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+# QDQ Placement Optimization Example
+
+This example demonstrates automated Q/DQ (Quantize/Dequantize) node placement optimization for ONNX models using TensorRT performance measurements.
+
+## Table of Contents
+
+<div align="center">
+
+| **Section** | **Description** | **Link** | **Docs** |
+| :------------: | :------------: | :------------: | :------------: |
+| Prerequisites | Get the model, set fixed batch size, and directory overview | [Link](#prerequisites) | |
+| Quick Start | Basic usage, FP8 quantization, and faster exploration | [Link](#quick-start) | |
+| Output Structure | Output workspace layout and files | [Link](#output-structure) | |
+| Region Inspection | Debug region discovery and partitioning | [Link](#region-inspection) | |
+| Using the Optimized Model | Deploy with TensorRT | [Link](#using-the-optimized-model) | |
+| Pattern Cache | Reuse learned patterns on similar models | [Link](#pattern-cache) | |
+| Optimize from Existing QDQ Model | Start from an existing quantized model | [Link](#optimize-from-existing-qdq-model) | |
+| Remote Autotuning with TensorRT | Offload autotuning to remote hardware | [Link](#remote-autotuning-with-tensorrt) | |
+| Programmatic API Usage | Python API and low-level control | [Link](#programmatic-api-usage) | |
+| Documentation | User guide and API reference | [Link](#documentation) | [docs](https://nvidia.github.io/Model-Optimizer/) |
+
+</div>
+
+## Prerequisites
+
+### Get the Model
+
+Download the ResNet50 model from the ONNX Model Zoo:
+
+```bash
+# Download ResNet50 from ONNX Model Zoo
+curl -L -o resnet50_Opset17.onnx https://github.com/onnx/models/raw/main/Computer_Vision/resnet50_Opset17_torch_hub/resnet50_Opset17.onnx
+```
+
+### Set Fixed Batch Size
+
+The downloaded model has a dynamic batch size. For best performance with TensorRT benchmarking, set a fixed batch size using Polygraphy:
+
+```bash
+polygraphy surgeon sanitize --override-input-shapes x:[128,3,1024,1024] -o resnet50_Opset17_bs128.onnx resnet50_Opset17.onnx
+```
+
+For other batch sizes, change the first dimension in the shape (e.g. `x:[1,3,1024,1024]` for batch size 1).
+
+### What's in This Directory
+
+- `README.md` - This guide
+
+**Note:** ONNX model files are not included in the repository (excluded via `.gitignore`). Download and prepare them using the instructions above.
+
+## Quick Start
+
+### Basic Usage
+
+Optimize the ResNet50 model with INT8 quantization:
+
+```bash
+# Using the fixed batch size model
+python3 -m modelopt.onnx.quantization.autotune \
+    --onnx_path resnet50_Opset17_bs128.onnx \
+    --output_dir ./resnet50_results \
+    --quant_type int8 \
+    --schemes_per_region 30
+
+# Or use the original dynamic batch size model, batch is set to 1 in benchmark
+python3 -m modelopt.onnx.quantization.autotune \
+    --onnx_path resnet50_Opset17.onnx \
+    --output_dir ./resnet50_results \
+    --quant_type int8 \
+    --schemes_per_region 30
+```
+
+Short options: `-m` for `--onnx_path`, `-o` for `--output_dir`, `-s` for `--schemes_per_region`. Default output directory is `./autotuner_output` if `--output_dir` is omitted.
+
+This will:
+
+1. Automatically discover optimization regions in the model
+2. Test 30 different Q/DQ placement schemes per region pattern
+3. Measure TensorRT performance for each scheme
+4. Export the best optimized model to `./resnet50_results/optimized_final.onnx`
+
+### FP8 Quantization
+
+For FP8 quantization:
+
+```bash
+python3 -m modelopt.onnx.quantization.autotune \
+    --onnx_path resnet50_Opset17_bs128.onnx \
+    --output_dir ./resnet50_fp8_results \
+    --quant_type fp8 \
+    --schemes_per_region 50
+```
+
+### Faster Exploration
+
+For quick experiments, reduce the number of schemes:
+
+```bash
+python3 -m modelopt.onnx.quantization.autotune \
+    --onnx_path resnet50_Opset17_bs128.onnx \
+    --output_dir ./resnet50_quick \
+    --schemes_per_region 15
+```
+
+## Output Structure
+
+After running, the output workspace will be:
+
+```log
+resnet50_results/
+├── optimized_final.onnx              # Optimized model
+├── baseline.onnx                     # Baseline for comparison
+├── autotuner_state.yaml              # Resume checkpoint
+├── autotuner_state_pattern_cache.yaml # Reusable pattern cache
+├── logs/
+│   ├── baseline.log                  # TensorRT baseline log
+│   ├── region_*_scheme_*.log         # Per-scheme logs
+│   └── final.log                     # Final model log
+└── region_models/                    # Best model per region (intermediate)
+    └── region_*_level_*.onnx
+```
+
+## Region Inspection
+
+To debug how the autotuner discovers and partitions regions in your model, use the `region_inspect` tool. It runs the same region search as the autotuner and prints the region hierarchy, node counts, and summary statistics (without running benchmarks).
