Examples

ResNet models tuning

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This example demonstrates how to use NVIDIA AITune to tune a ResNet model.

Environment Setup

You can use either of the following options to set up the environment:

Option 1 - virtual environment managed by you

Activate your virtual environment and install the dependencies:

$pip install --extra-index-url https://pypi.nvidia.com .

Option 2 - virtual environment managed by uv

Install dependencies:

$uv sync

Usage

To tune the ResNet model, run:

$tune --model-name resnet50

or for uv:

$uv run tune --model-name resnet50

After tuning, run classification with:

$inference --model-name resnet50 --image-path your_image

or for uv:

$uv run inference --model-name resnet50 --image-path your_image

User-provided dynamic shapes

The dynamic-shapes mode uses the same tuning flow while explicitly defining its batch and spatial ranges. With the defaults, it records batch sizes 1–4 at 224×224 and then runs inference at the unseen shape (2, 3, 256, 256):

$uv run tune --dynamic-shapes 1
$uv run inference --dynamic-shapes 1

The full-rank shape definition uses integers for fixed dimensions. Dimensions with the same name are shared, even when defined as separate objects:

1from aitune.torch import BatchDim, DynamicDim
2
3batch = BatchDim("batch", min=1, opt=4, max=4)
4height = DynamicDim("spatial", min=224, opt=224, max=256)
5width = DynamicDim("spatial", min=224, opt=224, max=256)
6dynamic_shapes = {"x": (batch, 3, height, width)}

Logging hardware metrics

If you would like to log hardware metrics during tuning or inference export AITUNE_HARDWARE_METRICS=True envorinment variable e.g.

$AITUNE_HARDWARE_METRICS=True uv run tune

AI Dynamo ResNet Deployment with Batching

Run ResNet as an AI Dynamo service with dynamic batching:

$pip install ".[dynamo]"
$tune --image-path dog.webp
$./run_dynamo.sh

This script starts the frontend and backend services, waits for them to be ready, then runs a test client to send a sample request. Once the test completes, all services are automatically shut down. This is meant as a functional check, not to provide a permanent server.

Dynamic batching

The service uses dynamic batching — requests are grouped and processed together for efficiency. Currently, there is one frontend and one worker. To support multiple workers, move batching to a separate service that handles request grouping.

Model Details

Can be found in following pages: