triton-model-navigator 0.4.2

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

Project description

The Triton Model Navigator automates the process of moving model from source to deployment on Triton Inference Server. The tool validate possible export and conversion paths to serializable formats like TensorRT and select the most promising format for production deployment.

The Triton Model Navigator is designed to provide a single entrypoint for each supported framework. The usage is simple as call a dedicated optimize function to start the process of searching for the best possible deployment by going through a broad spectrum of model conversions.

The optimize internally it performs model export, conversion, correctness testing, performance profiling, and saves all generated artifacts in the navigator_workspace, which is represented by a returned package object. The result of optimize process can be saved as a portable Navigator Package with the save function. Saved packages only contain the base model formats along with the best selected format based on latency and throughput. The package can be reused to recreate the process on same or different hardware. The configuration and execution status is saved in the status.yaml file located inside the workspace and the Navigator Package.

Finally, the Navigator Package can be used for model deployment on NVIDIA Triton Inference Server. Dedicated API helps with obtaining all necessary parameters and creating model_repository or receive the optimized model for inference in Python environment.

Installing

The package can be installed from pypi.org using:

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

Extras:

• tensorflow - Model Navigator for TensorFlow2
• jax - Model Navigator for JAX

Quick Start

The quick start presents how to optimize Python model for deployment on Triton Inference Server. In the example we are using a simple TensorFlow 2 model.

Export and optimize model

To use Triton Model Navigator you must prepare model and dataloader. We recommend to create following helper functions:

• get_model - return model object
• get_dataloader - generate samples required for export and conversion
• get_verify_func (optional) - validate the correctness of models based on implemented metric

Next you can use Triton Model Navigator optimize function with provided model, dataloader and verify function to export and convert model to all supported formats.

See the below example of optimizing a simple TensorFlow model.

import logging

import numpy as np
import tensorflow as tf

import model_navigator as nav

# enable tensorflow memory growth to avoid allocating all GPU memory
gpus = tf.config.experimental.list_physical_devices("GPU")
for gpu in gpus:
    tf.config.experimental.set_memory_growth(gpu, True)

LOGGER = logging.getLogger(__name__)


# dataloader is used for inference and finding input shapes of the model.
# If you do not have dataloader, create one with samples with min and max shapes.
def get_dataloader():
    return [np.random.rand(1, 224, 224, 3).astype("float32") for _ in range(10)]


def get_verify_function():
    def verify_func(ys_runner, ys_expected):
        for a, b in zip(ys_runner, ys_expected):
            if not (np.isclose(a["output__0"], b["output__0"], atol=0.01)).all():
                return False

        return True

    return verify_func


# Model inputs must be a Tensor to support deployment on Triton Inference Server.
def get_model():
    inp = tf.keras.layers.Input((224, 224, 3))
    layer_output = tf.keras.layers.Lambda(lambda x: x)(inp)
    layer_output = tf.keras.layers.Lambda(lambda x: x)(layer_output)
    layer_output = tf.keras.layers.Lambda(lambda x: x)(layer_output)
    layer_output = tf.keras.layers.Lambda(lambda x: x)(layer_output)
    layer_output = tf.keras.layers.Lambda(lambda x: x)(layer_output)
    model_output = tf.keras.layers.Lambda(lambda x: x)(layer_output)
    return tf.keras.Model(inp, model_output)

# Check documentation for more details about Profiler Configuration options.
def get_profiler_config():
    return nav.ProfilerConfig()


model = get_model()
dataloader = get_dataloader()
verify_func = get_verify_function()
profiler_config = get_profiler_config()

# Model Navigator optimize starts export, optimization and testing process.
# The resulting package represents all artifacts produced by Model Navigator.
package = nav.tensorflow.optimize(
    model=model,
    profiler_config=profiler_config,
    target_formats=(nav.Format.ONNX,),
    dataloader=dataloader,
    verify_func=verify_func,
)

# Save nav package that can be used for Triton Inference Server deployment or obtaining model runner later.
# The package contains base format checkpoints that can be used for all other conversions.
# Models with minimal latency and maximal throughput are added to the package.
nav.package.save(package=package, path="mlp.nav")

You can customize behavior of export and conversion steps passing [CustomConfig][model_navigator.api.config.CustomConfig] to optimize function.

NVIDIA Triton Inference Server deployment

If you prefer the standalone NVIDIA Triton Inference Server you can create and use model_repository.

import logging
import pathlib

from model_navigator.exceptions import (
    ModelNavigatorEmptyPackageError,
    ModelNavigatorError,
    ModelNavigatorWrongParameterError
)
import model_navigator as nav

LOGGER = logging.getLogger(__name__)

package = nav.package.load("mlp.nav", "load_workspace")

# Create model_repository for standalone Triton deployment
try:
    nav.triton.model_repository.add_model_from_package(
        model_repository_path=pathlib.Path("model_repository"), model_name="dummy_model", package=package
    )
except (ModelNavigatorWrongParameterError, ModelNavigatorEmptyPackageError, ModelNavigatorError) as e:
    LOGGER.warning(f"Model repository cannot be created.\n{str(e)}")

Use command to start server with provided model_repository:

$ docker run --gpus=1 --rm \
  -p8000:8000 \
  -p8001:8001 \
  -p8002:8002 \
  -v ${PWD}/model_repository:/models \
  nvcr.io/nvidia/tritonserver:23.01-py3 \
  tritonserver --model-repository=/models

Examples

We provide simple examples how to use Triton Model Navigator to optimize the PyTorch, TensorFlow2, JAX and ONNX models for deployment on Triton Inference Server.

Optimize for various frameworks

• PyTorch:

  • Linear Model
  • ResNet50
  • BERT

• TensorFlow:

  • Linear Model
  • EfficientNet
  • BERT

• JAX:

  • Linear Model
  • GPT-2

• ONNX:

  • Identity Model

Optimize Navigator Package

The Navigator Package can be reused for optimize e.g. on the new hardware or with newer libraries. The example code can be found in examples/package.

Using model on Triton Inference Server

The optimized model by Triton Model Navigator can be used for serving inference through Triton Inference Server. The example code can be found in examples/triton.

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/CHANGELOG.md
• Known Issues: https://github.com/triton-inference-server/model_navigator/docs/known_issues.md
• Contributing: https://github.com/triton-inference-server/model_navigator/CONTRIBUTING.md