update ai-hub to first optimize model for Workbench

Remove old examples

README.md

@@ -177,27 +177,35 @@ The expected output artifact is SageMaker’s `model.tar.gz`, normally containin
 ```
 qc-cli ai-hub upload <calibration.npz|calibration-dir> <inputs.npz|inputs.npy>
 qc-cli ai-hub upload <calibration> <inputs> --from-step validate
-qc-cli ai-hub quantize <calibration.npz|calibration-dir> [--onnx-path PATH] [--model-s3-uri URI] [--from-job NAME]
+qc-cli ai-hub optimize [--onnx-path PATH] [--model-s3-uri URI] [--from-job NAME]
+qc-cli ai-hub quantize <calibration.npz|calibration-dir> [--model-id ID] [--onnx-path PATH] [--model-s3-uri URI] [--from-job NAME]
 qc-cli ai-hub compile [--model-id ID] [--onnx-path PATH] [--model-s3-uri URI] [--from-job NAME]
 qc-cli ai-hub validate <inputs.npz|inputs.npy> [--model-id ID] [--input-name NAME]
 qc-cli ai-hub profile [--model-id ID]
 qc-cli ai-hub download [--model-id ID] [--output PATH]
 ```
 
-`ai-hub upload` runs the four Workbench upload steps in order: quantize, compile, validate, and profile. Use `--from-step compile`, `--from-step validate`, or `--from-step profile` to resume from saved local state after a completed earlier step.
+`ai-hub upload` optimizes to ONNX, quantizes, validates, and profiles. When `aihub.target_runtime` is not `onnx`, it
+also compiles the quantized model to that deployment runtime. The initial ONNX optimization gives external models
+Workbench provenance and applies compiler optimization passes before quantization.
 
 Resume behavior:
 
 ```text
---from-step quantize  Run quantize, compile, validate, and profile.
+--from-step optimize  Run optimize, quantize, optional final compile, validate, and profile.
---from-step compile   Skip quantize; compile the last quantized model unless an explicit source is passed.
+--from-step quantize  Quantize the last optimized ONNX, then optionally compile, validate, and profile.
---from-step validate  Skip quantize and compile; validate the last compiled model.
+--from-step compile   Skip optimize and quantize; finalize the last quantized model for the target runtime.
---from-step profile   Skip quantize, compile, and validate; profile the last compiled model.
+--from-step validate  Skip optimize, quantize, and compile; validate the last compiled model.
+--from-step profile   Skip optimize, quantize, compile, and validate; profile the last compiled model.
 ```
 
 When a step runs in the current command, `upload` passes its returned model ID directly to the next step. When a step is skipped, the next step resolves the needed model ID from `.qc-cli.json`. This avoids re-running earlier AI Hub jobs when you only need to continue from a later step.
 
-`ai-hub compile` resolves model sources in this order: `--model-id`, explicit source options (`--onnx-path`, `--model-s3-uri`, `--from-job`), last quantized model from state, then the last training job from local state. `ai-hub download` is separate because downloading the optimized artifact is outside the four-step Workbench upload loop.
+`ai-hub optimize` compiles an external model with `--target_runtime onnx`. `ai-hub quantize` uses an explicit
+`--model-id`, the last optimized ONNX model, or an explicit/local model source in that order. `ai-hub compile` resolves
+model sources in this order: `--model-id`, explicit source options, last quantized model, then the last training job.
+For `target_runtime: onnx`, upload treats the quantized ONNX as the final model and skips a redundant second compile.
+`ai-hub download` remains separate because downloading is outside the Workbench processing loop.
 
 AI Hub authentication currently uses the local `qai-hub` SDK configuration. A planned follow-up is to support AWS Systems Manager Parameter Store `SecureString` for team-managed tokens, where `config.yaml` stores only a parameter name such as `/qc-cli/aihub/token`, AWS KMS encrypts the token at rest, and the CLI retrieves it at runtime with `ssm:GetParameter` plus `kms:Decrypt` permissions.
 

examples/ai-hub/README.md

@@ -1,118 +0,0 @@
-# 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.

examples/ai-hub/prepare_inputs.py

@@ -1,74 +0,0 @@
-#!/usr/bin/env python3
-"""Prepare Qualcomm AI Hub calibration and validation inputs for the training example."""
-
-from __future__ import annotations
-
-import argparse
-from pathlib import Path
-
-import numpy as np
-from PIL import Image
-
-IMAGE_EXTENSIONS = {".jpg", ".jpeg", ".png"}
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument(
-        "--dataset-dir",
-        type=Path,
-        default=Path("examples/training/data/flower_photos_sagemaker"),
-        help="ImageFolder-style dataset used for training.",
-    )
-    parser.add_argument(
-        "--calibration-dir",
-        type=Path,
-        default=Path("examples/training/data/aihub_calibration"),
-        help="Directory where .npy calibration samples will be written.",
-    )
-    parser.add_argument(
-        "--input-file",
-        type=Path,
-        default=Path("examples/training/data/inputs.npz"),
-        help="Validation .npz input file for qc-cli ai-hub validate.",
-    )
-    parser.add_argument("--input-name", default="input", help="ONNX input name.")
-    parser.add_argument("--image-size", type=int, default=160, help="Square image size used by training.")
-    parser.add_argument("--samples", type=int, default=16, help="Number of calibration samples to write.")
-    return parser.parse_args()
-
-
-def preprocess_image(path: Path, image_size: int) -> np.ndarray:
-    image = Image.open(path).convert("RGB").resize((image_size, image_size), Image.Resampling.BILINEAR)
-    array = np.asarray(image, dtype=np.float32) / 255.0
-    array = np.transpose(array, (2, 0, 1))
-    mean = np.array([0.485, 0.456, 0.406], dtype=np.float32)[:, None, None]
-    std = np.array([0.229, 0.224, 0.225], dtype=np.float32)[:, None, None]
-    return ((array - mean) / std)[None, ...].astype("float32")
-
-
-def main() -> None:
-    args = parse_args()
-    images = sorted(p for p in args.dataset_dir.rglob("*") if p.suffix.lower() in IMAGE_EXTENSIONS)
-    if not images:
-        raise SystemExit(f"No images found under {args.dataset_dir}")
-    if args.samples < 1:
-        raise SystemExit("--samples must be at least 1")
-
-    args.calibration_dir.mkdir(parents=True, exist_ok=True)
-    args.input_file.parent.mkdir(parents=True, exist_ok=True)
-
-    sample_count = min(args.samples, len(images))
-    prepared = []
-    for index, image_path in enumerate(images[:sample_count]):
-        sample = preprocess_image(image_path, args.image_size)
-        np.save(args.calibration_dir / f"sample_{index:03d}.npy", sample)
-        prepared.append(sample)
-
-    np.savez(args.input_file, **{args.input_name: prepared[0]})
-    print(f"Wrote {sample_count} calibration samples to {args.calibration_dir}")
-    print(f"Wrote validation input to {args.input_file}")
-
-
-if __name__ == "__main__":
-    main()

