include steps for ai-hub

examples/meter-detection/README.md

@@ -153,7 +153,7 @@ Or pass the job name explicitly:
 qc-cli train status qc-cli-YYYYMMDD-HHMMSS
 ```
 
-## Outputs
+## SageMaker Outputs
 
 When the job completes, SageMaker packages the files written under `/opt/ml/model` into `model.tar.gz`.
 
@@ -167,6 +167,91 @@ metrics.json
 
 The archive is stored under the configured `s3.model_prefix`.
 
+## 6. Configure Qualcomm AI Hub
+
+Authenticate with Qualcomm AI Hub:
+
+```bash
+qai-hub configure --api_token
+```
+
+Add AI Hub settings to `config.yaml`. The input name and image size must match the ONNX model exported by this example:
+
+```yaml
+aihub:
+  device:
+    name: Dragonwing IQ-9075 EVK
+  target_runtime: tflite
+  input_specs:
+    images: [[1, 3, 640, 640], float32]
+  job_name: meter-detection
+  model_name: meter-detection
+  output_dir: build/qai-hub/meter-detection
+```
+
+Use the same image size configured in `sagemaker.training.hyperparameters.imgsz`. For example, a smoke-test model
+trained with `imgsz: 320` requires `images: [[1, 3, 320, 320], float32]`.
+
+## 7. Prepare AI Hub Inputs
+
+Generate calibration samples and a validation input from the downloaded dataset:
+
+```bash
+uv run python examples/meter-detection/prepare_aihub_inputs.py --image-size 640
+```
+
+This writes:
+
+```text
+examples/meter-detection/data/aihub_calibration/*.npy
+examples/meter-detection/data/inputs.npz
+```
+
+The script applies the preprocessing expected by the exported YOLO model: aspect-ratio-preserving letterboxing,
+RGB channel order, channel-first layout, and pixel values normalized to `[0, 1]`.
+
+Set `--image-size` to the training `imgsz` value when it is not `640`.
+
+## 8. Upload To Qualcomm AI Hub
+
+Use the SageMaker job name printed by `qc-cli train start`:
+
+```bash
+qc-cli ai-hub upload \
+  examples/meter-detection/data/aihub_calibration \
+  examples/meter-detection/data/inputs.npz \
+  --from-job qc-cli-YYYYMMDD-HHMMSS
+```
+
+The command downloads the job's `model.tar.gz`, finds `model.onnx`, uploads it to AI Hub, and runs quantization,
+compilation, validation, and profiling. The uploaded source model uses the configured
+`aihub.model_name`.
+
+If the meter-detection job is still the last training job in `.qc-cli.json`, `--from-job` can be omitted. Keeping it
+explicit prevents accidentally uploading an artifact from a different training run.
+
+To resume after a completed step, use one of:
+
+```bash
+qc-cli ai-hub upload \
+  examples/meter-detection/data/aihub_calibration \
+  examples/meter-detection/data/inputs.npz \
+  --from-step compile
+```
+
+```bash
+qc-cli ai-hub upload \
+  examples/meter-detection/data/aihub_calibration \
+  examples/meter-detection/data/inputs.npz \
+  --from-step validate
+```
+
+Download the compiled artifact after the workflow completes:
+
+```bash
+qc-cli ai-hub download --output build/qai-hub/meter-detection/model.tflite
+```
+
 ## Training Hyperparameters
 
 Values under `sagemaker.training.hyperparameters` are passed to `source/train.py` as command-line arguments.