add script to test steps in ai-hub

examples/ai-hub/README.md

@@ -0,0 +1,117 @@
+# 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: 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.