examples/ai-hub/run_ai_hub.sh

@@ -1,156 +0,0 @@
-#!/usr/bin/env bash
-set -euo pipefail
-
-CONFIG_PATH="config.yaml"
-CALIBRATION_PATH="examples/training/data/aihub_calibration"
-INPUT_FILE="examples/training/data/inputs.npz"
-FROM_STEP="quantize"
-FROM_JOB=""
-MODEL_S3_URI=""
-ONNX_PATH=""
-INPUT_NAME=""
-DOWNLOAD=true
-OUTPUT_PATH=""
-
-usage() {
-  cat <<EOF
-Usage: $0 [options]
-
-Options:
-  --config PATH              Path to qc-cli config file. Default: config.yaml
-  --calibration PATH         Calibration .npz file or directory of .npy samples.
-                              Default: ${CALIBRATION_PATH}
-  --input-file PATH          Validation .npz or .npy inputs. Default: ${INPUT_FILE}
-  --from-step STEP           Resume upload from: quantize, compile, validate, profile.
-                              Default: ${FROM_STEP}
-  --from-job NAME            SageMaker training job whose model artifact should upload.
-                              Defaults to the last training job in local qc-cli state.
-  --model-s3-uri URI         S3 URI of model.tar.gz to upload.
-  --onnx-path PATH           Local ONNX path or ONNX path inside extracted artifact.
-  --input-name NAME          Input name for .npy validation files.
-  --skip-download            Do not download the compiled AI Hub artifact after upload.
-  --output PATH              Destination file for ai-hub download.
-  -h, --help                 Show this help.
-EOF
-}
-
-while [[ $# -gt 0 ]]; do
-  case "$1" in
-    --config)
-      CONFIG_PATH="$2"
-      shift 2
-      ;;
-    --calibration)
-      CALIBRATION_PATH="$2"
-      shift 2
-      ;;
-    --input-file)
-      INPUT_FILE="$2"
-      shift 2
-      ;;
-    --from-step)
-      FROM_STEP="$2"
-      shift 2
-      ;;
-    --from-job)
-      FROM_JOB="$2"
-      shift 2
-      ;;
-    --model-s3-uri)
-      MODEL_S3_URI="$2"
-      shift 2
-      ;;
-    --onnx-path)
-      ONNX_PATH="$2"
-      shift 2
-      ;;
-    --input-name)
-      INPUT_NAME="$2"
-      shift 2
-      ;;
-    --skip-download)
-      DOWNLOAD=false
-      shift
-      ;;
-    --output)
-      OUTPUT_PATH="$2"
-      shift 2
-      ;;
-    -h|--help)
-      usage
-      exit 0
-      ;;
-    *)
-      echo "Unknown option: $1" >&2
-      usage >&2
-      exit 1
-      ;;
-  esac
-done
-
-if [[ ! -f "${CONFIG_PATH}" ]]; then
-  echo "Config not found: ${CONFIG_PATH}" >&2
-  exit 1
-fi
-
-case "${FROM_STEP}" in
-  quantize|compile|validate|profile)
-    ;;
-  *)
-    echo "--from-step must be one of: quantize, compile, validate, profile" >&2
-    exit 1
-    ;;
-esac
-
-if [[ ! -e "${CALIBRATION_PATH}" ]]; then
-  echo "Calibration path not found: ${CALIBRATION_PATH}" >&2
-  echo "Pass --calibration with a .npz file or directory of .npy samples." >&2
-  exit 1
-fi
-
-if [[ ! -f "${INPUT_FILE}" ]]; then
-  echo "Input file not found: ${INPUT_FILE}" >&2
-  echo "Pass --input-file with a validation .npz or .npy file." >&2
-  exit 1
-fi
-
-run() {
-  echo "+ $*"
-  "$@"
-}
-
-UPLOAD_ARGS=(
-  "${CALIBRATION_PATH}"
-  "${INPUT_FILE}"
-  --from-step "${FROM_STEP}"
-  --config "${CONFIG_PATH}"
-)
-
-if [[ -n "${FROM_JOB}" ]]; then
-  UPLOAD_ARGS+=(--from-job "${FROM_JOB}")
-fi
-
-if [[ -n "${MODEL_S3_URI}" ]]; then
-  UPLOAD_ARGS+=(--model-s3-uri "${MODEL_S3_URI}")
-fi
-
-if [[ -n "${ONNX_PATH}" ]]; then
-  UPLOAD_ARGS+=(--onnx-path "${ONNX_PATH}")
-fi
-
-if [[ -n "${INPUT_NAME}" ]]; then
-  UPLOAD_ARGS+=(--input-name "${INPUT_NAME}")
-fi
-
-run uv run qc-cli ai-hub upload "${UPLOAD_ARGS[@]}"
-
-if [[ "${DOWNLOAD}" == false ]]; then
-  exit 0
-fi
-
-DOWNLOAD_ARGS=(--config "${CONFIG_PATH}")
-if [[ -n "${OUTPUT_PATH}" ]]; then
-  DOWNLOAD_ARGS+=(--output "${OUTPUT_PATH}")
-fi
-
-run uv run qc-cli ai-hub download "${DOWNLOAD_ARGS[@]}"

examples/meter-detection/README.md

@@ -0,0 +1,266 @@
+# YOLO26 Electric Meter Detection Example
+
+This example trains a YOLO26 object detection model on the Roboflow Universe electric meter dataset using the existing `qc-cli` SageMaker training flow.
+
+The workflow is intentionally command driven. Run each step yourself so you can inspect the dataset, update `config.yaml`, and decide when to submit the SageMaker job.
+
+Dataset:
+
+```text
+https://universe.roboflow.com/kemals-workspace-kbc8l/electric-meter-detection-o4tfi/dataset/1
+```
+
+## Prerequisites
+
+- Install or sync the project dependencies: `uv sync`
+- The virtual environment is activated.
+- AWS credentials configured for the profile in `config.yaml`
+- Infrastructure already deployed with `qc-cli infra setup`
+
+## 1. Download The Dataset
+
+Register or sign in to Roboflow, then open the dataset page:
+
+```text
+https://universe.roboflow.com/kemals-workspace-kbc8l/electric-meter-detection-o4tfi/dataset/1
+```
+
+Download the dataset in YOLOv26 format from the Roboflow UI, then extract the downloaded archive into:
+
+```text
+examples/meter-detection/data/electric-meter-detection
+```
+
+The `data.yaml` file should be directly under that folder:
+
+```text
+examples/meter-detection/data/electric-meter-detection/data.yaml
+```
+
+Do not move `data.yaml` into the `train/` split folder.
+
+After extracting, confirm the dataset has a YOLO data file and image splits:
+
+```bash
+find examples/meter-detection/data/electric-meter-detection -maxdepth 2 -type d | sort
+find examples/meter-detection/data/electric-meter-detection -name data.yaml -print
+```
+
+Open `examples/meter-detection/data/electric-meter-detection/data.yaml` and make sure the split paths are relative to that folder:
+
+```yaml
+path: .
+train: train/images
+val: valid/images
+test: test/images
+```
+
+If your downloaded dataset does not include a `test/` folder, remove the `test:` line.
+
+The expected layout is similar to:
+
+```text
+examples/meter-detection/data/electric-meter-detection/
+  data.yaml
+  train/
+  valid/
+  test/
+```
+
+## 2. Configure SageMaker Training
+
+Update `config.yaml` so the training section points at this example's source directory:
+
+```yaml
+sagemaker:
+  training:
+    image_uri: 763104351884.dkr.ecr.us-east-1.amazonaws.com/pytorch-training:2.6-cpu-py312-ubuntu22.04-sagemaker-v1
+    instance_type: ml.g4dn.xlarge
+    instance_count: 1
+    source_dir: examples/meter-detection/source
+    entry_point: train.py
+    hyperparameters:
+      model: yolo26n.pt
+      epochs: 25
+      imgsz: 640
+      batch: 16
+      workers: 2
+```
+
+Use `yolo26n.pt` for a lightweight first YOLO26 run. If those weights are unavailable in the installed Ultralytics package, use `yolo11n.pt` as the established fallback:
+
+```yaml
+      model: yolo11n.pt
+```
+
+The `source/requirements.txt` file is installed by the SageMaker PyTorch container before running `train.py`.
+
+For a CPU smoke test, use a CPU instance and reduce the workload:
+
+```yaml
+sagemaker:
+  training:
+    image_uri: 763104351884.dkr.ecr.us-east-1.amazonaws.com/pytorch-training:2.6-cpu-py312-ubuntu22.04-sagemaker-v1
+    instance_type: ml.m4.xlarge
+    instance_count: 1
+    source_dir: examples/meter-detection/source
+    entry_point: train.py
+    hyperparameters:
+      model: yolo26n.pt
+      epochs: 1
+      imgsz: 320
+      batch: 4
+      workers: 2
+```
+
+## 3. Check Infrastructure
+
+Confirm the CLI can see the configured SageMaker role and S3 bucket:
+
+```bash
+qc-cli infra status
+```
+
+## 4. Upload The Dataset
+
+Upload the downloaded Roboflow dataset to the `s3.data_prefix` configured in `config.yaml`:
+
+```bash
+qc-cli upload examples/meter-detection/data/electric-meter-detection
+```
+
+Directory uploads preserve paths relative to the uploaded directory, so SageMaker receives the dataset root with `data.yaml` plus the split directories.
+
+In SageMaker, this uploaded dataset root is mounted at `/opt/ml/input/data/train`. That `train` path is the SageMaker channel name, not the YOLO `train/` split folder.
+
+## 5. Start Training
+
+Submit the SageMaker training job:
+
+```bash
+qc-cli train start
+```
+
+The command prints the submitted SageMaker job name. Check progress with:
+
+```bash
+qc-cli train status
+```
+
+Or pass the job name explicitly:
+
+```bash
+qc-cli train status qc-cli-YYYYMMDD-HHMMSS
+```
+
+## SageMaker Outputs
+
+When the job completes, SageMaker packages the files written under `/opt/ml/model` into `model.tar.gz`.
+
+This example writes:
+
+```text
+best.pt
+model.onnx
+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: onnx
+  input_specs:
+    images: [[1, 3, 640, 640], float32]
+  job_name: meter-detection
+  model_name: meter-detection
+  output_dir: build/qai-hub/meter-detection
+```
+
+The ONNX graph is the source of truth. The export normally uses the same value as `sagemaker.training.hyperparameters.imgsz`, but changing `config.yaml` after training does not resize an existing model. For example, a model exported 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]`.
+
+## 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`, and runs the following AI Hub workflow:
+
+1. Compile the external ONNX to a Workbench-optimized ONNX model.
+2. Quantize the optimized ONNX model.
+3. Compile the quantized model when the configured deployment runtime is not `onnx`.
+4. Validate and profile the final model.
+
+The training example sanitizes the Ultralytics ONNX export before saving `model.onnx`. This removes graph input or output names, such as `output0`, that are duplicated in the ONNX `value_info` metadata and rejected by AI Hub.
+
+For a model already downloaded by a failed upload attempt, sanitize the extracted ONNX file and retry using the local model. Replace the job name in both paths:
+
+```bash
+uv run --with onnx python examples/meter-detection/source/sanitize_onnx.py \
+  build/qai-hub/meter-detection/qc-cli-YYYYMMDD-HHMMSS/source/extracted/model.onnx \
+  --output build/qai-hub/meter-detection/model.aihub.onnx
+
+qc-cli ai-hub upload \
+  examples/meter-detection/data/aihub_calibration \
+  examples/meter-detection/data/inputs.npz \
+  --onnx-path build/qai-hub/meter-detection/model.aihub.onnx
+```
+
+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.
+
+| Name | Type | Default | Description |
+|---|---:|---:|---|
+| `model` | string | `yolo26n.pt` | Ultralytics model weights or model YAML. |
+| `epochs` | int | `25` | Number of training epochs. |
+| `imgsz` | int | `640` | Square training image size. |
+| `batch` | int | `16` | Images per training batch. |
+| `workers` | int | `2` | DataLoader worker count. |
+| `patience` | int | `20` | Early stopping patience. |
+| `device` | string | auto | Optional Ultralytics device value such as `0` or `cpu`. |
+| `data-yaml` | string | auto | Optional path to `data.yaml`; normally discovered from the uploaded dataset root. |
+| `dataset-dir` | string | `SM_CHANNEL_TRAIN` | Uploaded dataset root mounted by SageMaker. |
+
+Do not set `dataset-dir` or `model-dir` in normal SageMaker runs. SageMaker sets those automatically through `SM_CHANNEL_TRAIN` and `SM_MODEL_DIR`.

examples/meter-detection/prepare_aihub_inputs.py

@@ -0,0 +1,92 @@
+#!/usr/bin/env python3
+"""Prepare Qualcomm AI Hub calibration and validation inputs for the meter detector."""
+
+from __future__ import annotations
+
+import argparse
+from pathlib import Path
+
+import numpy as np
+from PIL import Image
+
+IMAGE_EXTENSIONS = {".jpg", ".jpeg", ".png"}
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        "--dataset-dir",
+        type=Path,
+        default=Path("examples/meter-detection/data/electric-meter-detection"),
+        help="Root of the extracted Roboflow dataset.",
+    )
+    parser.add_argument(
+        "--calibration-dir",
+        type=Path,
+        default=Path("examples/meter-detection/data/aihub_calibration"),
+        help="Directory where .npy calibration samples will be written.",
+    )
+    parser.add_argument(
+        "--input-file",
+        type=Path,
+        default=Path("examples/meter-detection/data/inputs.npz"),
+        help="Validation .npz input file for qc-cli ai-hub validate.",
+    )
+    parser.add_argument("--input-name", default="images", help="ONNX input name.")
+    parser.add_argument("--image-size", type=int, default=640, help="Square image size used for ONNX export.")
+    parser.add_argument("--samples", type=int, default=16, help="Number of calibration samples to write.")
+    return parser.parse_args()
+
+
+def preprocess_image(path: Path, image_size: int) -> np.ndarray:
+    """Apply Ultralytics-style letterboxing and produce an NCHW float32 tensor."""
+    with Image.open(path) as source:
+        image = source.convert("RGB")
+
+    scale = min(image_size / image.width, image_size / image.height)
+    resized_width = round(image.width * scale)
+    resized_height = round(image.height * scale)
+    image = image.resize((resized_width, resized_height), Image.Resampling.BILINEAR)
+
+    canvas = Image.new("RGB", (image_size, image_size), (114, 114, 114))
+    left = round((image_size - resized_width) / 2 - 0.1)
+    top = round((image_size - resized_height) / 2 - 0.1)
+    canvas.paste(image, (left, top))
+
+    array = np.asarray(canvas, dtype=np.float32) / 255.0
+    return np.transpose(array, (2, 0, 1))[None, ...].astype(np.float32)
+
+
+def main() -> None:
+    args = parse_args()
+    if args.image_size < 1:
+        raise SystemExit("--image-size must be at least 1")
+    if args.samples < 1:
+        raise SystemExit("--samples must be at least 1")
+
+    images = sorted(
+        path
+        for path in args.dataset_dir.rglob("*")
+        if path.is_file() and path.suffix.lower() in IMAGE_EXTENSIONS and path.parent.name == "images"
+    )
+    if not images:
+        raise SystemExit(f"No images found under {args.dataset_dir}")
+
+    args.calibration_dir.mkdir(parents=True, exist_ok=True)
+    args.input_file.parent.mkdir(parents=True, exist_ok=True)
+    for stale_sample in args.calibration_dir.glob("sample_*.npy"):
+        stale_sample.unlink()
+
+    prepared: list[np.ndarray] = []
+    for index, image_path in enumerate(images[: args.samples]):
+        sample = preprocess_image(image_path, args.image_size)
+        np.save(args.calibration_dir / f"sample_{index:03d}.npy", sample)
+        prepared.append(sample)
+
+    np.savez(args.input_file, **{args.input_name: prepared[0]})  # pyright: ignore[reportArgumentType]
+    print(f"Wrote {len(prepared)} calibration samples to {args.calibration_dir}")
+    print(f"Wrote validation input to {args.input_file}")
+
+
+if __name__ == "__main__":
+    main()

examples/meter-detection/source/requirements.txt

@@ -0,0 +1,3 @@
+ultralytics>=8.3.0
+pyyaml>=6.0.3
+onnx>=1.16.0

examples/meter-detection/source/sanitize_onnx.py

@@ -0,0 +1,38 @@
+#!/usr/bin/env python3
+"""Remove ONNX value_info entries that duplicate graph inputs or outputs."""
+
+from __future__ import annotations
+
+import argparse
+from pathlib import Path
+
+import onnx  # type: ignore[reportMissingImports]
+
+
+def sanitize_onnx(path: Path, output_path: Path | None = None) -> Path:
+    model = onnx.load(path)
+    io_names = {value.name for value in (*model.graph.input, *model.graph.output)}
+    retained_value_info = [value for value in model.graph.value_info if value.name not in io_names]
+
+    destination = output_path or path
+    if len(retained_value_info) != len(model.graph.value_info):
+        del model.graph.value_info[:]
+        model.graph.value_info.extend(retained_value_info)
+
+    destination.parent.mkdir(parents=True, exist_ok=True)
+    onnx.save(model, destination)
+    return destination
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("onnx_path", type=Path)
+    parser.add_argument("--output", type=Path)
+    args = parser.parse_args()
+
+    written = sanitize_onnx(args.onnx_path, args.output)
+    print(f"Saved sanitized ONNX model to {written}")
+
+
+if __name__ == "__main__":
+    main()

