Merge branch 'main' into ai-hub

README.md

@@ -65,6 +65,17 @@ sagemaker:
     entry_point: null       # Optional: script inside source_dir
     source_dir: null        # Optional: local dir packaged and uploaded automatically
     hyperparameters: {}
+
+aihub:
+  device: Samsung Galaxy S25 (Family)
+  target_runtime: tflite
+  input_specs: {}           # Required before running qc-cli ai-hub commands
+  job_name: null            # Optional prefix for AI Hub Workbench jobs
+  model_name: null          # Optional name for uploaded local ONNX models
+  compile_options: null
+  profile_options: null
+  quantize_options: null
+  output_dir: build/qai-hub
 ```
 
 `qc-cli init` generates the `infra.stack_name` and `s3.bucket` namespace once and writes it to `config.yaml`. Keep these values stable for a deployment; changing them points the CLI at different infrastructure.
@@ -155,6 +166,24 @@ qc-cli train list --limit 3     Show a custom number of recent jobs
 
 The expected output artifact is SageMaker’s `model.tar.gz`, normally containing the trained model file your container writes to `/opt/ml/model`.
 
+### `ai-hub`
+
+```
+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 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 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 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.
+
 ## Model lifecycle
 
 The CLI uses neutral experiment naming for trained artifacts and reserves release terminology for an explicit promotion step.
@@ -168,12 +197,9 @@ Current behavior:
    - `qc_cli.artifact_kind=trained_source`
    - `qc_cli.source=sagemaker`
 4. The MLflow alias `experiment-latest` points at the most recently registered experiment version.
+5. AI Hub upload commands create deployable derived artifacts from a trained-source experiment or local ONNX model.
 
-Planned AI Hub extension:
-
-1. AI Hub compile or quantize will create deployable derived artifacts from a trained-source experiment.
-2. Derived artifacts will keep lineage back to the source experiment version instead of replacing it.
-3. Release aliases such as `v1` or `production` will point at the selected deployable artifact.
+Future release aliases such as `v1` or `production` can point at a selected deployable artifact.
 
 Example future metadata:
 

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.

examples/ai-hub/prepare_inputs.py

@@ -0,0 +1,74 @@
+#!/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

@@ -0,0 +1,156 @@
+#!/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/training/source/train.py

@@ -126,10 +126,6 @@ def export_onnx(model: nn.Module, model_dir: Path, image_size: int) -> None:
         do_constant_folding=True,
         input_names=["input"],
         output_names=["logits"],
-        dynamic_axes={
-            "input": {0: "batch_size"},
-            "logits": {0: "batch_size"},
-        },
     )
 
 

pyproject.toml

@@ -13,8 +13,10 @@ dependencies = [
     "boto3>=1.34,<1.42",
     "constructs>=10.0.0",
     "mlflow>=3.0",
+    "numpy>=1.26",
     "pydantic>=2.13.3",
     "pyyaml>=6.0.3",
+    "qai-hub>=0.49.0",
     "sagemaker-mlflow>=0.4.0",
 ]
 
@@ -28,6 +30,7 @@ packages = ["src"]
 dev = [
     "boto3-stubs[iam,s3,sagemaker]",
     "pytest>=8.0",
+    "pytest-mock>=3.12",
     "pyright>=1.1.409",
     "types-PyYAML",
     "ruff>=0.4",

src/aws/s3.py

@@ -21,6 +21,24 @@ def upload_file(
     return f"s3://{bucket}/{s3_key}"
 
 
+def download_file(
+    region: str,
+    profile: str,
+    s3_uri: str,
+    local_path: str,
+) -> str:
+    if not s3_uri.startswith("s3://"):
+        raise ValueError(f"Expected S3 URI, got: {s3_uri}")
+    bucket_key = s3_uri.removeprefix("s3://")
+    bucket, _, key = bucket_key.partition("/")
+    if not bucket or not key:
+        raise ValueError(f"Expected S3 URI with bucket and key, got: {s3_uri}")
+    dest = Path(local_path)
+    dest.parent.mkdir(parents=True, exist_ok=True)
+    _client(region, profile).download_file(bucket, key, str(dest))
+    return str(dest)
+
+
 def upload_dir(
     region: str,
     profile: str,

src/aws/sagemaker.py

@@ -121,6 +121,16 @@ def get_training_job_status(session: Boto3SessionKwargs, job_name: str) -> Train
     )
 
 
+def get_model_artifacts(region: str, profile: str, job_name: str) -> str:
+    resp = boto3.Session(profile_name=profile, region_name=region).client("sagemaker").describe_training_job(
+        TrainingJobName=job_name
+    )
+    artifact = resp.get("ModelArtifacts", {}).get("S3ModelArtifacts")
+    if not artifact:
+        raise RuntimeError(f"Training job '{job_name}' does not have model artifacts yet.")
+    return str(artifact)
+
+
 def list_training_jobs(
     session: Boto3SessionKwargs,
     max_results: int = 10,

src/commands/ai_hub.py

@@ -0,0 +1,374 @@
+from collections.abc import Mapping, Sequence
+from datetime import datetime
+from enum import StrEnum
+from pathlib import Path
+from typing import Any
+
+import typer
+
+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
+
+app = typer.Typer(help="Quantize, compile, validate, profile, and download models with Qualcomm AI Hub")
+
+_RUNTIME_EXTENSIONS = {
+    "tflite": "tflite",
+    "qnn_context_binary": "bin",
+    "onnx": "onnx",
+}
+
+
+class UploadStep(StrEnum):
+    quantize = "quantize"
+    compile = "compile"
+    validate = "validate"
+    profile = "profile"
+
+
+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:
+        CONSOLE.print("[red]aihub.input_specs must define at least one input.[/red]")
+        raise typer.Exit(1)
+    return specs
+
+
+def _load_inputs(
+    input_file: Path,
+    specs: Mapping[str, tuple[Sequence[int], str]],
+    input_name: str | None = None,
+) -> dict[str, Any]:
+    import numpy as np
+
+    if not input_file.exists():
+        raise FileNotFoundError(f"File not found: {input_file}")
+
+    if input_file.suffix == ".npz":
+        loaded = np.load(input_file)
+        missing = set(specs) - set(loaded.files)
+        if missing:
+            raise ValueError(f"Missing input(s) in NPZ: {', '.join(sorted(missing))}")
+        return {name: loaded[name] for name in specs}
+
+    if input_file.suffix == ".npy":
+        if input_name is None:
+            if len(specs) != 1:
+                raise ValueError("--input-name is required when config has multiple inputs")
+            input_name = next(iter(specs))
+        if input_name not in specs:
+            raise ValueError(f"Input name '{input_name}' is not defined in aihub.input_specs")
+        return {input_name: np.load(input_file)}
+
+    raise ValueError("Input file must be .npz or .npy")
+
+
+def _load_calibration(path: Path, specs: Mapping[str, tuple[Sequence[int], str]]) -> dict[str, Any]:
+    import numpy as np
+
+    if path.is_file():
+        return _load_inputs(path, specs)
+
+    if not path.is_dir():
+        raise FileNotFoundError(f"Calibration path not found: {path}")
+
+    if len(specs) != 1:
+        raise ValueError("Directory calibration data is supported only for single-input models.")
+    input_name = next(iter(specs))
+    samples = [np.load(p) for p in sorted(path.glob("*.npy"))]
+    if not samples:
+        raise ValueError(f"No .npy calibration samples found in {path}")
+    return {input_name: samples}
+
+
+def _job_name(cfg: Config, operation: str) -> str | None:
+    if not cfg.aihub.job_name:
+        return None
+    return f"{cfg.aihub.job_name}-{operation}"
+
+
+def _model_id_or_state(config_path: str, model_id: str | None, *, quantized: bool = False) -> str:
+    st = state_ops.store(config_path)
+    resolved = model_id or (st.get_last_quantized_model_id() if quantized else st.get_last_compiled_model_id())
+    if not resolved:
+        source = "quantized" if quantized else "compiled"
+        CONSOLE.print(f"[red]No {source} model found. Pass --model-id or run the previous AI Hub step first.[/red]")
+        raise typer.Exit(1)
+    return resolved
+
+
+def _quantize_step(
+    cfg: Config,
+    config_path: str,
+    calibration_path: Path,
+    from_job: str | None,
+    model_s3_uri: str | None,
+    onnx_path: str | None,
+) -> str:
+    st = state_ops.store(config_path)
+    specs = _input_specs(cfg)
+    try:
+        resolved = resolve_onnx(
+            cfg=cfg,
+            output_dir=cfg.aihub.output_dir,
+            from_job=from_job,
+            model_s3_uri=model_s3_uri or st.get_last_model_artifact(),
+            onnx_path=onnx_path,
+            last_training_job=st.get_last_training_job(),
+        )
+        calibration_data = _load_calibration(calibration_path, specs)
+    except (FileNotFoundError, ValueError) as e:
+        CONSOLE.print(f"[red]{e}[/red]")
+        raise typer.Exit(1)
+
+    try:
+        result = aihub_jobs.submit_quantize_job(
+            resolved.onnx_path,
+            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(
+        last_model_artifact=resolved.model_artifact,
+        last_quantize_job_id=result["job_id"],
+        last_quantized_model_id=result["model_id"],
+    )
+    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 _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,
+) -> str:
+    st = state_ops.store(config_path)
+    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(
+                cfg=cfg,
+                output_dir=cfg.aihub.output_dir,
+                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:
+        result = aihub_jobs.submit_compile_job(
+            model=model,
+            device_name=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]")
+        raise typer.Exit(1)
+
+    updates: dict[str, Any] = {
+        "last_compile_job_id": result["job_id"],
+        "last_compiled_model_id": result["model_id"],
+    }
+    if model_artifact:
+        updates["last_model_artifact"] = 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]")
+    return str(result["model_id"])
+
+
+def _validate_step(
+    cfg: Config,
+    config_path: str,
+    input_file: Path,
+    model_id: str | None,
+    input_name: str | None,
+) -> str:
+    specs = _input_specs(cfg)
+    resolved_model_id = _model_id_or_state(config_path, model_id)
+    try:
+        inputs = _load_inputs(input_file, specs, input_name)
+    except (FileNotFoundError, ValueError) as e:
+        CONSOLE.print(f"[red]{e}[/red]")
+        raise typer.Exit(1)
+
+    run = datetime.now().strftime("%Y%m%d-%H%M%S")
+    out_dir = Path(cfg.aihub.output_dir) / run / "validation"
+    try:
+        result = aihub_jobs.submit_inference_job(
+            resolved_model_id,
+            cfg.aihub.device,
+            inputs,
+            out_dir,
+            job_name=_job_name(cfg, "validate"),
+        )
+    except Exception as e:
+        CONSOLE.print(f"[red]AI Hub inference failed: {e}[/red]")
+        raise typer.Exit(1)
+
+    state_ops.store(config_path).update(last_inference_job_id=result["job_id"])
+    CONSOLE.print(f"[green]✓[/green] Inference job: [bold]{result['job_id']}[/bold]")
+    outputs = result.get("outputs")
+    if isinstance(outputs, dict):
+        for name, value in outputs.items():
+            CONSOLE.print(f"  {name}: shape={getattr(value, 'shape', '?')}")
+    CONSOLE.print(f"Outputs: [cyan]{out_dir}[/cyan]")
+    return str(result["job_id"])
+
+
+def _profile_step(cfg: Config, config_path: str, model_id: str | None) -> str:
+    resolved_model_id = _model_id_or_state(config_path, model_id)
+    try:
+        result = aihub_jobs.submit_profile_job(
+            resolved_model_id,
+            cfg.aihub.device,
+            cfg.aihub.profile_options,
+            job_name=_job_name(cfg, "profile"),
+        )
+    except Exception as e:
+        CONSOLE.print(f"[red]AI Hub profile failed: {e}[/red]")
+        raise typer.Exit(1)
+    state_ops.store(config_path).update(last_profile_job_id=result["job_id"])
+    CONSOLE.print(f"[green]✓[/green] Profile job: [bold]{result['job_id']}[/bold]")
+    return str(result["job_id"])
+
+
+@app.command()
+def quantize(
+    calibration_path: Path = typer.Argument(..., help="Calibration .npz file or directory of .npy samples"),
+    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(
+        None, "--onnx-path", help="Local ONNX path or ONNX path inside extracted artifact"
+    ),
+    config: str = CONFIG_OPT,
+) -> None:
+    """Quantize an ONNX model to INT8."""
+    cfg = load_cfg(config)
+    _quantize_step(cfg, config, calibration_path, from_job, model_s3_uri, onnx_path)
+
+
+@app.command()
+def compile(
+    model_id: str | None = typer.Option(None, "--model-id", help="AI Hub model ID to compile"),
+    from_job: str | None = typer.Option(None, "--from-job", help="Training job name whose model artifact should compile"),
+    model_s3_uri: str | None = typer.Option(None, "--model-s3-uri", help="S3 URI of model.tar.gz to compile"),
+    onnx_path: str | None = typer.Option(
+        None, "--onnx-path", help="Local ONNX path or ONNX path inside extracted artifact"
+    ),
+    config: str = CONFIG_OPT,
+) -> 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)
+
+
+@app.command()
+def validate(
+    input_file: Path = typer.Argument(..., help="NumPy .npz or .npy inputs to run on device"),
+    model_id: str | None = typer.Option(None, "--model-id", help="AI Hub compiled model ID"),
+    input_name: str | None = typer.Option(None, "--input-name", help="Input name for .npy files"),
+    config: str = CONFIG_OPT,
+) -> None:
+    """Run an AI Hub inference job using sample inputs."""
+    cfg = load_cfg(config)
+    _validate_step(cfg, config, input_file, model_id, input_name)
+
+
+@app.command()
+def profile(
+    model_id: str | None = typer.Option(None, "--model-id", help="AI Hub compiled model ID"),
+    config: str = CONFIG_OPT,
+) -> None:
+    """Profile a compiled model on the configured AI Hub device."""
+    cfg = load_cfg(config)
+    _profile_step(cfg, config, model_id)
+
+
+@app.command()
+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_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(
+        None, "--onnx-path", help="Local ONNX path or ONNX path inside extracted artifact"
+    ),
+    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."""
+    cfg = load_cfg(config)
+    steps = [UploadStep.quantize, UploadStep.compile, UploadStep.validate, UploadStep.profile]
+    selected = steps[steps.index(from_step) :]
+
+    quantized_model_id: str | None = None
+    compiled_model_id: str | None = None
+    if UploadStep.quantize in selected:
+        quantized_model_id = _quantize_step(cfg, config, calibration_path, from_job, model_s3_uri, onnx_path)
+    if UploadStep.compile in selected:
+        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)
+    if UploadStep.profile in selected:
+        _profile_step(cfg, config, compiled_model_id)
+
+
+@app.command()
+def download(
+    model_id: str | None = typer.Option(None, "--model-id", help="AI Hub compiled model ID"),
+    output: Path | None = typer.Option(None, "--output", "-o", help="Destination file path"),
+    config: str = CONFIG_OPT,
+) -> None:
+    """Download the last compiled deployable artifact from AI Hub."""
+    cfg = load_cfg(config)
+    resolved_model_id = _model_id_or_state(config, model_id)
+    ext = _RUNTIME_EXTENSIONS.get(cfg.aihub.target_runtime, cfg.aihub.target_runtime)
+    dest = output or (Path(cfg.aihub.output_dir) / f"model.{ext}")
+
+    try:
+        written = aihub_jobs.download_model(resolved_model_id, dest)
+    except Exception as e:
+        CONSOLE.print(f"[red]AI Hub download failed: {e}[/red]")
+        raise typer.Exit(1)
+    state_ops.store(config).update(last_downloaded_model=written)
+    CONSOLE.print(f"[green]✓[/green] Downloaded model: [cyan]{written}[/cyan]")

src/config.py

@@ -80,6 +80,18 @@ class SageMakerConfig(BaseModel):
     training: TrainingConfig = Field(default_factory=TrainingConfig)
 
 
+class AIHubConfig(BaseModel):
+    device: str = "Samsung Galaxy S25 (Family)"
+    target_runtime: str = "tflite"
+    input_specs: dict[str, tuple[list[int], str]] = Field(default_factory=dict)
+    job_name: str | None = None
+    model_name: str | None = None
+    compile_options: str | None = None
+    profile_options: str | None = None
+    quantize_options: str | None = None
+    output_dir: str = "build/qai-hub"
+
+
 class MlflowConfig(BaseModel):
     mode: MlflowMode = MlflowMode.disabled
     tracking_server_name: str | None = None
@@ -104,6 +116,7 @@ class Config(BaseModel):
     aws: AwsConfig = Field(default_factory=AwsConfig)
     s3: S3Config = Field(default_factory=S3Config)
     sagemaker: SageMakerConfig = Field(default_factory=SageMakerConfig)
+    aihub: AIHubConfig = Field(default_factory=AIHubConfig)
     mlflow: MlflowConfig = Field(default_factory=MlflowConfig)
 
     @property

src/main.py

@@ -7,7 +7,7 @@ from rich.console import Console
 from rich.progress import BarColumn, Progress, SpinnerColumn, TaskProgressColumn, TextColumn
 
 from src.aws import s3 as s3_ops
-from src.commands import infra, train
+from src.commands import ai_hub, infra, train
 from src.commands.utils import CONFIG_OPT, load_cfg
 from src.config import GENERATED_STACK_PREFIX, Config, InfraConfig, S3Config
 
@@ -17,6 +17,7 @@ app = typer.Typer(
 )
 app.add_typer(infra.app, name="infra")
 app.add_typer(train.app, name="train")
+app.add_typer(ai_hub.app, name="ai-hub")
 
 console = Console()
 

src/qualcomm/__init__.py

@@ -0,0 +1 @@
+

src/qualcomm/aihub_jobs.py

@@ -0,0 +1,129 @@
+from pathlib import Path
+from typing import Any, TypedDict
+
+import qai_hub.hub as hub
+from qai_hub.client import CompileJob, Device, InferenceJob, Model, ProfileJob, QuantizeDtype, QuantizeJob
+
+
+class ModelJobResult(TypedDict):
+    job: CompileJob | QuantizeJob
+    job_id: str
+    model: Model
+    model_id: str
+
+
+class InferenceJobResult(TypedDict):
+    job: InferenceJob
+    job_id: str
+    outputs: Any
+
+
+class ProfileJobResult(TypedDict):
+    job: ProfileJob
+    job_id: str
+
+
+def _dataset_entries(inputs: dict[str, Any]) -> dict[str, list[Any]]:
+    return {name: value if isinstance(value, list) else [value] for name, value in inputs.items()}
+
+
+def submit_compile_job(
+    model: Any,
+    device_name: str,
+    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,
+        device=Device(device_name),
+        name=job_name,
+        input_specs=input_specs,
+        options=compile_options,
+    )
+    target_model = job.get_target_model()
+    if target_model is None:
+        raise RuntimeError(f"Compile job {job.job_id} did not produce a target model.")
+    return {"job": job, "job_id": str(job.job_id), "model": target_model, "model_id": str(target_model.model_id)}
+
+
+def submit_inference_job(
+    model_id: str,
+    device_name: str,
+    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),
+        device=Device(device_name),
+        inputs=_dataset_entries(inputs),
+        name=job_name,
+    )
+    out = Path(output_dir)
+    out.mkdir(parents=True, exist_ok=True)
+    data = job.download_output_data(str(out))
+    return {"job": job, "job_id": str(job.job_id), "outputs": data}
+
+
+def submit_profile_job(
+    model_id: str,
+    device_name: str,
+    options: str | None = None,
+    job_name: str | None = None,
+) -> ProfileJobResult:
+    job = hub.submit_profile_job(
+        model=hub.get_model(model_id),
+        device=Device(device_name),
+        name=job_name,
+        options=options or "",
+    )
+    return {"job": job, "job_id": str(job.job_id)}
+
+
+def submit_quantize_job(
+    model: str | Path,
+    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,
+        calibration_data=_dataset_entries(calibration_data),
+        weights_dtype=QuantizeDtype.INT8,
+        activations_dtype=QuantizeDtype.INT8,
+        name=job_name,
+        options=options or "",
+    )
+    target_model = job.get_target_model()
+    if target_model is None:
+        raise RuntimeError(f"Quantize job {job.job_id} did not produce a target model.")
+    return {"job": job, "job_id": str(job.job_id), "model": target_model, "model_id": str(target_model.model_id)}
+
+
+def download_model(model_id: str, output_path: str | Path) -> str:
+    dest = Path(output_path)
+    dest.parent.mkdir(parents=True, exist_ok=True)
+    model = hub.get_model(model_id)
+    result = model.download(str(dest))
+    return str(result or dest)

src/qualcomm/artifacts.py

@@ -0,0 +1,83 @@
+import tarfile
+from dataclasses import dataclass
+from pathlib import Path
+
+from src.aws import s3 as s3_ops
+from src.aws import sagemaker as sm_ops
+from src.config import Config
+
+
+@dataclass(frozen=True)
+class ResolvedOnnx:
+    onnx_path: Path
+    model_artifact: str | None
+    run_name: str
+
+
+def _safe_extract(tar: tarfile.TarFile, dest: Path) -> None:
+    dest_root = dest.resolve()
+    for member in tar.getmembers():
+        target = (dest / member.name).resolve()
+        if dest_root != target and dest_root not in target.parents:
+            raise ValueError(f"Unsafe tar member path: {member.name}")
+    tar.extractall(dest, filter="data")
+
+
+def _find_onnx(root: Path, explicit: str | None = None) -> Path:
+    if explicit:
+        p = Path(explicit)
+        if not p.is_absolute():
+            p = root / p
+        if not p.exists():
+            raise FileNotFoundError(f"ONNX file not found: {p}")
+        return p
+
+    matches = sorted(root.rglob("model.onnx"))
+    if not matches:
+        matches = sorted(root.rglob("*.onnx"))
+    if not matches:
+        raise FileNotFoundError(f"No ONNX file found under {root}")
+    if len(matches) > 1:
+        joined = ", ".join(str(p.relative_to(root)) for p in matches)
+        raise ValueError(f"Multiple ONNX files found ({joined}). Pass --onnx-path.")
+    return matches[0]
+
+
+def resolve_onnx(
+    cfg: Config,
+    output_dir: str,
+    from_job: str | None = None,
+    model_s3_uri: str | None = None,
+    onnx_path: str | None = None,
+    last_training_job: str | None = None,
+) -> ResolvedOnnx:
+    if onnx_path:
+        path = Path(onnx_path)
+        if path.exists():
+            return ResolvedOnnx(onnx_path=path, model_artifact=None, run_name=path.stem)
+
+    job = from_job or last_training_job
+    artifact = model_s3_uri
+    if not artifact:
+        if not job:
+            raise ValueError("No model source found. Pass --onnx-path, --model-s3-uri, --from-job, or run training first.")
+        artifact = sm_ops.get_model_artifacts(cfg.aws.region, cfg.aws.profile, job)
+
+    run_name = job or Path(artifact).name.removesuffix(".tar.gz").replace("/", "-")
+    root = Path(output_dir) / run_name / "source"
+    tar_path = root / "model.tar.gz"
+    s3_ops.download_file(cfg.aws.region, cfg.aws.profile, artifact, str(tar_path))
+
+    extract_dir = root / "extracted"
+    extract_dir.mkdir(parents=True, exist_ok=True)
+    try:
+        with tarfile.open(tar_path, "r:gz") as tar:
+            _safe_extract(tar, extract_dir)
+    except tarfile.TarError as e:
+        raise ValueError(f"Invalid model tarball: {tar_path}") from e
+
+    return ResolvedOnnx(
+        onnx_path=_find_onnx(extract_dir, onnx_path),
+        model_artifact=artifact,
+        run_name=run_name,
+    )

src/state.py

@@ -33,6 +33,22 @@ class CliStateStore:
         value = self.get("last_training_job")
         return str(value) if value else None
 
+    def get_last_model_artifact(self) -> str | None:
+        value = self.get("last_model_artifact")
+        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
+
+    def get_last_compiled_model_id(self) -> str | None:
+        value = self.get("last_compiled_model_id")
+        return str(value) if value else None
+
+    def get_last_downloaded_model(self) -> str | None:
+        value = self.get("last_downloaded_model")
+        return str(value) if value else None
+
     def set_last_training_job(self, job_name: str) -> None:
         self.update(last_training_job=job_name)
 

uv.lock

@@ -210,6 +210,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/06/7c/1e7964f0f267301bb5026fed45369961f7311073412bcd36e09fbe4df0de/aws_cdk_lib-2.253.1-py3-none-any.whl", hash = "sha256:03a6f5080978f9e3576f490d06fbd1f41f159280d34dbca50721de4a19694136", size = 50271288, upload-time = "2026-05-08T16:04:41.956Z" },
 ]
 
+[[package]]
+name = "backoff"
+version = "2.2.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/47/d7/5bbeb12c44d7c4f2fb5b56abce497eb5ed9f34d85701de869acedd602619/backoff-2.2.1.tar.gz", hash = "sha256:03f829f5bb1923180821643f8753b0502c3b682293992485b0eef2807afa5cba", size = 17001, upload-time = "2022-10-05T19:19:32.061Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/df/73/b6e24bd22e6720ca8ee9a85a0c4a2971af8497d8f3193fa05390cbd46e09/backoff-2.2.1-py3-none-any.whl", hash = "sha256:63579f9a0628e06278f7e47b7d7d5b6ce20dc65c5e96a6f3ca99a6adca0396e8", size = 15148, upload-time = "2022-10-05T19:19:30.546Z" },
+]
+
 [[package]]
 name = "blinker"
 version = "1.9.0"
@@ -591,6 +600,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/e3/43/33806117fc8e0992aae890be73990b31d802b66e8a423bf87b80990fce66/databricks_sdk-0.111.0-py3-none-any.whl", hash = "sha256:d14ba186afd2bea03c7157d2f03e0f861a0b8eff528cfdba926d07b9e20384b8", size = 901536, upload-time = "2026-05-25T09:29:58.057Z" },
 ]
 
+[[package]]
+name = "deprecation"
+version = "2.1.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "packaging" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/5a/d3/8ae2869247df154b64c1884d7346d412fed0c49df84db635aab2d1c40e62/deprecation-2.1.0.tar.gz", hash = "sha256:72b3bde64e5d778694b0cf68178aed03d15e15477116add3fb773e581f9518ff", size = 173788, upload-time = "2020-04-20T14:23:38.738Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/02/c3/253a89ee03fc9b9682f1541728eb66db7db22148cd94f89ab22528cd1e1b/deprecation-2.1.0-py2.py3-none-any.whl", hash = "sha256:a10811591210e1fb0e768a8c25517cabeabcba6f0bf96564f8ff45189f90b14a", size = 11178, upload-time = "2020-04-20T14:23:36.581Z" },
+]
+
 [[package]]
 name = "docker"
 version = "7.1.0"
@@ -908,6 +929,41 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/04/4b/29cac41a4d98d144bf5f6d33995617b185d14b22401f75ca86f384e87ff1/h11-0.16.0-py3-none-any.whl", hash = "sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86", size = 37515, upload-time = "2025-04-24T03:35:24.344Z" },
 ]
 
+[[package]]
+name = "h5py"
+version = "3.16.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
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     { name = "typer", specifier = "==0.25.0" },
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     { name = "pyright", specifier = ">=1.1.409" },
     { name = "pytest", specifier = ">=8.0" },
+    { name = "pytest-mock", specifier = ">=3.12" },
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+version = "1.0.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
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 version = "15.0.0"
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+[[package]]
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+version = "3.0.4"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/72/d1/d3159231aec234a59dd7d601e9dd9fe96f3afff15efd33c1070019b26132/semver-3.0.4.tar.gz", hash = "sha256:afc7d8c584a5ed0a11033af086e8af226a9c0b206f313e0301f8dd7b6b589602", size = 269730, upload-time = "2025-01-24T13:19:27.617Z" }
+wheels = [
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+]
+
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 version = "1.5.4"
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+[[package]]
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+version = "4.67.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
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+wheels = [
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