Machine Learning Frameworks, Model Tooling, and Deployment Strategies in the ML Pipeline

Machine learning frameworks, model tooling, and deployment solutions each serve different purposes within a machine learning (ML) workflow. Every stage of model creation, training, tuning, rollout, and inference exposes specific strengths and weaknesses for these technologies.

In this guide, we take a close look at five key tools and related technologies. PyTorch, TensorFlow, LiteRT (previously known as TensorFlow Lite), TensorRT, and ONNX. We outline their capabilities, benefits, trade-offs, and how they fit into a typical machine learning lifecycle. We also highlight how these tools can interoperate, present common deployment patterns, and include code examples to clarify the underlying ideas.

Key Takeaways for ML Tools and Deployment Workflows

• Align tools with pipeline phases: Use PyTorch or TensorFlow primarily for training, rely on ONNX to move models between ecosystems, apply TensorRT to optimize workloads on NVIDIA GPUs, and target mobile or edge environments with LiteRT.
• Leverage specialized optimizations: TensorRT focuses on lowering latency and increasing throughput on GPU hardware, whereas LiteRT is designed to reduce binary size and memory consumption on constrained devices.
• Prepare production infrastructure: TensorFlow supplies integrated production pipelines (for example, TFX and TensorFlow Serving), while PyTorch setups typically combine TorchServe and ONNX Runtime or TensorRT with additional custom integration code.
• Use interoperability to your advantage: A common pattern is PyTorch → ONNX → TensorRT for GPU-based serving, or TensorFlow → LiteRT when targeting on-device or edge applications.
• Simplify operations with a managed platform: A managed AI environment at centron can streamline training and deployment for PyTorch and TensorFlow while incorporating ONNX, TensorRT, and LiteRT into a unified workflow.

PyTorch: Flexible Training and Research-Oriented Design

PyTorch is an open-source deep learning framework that relies on a dynamic (define-by-run) computation graph and a Python-centric programming style. The framework is built around flexibility: models are expressed as regular Python code, making them intuitive to write, modify, and debug.

Although PyTorch emphasizes dynamic behavior, its optimized C++ core and tensor libraries (including GPU-accelerated backends such as cuDNN) allow it to deliver performance on par with or superior to static graph solutions. Over time, PyTorch has matured from a research-first tool into a framework suitable for development and production, thanks to features like TorchScript (for serializing models into optimized artifacts usable from C++ or on mobile) and TorchServe for model serving.

PyTorch: Strengths and Weaknesses

The following sections summarize where PyTorch shines and where certain compromises remain.

Strengths of PyTorch

• Python-native API: The interface feels like idiomatic Python, which keeps model authoring and debugging straightforward.
• Dynamic computation graph: The define-by-run approach enables rapid experimentation and more intuitive debugging workflows.
• Broad ecosystem and active community: Strong support for computer vision, NLP, utilities, and third-party libraries.
• Integration with Python tooling: Works smoothly with standard Python debuggers, profilers, and development tools.
• Productivity-focused improvements: JIT compilation and Ahead-of-Time (AOT) compilation in PyTorch 2.x help accelerate inference and simplify deployment.
• GPU-first design: Straightforward CUDA integration and solid support for distributed and multi-GPU training and serving.
• Evolving deployment story: TorchScript, TorchServe, and Torch-TensorRT are steadily narrowing the gap between research environments and hardened production systems.

Weaknesses of PyTorch

• Not fully turnkey for production compared to TensorFlow: A typical PyTorch production stack involves TorchServe combined with ONNX Runtime or TensorRT plus custom CI/CD and orchestration, which increases the amount of integration work and operational overhead.
• Additional hardening required for shipping: Because PyTorch is based on a dynamic graph, extra steps like TorchScript conversion or AOT compilation are needed to create deterministic, reproducible artifacts. Rigorous parity testing is crucial to prevent behavior differences between development and production environments.
• Fragmented MLOps ecosystem: PyTorch usually depends on a mix of different tools, which enlarges the surface area for version drift, upgrades, and security maintenance.
• Large runtime footprint: The complete PyTorch runtime (including CUDA and other accelerator dependencies) is relatively heavy, making it less suitable for highly resource-constrained mobile or edge deployments.