examples/meter-detection/source/train.py

@@ -0,0 +1,126 @@
+#!/usr/bin/env python3
+"""SageMaker entry point for YOLO electric meter detection training."""
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import shutil
+from pathlib import Path
+from typing import Any
+
+import yaml
+from sanitize_onnx import sanitize_onnx
+from ultralytics import YOLO  # type: ignore[reportMissingImports]
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model", default="yolo26n.pt")
+    parser.add_argument("--epochs", type=int, default=25)
+    parser.add_argument("--imgsz", type=int, default=640)
+    parser.add_argument("--batch", type=int, default=16)
+    parser.add_argument("--workers", type=int, default=2)
+    parser.add_argument("--patience", type=int, default=20)
+    parser.add_argument("--device", default=None)
+    parser.add_argument("--data-yaml", default=None)
+    parser.add_argument("--dataset-dir", default=os.environ.get("SM_CHANNEL_TRAIN", "/opt/ml/input/data/train"))
+    parser.add_argument("--train-dir", dest="dataset_dir", help=argparse.SUPPRESS)
+    parser.add_argument("--model-dir", default=os.environ.get("SM_MODEL_DIR", "/opt/ml/model"))
+    return parser.parse_args()
+
+
+def find_data_yaml(dataset_dir: Path, explicit_path: str | None) -> Path:
+    if explicit_path:
+        data_yaml = Path(explicit_path)
+        if data_yaml.is_file():
+            return data_yaml
+        raise FileNotFoundError(f"Configured data.yaml does not exist: {data_yaml}")
+
+    matches = sorted(dataset_dir.rglob("data.yaml"))
+    if not matches:
+        raise FileNotFoundError(f"Could not find data.yaml under {dataset_dir}")
+    if len(matches) > 1:
+        print(f"Found multiple data.yaml files; using {matches[0]}")
+    return matches[0]
+
+
+def prepare_data_yaml(data_yaml: Path) -> Path:
+    """Write a SageMaker-local data file rooted at the uploaded dataset."""
+    dataset_root = data_yaml.parent
+    data = yaml.safe_load(data_yaml.read_text(encoding="utf-8"))
+    if not isinstance(data, dict):
+        raise ValueError(f"Expected a mapping in {data_yaml}")
+
+    normalized = dict(data)
+    normalized["path"] = str(dataset_root)
+    if "val" not in normalized and "valid" in normalized:
+        normalized["val"] = normalized.pop("valid")
+
+    prepared_path = dataset_root / "data.sagemaker.yaml"
+    prepared_path.write_text(yaml.safe_dump(normalized, sort_keys=False), encoding="utf-8")
+    print(f"Prepared dataset config: {prepared_path}")
+    return prepared_path
+
+
+def copy_if_exists(source: Path, destination: Path) -> None:
+    if source.exists():
+        shutil.copy2(source, destination)
+        print(f"Saved {destination}")
+
+
+def main() -> None:
+    args = parse_args()
+    dataset_dir = Path(args.dataset_dir)
+    model_dir = Path(args.model_dir)
+    model_dir.mkdir(parents=True, exist_ok=True)
+
+    data_yaml = prepare_data_yaml(find_data_yaml(dataset_dir, args.data_yaml))
+    model = YOLO(args.model)
+
+    train_kwargs: dict[str, Any] = {
+        "data": str(data_yaml),
+        "epochs": args.epochs,
+        "imgsz": args.imgsz,
+        "batch": args.batch,
+        "workers": args.workers,
+        "patience": args.patience,
+        "project": str(model_dir / "runs"),
+        "name": "train",
+        "exist_ok": True,
+    }
+    if args.device:
+        train_kwargs["device"] = args.device
+
+    results = model.train(**train_kwargs)
+    save_dir = Path(results.save_dir)
+    best_pt = save_dir / "weights" / "best.pt"
+    last_pt = save_dir / "weights" / "last.pt"
+    trained_weights = best_pt if best_pt.exists() else last_pt
+    if not trained_weights.exists():
+        raise FileNotFoundError(f"Could not find trained weights in {save_dir / 'weights'}")
+
+    copy_if_exists(trained_weights, model_dir / "best.pt")
+    trained_model = YOLO(str(trained_weights))
+    onnx_path = Path(trained_model.export(format="onnx", imgsz=args.imgsz))
+    saved_onnx_path = sanitize_onnx(onnx_path, model_dir / "model.onnx")
+    print(f"Saved {saved_onnx_path}")
+
+    metrics = {
+        "model": args.model,
+        "epochs": args.epochs,
+        "imgsz": args.imgsz,
+        "batch": args.batch,
+        "workers": args.workers,
+        "patience": args.patience,
+        "data_yaml": str(data_yaml),
+        "weights": str(trained_weights),
+        "onnx": str(saved_onnx_path),
+    }
+    (model_dir / "metrics.json").write_text(json.dumps(metrics, indent=2), encoding="utf-8")
+    print(f"Saved model artifacts to {model_dir}")
+
+
+if __name__ == "__main__":
+    main()

examples/training/README.md

@@ -1,89 +0,0 @@
-# SageMaker Training Example
-
-This example downloads a small image-classification dataset, uploads it through `qc-cli`, and submits a live SageMaker training job.
-
-## Prerequisites
-
-- AWS credentials configured for the profile in `config.yaml`
-- Infrastructure already deployed with `qc-cli infra setup`
-- `config.yaml` updated with:
-
-```yaml
-s3:
-  bucket: your-bucket-name
-
-sagemaker:
-  training:
-    image_uri: 763104351884.dkr.ecr.us-east-1.amazonaws.com/pytorch-training:2.6-cpu-py312-ubuntu22.04-sagemaker-v1
-    instance_type: ml.m4.xlarge
-    instance_count: 1
-    source_dir: examples/training/source
-    entry_point: train.py
-    hyperparameters:
-      epochs: 1
-      batch-size: 32
-      learning-rate: 0.001
-      image-size: 160
-      validation-split: 0.2
-```
-
-## Training Hyperparameters
-
-Values under `sagemaker.training.hyperparameters` are passed to the training entry point as command-line arguments. For this example, they map to arguments defined in [source/train.py](source/train.py).
-
-Supported by this example:
-
-| Name | Type | Default | Description |
-|---|---:|---:|---|
-| `epochs` | int | `1` | Number of training epochs. |
-| `batch-size` | int | `32` | Images per training batch. |
-| `learning-rate` | float | `0.001` | Adam optimizer learning rate. |
-| `image-size` | int | `160` | Resize images to square `image-size x image-size`. |
-| `validation-split` | float | `0.2` | Fraction of data used for validation. |
-| `max-samples` | int | `0` | Optional cap for smoke tests; `0` means use all images. |
-| `seed` | int | `13` | Random seed for reproducible splitting. |
-| `num-workers` | int | `2` | DataLoader worker count. |
-
-Do not set `train-dir` or `model-dir` in normal SageMaker runs. SageMaker sets those automatically through `SM_CHANNEL_TRAIN` and `SM_MODEL_DIR`.
-
-## 1. Download The Dataset
-
-```bash
-bash examples/training/download_flower_photos.sh
-```
-
-This creates:
-
-```text
-examples/training/data/flower_photos_sagemaker/
-  daisy/
-  dandelion/
-  roses/
-  sunflowers/
-  tulips/
-```
-
-## 2. Run Training
-
-Run the training script and wait until it finishes:
-
-```bash
-bash examples/training/run_training.sh --config config.yaml --wait
-```
-
-Use a dataset that is already uploaded to `s3.data_prefix`:
-
-```bash
-bash examples/training/run_training.sh \
-  --config config.yaml \
-  --skip-upload \
-  --wait
-```
-
-## Notes
-
-- The default dataset path is `examples/training/data/flower_photos_sagemaker`.
-- Uploaded data uses the `s3.bucket` and `s3.data_prefix` values from `config.yaml`.
-- Training artifacts are written under `s3://<bucket>/<model_prefix>/`.
-- The SageMaker `model.tar.gz` contains `model.onnx`, `model.pt`, `class_to_idx.json`, and `metrics.json`.
-- SageMaker packages `examples/training/source`, installs `requirements.txt`, and runs `train.py`.

examples/training/download_flower_photos.sh

@@ -1,40 +0,0 @@
-#!/usr/bin/env bash
-set -euo pipefail
-
-DATASET_URL="https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz"
-DEST_DIR="${1:-examples/training/data}"
-ARCHIVE_PATH="${DEST_DIR}/flower_photos.tgz"
-RAW_DATASET_DIR="${DEST_DIR}/flower_photos"
-DATASET_DIR="${DEST_DIR}/flower_photos_sagemaker"
-CLASS_NAMES=("daisy" "dandelion" "roses" "sunflowers" "tulips")
-
-mkdir -p "${DEST_DIR}"
-
-if [[ -d "${DATASET_DIR}" ]]; then
-  echo "Dataset already exists: ${DATASET_DIR}"
-  echo "Use this path with run_training.py:"
-  echo "  ${DATASET_DIR}"
-  exit 0
-fi
-
-echo "Downloading TensorFlow flower_photos dataset..."
-if command -v curl >/dev/null 2>&1; then
-  curl -L "${DATASET_URL}" -o "${ARCHIVE_PATH}"
-elif command -v wget >/dev/null 2>&1; then
-  wget -O "${ARCHIVE_PATH}" "${DATASET_URL}"
-else
-  echo "Either curl or wget is required." >&2
-  exit 1
-fi
-
-echo "Extracting dataset..."
-tar -xzf "${ARCHIVE_PATH}" -C "${DEST_DIR}"
-
-echo "Preparing SageMaker directory layout..."
-mkdir -p "${DATASET_DIR}"
-for class_name in "${CLASS_NAMES[@]}"; do
-  cp -R "${RAW_DATASET_DIR}/${class_name}" "${DATASET_DIR}/${class_name}"
-done
-
-echo "Dataset ready: ${DATASET_DIR}"
-find "${DATASET_DIR}" -mindepth 1 -maxdepth 1 -type d -print | sort

examples/training/run_training.sh

@@ -1,112 +0,0 @@
-#!/usr/bin/env bash
-set -euo pipefail
-
-CONFIG_PATH="config.yaml"
-DATASET_DIR="examples/training/data/flower_photos_sagemaker"
-WAIT=false
-SKIP_UPLOAD=false
-POLL_SECONDS=60
-
-usage() {
-  cat <<EOF
-Usage: $0 [options]
-
-Options:
-  --config PATH          Path to qc-cli config file. Default: config.yaml
-  --dataset-dir PATH     Dataset directory to upload. Default: ${DATASET_DIR}
-  --skip-upload          Train against data already uploaded to s3.data_prefix.
-  --wait                 Poll until training completes.
-  -h, --help             Show this help.
-EOF
-}
-
-while [[ $# -gt 0 ]]; do
-  case "$1" in
-    --config)
-      CONFIG_PATH="$2"
-      shift 2
-      ;;
-    --dataset-dir)
-      DATASET_DIR="$2"
-      shift 2
-      ;;
-    --skip-upload)
-      SKIP_UPLOAD=true
-      shift
-      ;;
-    --wait)
-      WAIT=true
-      shift
-      ;;
-    -h|--help)
-      usage
-      exit 0
-      ;;
-    *)
-      echo "Unknown option: $1" >&2
-      usage >&2
-      exit 1
-      ;;
-  esac
-done
-
-if [[ ! -f "${CONFIG_PATH}" ]]; then
-  echo "Config not found: ${CONFIG_PATH}" >&2
-  exit 1
-fi
-
-if [[ "${SKIP_UPLOAD}" == false && ! -d "${DATASET_DIR}" ]]; then
-  echo "Dataset not found: ${DATASET_DIR}" >&2
-  echo "Run: bash examples/training/download_flower_photos.sh" >&2
-  exit 1
-fi
-
-run() {
-  echo "+ $*"
-  "$@"
-}
-
-run uv run qc-cli infra status --config "${CONFIG_PATH}"
-
-if [[ "${SKIP_UPLOAD}" == false ]]; then
-  run uv run qc-cli upload "${DATASET_DIR}" --config "${CONFIG_PATH}"
-fi
-
-TRAIN_OUTPUT_FILE="$(mktemp)"
-trap 'rm -f "${TRAIN_OUTPUT_FILE}"' EXIT
-run uv run qc-cli train start --config "${CONFIG_PATH}" | tee "${TRAIN_OUTPUT_FILE}"
-
-JOB_NAME="$(grep -Eo 'qc-cli-[0-9]{8}-[0-9]{6}' "${TRAIN_OUTPUT_FILE}" | tail -n 1)"
-if [[ -z "${JOB_NAME}" ]]; then
-  echo "Could not find training job name in qc-cli output." >&2
-  exit 1
-fi
-
-echo "Submitted SageMaker training job: ${JOB_NAME}"
-
-if [[ "${WAIT}" == false ]]; then
-  run uv run qc-cli train status "${JOB_NAME}" --config "${CONFIG_PATH}"
-  exit 0
-fi
-
-while true; do
-  STATUS_OUTPUT="$(uv run qc-cli train status "${JOB_NAME}" --config "${CONFIG_PATH}")"
-  echo "${STATUS_OUTPUT}"
-
-  if printf '%s\n' "${STATUS_OUTPUT}" | grep -q 'Status:.*Completed'; then
-    echo "Training completed successfully."
-    exit 0
-  fi
-
-  if printf '%s\n' "${STATUS_OUTPUT}" | grep -q 'Status:.*Failed'; then
-    echo "Training failed." >&2
-    exit 1
-  fi
-
-  if printf '%s\n' "${STATUS_OUTPUT}" | grep -q 'Status:.*Stopped'; then
-    echo "Training stopped." >&2
-    exit 1
-  fi
-
-  sleep "${POLL_SECONDS}"
-done

