | Automates models and pipelines deployment on PyTriton and Triton Inference Server through dedicated API                                          |



## Documentation



Learn more about Triton Model Navigator features in [documentation](https://triton-inference-server.github.io/model_navigator).



## Prerequisites



Before proceeding with the installation of Triton Model Navigator, ensure your system meets the following criteria:



- **Operating System**: Linux (Ubuntu 20.04+ recommended)

- **Python**: Version `3.8` or newer

- NVIDIA GPU



You can use NGC Containers for PyTorch and TensorFlow which contain all necessary dependencies:



- [PyTorch](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch)

- [TensorFlow](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/tensorflow)



## Install



The Triton Model Navigator can be installed from `pypi.org`.



### Installing with PyTorch extras



For installing with PyTorch dependencies, use:



```shell

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

```



### Installing with TensorFlow extras



For installing with TensorFlow dependencies, use:



```shell

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

```



### Installing with onnxruntime-gpu for CUDA 11



The default CUDA version for ONNXRuntime since 1.19.0 is CUDA 12. To install with CUDA 11 support use following extra index url:

```shell

.. --extra-index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/onnxruntime-cuda-11/pypi/simple/ ..

```



## Quick Start



The quick start section provides examples of possible optimization and deployment paths provided in Triton Model Navigator.



### Optimize Stable Diffusion with Inplace



The Inplace Optimize allows seamless optimization of models for deployment, such as converting

them to TensorRT, without requiring any changes to the original Python pipelines.



The below code presents Stable Diffusion pipeline optimization. But first, before you run the example install the required

packages:



```shell

pip install transformers diffusers torch

```



Then, initialize the pipeline and wrap the model components with `nav.Module`::



```python

import model_navigator as nav

from transformers.modeling_outputs import BaseModelOutputWithPooling

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline



def get_pipeline():

    # Initialize Stable Diffusion pipeline and wrap modules for optimization

    pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1")

    pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)

    pipe = pipe.to("cuda")

    pipe.text_encoder = nav.Module(

        pipe.text_encoder,

        name="clip",

        output_mapping=lambda output: BaseModelOutputWithPooling(**output),

    )

    pipe.unet = nav.Module(

        pipe.unet,

        name="unet",

    )

    pipe.vae.decoder = nav.Module(

        pipe.vae.decoder,

        name="vae",

    )

    return pipe

```



Prepare a simple dataloader:



```python

# Please mind, the first element in tuple need to be a batch size

def get_dataloader():

    return [(1, "a photo of an astronaut riding a horse on mars")]

```



Execute model optimization:



```python

pipe = get_pipeline()

dataloader = get_dataloader()



nav.optimize(pipe, dataloader)

```

Once the pipeline has been optimized, you can load explicit the most performant version of the modules executing:



```python

nav.load_optimized()

```



At this point, you can simply use the original pipeline to generate prediction with optimized models directly in Python:

```python

pipe.to("cuda")



images = pipe(["a photo of an astronaut riding a horse on mars"])

image = images[0][0]



image.save("an_astronaut_riding_a_horse.png")

```



An example of how to serve a Stable Diffusion pipeline through PyTriton can be found [here](https://github.com/triton-inference-server/pytriton/tree/main/examples/huggingface_stable_diffusion).



Please read [Error isolation when running Python script](#error-isolation-when-running-python-script) when you plan

to place code in Python script.



### Optimize ResNET and deploy on Triton



Triton Model Navigator support also optimization path for deployment on Triton. This path is supported for nn.Module,

keras.Model or ONNX files which inputs are tensors.



To optimize ResNet50 model from TorchHub run the following code:



```python

import torch

import model_navigator as nav



# Initialize the model

resnet50 = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_resnet50', pretrained=True).eval()



# Wrap model in nav.Module

resnet50 = nav.Module(resnet50, name="resnet50")



# Optimize Torch model loaded from TorchHub

nav.optimize(resnet50, dataloader=[(1, [torch.randn(1, 3, 256, 256)])])

```



Once optimization is done, creating a model store for deployment on Triton is simple as following code:



```python

import pathlib



# Generate the model store from optimized model

resnet50.triton_model_store(

    model_repository_path=pathlib.Path("model_repository"),

)

```



Please read [Error isolation when running Python script](#error-isolation-when-running-python-script) when you plan

to place code in Python script.



### Profile any model or callable in Python



Triton Model Navigator enhances models and pipelines and provides a uniform method for profiling any Python

function, callable, or model. At present, our support is limited strictly to static batch profiling scenarios.



As an example, we will use a simple function that simply sleeps for 50ms:



```python

import time



def custom_fn(input_):

    # wait 50ms

    time.sleep(0.05)

    return input_

```



Let's provide a dataloader we will use for profiling:



```python

# Tuple of batch size and data sample

dataloader = [(1, ["This is example input"])]

```



Finally, run the profiling of the function with prepared dataloader:



```python

nav.profile(custom_fn, dataloader)

```



## Error isolation when running Python script



**Important**: Please review below section to prevent unexpected issues when running `optimize`.



For better error isolation, some conversions and exports are run in separate child processes using multiprocessing in

the `spawn` mode. This means that everything in a global scope will be run in a child process. You can encounter

unexpected issue when the optimization code is place in Python script and executed as:

```shell

python optimize.py

```

To prevent nested optimization, you have to either put the optimize code in:

```python

if __name__ == "__main__":

    # optimization goes here

```

or

```python

import multiprocessing as mp

if mp.current_process().name == "MainProcess":

    # optimization goes here

```



If none of the above works for you, you can run all optimization in a single process at the cost of error isolation by

setting the following environment variable:

```bash

NAVIGATOR_USE_MULTIPROCESSING=False

```



## Examples



We offer comprehensive, step-by-step [guides](examples) that showcase the utilization of the Triton Model Navigator’s

diverse features. These guides are designed to elucidate the processes of optimization, profiling, testing, and

deployment of models using [PyTriton](https://github.com/triton-inference-server/pytriton) and [Triton Inference Server](https://github.com/triton-inference-server/server).



## Useful Links



* [Changelog](CHANGELOG.md)

* [Support Matrix](docs/support_matrix.md)

* [Known Issues](docs/known_issues.md)

* [Contributing](CONTRIBUTING.md)