PyTorch in Action: Training and Deployment Workflow

PyTorch is most commonly used during the stages of model design and training. For deployment, PyTorch can still be used directly for inference—for example, running models on a server, possibly exposed through a web service with frameworks like Flask or via TorchServe. Alternatively, models can be converted into more lightweight formats for specialized deployment targets.

PyTorch Example: Model Training Workflow

The following example constructs a simple fully connected neural network and trains it using PyTorch’s nn.Module abstractions and optimization utilities. Thanks to the dynamic computation graph, the training loop is implemented in plain Python, where each iteration simply calls the model’s forward pass. After training, the model’s parameters are stored. Later on, the weights can be loaded back into PyTorch or exported to ONNX for use with other runtimes (as discussed later in this guide).

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
from torch.utils.data import DataLoader

# Define a simple neural network
class SimpleNet(nn.Module):
    def __init__(self):
        super(SimpleNet, self).__init__()
        self.fc1 = nn.Linear(784, 128)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(128, 10)
    def forward(self, x):
        x = x.view(-1, 784)  # Flatten images
        x = self.relu(self.fc1(x))
        return self.fc2(x)

# Prepare the training dataset and DataLoader
transform = transforms.Compose([transforms.ToTensor()])
train_dataset = datasets.MNIST(root="./data", train=True, transform=transform, download=True)
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)

# Model setup
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = SimpleNet().to(device)
optimizer = optim.Adam(model.parameters(), lr=1e-3)
criterion = nn.CrossEntropyLoss()

# Training loop
for epoch in range(5):
    for batch_x, batch_y in train_loader:
        batch_x, batch_y = batch_x.to(device), batch_y.to(device)
        optimizer.zero_grad()
        outputs = model(batch_x)
        loss = criterion(outputs, batch_y)
        loss.backward()
        optimizer.step()

print("Training completed.")
torch.save(model.state_dict(), "model_weights.pth")

TensorFlow: Production-Ready Framework and Comprehensive Ecosystem

TensorFlow is another major deep learning framework, initially developed at Google. Early TensorFlow releases popularized static computation graphs, where a model’s graph is defined first and then executed. This approach made it easier to apply whole-graph optimizations and efficiently deploy models across many platforms, though it sacrificed some ease of use and flexibility. In response to user feedback and competition from PyTorch, TensorFlow 2.x introduced eager execution (a more dynamic style similar to PyTorch) as the default, while still allowing developers to benefit from static graph optimizations through the tf.function decorator and the XLA compiler.

TensorFlow also adopts Keras as its primary high-level API for building models. Beyond core training, TensorFlow ships with an extensive production ecosystem. Models can be served on standard servers via TensorFlow Serving, deployed on mobile or embedded hardware through TensorFlow Lite (now LiteRT), executed in the browser with TensorFlow.js, or run on specialized accelerators such as Google’s TPUs.

TensorFlow: Strengths and Weaknesses

TensorFlow is widely adopted in industry for large-scale training and production ML workloads. Its ecosystem delivers end-to-end capabilities but introduces its own trade-offs for engineers.

Strengths of TensorFlow

• High scalability: Built-in support for distributed training across multiple GPUs and even multiple machines.
• End-to-end, production-focused ecosystem: TensorFlow Extended (TFX) offers components for data ingestion, validation, and serving, supporting enterprise-grade ML pipelines.
• Optimized computation path: When static graph execution and XLA are enabled, TensorFlow can provide substantial performance improvements.
• Deep TPU support: Native integration with Google TPUs for both training and inference of large-scale workloads.
• Smooth deployment flow: The SavedModel format integrates directly with TensorFlow Serving, enabling straightforward production inference setups.
• Ready for edge and mobile: Converting TensorFlow models to LiteRT is a relatively direct process for on-device deployments.
• Abundant learning resources: A large user community, extensive documentation, and many pre-trained models facilitate transfer learning and rapid experimentation.