+
+```bash
+# Basic inspection (regions with quantizable ops only)
+python3 -m modelopt.onnx.quantization.autotune.region_inspect --model resnet50_Opset17_bs128.onnx
+
+# Verbose mode for detailed debug logging
+python3 -m modelopt.onnx.quantization.autotune.region_inspect --model resnet50_Opset17_bs128.onnx --verbose
+
+# Custom maximum sequence region size
+python3 -m modelopt.onnx.quantization.autotune.region_inspect --model resnet50_Opset17_bs128.onnx --max-sequence-size 20
+
+# Include all regions (including those without Conv/MatMul etc.)
+python3 -m modelopt.onnx.quantization.autotune.region_inspect --model resnet50_Opset17_bs128.onnx --include-all-regions
+```
+
+Short option: `-m` for `--model`, `-v` for `--verbose`. Use this to verify region boundaries and counts before or during autotuning.
+
+## Using the Optimized Model
+
+Deploy with TensorRT:
+
+```bash
+trtexec --onnx=resnet50_results/optimized_final.onnx \
+        --saveEngine=resnet50.engine \
+        --stronglyTyped
+```
+
+## Pattern Cache
+
+Reuse learned patterns on similar models (warm-start):
+
+```bash
+# First optimization on ResNet50
+python3 -m modelopt.onnx.quantization.autotune \
+    --onnx_path resnet50_Opset17_bs128.onnx \
+    --output_dir ./resnet50_run
+
+# Download and prepare ResNet101 (or any similar model)
+curl -L -o resnet101_Opset17.onnx https://github.com/onnx/models/raw/main/Computer_Vision/resnet101_Opset17_torch_hub/resnet101_Opset17.onnx
+polygraphy surgeon sanitize --override-input-shapes x:[128,3,1024,1024] -o resnet101_Opset17_bs128.onnx resnet101_Opset17.onnx
+
+# Reuse patterns from ResNet50 on ResNet101 
+python3 -m modelopt.onnx.quantization.autotune \
+    --onnx_path resnet101_Opset17_bs128.onnx \
+    --output_dir ./resnet101_run \
+    --pattern_cache ./resnet50_run/autotuner_state_pattern_cache.yaml
+```
+
+## Optimize from Existing QDQ Model
+
+If the user already have a quantized model, he can use it as a starting point to potentially find even better Q/DQ placements:
+
+```bash
+# Use an existing QDQ model as baseline (imports quantization patterns)
+python3 -m modelopt.onnx.quantization.autotune \
+    --onnx_path resnet50_Opset17_bs128.onnx \
+    --output_dir ./resnet50_improved \
+    --qdq_baseline resnet50_quantized.onnx \
+    --schemes_per_region 40
+```
+
+This will:
+
+1. Extract Q/DQ insertion points from the baseline model
+2. Import them into the pattern cache as seed schemes
+3. Generate and test variations to find better placements
+4. Compare against the baseline performance
+
+**Use cases:**
+
+- **Improve existing quantization**: Fine-tune manually quantized models
+- **Compare tools**: Test if autotuner can beat other quantization methods
+- **Bootstrap optimization**: Start from expert-tuned schemes
+
+**Example workflow:**
+
+```bash
+# Step 1: Create initial quantized model with modelopt 
+# For example, using modelopt's quantize function:
+python3 -c "
+import numpy as np
+from modelopt.onnx.quantization import quantize
+
+# Create dummy calibration data (replace with real data for production)
+dummy_input = np.random.randn(128, 3, 224, 224).astype(np.float32)
+quantize(
+    'resnet50_Opset17_bs128.onnx',
+    calibration_data=dummy_input,
+    calibration_method='entropy',
+    output_path='resnet50_quantized.onnx'
+)
+"
+
+# Step 2: Use the quantized baseline for autotuning
+# The autotuner will try to find better Q/DQ placements than the initial quantization
+python3 -m modelopt.onnx.quantization.autotune \
+    --onnx_path resnet50_Opset17_bs128.onnx \
+    --output_dir ./resnet50_autotuned \
+    --qdq_baseline resnet50_quantized.onnx \
+    --schemes_per_region 50
+```
+
+**Note:** This example uses dummy calibration data. For production use, provide real calibration data representative of the inference workload.