examples/training/source/requirements.txt

@@ -1 +0,0 @@
-onnx==1.21.0

examples/training/source/train.py

@@ -1,188 +0,0 @@
-#!/usr/bin/env python3
-"""SageMaker entry point for CPU image-classification training."""
-
-from __future__ import annotations
-
-import argparse
-import json
-import os
-import random
-from pathlib import Path
-
-import torch
-from torch import nn
-from torch.utils.data import DataLoader, Subset, random_split
-from torchvision import datasets, transforms
-
-
-class SmallImageClassifier(nn.Module):
-    def __init__(self, class_count: int) -> None:
-        super().__init__()
-        self.features = nn.Sequential(
-            nn.Conv2d(3, 16, kernel_size=3, padding=1),
-            nn.ReLU(inplace=True),
-            nn.MaxPool2d(2),
-            nn.Conv2d(16, 32, kernel_size=3, padding=1),
-            nn.ReLU(inplace=True),
-            nn.MaxPool2d(2),
-            nn.Conv2d(32, 64, kernel_size=3, padding=1),
-            nn.ReLU(inplace=True),
-            nn.MaxPool2d(2),
-            nn.AdaptiveAvgPool2d((1, 1)),
-        )
-        self.classifier = nn.Linear(64, class_count)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.features(x)
-        x = torch.flatten(x, 1)
-        return self.classifier(x)
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--epochs", type=int, default=1)
-    parser.add_argument("--batch-size", type=int, default=32)
-    parser.add_argument("--learning-rate", type=float, default=0.001)
-    parser.add_argument("--image-size", type=int, default=160)
-    parser.add_argument("--validation-split", type=float, default=0.2)
-    parser.add_argument("--max-samples", type=int, default=0)
-    parser.add_argument("--seed", type=int, default=13)
-    parser.add_argument("--num-workers", type=int, default=2)
-    parser.add_argument("--train-dir", default=os.environ.get("SM_CHANNEL_TRAIN", "/opt/ml/input/data/train"))
-    parser.add_argument("--model-dir", default=os.environ.get("SM_MODEL_DIR", "/opt/ml/model"))
-    return parser.parse_args()
-
-
-def build_datasets(args: argparse.Namespace) -> tuple[Subset, Subset, dict[str, int]]:
-    transform = transforms.Compose(
-        [
-            transforms.Resize((args.image_size, args.image_size)),
-            transforms.ToTensor(),
-            transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
-        ]
-    )
-    dataset = datasets.ImageFolder(args.train_dir, transform=transform)
-    if len(dataset.classes) < 2:
-        raise ValueError(f"Expected at least two classes in {args.train_dir}. Found: {dataset.classes}")
-
-    if args.max_samples > 0 and args.max_samples < len(dataset):
-        indices = list(range(len(dataset)))
-        random.Random(args.seed).shuffle(indices)
-        dataset = Subset(dataset, indices[: args.max_samples])
-
-    validation_size = max(1, int(len(dataset) * args.validation_split))
-    train_size = len(dataset) - validation_size
-    if train_size < 1:
-        raise ValueError("Not enough images to create a train/validation split.")
-
-    generator = torch.Generator().manual_seed(args.seed)
-    train_dataset, validation_dataset = random_split(dataset, [train_size, validation_size], generator=generator)
-    return train_dataset, validation_dataset, getattr(dataset, "dataset", dataset).class_to_idx
-
-
-def run_epoch(
-    model: nn.Module,
-    data_loader: DataLoader,
-    criterion: nn.Module,
-    optimizer: torch.optim.Optimizer | None,
-    device: torch.device,
-) -> tuple[float, float]:
-    training = optimizer is not None
-    model.train(training)
-
-    total_loss = 0.0
-    total_correct = 0
-    total_examples = 0
-
-    for images, labels in data_loader:
-        images = images.to(device)
-        labels = labels.to(device)
-
-        with torch.set_grad_enabled(training):
-            logits = model(images)
-            loss = criterion(logits, labels)
-
-            if training:
-                optimizer.zero_grad()
-                loss.backward()
-                optimizer.step()
-
-        total_loss += loss.item() * images.size(0)
-        total_correct += (logits.argmax(dim=1) == labels).sum().item()
-        total_examples += images.size(0)
-
-    return total_loss / total_examples, total_correct / total_examples
-
-
-def export_onnx(model: nn.Module, model_dir: Path, image_size: int) -> None:
-    model.eval()
-    dummy_input = torch.randn(1, 3, image_size, image_size)
-    torch.onnx.export(
-        model,
-        dummy_input,
-        model_dir / "model.onnx",
-        export_params=True,
-        opset_version=17,
-        do_constant_folding=True,
-        input_names=["input"],
-        output_names=["logits"],
-    )
-
-
-def main() -> None:
-    args = parse_args()
-    random.seed(args.seed)
-    torch.manual_seed(args.seed)
-
-    train_dataset, validation_dataset, class_to_idx = build_datasets(args)
-    train_loader = DataLoader(
-        train_dataset,
-        batch_size=args.batch_size,
-        shuffle=True,
-        num_workers=args.num_workers,
-    )
-    validation_loader = DataLoader(
-        validation_dataset,
-        batch_size=args.batch_size,
-        shuffle=False,
-        num_workers=args.num_workers,
-    )
-
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    model = SmallImageClassifier(class_count=len(class_to_idx)).to(device)
-    criterion = nn.CrossEntropyLoss()
-    optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
-
-    print(f"Training on {device}. Classes: {sorted(class_to_idx)}")
-    metrics = []
-    for epoch in range(1, args.epochs + 1):
-        train_loss, train_accuracy = run_epoch(model, train_loader, criterion, optimizer, device)
-        validation_loss, validation_accuracy = run_epoch(model, validation_loader, criterion, None, device)
-        epoch_metrics = {
-            "epoch": epoch,
-            "train_loss": train_loss,
-            "train_accuracy": train_accuracy,
-            "validation_loss": validation_loss,
-            "validation_accuracy": validation_accuracy,
-        }
-        metrics.append(epoch_metrics)
-        print(json.dumps(epoch_metrics, sort_keys=True))
-
-    model_dir = Path(args.model_dir)
-    model_dir.mkdir(parents=True, exist_ok=True)
-    torch.save(
-        {
-            "model_state_dict": model.cpu().state_dict(),
-            "class_to_idx": class_to_idx,
-            "image_size": args.image_size,
-        },
-        model_dir / "model.pt",
-    )
-    export_onnx(model, model_dir, args.image_size)
-    (model_dir / "class_to_idx.json").write_text(json.dumps(class_to_idx, indent=2), encoding="utf-8")
-    (model_dir / "metrics.json").write_text(json.dumps(metrics, indent=2), encoding="utf-8")
-    print(f"Saved model artifacts to {model_dir}")
-
-
-if __name__ == "__main__":
-    main()

src/commands/ai_hub.py

@@ -1,4 +1,5 @@
 from collections.abc import Mapping, Sequence
+from dataclasses import dataclass
 from datetime import datetime
 from enum import StrEnum
 from pathlib import Path
@@ -12,9 +13,9 @@ from src import state as state_ops
 from src.commands.utils import CONFIG_OPT, CONSOLE, load_cfg
 from src.config import Config
 from src.qualcomm import aihub_jobs
-from src.qualcomm.artifacts import resolve_onnx
+from src.qualcomm.artifacts import ResolvedOnnx, resolve_onnx
 
-app = typer.Typer(help="Quantize, compile, validate, profile, and download models with Qualcomm AI Hub")
+app = typer.Typer(help="Optimize, quantize, compile, validate, profile, and download models with Qualcomm Workbench")
 
 _RUNTIME_EXTENSIONS = {
     "tflite": "tflite",
@@ -24,12 +25,19 @@ _RUNTIME_EXTENSIONS = {
 
 
 class UploadStep(StrEnum):
+    optimize = "optimize"
     quantize = "quantize"
     compile = "compile"
     validate = "validate"
     profile = "profile"
 
 
+@dataclass(frozen=True)
+class ResolvedModelSource:
+    model: str | Path
+    model_artifact: str | None = None
+
+
 def _input_specs(cfg: Config) -> dict[str, tuple[tuple[int, ...], str]]:
     specs = {name: (tuple(shape), dtype) for name, (shape, dtype) in cfg.aihub.input_specs.items()}
     if not specs:
@@ -101,6 +109,57 @@ def _model_id_or_state(config_path: str, model_id: str | None, *, quantized: boo
     return resolved
 
 
+def _resolve_model_source(
+    cfg: Config,
+    config_path: str,
+    *,
+    model_id: str | None = None,
+    previous_model_id: str | None = None,
+    from_job: str | None = None,
+    model_s3_uri: str | None = None,
+    onnx_path: str | None = None,
+) -> ResolvedModelSource:
+    if model_id:
+        return ResolvedModelSource(model_id)
+
+    has_explicit_source = bool(from_job or model_s3_uri or onnx_path)
+    if previous_model_id and not has_explicit_source:
+        return ResolvedModelSource(previous_model_id)
+
+    resolved = _resolve_onnx_source(
+        cfg,
+        config_path,
+        from_job=from_job,
+        model_s3_uri=model_s3_uri,
+        onnx_path=onnx_path,
+    )
+    return ResolvedModelSource(resolved.onnx_path, resolved.model_artifact)
+
+
+def _resolve_onnx_source(
+    cfg: Config,
+    config_path: str,
+    *,
+    from_job: str | None = None,
+    model_s3_uri: str | None = None,
+    onnx_path: str | None = None,
+) -> ResolvedOnnx:
+    st = state_ops.store(config_path)
+    last_training_job = st.get_last_training_job()
+    saved_model_artifact = None
+    if not from_job and not model_s3_uri and not onnx_path and not last_training_job:
+        saved_model_artifact = st.get_last_model_artifact()
+
+    return resolve_onnx(
+        cfg=cfg,
+        output_dir=cfg.aihub.output_dir,
+        from_job=from_job,
+        model_s3_uri=model_s3_uri or saved_model_artifact,
+        onnx_path=onnx_path,
+        last_training_job=last_training_job,
+    )
+
+
 def _device_selector(device: Device) -> str:
     parts: list[str] = []
     if device.name:
@@ -132,20 +191,23 @@ def _quantize_step(
     cfg: Config,
     config_path: str,
     calibration_path: Path,
-    from_job: str | None,
+    *,
-    model_s3_uri: str | None,
+    model_id: str | None = None,
-    onnx_path: str | None,
+    from_job: str | None = None,
+    model_s3_uri: str | None = None,
+    onnx_path: str | None = None,
 ) -> str:
     st = state_ops.store(config_path)
     specs = _input_specs(cfg)
     try:
-        resolved = resolve_onnx(
+        source = _resolve_model_source(
-            cfg=cfg,
+            cfg,
-            output_dir=cfg.aihub.output_dir,
+            config_path,
+            model_id=model_id,
+            previous_model_id=st.get_last_optimized_model_id(),
             from_job=from_job,
-            model_s3_uri=model_s3_uri or st.get_last_model_artifact(),
+            model_s3_uri=model_s3_uri,
             onnx_path=onnx_path,
-            last_training_job=st.get_last_training_job(),
         )
         calibration_data = _load_calibration(calibration_path, specs)
     except (FileNotFoundError, ValueError) as e:
@@ -153,73 +215,117 @@ def _quantize_step(
         raise typer.Exit(1)
 