Weaknesses of TensorFlow

• Historically steep learning curve: TensorFlow 1.x required explicit management of graphs and sessions, which often felt unintuitive for newcomers.
• Challenging debugging: In TensorFlow 2.x, using the @tf.function decorator and graph mode can still limit line-by-line execution, complicating debugging workflows.
• Bulky runtime: The full TensorFlow package is large, making it impractical for direct deployment on many devices and motivating the need for LiteRT/TFLite.
• Complex custom operation development: Implementing new kernels or operations generally demands deeper insight into TensorFlow’s internal architecture.
• Slower to adopt cutting-edge research: Novel layers and methods often emerge first in PyTorch before being ported into TensorFlow.
• Less flexible for rapid experimentation: Static graph behavior can feel restrictive compared to PyTorch’s dynamic graph style when iterating quickly on prototypes.

TensorFlow End-to-End: From Model Development to Deployment

With TensorFlow, you can build and train a model—typically through the Keras API—export that model, and then move it into a deployment environment.

The exported model can power inference on a server, for example via TensorFlow Serving or the TensorFlow C++ API. For edge targets such as mobile phones, IoT hardware, or other embedded devices, the same model can be converted to LiteRT (TFLite) format and executed directly on the device. TensorFlow also integrates with TensorRT (TF-TRT) to speed up GPU inference. In one reported scenario, a ResNet-50 model running on an NVIDIA T4 GPU achieved about 2.4× higher inference throughput compared to unoptimized TensorFlow GPU execution.

TensorFlow Example – Keras Model Training

The following snippet defines a simple feed-forward neural network for image classification using Keras. The model is compiled and trained on the prepared MNIST data (x_train, y_train), then stored on disk. The SavedModel file (model_saved.keras) can later be reloaded for inference or transformed for deployment. TensorFlow’s high-level API hides the explicit construction of the low-level computation graph but can still optimize that graph internally when running in production.

import tensorflow as tf

# Load MNIST dataset
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()

# Normalize the pixel values to [0, 1]
x_train = x_train / 255.0
x_test = x_test / 255.0

# Define a simple model using Keras (e.g., for MNIST classification)
model = tf.keras.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10)
])

# Compile the model with optimizer, loss, and metrics
model.compile(
    optimizer='adam',
    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    metrics=['accuracy']
)

# Train the model on training data
model.fit(x_train, y_train, epochs=5, batch_size=32)

# Evaluate on test data
test_loss, test_acc = model.evaluate(x_test, y_test, verbose=2)
print(f"\nTest accuracy: {test_acc:.4f}")

# Save the trained model to disk (SavedModel format)
model.save("model_saved.keras")

LiteRT: Lightweight On-Device Inference Runtime

LiteRT (Lite Runtime) is a compact inference engine that evolved from TensorFlow Lite. It is designed to run pre-trained models on devices with limited resources, such as smartphones, tablets, IoT hardware, edge systems, and even microcontrollers.

Initially, LiteRT primarily targeted models built in TensorFlow. Over time, Google’s AI Edge team extended it so that models originating from other frameworks can also be supported. Conversion tooling can transform models authored in PyTorch, JAX, or TensorFlow into the FlatBuffers-based .tflite format.

LiteRT: Strengths and Weaknesses

LiteRT is focused on on-device inference for mobile, embedded, and edge-machine learning scenarios. The following sections outline the main advantages and the associated trade-offs.

Strengths of LiteRT

• Extremely small runtime: The engine can be compiled into a very small binary, with the leanest builds around ~300 KB, which is crucial for mobile and embedded applications where every kilobyte matters.
• Support for hardware acceleration: LiteRT integrates with Android NNAPI for DSP/NPU offload, iOS Core ML, and offers a GPU delegate for mobile GPUs.
• Model-level optimizations: Post-training quantization (int8 or float16), pruning, and clustering can be applied to shrink model size and reduce latency while preserving accuracy as much as possible.
• Tailored for on-device performance: Aggressive optimizations and hardware delegates help achieve real-time inference and lower power consumption on supported devices.
• Cross-platform reach: LiteRT works on Android, iOS, Linux (including platforms such as Raspberry Pi), and microcontrollers (via LiteRT Micro).
• Offline and privacy-friendly operation: Because inference runs locally, applications avoid network latency and keep user data on the device.

Weaknesses of LiteRT

• Inference-only design: It does not provide general-purpose on-device training; only limited transfer-learning-style scenarios are supported in specific cases.
• Gaps in operator coverage: Not every TensorFlow or PyTorch operation is implemented. In some situations, models must be altered or custom operators created.
• Conversion friction: To obtain a .tflite model, you may need to partition graphs or apply quantization and other transformations, which can complicate conversion.
• Challenging debugging experience: Static FlatBuffer models are more difficult to inspect and debug compared to framework-native representations.
• Resource limitations: Very large models, such as many transformer-scale architectures, may still exceed feasible latency or memory budgets for target devices.
• Potential need for server offload: For models that cannot be executed efficiently on-device, hybrid approaches that split work between client and server may be necessary.

LiteRT in the Machine Learning Pipeline

A typical LiteRT pipeline looks like this: you train a model in TensorFlow or PyTorch, then convert the resulting model to the .tflite format using the appropriate converter. The resulting .tflite file is bundled into a mobile application or deployed to an embedded platform that runs it with the LiteRT runtime.

During conversion, you can apply optimizations such as quantization or pruning. The resulting model is executed in your software through the LiteRT interpreter, which is accessible from several programming languages (for example Java or Kotlin on Android, Swift on iOS, C++ for native applications, or Python for quick experiments).

Compared to shipping a full framework runtime on-device, LiteRT typically delivers substantially better performance and lower resource usage. In one sample benchmark on a Samsung S21, an image classification model ran at about 23 ms per inference and used roughly 89 MB of memory with TensorFlow Lite. By contrast, the same model achieved around 31 ms (112 MB) with ONNX Runtime and approximately 38 ms (126 MB) with PyTorch Mobile. These results underline LiteRT’s emphasis on low-latency, memory-efficient execution on mobile hardware.

LiteRT Example – Converting and Running a Model

The following example demonstrates how to take a TensorFlow model trained in Python, convert it to LiteRT format, and then perform inference with it.

# ready MNIST → SavedModel → LiteRT(TFLite) → Inference pipeline
import tensorflow as tf
import numpy as np

print("TF version:", tf.__version__)

# 1) Load & prep MNIST
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
# Normalize to [0,1]
x_train = (x_train / 255.0).astype("float32")
x_test  = (x_test  / 255.0).astype("float32")

# 2) Define & train a simple Keras model
model = tf.keras.Sequential([
    tf.keras.layers.Input(shape=(28, 28)),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(128, activation="relu"),
    tf.keras.layers.Dense(10)  # logits
])

model.compile(optimizer="adam",
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=["accuracy"])

model.fit(x_train, y_train, epochs=2, batch_size=128, validation_split=0.1, verbose=1)

# Quick test set eval
test_loss, test_acc = model.evaluate(x_test, y_test, verbose=0)
print(f"TF model test accuracy: {test_acc:.4f}")

# 3) Export a TensorFlow SavedModel (needed for TFLite conversion)
# In TF 2.15+, prefer model.export(). If not available, fallback to tf.saved_model.save
if hasattr(model, "export"):
    model.export("model_saved")            # TF ≥ 2.15
else:
    tf.saved_model.save(model, "model_saved")  # Older TF fallback

# 4) Convert to LiteRT/TFLite (FP32 with default optimizations)
converter = tf.lite.TFLiteConverter.from_saved_model("model_saved")
converter.optimizations = [tf.lite.Optimize.DEFAULT]   # dynamic range quantization if weights permit
tflite_model = converter.convert()

with open("model_fp32.tflite", "wb") as f:
    f.write(tflite_model)
print("Wrote model_fp32.tflite")

# --- OPTIONAL: Full INT8 quantization with representative dataset ---
do_full_int8 = True
if do_full_int8:
    def rep_data():
        # Yield a few hundred samples to calibrate ranges
        for i in range(500):
            # TFLite expects a batch dimension
            yield [np.expand_dims(x_train[i], 0)]
    converter_int8 = tf.lite.TFLiteConverter.from_saved_model("model_saved")
    converter_int8.optimizations = [tf.lite.Optimize.DEFAULT]
    converter_int8.representative_dataset = rep_data
    # Force int8 I/O where supported
    converter_int8.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
    converter_int8.inference_input_type = tf.int8
    converter_int8.inference_output_type = tf.int8
    try:
        tflite_int8 = converter_int8.convert()
        with open("model_int8.tflite", "wb") as f:
            f.write(tflite_int8)
        print("Wrote model_int8.tflite")
    except Exception as e:
        print("INT8 conversion fell back / failed:", e)
        tflite_int8 = None

# 5) Run inference with the TFLite Interpreter (FP32 model)
import tensorflow.lite as tflite

def tflite_predict(tflite_path, image_28x28):
    interpreter = tflite.Interpreter(model_path=tflite_path)
    interpreter.allocate_tensors()
    in_details = interpreter.get_input_details()
    out_details = interpreter.get_output_details()

    inp = image_28x28
    # Match dtype & shape expected by the model
    if in_details[0]["dtype"] == np.float32:
        inp = inp.astype(np.float32)
    elif in_details[0]["dtype"] == np.int8:
        # Quantized model expects int8; apply quantization params
        scale, zero_point = in_details[0]["quantization"]
        if scale == 0:
            # Safety: if no scale provided (rare), just cast
            inp = inp.astype(np.int8)
        else:
            inp = (inp / scale + zero_point).round().astype(np.int8)

    # Add batch dimension
    inp = np.expand_dims(inp, 0)

    interpreter.set_tensor(in_details[0]["index"], inp)
    interpreter.invoke()
    out = interpreter.get_tensor(out_details[0]["index"])

    # If output is int8, dequantize back to float for softmax/argmax
    if out_details[0]["dtype"] == np.int8:
        scale, zero_point = out_details[0]["quantization"]
        if scale != 0:
            out = (out.astype(np.float32) - zero_point) * scale

    # Convert logits to probabilities and pick class
    probs = tf.nn.softmax(out, axis=-1).numpy()[0]
    pred  = int(np.argmax(probs))
    conf  = float(probs[pred])
    return pred, conf

# Test on a few MNIST samples with FP32 model
for idx in [0, 1, 2]:
    pred, conf = tflite_predict("model_fp32.tflite", x_test[idx])
    print(f"[FP32] Sample {idx}: pred={pred}, conf={conf:.3f}, true={y_test[idx]}")

# If INT8 model exists, test it as well
if 'tflite_int8' in locals() and tflite_int8 is not None:
    for idx in [0, 1, 2]:
        pred, conf = tflite_predict("model_int8.tflite", x_test[idx])
        print(f"[INT8] Sample {idx}: pred={pred}, conf={conf:.3f}, true={y_test[idx]}")

This script first loads and normalizes the MNIST dataset, then defines and trains a compact fully connected network and evaluates its accuracy. The trained model is exported as a SavedModel, which serves as input to the TFLiteConverter. The converter produces two variants: a default FP32 LiteRT model and, optionally, a fully quantized INT8 model built with a representative dataset to calibrate ranges. Finally, a helper function tflite_predict() is implemented to open a .tflite file, prepare and (de)quantize data as required, run inference, and return both the predicted digit and associated confidence. A few sample inputs are passed through the FP32 and INT8 models to validate deployment and illustrate the resulting outputs.

TensorRT: High-Performance Inference on NVIDIA GPUs

NVIDIA TensorRT is an SDK and runtime environment focused on low-latency, high-throughput deployment of neural networks on NVIDIA GPUs. You can think of TensorRT as a deep learning model compiler: you provide a trained model (often in ONNX format or a framework-specific representation), and TensorRT runs a sequence of optimizations to produce a highly tuned inference engine for the GPU.

These optimizations include:

• Layer fusion: Combining compatible operations to cut down on memory transfers and kernel launch overhead.
• Kernel auto-tuning: Automatically selecting the fastest CUDA kernels based on tensor shapes and target hardware.
• Memory planning: Optimizing tensor lifetimes and workspace usage to reduce copies and memory peaks.
• Reduced-precision execution: Enabling FP16 and INT8 (backed by calibration or quantization-aware training) for substantial speedups and lower bandwidth requirements.
• Dynamic shape handling and profile caching: Building execution profiles for different shape ranges to eliminate repeated optimizations at runtime.

The result is an optimized binary engine that can execute the model’s forward pass significantly faster than standard framework implementations.

TensorRT: Strengths and Weaknesses

This section outlines TensorRT’s main advantages and the practical trade-offs that teams should be aware of.

Strengths of TensorRT

• High GPU performance: By leveraging FP16/INT8 computation and NVIDIA architecture–specific optimizations, TensorRT can dramatically speed up inference—often achieving up to roughly 40× acceleration over CPU-based execution.
• Efficient GPU utilization: It can deliver 2–5× lower latency or significantly higher throughput than unoptimized TensorFlow or PyTorch running on the same GPU.
• Support for batching and concurrency: It is well-suited to large-scale inference services that must handle many concurrent requests where throughput is critical.
• Flexible deployment options: TensorRT can run standalone, be integrated into Triton Inference Server, be used as an execution provider alongside ONNX Runtime, or be tied into TensorFlow.
• Rich ecosystem integrations: It works with NVIDIA SDKs such as DeepStream (for video analytics) and Riva (for speech and conversational AI) to deliver complete end-to-end solutions.
• Multiple language bindings: Python and C++ APIs allow integration into custom data processing and serving pipelines.

Weaknesses of TensorRT

• NVIDIA hardware dependency: TensorRT is limited to NVIDIA GPUs; inference setups built on TensorRT cannot be directly ported to CPU-only infrastructures or other accelerators.
• Model conversion complexity: Models must be transformed into TensorRT engine format, which might require adjustments for unsupported layers or the development of custom plugins.
• Manual tuning effort: Achieving the best performance often involves hand-tuning parameters such as workspace size, precision modes, and input shape profiles.
• Hardware-specific engines: TensorRT engines are tuned for particular GPU families and architectures, which means that porting to a different device (for example, from a data center GPU to a Jetson edge device) usually requires rebuilding the engine.
• Re-optimization on model changes: Any modifications to the underlying model call for a fresh conversion and optimization cycle to maintain peak performance.
• Higher deployment complexity: Compared with more integrated options like TensorFlow Serving or ONNX Runtime alone, setting up and maintaining TensorRT-based deployments tends to require more engineering work.

TensorRT in the Pipeline: Inference-Only Role

TensorRT is focused solely on inference. A common workflow is to train a model in PyTorch or TensorFlow using GPUs, export it to ONNX (a format TensorRT natively supports), and then use TensorRT APIs or tools to deploy that ONNX model. During this process, you build an engine (possibly performing INT8 calibration with sample data) and then run that engine inside your application.

Once the engine is created, you load it in a C++ server application or through Python bindings, depending on scale and architecture. For high-throughput production environments handling large volumes of queries, it is standard practice to run TensorRT engines within NVIDIA’s Triton Inference Server, which manages multiple models and concurrent requests.

TensorRT Example – Converting an ONNX Model to a TensorRT Engine

The following simplified pseudo-code uses TensorRT’s Python API to build an engine from an ONNX model and perform inference. It illustrates the overall flow without going into full API detail.

import tensorrt as trt

onnx_file = "model.onnx"
engine_file = "model.plan"  # TensorRT engine file

# Set up TensorRT logger and builder
logger = trt.Logger(trt.Logger.INFO)
builder = trt.Builder(logger)
network = builder.create_network(flags=1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
parser = trt.OnnxParser(network, logger)

# Parse the ONNX model to populate the TensorRT network
with open(onnx_file, "rb") as f:
    parser.parse(f.read())
# (In practice, check parser.error for unsupported ops here.)

# Configure builder
builder.max_batch_size = 1
config = builder.create_builder_config()
config.max_workspace_size = 1 << 30  # 1GB workspace for optimization
config.flags |= trt.BuilderFlag.FP16  # enable FP16 precision if supported

# Build the TensorRT engine
engine = builder.build_engine(network, config)
with open(engine_file, "wb") as f:
    f.write(engine.serialize())  # save the engine to file

# Use the engine for inference
runtime = trt.Runtime(logger)
with open(engine_file, "rb") as f:
    engine_bytes = f.read()
engine = runtime.deserialize_cuda_engine(engine_bytes)
context = engine.create_execution_context()

# Assuming a single input and single output for simplicity
input_shape = engine.get_binding_shape(0)
output_shape = engine.get_binding_shape(1)
# Allocate device memory for inputs and outputs (using PyCUDA or similar)
# ... (omitted for brevity)
# Execute inference
context.execute_v2(bindings=[d_input_ptr, d_output_ptr])
# Copy results from device memory to host and use the output

This code outlines the core stages involved in turning an ONNX model into a TensorRT engine. A Builder and OnnxParser are created to read the model graph, then builder configuration (such as workspace size and FP16 support) is applied. The build_engine call runs TensorRT’s optimizations and returns an engine that is serialized to model.plan. Later, the engine can be deserialized, an execution context can be created, and inference can be performed.

Conceptually, using TensorRT for inference involves the following steps:

• Parse the model: Load and interpret the model using TensorRT’s parser or related tools to construct an internal network representation.
• Build an optimized engine: Generate a hardware-specific engine from the parsed model, typically involving optimizations like layer fusion and precision calibration.
• Run inference: Execute inference via the optimized engine, which requires managing device memory for inputs and outputs and invoking execute_v2 on the execution context.

In real-world systems, additional concerns such as dynamic shapes or multiple input and output bindings come into play, which call for more detailed TensorRT code. In practice, many teams rely on higher-level wrappers or use ONNX Runtime with a TensorRT backend to avoid writing low-level integration code themselves.

ONNX: Model Interoperability and Cross-Platform Deployment

ONNX (Open Neural Network Exchange) is not a training framework but an open model representation format that also offers a runtime, ONNX Runtime, for executing models.

With ONNX, you can train a model in one framework (for example, PyTorch or TensorFlow), export it to the ONNX format (a computation graph built from standardized operations), and then execute it using another tool or even on a different hardware backend. This separation between training framework and inference runtime is especially powerful in production, where you might prefer one framework for development and another runtime for cost, performance, or operational reasons when deploying.

ONNX: Strengths and Weaknesses

The following summary highlights where ONNX and ONNX Runtime perform well and where their current limitations lie.

Strengths of ONNX / ONNX Runtime

• Flexibility and interoperability: Train in one framework (for instance, PyTorch) and deploy with another (such as ONNX Runtime in C++).
• Lightweight, inference-centric runtime: Smaller installation footprint than full frameworks, focused purely on inference without training overhead.
• Cross-platform support: Runs on Windows, Linux, macOS, and mobile targets via ONNX Runtime Mobile with a reduced operator set.
• Strong performance: Often matches or exceeds native framework inference, especially on CPUs; for some batch-1 GPU scenarios, ONNX Runtime can be faster after applying graph optimizations (for example, around 24.2 ms vs. roughly 30.4 ms on ResNet-50).
• Proven at scale: In certain Microsoft production workloads, ONNX Runtime has reportedly outperformed TorchScript.
• Healthy ecosystem: A robust ecosystem of converters, public model collections, and utilities such as Netron and onnxoptimizer.

