TensorRT CUDA 12 Bindings

Common errors

• No module named 'tensorrt'

  cause The `tensorrt-cuXX-bindings` package is not installed or the Python environment is incorrect.
  fix Ensure you have installed the correct package using `pip install tensorrt-cu12-bindings` in your active Python environment.

• [TRT] ERROR: [checkRuntime.cpp::checkRuntime::0] CUDA driver version is insufficient for CUDA runtime version

  cause The NVIDIA CUDA driver installed on your system is too old or incompatible with the CUDA runtime version linked by the `tensorrt-cu12-bindings` package.
  fix Upgrade your NVIDIA GPU drivers to a version compatible with CUDA 12.x. Check NVIDIA's official documentation for driver requirements for specific CUDA versions.

• [TRT] ERROR: Network must have at least one output.

  cause The TensorRT network definition was created but no output tensor was explicitly marked using `network.mark_output()`.
  fix After defining the computational graph, ensure you call `network.mark_output(output_tensor)` for all desired output tensors.

• [TRT] ERROR: [builder.cpp::buildEngine::] Error Code 4: Internal Error (Could not find any implementation for node...)

  cause TensorRT's builder could not find an implementation for a specific layer or operation on the target hardware, possibly due to unsupported operations, invalid input shapes, or insufficient `max_workspace_size`.
  fix Review your network definition for unsupported operations, ensure input dimensions are valid, and consider increasing `config.max_workspace_size` if memory is an issue. Check TensorRT documentation for layer support.

• TypeError: argument of type 'NoneType' is not iterable (or similar NoneType errors after API calls)

  cause A TensorRT API call (e.g., `network.add_input`, `network.add_convolution`) failed to create the intended object and returned `None`, but the subsequent code tried to operate on it as if it were a valid object.
  fix Check the return values of TensorRT API calls, especially after defining layers or adding inputs/outputs. This often indicates invalid parameters passed to the function or an internal TensorRT error preventing object creation.

Warnings

• breaking The `tensorrt-cuXX-bindings` packages are tightly coupled with specific CUDA versions (e.g., `cu12` for CUDA 12.x). Using a mismatched CUDA driver or toolkit version on your system will lead to runtime failures or import errors.
  Fix: Verify your NVIDIA CUDA Toolkit and driver versions precisely match the `cuXX` suffix of the installed package. Upgrade or downgrade system CUDA components as necessary.
• breaking TensorRT 10.13.2 dropped official support for CUDA 11.X. Additionally, official samples and demos now require Python 3.10 or newer.
  Fix: For full compatibility and access to samples/demos, ensure you are running a CUDA 12.x (or newer) environment and Python 3.10 or newer.
• breaking Custom TensorRT plugins (e.g., those implementing `IPluginV2`) are being migrated to `IPluginV3`. Older plugin versions may be deprecated and removed in future releases.
  Fix: If you maintain custom plugins, review the TensorRT documentation for plugin migration guides and update them to `IPluginV3` or later to ensure forward compatibility.
• deprecated Official TensorRT Python samples and tools have transitioned from `pycuda` to `cuda-python` for low-level CUDA interactions.
  Fix: Update any custom Python code that directly uses `pycuda` for CUDA context management or memory operations to use `cuda-python` for alignment with official practices and future compatibility.
• gotcha Starting with TensorRT 10.14, samples and demos are no longer included directly within the `tensorrt-cuXX-bindings` Python packages.
  Fix: Access all TensorRT samples, demos, and associated scripts directly from the official NVIDIA TensorRT GitHub repository (github.com/nvidia/tensorrt/tree/main/samples).

Install

• pip install tensorrt-cu12-bindings Install for CUDA 12.x

Imports

• tensorrt

  import tensorrt as trt

Quickstart

This quickstart demonstrates how to initialize the TensorRT builder, define a simple identity network with an explicit batch dimension, configure the builder, and build a TensorRT engine. This is the fundamental process for optimizing and compiling deep learning models for NVIDIA GPUs.

import tensorrt as trt
import numpy as np

# 1. Create a logger (TRT_LOGGER = trt.Logger(trt.Logger.INFO) for more verbose output)
TRT_LOGGER = trt.Logger(trt.Logger.WARNING)

# 2. Create builder, network, and configuration
builder = trt.Builder(TRT_LOGGER)
# Explicit batch is required for some features (e.g., dynamic shapes)
network = builder.create_network(1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
config = builder.create_builder_config()

# Configure builder options
# max_workspace_size: The maximum GPU memory size (in bytes) that TensorRT can use for temporary buffers.
config.max_workspace_size = 1 << 20  # 1 MiB (adjust as needed for larger models)

# 3. Define the network: a simple identity layer for demonstration
# Input shape (batch_size, channels, height, width)
input_shape = (1, 3, 224, 224)
input_tensor = network.add_input(name="input_tensor", dtype=trt.float32, shape=input_shape)

# Add an identity layer as a simple example operation
identity_layer = network.add_identity(input_tensor)
output_tensor = identity_layer.get_output(0)

# Mark the output tensor
network.mark_output(output_tensor)
output_tensor.name = "output_tensor"

# 4. Build the engine
print(f"Building TensorRT engine with input shape {input_shape}...")
engine = builder.build_engine(network, config)

if engine:
    print("TensorRT engine built successfully!")
    # Example: serialize the engine to disk
    # with open("my_identity_engine.trt", "wb") as f:
    #     f.write(engine.serialize())
    # print("Engine serialized to my_identity_engine.trt")
else:
    print("Failed to build TensorRT engine.")

# Cleanup
del network, builder, config, engine