     try:
+        hub_model = (
+            hub.upload_model(str(source.model), name=cfg.aihub.model_name)
+            if isinstance(source.model, Path)
+            else hub.get_model(source.model)
+        )
         result = aihub_jobs.submit_quantize_job(
-            resolved.onnx_path,
+            hub_model,
             calibration_data,
             cfg.aihub.quantize_options,
             job_name=_job_name(cfg, "quantize"),
-            model_name=cfg.aihub.model_name,
         )
     except Exception as e:
         CONSOLE.print(f"[red]AI Hub quantize failed: {e}[/red]")
         raise typer.Exit(1)
 
-    st.update(
+    updates: dict[str, Any] = {
-        last_model_artifact=resolved.model_artifact,
+        "last_quantize_job_id": result["job_id"],
-        last_quantize_job_id=result["job_id"],
+        "last_quantized_model_id": result["model_id"],
-        last_quantized_model_id=result["model_id"],
+    }
-    )
+    if source.model_artifact:
+        updates["last_model_artifact"] = source.model_artifact
+    st.update(**updates)
     CONSOLE.print(f"[green]✓[/green] Quantize job: [bold]{result['job_id']}[/bold]")
     CONSOLE.print(f"[green]✓[/green] Quantized model: [bold]{result['model_id']}[/bold]")
     return str(result["model_id"])
 
 
+def _optimize_step(
+    cfg: Config,
+    config_path: str,
+    from_job: str | None,
+    model_s3_uri: str | None,
+    onnx_path: str | None,
+) -> str:
+    st = state_ops.store(config_path)
+    _validate_device(cfg)
+    specs = _input_specs(cfg)
+    try:
+        source = _resolve_onnx_source(
+            cfg,
+            config_path,
+            from_job=from_job,
+            model_s3_uri=model_s3_uri,
+            onnx_path=onnx_path,
+        )
+    except (FileNotFoundError, ValueError) as e:
+        CONSOLE.print(f"[red]{e}[/red]")
+        raise typer.Exit(1)
+
+    try:
+        hub_model = hub.upload_model(str(source.onnx_path), name=cfg.aihub.model_name)
+        result = aihub_jobs.submit_compile_job(
+            model=hub_model,
+            device=cfg.aihub.device,
+            input_specs=specs,
+            target_runtime="onnx",
+            job_name=_job_name(cfg, "optimize"),
+        )
+    except Exception as e:
+        CONSOLE.print(f"[red]AI Hub ONNX optimization failed: {e}[/red]")
+        raise typer.Exit(1)
+
+    st.update(
+        last_model_artifact=source.model_artifact,
+        last_optimize_job_id=result["job_id"],
+        last_optimized_model_id=result["model_id"],
+    )
+    CONSOLE.print(f"[green]✓[/green] ONNX optimization job: [bold]{result['job_id']}[/bold]")
+    CONSOLE.print(f"[green]✓[/green] Optimized ONNX model: [bold]{result['model_id']}[/bold]")
+    return str(result["model_id"])
+
+
 def _compile_step(
     cfg: Config,
     config_path: str,
-    model_id: str | None,
-    from_job: str | None,
-    model_s3_uri: str | None,
-    onnx_path: str | None,
     *,
-    prefer_quantized: bool,
+    model_id: str | None = None,
+    from_job: str | None = None,
+    model_s3_uri: str | None = None,
+    onnx_path: str | None = None,
 ) -> str:
     st = state_ops.store(config_path)
     _validate_device(cfg)
     specs = _input_specs(cfg)
-
-    model: Any
-    model_artifact: str | None = None
-    has_explicit_source = bool(from_job or model_s3_uri or onnx_path)
-    if model_id:
-        model = model_id
-    elif prefer_quantized and not has_explicit_source and st.get_last_quantized_model_id():
-        model = st.get_last_quantized_model_id()
-    else:
     try:
-            resolved = resolve_onnx(
+        source = _resolve_model_source(
-                cfg=cfg,
+            cfg,
-                output_dir=cfg.aihub.output_dir,
+            config_path,
+            model_id=model_id,
+            previous_model_id=st.get_last_quantized_model_id(),
             from_job=from_job,
             model_s3_uri=model_s3_uri,
             onnx_path=onnx_path,
-                last_training_job=st.get_last_training_job(),
         )
     except (FileNotFoundError, ValueError) as e:
         CONSOLE.print(f"[red]{e}[/red]")
         raise typer.Exit(1)
-        model = resolved.onnx_path
-        model_artifact = resolved.model_artifact
 
     try:
+        hub_model = (
+            hub.upload_model(str(source.model), name=cfg.aihub.model_name)
+            if isinstance(source.model, Path)
+            else hub.get_model(source.model)
+        )
         result = aihub_jobs.submit_compile_job(
-            model=model,
+            model=hub_model,
             device=cfg.aihub.device,
             input_specs=specs,
             target_runtime=cfg.aihub.target_runtime,
             options=cfg.aihub.compile_options,
             job_name=_job_name(cfg, "compile"),
-            model_name=cfg.aihub.model_name if isinstance(model, Path) else None,
         )
     except Exception as e:
         CONSOLE.print(f"[red]AI Hub compile failed: {e}[/red]")
@@ -229,8 +335,8 @@ def _compile_step(
         "last_compile_job_id": result["job_id"],
         "last_compiled_model_id": result["model_id"],
     }
-    if model_artifact:
+    if source.model_artifact:
-        updates["last_model_artifact"] = model_artifact
+        updates["last_model_artifact"] = source.model_artifact
     st.update(**updates)
     CONSOLE.print(f"[green]✓[/green] Compile job: [bold]{result['job_id']}[/bold]")
     CONSOLE.print(f"[green]✓[/green] Compiled model: [bold]{result['model_id']}[/bold]")
@@ -256,8 +362,9 @@ def _validate_step(
     run = datetime.now().strftime("%Y%m%d-%H%M%S")
     out_dir = Path(cfg.aihub.output_dir) / run / "validation"
     try:
+        hub_model = hub.get_model(resolved_model_id)
         result = aihub_jobs.submit_inference_job(
-            resolved_model_id,
+            hub_model,
             cfg.aihub.device,
             inputs,
             out_dir,
@@ -281,8 +388,9 @@ def _profile_step(cfg: Config, config_path: str, model_id: str | None) -> str:
     _validate_device(cfg)
     resolved_model_id = _model_id_or_state(config_path, model_id)
     try:
+        hub_model = hub.get_model(resolved_model_id)
         result = aihub_jobs.submit_profile_job(
-            resolved_model_id,
+            hub_model,
             cfg.aihub.device,
             cfg.aihub.profile_options,
             job_name=_job_name(cfg, "profile"),
@@ -295,9 +403,24 @@ def _profile_step(cfg: Config, config_path: str, model_id: str | None) -> str:
     return str(result["job_id"])
 
 
+@app.command()
+def optimize(
+    from_job: str | None = typer.Option(None, "--from-job", help="Training job name whose model artifact should optimize"),
+    model_s3_uri: str | None = typer.Option(None, "--model-s3-uri", help="S3 URI of model.tar.gz to optimize"),
+    onnx_path: str | None = typer.Option(
+        None, "--onnx-path", help="Local ONNX path or ONNX path inside extracted artifact"
+    ),
+    config: str = CONFIG_OPT,
+) -> None:
+    """Optimize an external model into a Workbench-produced ONNX model."""
+    cfg = load_cfg(config)
+    _optimize_step(cfg, config, from_job, model_s3_uri, onnx_path)
+
+
 @app.command()
 def quantize(
     calibration_path: Path = typer.Argument(..., help="Calibration .npz file or directory of .npy samples"),
+    model_id: str | None = typer.Option(None, "--model-id", help="AI Hub optimized ONNX model ID"),
     from_job: str | None = typer.Option(None, "--from-job", help="Training job name whose model artifact should quantize"),
     model_s3_uri: str | None = typer.Option(None, "--model-s3-uri", help="S3 URI of model.tar.gz to quantize"),
     onnx_path: str | None = typer.Option(
@@ -307,7 +430,15 @@ def quantize(
 ) -> None:
     """Quantize an ONNX model to INT8."""
     cfg = load_cfg(config)
-    _quantize_step(cfg, config, calibration_path, from_job, model_s3_uri, onnx_path)
+    _quantize_step(
+        cfg,
+        config,
+        calibration_path,
+        model_id=model_id,
+        from_job=from_job,
+        model_s3_uri=model_s3_uri,
+        onnx_path=onnx_path,
+    )
 
 
 @app.command()
@@ -322,7 +453,14 @@ def compile(
 ) -> None:
     """Compile a model for the configured Qualcomm AI Hub target."""
     cfg = load_cfg(config)
-    _compile_step(cfg, config, model_id, from_job, model_s3_uri, onnx_path, prefer_quantized=True)
+    _compile_step(
+        cfg,
+        config,
+        model_id=model_id,
+        from_job=from_job,
+        model_s3_uri=model_s3_uri,
+        onnx_path=onnx_path,
+    )
 
 
 @app.command()
@@ -351,7 +489,7 @@ def profile(
 def upload(
     calibration_path: Path = typer.Argument(..., help="Calibration .npz file or directory of .npy samples"),
     input_file: Path = typer.Argument(..., help="Validation .npz or .npy inputs to run on device"),
-    from_step: UploadStep = typer.Option(UploadStep.quantize, "--from-step", help="Resume from this Workbench step"),
+    from_step: UploadStep = typer.Option(UploadStep.optimize, "--from-step", help="Resume from this Workbench step"),
     from_job: str | None = typer.Option(None, "--from-job", help="Training job name whose model artifact should upload"),
     model_s3_uri: str | None = typer.Option(None, "--model-s3-uri", help="S3 URI of model.tar.gz to upload"),
     onnx_path: str | None = typer.Option(
@@ -360,24 +498,47 @@ def upload(
     input_name: str | None = typer.Option(None, "--input-name", help="Input name for .npy validation files"),
     config: str = CONFIG_OPT,
 ) -> None:
-    """Run the four Workbench upload steps: quantize, compile, validate, and profile."""
+    """Optimize, quantize, optionally compile, validate, and profile a model."""
     cfg = load_cfg(config)
-    steps = [UploadStep.quantize, UploadStep.compile, UploadStep.validate, UploadStep.profile]
+    steps = [UploadStep.optimize, UploadStep.quantize, UploadStep.compile, UploadStep.validate, UploadStep.profile]
     selected = steps[steps.index(from_step) :]
 
+    optimized_model_id: str | None = None
     quantized_model_id: str | None = None
     compiled_model_id: str | None = None
+    if UploadStep.optimize in selected:
+        optimized_model_id = _optimize_step(cfg, config, from_job, model_s3_uri, onnx_path)
     if UploadStep.quantize in selected:
-        quantized_model_id = _quantize_step(cfg, config, calibration_path, from_job, model_s3_uri, onnx_path)
+        if UploadStep.optimize not in selected:
+            optimized_model_id = state_ops.store(config).get_last_optimized_model_id()
+            if not optimized_model_id:
+                CONSOLE.print(
+                    "[red]No optimized ONNX model found. Resume from --from-step optimize or run "
+                    "'qc-cli ai-hub optimize' first.[/red]"
+                )
+                raise typer.Exit(1)
+        quantized_model_id = _quantize_step(
+            cfg,
+            config,
+            calibration_path,
+            model_id=optimized_model_id,
+        )
     if UploadStep.compile in selected:
+        if cfg.aihub.target_runtime == "onnx":
+            compiled_model_id = quantized_model_id or state_ops.store(config).get_last_quantized_model_id()
+            if not compiled_model_id:
+                CONSOLE.print(
+                    "[red]No quantized ONNX model found. Resume from --from-step quantize or run "
+                    "'qc-cli ai-hub quantize' first.[/red]"
+                )
+                raise typer.Exit(1)
+            state_ops.store(config).update(last_compiled_model_id=compiled_model_id)
+            CONSOLE.print("[green]✓[/green] Target runtime is ONNX; skipping final compile.")
+        else:
             compiled_model_id = _compile_step(
                 cfg,
                 config,
                 model_id=quantized_model_id,
-            from_job=from_job,
-            model_s3_uri=model_s3_uri,
-            onnx_path=onnx_path,
-            prefer_quantized=True,
             )
     if UploadStep.validate in selected:
         _validate_step(cfg, config, input_file, compiled_model_id, input_name)

src/qualcomm/__init__.py

@@ -1 +0,0 @@
-

src/qualcomm/aihub_jobs.py

@@ -28,30 +28,19 @@ def _dataset_entries(inputs: dict[str, Any]) -> dict[str, list[Any]]:
 
 
 def submit_compile_job(
-    model: Any,
+    model: Model,
     device: Device,
     input_specs: dict[str, tuple[tuple[int, ...], str]],
     target_runtime: str,
     options: str | None = None,
     job_name: str | None = None,
-    model_name: str | None = None,
 ) -> ModelJobResult:
     compile_options = f"--target_runtime {target_runtime}"
     if options:
         compile_options = f"{compile_options} {options}"
 
-    model_arg = model
-    if isinstance(model, Path):
-        model_arg = str(model)
-    elif isinstance(model, str):
-        candidate = Path(model)
-        model_arg = model if candidate.exists() or candidate.suffix else hub.get_model(model)
-
-    if model_name and isinstance(model_arg, str) and Path(model_arg).exists():
-        model_arg = hub.upload_model(model_arg, name=model_name)
-
     job = hub.submit_compile_job(
-        model=model_arg,
+        model=model,
         device=device,
         name=job_name,
         input_specs=input_specs,
@@ -64,14 +53,14 @@ def submit_compile_job(
 
 
 def submit_inference_job(
-    model_id: str,
+    model: Model,
     device: Device,
     inputs: dict[str, Any],
     output_dir: str | Path,
     job_name: str | None = None,
 ) -> InferenceJobResult:
     job = hub.submit_inference_job(
-        model=hub.get_model(model_id),
+        model=model,
         device=device,
         inputs=_dataset_entries(inputs),
         name=job_name,
@@ -83,13 +72,13 @@ def submit_inference_job(
 
 
 def submit_profile_job(
-    model_id: str,
+    model: Model,
     device: Device,
     options: str | None = None,
     job_name: str | None = None,
 ) -> ProfileJobResult:
     job = hub.submit_profile_job(
-        model=hub.get_model(model_id),
+        model=model,
         device=device,
         name=job_name,
         options=options or "",
@@ -98,17 +87,13 @@ def submit_profile_job(
 
 
 def submit_quantize_job(
-    model: str | Path,
+    model: Model,
     calibration_data: dict[str, Any],
     options: str | None = None,
     job_name: str | None = None,
-    model_name: str | None = None,
 ) -> ModelJobResult:
-    model_arg = str(model)
-    if model_name and Path(model_arg).exists():
-        model_arg = hub.upload_model(model_arg, name=model_name)
     job = hub.submit_quantize_job(
-        model=model_arg,
+        model=model,
         calibration_data=_dataset_entries(calibration_data),
         weights_dtype=QuantizeDtype.INT8,
         activations_dtype=QuantizeDtype.INT8,

src/state.py

@@ -37,6 +37,10 @@ class CliStateStore:
         value = self.get("last_model_artifact")
         return str(value) if value else None
 
+    def get_last_optimized_model_id(self) -> str | None:
+        value = self.get("last_optimized_model_id")
+        return str(value) if value else None
+
     def get_last_quantized_model_id(self) -> str | None:
         value = self.get("last_quantized_model_id")
         return str(value) if value else None