Weaknesses of ONNX / ONNX Runtime

• Conversion complexity: Exporting can fail or lead to suboptimal performance when models rely on operators that are not yet fully standardized or supported.
• Custom operator gaps: Architectures built with non-standard layers may require fallbacks or custom plugins, which can become opaque “black boxes.”
• More difficult debugging: Static ONNX graphs are detached from the original framework, its source code, and related tooling, which makes debugging more cumbersome.
• Less “batteries-included”: Steps like pre-processing, post-processing, and other pipeline glue code typically live outside ONNX Runtime.
• Additional workflow overhead: Introducing ONNX adds a separate export and validation stage that must be maintained over time.

ONNX as the Handoff Layer: Train Anywhere, Deploy Everywhere

ONNX often sits in the middle of an ML pipeline. A typical pattern is to train a model in PyTorch and then use torch.onnx.export to emit an model.onnx file. That ONNX model can then be deployed into a production service powered by ONNX Runtime, implemented in C++ for efficiency or in Python if that better suits the application.

Consider a situation where you have a TensorFlow model but want to use TensorRT directly instead of going through TensorFlow’s built-in integration. In that case, you can convert the TensorFlow model to ONNX and then pass the resulting model to TensorRT, which natively understands ONNX.

ONNX is also common in model compression and quantization workflows. For instance, you can export a trained model to ONNX and then apply post-training quantization to it using tools included with ONNX Runtime.

Interoperability and Typical ML Tooling Workflows

End-to-end interoperability is often critical for experienced ML practitioners. In real-world systems, none of the tools discussed here are used entirely in isolation. Instead, you typically combine two or three of them to cover training, optimization, and deployment requirements. The examples below illustrate representative pipelines and how these components fit together.

In this ecosystem, PyTorch and TensorFlow act as the “front-end” frameworks used to create and train models. ONNX serves as a shared “handoff” format that transports models between frameworks and runtimes. TensorRT and LiteRT act as specialized “endpoints,” each tuned for specific hardware—GPUs in the case of TensorRT and edge or mobile devices for LiteRT.

FAQ

PyTorch vs. TensorFlow – when should I choose each?

PyTorch is generally the better fit for rapid research iteration and Python-friendly debugging, while TensorFlow is often preferred for end-to-end, production-grade ML pipelines (for example, TFX, TensorFlow Serving, and TPU integration) and smoother enterprise operations.

What is LiteRT (formerly TFLite), and when should I use it?

LiteRT is a lightweight runtime for on-device inference, built with mobile and edge scenarios in mind. You train your model in TensorFlow or PyTorch, convert it to .tflite, and then run it with hardware delegates—such as NNAPI, Core ML, or GPU delegates—to achieve low-latency, energy-efficient inference.

How do ONNX and TensorRT work together?

You export your trained model to the ONNX format, then use TensorRT to compile that ONNX graph into a highly optimized engine for NVIDIA GPUs (leveraging FP16/INT8 and kernel fusion). In this setup, ONNX provides the interoperability bridge, while TensorRT acts as the high-performance GPU accelerator.

Conclusion

Tool selection should be driven by where your models run and how you plan to scale them: use PyTorch or TensorFlow for model development and training; ONNX to decouple training from inference; TensorRT when you need maximum performance on NVIDIA GPUs; and LiteRT for compact, low-latency, on-device inference. Successful production stacks are typically multi-framework—such as PyTorch → ONNX → TensorRT for GPU serving or TensorFlow → LiteRT for mobile—while converging on a single exported artifact that you can benchmark, validate, and ship.

A practical way to realize this at centron is to leverage centron’s managed AI and GPU infrastructure. You can spin up managed GPU notebooks, train models, and expose accelerated endpoints without handling the underlying infrastructure yourself. This enables you to work with PyTorch or TensorFlow together with ONNX, TensorRT, and LiteRT in one cohesive, streamlined workflow.