# Qualcomm AI Hub Example

This example takes the ONNX model produced by the SageMaker training example and runs the Qualcomm AI Hub upload workflow:

1. Quantize
2. Compile
3. Validate
4. Profile
5. Download the compiled artifact

## Prerequisites

Run the training example first and wait for it to complete:

```bash
bash examples/training/run_training.sh --config config.yaml --wait
```

If the dataset is already uploaded to S3, use:

```bash
bash examples/training/run_training.sh --config config.yaml --skip-upload --wait
```

The training artifact must contain a static-shape `model.onnx`. The training example exports an input named `input` with shape `1x3x160x160`.

Your `config.yaml` must include AI Hub settings:

```yaml
aihub:
  device:
    name: Samsung Galaxy S25 (Family)
  target_runtime: tflite
  input_specs:
    input: [[1, 3, 160, 160], float32]
  output_dir: build/qai-hub
```

You also need local Qualcomm AI Hub SDK authentication configured.

## Prepare Inputs

AI Hub does not consume the raw JPG training images directly. It needs NumPy tensors that match the ONNX model input shape and preprocessing.

Generate calibration and validation inputs:

```bash
uv run python examples/ai-hub/prepare_inputs.py
```

This writes:

```text
examples/training/data/aihub_calibration/*.npy
examples/training/data/inputs.npz
```

The script applies the same image preprocessing used by the training example:

- resize to `160x160`
- convert to channel-first `1x3x160x160`
- normalize with ImageNet mean and standard deviation

Useful options:

```bash
uv run python examples/ai-hub/prepare_inputs.py \
  --dataset-dir examples/training/data/flower_photos_sagemaker \
  --calibration-dir examples/training/data/aihub_calibration \
  --input-file examples/training/data/inputs.npz \
  --samples 16
```

## Run AI Hub

After training completes and inputs are prepared:

```bash
bash examples/ai-hub/run_ai_hub.sh --config config.yaml
```

By default, the script uses the last SageMaker training job recorded in `.qc-cli.json`. It downloads that job's `model.tar.gz`, extracts `model.onnx`, runs the AI Hub workflow, and downloads the compiled artifact.

To use a specific training job:

```bash
bash examples/ai-hub/run_ai_hub.sh \
  --config config.yaml \
  --from-job qc-cli-YYYYMMDD-HHMMSS
```

To resume from a later Workbench step:

```bash
bash examples/ai-hub/run_ai_hub.sh \
  --config config.yaml \
  --from-step validate
```

To skip downloading the compiled artifact:

```bash
bash examples/ai-hub/run_ai_hub.sh \
  --config config.yaml \
  --skip-download
```

## Troubleshooting

If AI Hub reports dynamic input shapes, rerun training with the current training source. AI Hub quantization requires the exported ONNX model to use static input shapes.

If `run_ai_hub.sh` reports missing calibration or input files, run:

```bash
uv run python examples/ai-hub/prepare_inputs.py
```

If validation fails with a missing input name, make sure `config.yaml` and the generated `.npz` both use `input` as the input name.