+
+## Remote Autotuning with TensorRT
+
+TensorRT 10.16+ supports remote autotuning, which allows TensorRT's optimization process to be offloaded to a remote hardware. This is useful when optimizing models for different target GPUs without having direct access to them.
+
+To use remote autotuning during Q/DQ placement optimization, run with `trtexec` and pass extra args:
+
+```bash
+python3 -m modelopt.onnx.quantization.autotune \
+    --onnx_path resnet50_Opset17_bs128.onnx \
+    --output_dir ./resnet50_remote_autotuned \
+    --schemes_per_region 50 \
+    --use_trtexec \
+    --trtexec_benchmark_args "--remoteAutoTuningConfig=\"<remote autotuning config>\""
+```
+
+**Requirements:**
+
+- TensorRT 10.16 or later
+- Valid remote autotuning configuration
+- `--use_trtexec` must be set (benchmarking uses `trtexec` instead of the TensorRT Python API)
+
+Replace `<remote autotuning config>` with user's actual remote autotuning configuration string. Other TensorRT benchmark options (e.g. `--timing_cache`, `--warmup_runs`, `--timing_runs`, `--plugin_libraries`) are also available; run `--help` for details.
+
+## Programmatic API Usage
+
+All examples above use the command-line interface. For **low-level programmatic control** in Python code, use the Python API directly. This allows user to:
+
+- Integrate autotuning into custom pipelines
+- Implement custom evaluation functions
+- Control state management and checkpointing
+- Build custom optimization workflows
+
+**See the API Reference documentation for low-level usage:**
+
+- [`docs/source/reference/2_qdq_placement.rst`](../../docs/source/reference/2_qdq_placement.rst)
+
+The API docs include detailed examples of:
+
+- Using the `QDQAutotuner` class and `region_pattern_autotuning_workflow`
+- Customizing region discovery and scheme generation
+- Managing optimization state and pattern cache programmatically
+- Implementing custom performance evaluators (e.g. via `init_benchmark_instance` and `benchmark_onnx_model`)
+
+## Documentation
+
+For comprehensive documentation on QDQ placement optimization, see:
+
+- **User Guide**: [`docs/source/guides/9_qdq_placement.rst`](../../docs/source/guides/9_qdq_placement.rst)
+  - Detailed explanations of how the autotuner works
+  - Advanced usage patterns and best practices
+  - Configuration options and performance tuning
+  - Troubleshooting common issues
+
+- **API Reference**: [`docs/source/reference/2_qdq_placement.rst`](../../docs/source/reference/2_qdq_placement.rst)
+  - Complete API documentation for all classes and functions
+  - Low-level usage examples
+  - State management and pattern cache details
+
+For command-line help and all options (e.g. `--state_file`, `--node_filter_list`, `--default_dq_dtype`, `--verbose`):
+
+```bash
+python3 -m modelopt.onnx.quantization.autotune --help
+```
diff --git a/modelopt/onnx/logging_config.py b/modelopt/onnx/logging_config.py
index 99468b87f..bcc83cdc9 100644
--- a/modelopt/onnx/logging_config.py
+++ b/modelopt/onnx/logging_config.py
@@ -38,7 +38,7 @@ def configure_logging(level=logging.INFO, log_file=None):
     for handler in logger.handlers[:]:
         logger.removeHandler(handler)
 
-    formatter = logging.Formatter("[modelopt][onnx] - %(levelname)s - %(message)s")
+    formatter = logging.Formatter("%(asctime)s - [modelopt][onnx] - %(levelname)s - %(message)s")
 
     # Add file handler if log_file is specified
     if log_file: