update

match
move mlflow to its own command
2026-06-08 14:59:44 -04:00 · 2026-06-08 14:54:13 -04:00 · 2026-06-05 11:47:38 -04:00 · 2026-06-05 11:25:04 -04:00 · 2026-06-04 17:28:17 -04:00 · 2026-06-03 17:13:00 -04:00
17 changed files with 252 additions and 422 deletions
--- a/README.md
+++ b/README.md
@@ -67,7 +67,8 @@ sagemaker:
    hyperparameters: {}

 aihub:
-  device: Samsung Galaxy S25 (Family)
+  device:
+    name: 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
@@ -109,10 +110,10 @@ When MLflow is enabled, `train start` creates an MLflow run for the SageMaker jo
 To open the managed SageMaker MLflow UI, request a fresh presigned URL:

 ```bash
-qc-cli infra mlflow-url --config config.yaml
+qc-cli mlflow open --config config.yaml
 ```

-This works for `mode: create` and for `mode: existing` when the existing server is managed by Amazon SageMaker. In `create` mode, the command uses the CLI-managed tracking server name. In `existing` mode, it uses `mlflow.tracking_server_name`. If the existing MLflow server is external to SageMaker, open it with that server's own URL instead.
+This opens a browser to a fresh presigned URL. It works for `mode: create` and for `mode: existing` when the existing server is managed by Amazon SageMaker. In `create` mode, the command uses the CLI-managed tracking server name. In `existing` mode, it uses `mlflow.tracking_server_name`. If the existing MLflow server is external to SageMaker, open it with that server's own URL instead.

 ## Commands

@@ -124,6 +125,12 @@ qc-cli init --output <path>  Write config to a custom path
 qc-cli init --force          Overwrite an existing config file
 ```

+### `mlflow`
+
+```
+qc-cli mlflow open  Open a presigned MLflow UI URL in a browser
+```
+
 ### `infra`

 ```
@@ -131,7 +138,6 @@ qc-cli infra setup                         Deploy the CDK stack
 qc-cli infra setup --no-bootstrap          Deploy without running CDK bootstrap
 qc-cli infra setup --cloudformation-execution-policy <arn> Set CDK bootstrap execution policy ARN
 qc-cli infra status                        Show CDK stack/resource status
-qc-cli infra mlflow-url                    Print a presigned MLflow UI URL
 qc-cli infra destroy                       Destroy stack, retaining S3 data
 qc-cli infra destroy --yes                 Destroy stack without confirmation
 qc-cli infra destroy --delete-bucket-data  Destroy stack and delete S3 data
@@ -180,6 +186,17 @@ 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.

+Resume behavior:
+
+```text
+--from-step quantize  Run quantize, compile, validate, and profile.
+--from-step compile   Skip quantize; compile the last quantized model unless an explicit source is passed.
+--from-step validate  Skip quantize and compile; validate the last compiled model.
+--from-step profile   Skip 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 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.
--- a/examples/ai-hub/README.md
+++ b/examples/ai-hub/README.md
@@ -13,38 +13,35 @@ This example takes the ONNX model produced by the SageMaker training example and
 Run the training example first and wait for it to complete:

 ```bash
-bash examples/training/run_training.sh --config config.yaml --wait
+examples/training/run_training.sh --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:
+The `config.yaml` file must include AI Hub settings:

 ```yaml
 aihub:
-  device: Samsung Galaxy S25 (Family)
+  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.
+Finally, the user needs to authenticate with Qualcomm AI Hub using:
+
+```bash
+qai-hub configure --api_token
+```

 ## 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:
+To generate calibration and validation inputs:

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

 This writes:
@@ -60,58 +57,23 @@ The script applies the same image preprocessing used by the training example:
 - convert to channel-first `1x3x160x160`
 - normalize with ImageNet mean and standard deviation

-Useful options:
+## Upload Model to Qualcomm Workbench
+
+The model can be uploaded to Qualcomm Workbench using:

 ```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
+qc-cli ai-hub upload examples/training/data/aihub_calibration examples/training/data/inputs.npz
 ```

-## Run AI Hub
+The first argument is the calibration path for the model and the second argument is the input file, both of which were created by the `prepare_inputs.py` script. For more details, add `--help` after the `upload` command.

-After training completes and inputs are prepared:
+The `upload` command runs the following commands in order:
+1. `qc-cli ai-hub quantize`
+2. `qc-cli ai-hub compile`
+3. `qc-cli ai-hub validate`
+4. `qc-cli ai-hub profile`

+Finally the user can download the model from AI Workbench using the command
 ```bash
-bash examples/ai-hub/run_ai_hub.sh --config config.yaml
+qc-cli ai-hub download
 ```
-
-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.
--- a/examples/ai-hub/prepare_inputs.py
+++ b/examples/ai-hub/prepare_inputs.py
@@ -9,7 +9,6 @@ from pathlib import Path
 import numpy as np
 from PIL import Image

-
 IMAGE_EXTENSIONS = {".jpg", ".jpeg", ".png"}


--- a/examples/ai-hub/run_ai_hub.sh
+++ b/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[@]}"
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -5,7 +5,7 @@ build-backend = "hatchling.build"
 [project]
 name = "qc-cli"
 version = "0.1.0"
-description = "CLI for SageMaker ONNX training and Qualcomm AI Hub optimization"
+description = "CLI for training and deploying models for Qualcomm AI Hub"
 requires-python = ">=3.13"
 dependencies = [
    "aws-cdk-lib>=2.180.0",
@@ -29,8 +29,6 @@ packages = ["src"]
 [dependency-groups]
 dev = [
    "boto3-stubs[iam,s3,sagemaker]",
-    "pytest>=8.0",
-    "pytest-mock>=3.12",
    "pyright>=1.1.409",
    "types-PyYAML",
    "ruff>=0.4",
--- a/src/cloud/init.py
+++ b/src/cloud/init.py
--- a/src/commands/ai_hub.py
+++ b/src/commands/ai_hub.py
@@ -4,7 +4,9 @@ from enum import StrEnum
 from pathlib import Path
 from typing import Any

+import qai_hub.hub as hub
 import typer
+from qai_hub.client import Device

 from src import state as state_ops
 from src.commands.utils import CONFIG_OPT, CONSOLE, load_cfg
@@ -12,7 +14,7 @@ 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")
+app = typer.Typer(help="Quantize, compile, validate, profile, and download models with Qualcomm Workbench")

 _RUNTIME_EXTENSIONS = {
    "tflite": "tflite",
@@ -99,6 +101,33 @@ def _model_id_or_state(config_path: str, model_id: str | None, *, quantized: boo
    return resolved


+def _device_selector(device: Device) -> str:
+    parts: list[str] = []
+    if device.name:
+        parts.append(f"name={device.name!r}")
+    if device.os:
+        parts.append(f"os={device.os!r}")
+    if device.attributes:
+        parts.append(f"attributes={device.attributes!r}")
+    return ", ".join(parts) if parts else "empty selector"
+
+
+def _validate_device(cfg: Config) -> None:
+    device = cfg.aihub.device
+    try:
+        matches = hub.get_devices(name=device.name, os=device.os, attributes=device.attributes)
+    except Exception as e:
+        CONSOLE.print(f"[red]Unable to validate AI Hub device {_device_selector(device)}: {e}[/red]")
+        raise typer.Exit(1)
+
+    if matches:
+        return
+
+    CONSOLE.print(f"[red]AI Hub device not found: {_device_selector(device)}[/red]")
+    CONSOLE.print("Run [bold]qai-hub list-devices[/bold] to see valid device names.")
+    raise typer.Exit(1)
+
+
 def _quantize_step(
    cfg: Config,
    config_path: str,
@@ -156,6 +185,7 @@ def _compile_step(
    prefer_quantized: bool,
 ) -> str:
    st = state_ops.store(config_path)
+    _validate_device(cfg)
    specs = _input_specs(cfg)

    model: Any
@@ -184,7 +214,7 @@ def _compile_step(
    try:
        result = aihub_jobs.submit_compile_job(
            model=model,
-            device_name=cfg.aihub.device,
+            device=cfg.aihub.device,
            input_specs=specs,
            target_runtime=cfg.aihub.target_runtime,
            options=cfg.aihub.compile_options,
@@ -214,6 +244,7 @@ def _validate_step(
    model_id: str | None,
    input_name: str | None,
 ) -> str:
+    _validate_device(cfg)
    specs = _input_specs(cfg)
    resolved_model_id = _model_id_or_state(config_path, model_id)
    try:
@@ -247,6 +278,7 @@ def _validate_step(


 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:
        result = aihub_jobs.submit_profile_job(
--- a/src/commands/infra.py
+++ b/src/commands/infra.py
@@ -150,35 +150,6 @@ def status(config: str = CONFIG_OPT) -> None:
    CONSOLE.print(table)


-@app.command(name="mlflow-url")
-def mlflow_url(config: str = CONFIG_OPT) -> None:
-    """Print a presigned URL for the configured MLflow tracking server."""
-    cfg = load_cfg(config)
-    tracking_server_name = cfg.effective_mlflow_tracking_server_name
-    if not tracking_server_name:
-        CONSOLE.print("[red]MLflow is disabled in config.yaml.[/red]")
-        raise typer.Exit(1)
-
-    try:
-        url = mlflow.create_presigned_tracking_server_url(
-            cfg.aws.region,
-            cfg.aws.profile,
-            tracking_server_name,
-        )
-    except Exception as e:
-        CONSOLE.print("[yellow]Could not create a SageMaker MLflow UI URL.[/yellow]")
-        CONSOLE.print(f"Tracking server: [cyan]{tracking_server_name}[/cyan]")
-        CONSOLE.print(f"Reason: {e}")
-        CONSOLE.print(
-            "This command can create presigned URLs only for MLflow tracking servers managed by "
-            "Amazon SageMaker. If this is an external MLflow server, open it with that server's own URL."
-        )
-        raise typer.Exit(1)
-
-    CONSOLE.print(f"MLflow tracking server: [cyan]{tracking_server_name}[/cyan]")
-    CONSOLE.print(f"MLflow UI: {url}")
-
-
@app.command()
 def destroy(
    config: str = CONFIG_OPT,
--- a/src/commands/init.py
+++ b/src/commands/init.py
@@ -0,0 +1,40 @@
+import secrets
+from pathlib import Path
+
+import typer
+import yaml
+
+from src.commands.utils import CONSOLE
+from src.config import GENERATED_STACK_PREFIX, Config, InfraConfig, S3Config
+
+app = typer.Typer()
+
+
+@app.command()
+def init(
+    output: str = typer.Option("config.yaml", help="Destination path for the config file"),
+    force: bool = typer.Option(False, "--force", "-f", help="Overwrite an existing config file"),
+) -> None:
+    """Write a starter config.yaml to the current directory."""
+    dest = Path(output)
+    if dest.exists() and not force:
+        CONSOLE.print(f"[yellow]{dest} already exists.[/yellow] Use --force to overwrite.")
+        raise typer.Exit(1)
+
+    config = _new_isolated_config()
+    dest.parent.mkdir(parents=True, exist_ok=True)
+    config_data = config.model_dump(mode="json")
+    config_data["sagemaker"].pop("role_name", None)
+    with open(dest, "w") as f:
+        yaml.safe_dump(config_data, f, sort_keys=False)
+
+    CONSOLE.print(f"[green]✓[/green] Config written to [bold]{dest}[/bold]")
+    CONSOLE.print("Edit [cyan]sagemaker.training.image_uri[/cyan] before running training commands.")
+
+
+def _new_isolated_config() -> Config:
+    suffix = secrets.token_hex(6)
+    namespace = f"{GENERATED_STACK_PREFIX}{suffix}"
+    config = Config(infra=InfraConfig(stack_name=namespace))
+    config.s3 = S3Config(bucket=f"{namespace}-data")
+    return config
--- a/src/commands/mlflow.py
+++ b/src/commands/mlflow.py
@@ -0,0 +1,41 @@
+import webbrowser
+
+import typer
+
+from src.aws import mlflow as aws_mlflow
+from src.commands.utils import CONFIG_OPT, CONSOLE, load_cfg
+
+app = typer.Typer(help="Manage MLflow tracking server access")
+
+
+@app.command(name="open")
+def open_mlflow(config: str = CONFIG_OPT) -> None:
+    """Open a presigned URL for the configured MLflow tracking server."""
+    cfg = load_cfg(config)
+    tracking_server_name = cfg.effective_mlflow_tracking_server_name
+    if not tracking_server_name:
+        CONSOLE.print("[red]MLflow is disabled in config.yaml.[/red]")
+        raise typer.Exit(1)
+
+    try:
+        url = aws_mlflow.create_presigned_tracking_server_url(
+            cfg.aws.region,
+            cfg.aws.profile,
+            tracking_server_name,
+        )
+    except Exception as e:
+        CONSOLE.print("[yellow]Could not create a SageMaker MLflow UI URL.[/yellow]")
+        CONSOLE.print(f"Tracking server: [cyan]{tracking_server_name}[/cyan]")
+        CONSOLE.print(f"Reason: {e}")
+        CONSOLE.print(
+            "This command can create presigned URLs only for MLflow tracking servers managed by "
+            "Amazon SageMaker. If this is an external MLflow server, open it with that server's own URL."
+        )
+        raise typer.Exit(1)
+
+    CONSOLE.print(f"MLflow tracking server: [cyan]{tracking_server_name}[/cyan]")
+    CONSOLE.print(f"MLflow UI: {url}")
+    if webbrowser.open(url):
+        CONSOLE.print("[green]✓[/green] Opened MLflow UI in your browser.")
+    else:
+        CONSOLE.print("[yellow]Could not open a browser automatically. Open the URL above manually.[/yellow]")
--- a/src/commands/train.py
+++ b/src/commands/train.py
@@ -101,7 +101,7 @@ def start(config: str = CONFIG_OPT) -> None:
    CONSOLE.print(f"[green]✓[/green] Job submitted: [bold]{job_name}[/bold]")
    if run_id:
        CONSOLE.print(f"MLflow run: [cyan]{run_id}[/cyan]")
-        CONSOLE.print("Open MLflow: [cyan]qc-cli infra mlflow-url[/cyan]")
+        CONSOLE.print("Open MLflow: [cyan]qc-cli mlflow open[/cyan]")
    CONSOLE.print("Track progress: [cyan]qc-cli train status[/cyan]")


@@ -151,7 +151,7 @@ def status(
            st.set_latest_experiment_model_version(version)
            CONSOLE.print(f"MLflow model version: [cyan]{version}[/cyan] ([cyan]experiment-latest[/cyan])")
    if run_id and cfg.mlflow.mode is not MlflowMode.disabled:
-        CONSOLE.print("Open MLflow: [cyan]qc-cli infra mlflow-url[/cyan]")
+        CONSOLE.print("Open MLflow: [cyan]qc-cli mlflow open[/cyan]")


@app.command(name="list")
--- a/src/commands/upload.py
+++ b/src/commands/upload.py
@@ -0,0 +1,70 @@
+from pathlib import Path
+
+import typer
+from rich.progress import BarColumn, Progress, SpinnerColumn, TaskProgressColumn, TextColumn
+
+from src.aws import s3 as s3_ops
+from src.commands.utils import CONFIG_OPT, CONSOLE, load_cfg
+
+app = typer.Typer()
+
+
+@app.command()
+def upload(
+    path: Path = typer.Argument(..., help="Local file or directory to upload"),
+    s3_key: str | None = typer.Option(None, "--s3-key", help="S3 key for file uploads"),
+    config: str = CONFIG_OPT,
+) -> None:
+    """Upload a local file or directory to S3."""
+    cfg = load_cfg(config)
+
+    if path.is_file():
+        key = s3_key or f"{cfg.s3.data_prefix.rstrip('/')}/{path.name}"
+        try:
+            with CONSOLE.status(f"Uploading {path.name}..."):
+                uri = s3_ops.upload_file(cfg.aws.region, cfg.aws.profile, cfg.s3.bucket, str(path), key)
+        except Exception as e:
+            CONSOLE.print(f"[red]Upload failed: {e}[/red]")
+            raise typer.Exit(1)
+
+        CONSOLE.print(f"[green]✓[/green] {path.name} -> {uri}")
+        return
+
+    if path.is_dir():
+        if s3_key is not None:
+            CONSOLE.print("[red]--s3-key can only be used when uploading a single file.[/red]")
+            raise typer.Exit(1)
+
+        files = [file for file in path.rglob("*") if file.is_file()]
+        if not files:
+            CONSOLE.print("[yellow]No files found in directory.[/yellow]")
+            raise typer.Exit(0)
+
+        prefix = cfg.s3.data_prefix
+        CONSOLE.print(f"Uploading {len(files)} files to s3://{cfg.s3.bucket}/{prefix.rstrip('/')}/")
+        try:
+            with Progress(
+                SpinnerColumn(),
+                TextColumn("[progress.description]{task.description}"),
+                BarColumn(),
+                TaskProgressColumn(),
+                console=CONSOLE,
+            ) as progress:
+                task = progress.add_task("Uploading...", total=len(files))
+                count = s3_ops.upload_dir(
+                    cfg.aws.region,
+                    cfg.aws.profile,
+                    cfg.s3.bucket,
+                    str(path),
+                    prefix,
+                    on_progress=lambda: progress.advance(task),
+                )
+        except Exception as e:
+            CONSOLE.print(f"[red]Upload failed: {e}[/red]")
+            raise typer.Exit(1)
+
+        CONSOLE.print(f"[green]✓[/green] Uploaded {count} files to s3://{cfg.s3.bucket}/{prefix.rstrip('/')}/")
+        return
+
+    CONSOLE.print(f"[red]Path not found: {path}[/red]")
+    raise typer.Exit(1)
--- a/src/config.py
+++ b/src/config.py
@@ -4,7 +4,8 @@ from typing import Any, Literal, TypedDict

 from mypy_boto3_s3.literals import BucketLocationConstraintType
 from mypy_boto3_sagemaker.literals import TrainingInstanceTypeType
-from pydantic import BaseModel, Field, model_validator
+from pydantic import BaseModel, Field, field_validator, model_validator
+from qai_hub.client import Device


 class MlflowMode(StrEnum):
@@ -81,7 +82,7 @@ class SageMakerConfig(BaseModel):


 class AIHubConfig(BaseModel):
-    device: str = "Samsung Galaxy S25 (Family)"
+    device: Device = Field(default_factory=lambda: Device("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
@@ -91,6 +92,13 @@ class AIHubConfig(BaseModel):
    quantize_options: str | None = None
    output_dir: str = "build/qai-hub"

+    @field_validator("device", mode="before")
+    @classmethod
+    def parse_device(cls, value: Any) -> Any:
+        if isinstance(value, str):
+            return Device(value)
+        return value
+

 class MlflowConfig(BaseModel):
    mode: MlflowMode = MlflowMode.disabled
--- a/src/main.py
+++ b/src/main.py
@@ -1,115 +1,14 @@
-import secrets
-from pathlib import Path
-
 import typer
-import yaml
-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 ai_hub, infra, train
-from src.commands.utils import CONFIG_OPT, load_cfg
-from src.config import GENERATED_STACK_PREFIX, Config, InfraConfig, S3Config
+from src.commands import ai_hub, infra, init, mlflow, train, upload

 app = typer.Typer(
    help="qc-cli: End-to-end model managment for Qualcomm AI Hub.",
    no_args_is_help=True,
 )
+app.add_typer(init.app)
+app.add_typer(upload.app)
+app.add_typer(mlflow.app, name="mlflow")
 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()
-
-
-@app.command()
-def init(
-    output: str = typer.Option("config.yaml", help="Destination path for the config file"),
-    force: bool = typer.Option(False, "--force", "-f", help="Overwrite an existing config file"),
-) -> None:
-    """Write a starter config.yaml to the current directory."""
-    dest = Path(output)
-    if dest.exists() and not force:
-        console.print(f"[yellow]{dest} already exists.[/yellow] Use --force to overwrite.")
-        raise typer.Exit(1)
-
-    config = _new_isolated_config()
-    dest.parent.mkdir(parents=True, exist_ok=True)
-    config_data = config.model_dump(mode="json")
-    config_data["sagemaker"].pop("role_name", None)
-    with open(dest, "w") as f:
-        yaml.safe_dump(config_data, f, sort_keys=False)
-
-    console.print(f"[green]✓[/green] Config written to [bold]{dest}[/bold]")
-    console.print(
-        "Edit [cyan]sagemaker.training.image_uri[/cyan] before running training commands."
-    )
-
-
-def _new_isolated_config() -> Config:
-    suffix = secrets.token_hex(6)
-    namespace = f"{GENERATED_STACK_PREFIX}{suffix}"
-    config = Config(infra=InfraConfig(stack_name=namespace))
-    config.s3 = S3Config(bucket=f"{namespace}-data")
-    return config
-
-
-@app.command()
-def upload(
-    path: Path = typer.Argument(..., help="Local file or directory to upload"),
-    s3_key: str | None = typer.Option(None, "--s3-key", help="S3 key for file uploads"),
-    config: str = CONFIG_OPT,
-) -> None:
-    """Upload a local file or directory to S3."""
-    cfg = load_cfg(config)
-
-    if path.is_file():
-        key = s3_key or f"{cfg.s3.data_prefix.rstrip('/')}/{path.name}"
-        try:
-            with console.status(f"Uploading {path.name}..."):
-                uri = s3_ops.upload_file(cfg.aws.region, cfg.aws.profile, cfg.s3.bucket, str(path), key)
-        except Exception as e:
-            console.print(f"[red]Upload failed: {e}[/red]")
-            raise typer.Exit(1)
-
-        console.print(f"[green]✓[/green] {path.name} -> {uri}")
-        return
-
-    if path.is_dir():
-        if s3_key is not None:
-            console.print("[red]--s3-key can only be used when uploading a single file.[/red]")
-            raise typer.Exit(1)
-
-        files = [file for file in path.rglob("*") if file.is_file()]
-        if not files:
-            console.print("[yellow]No files found in directory.[/yellow]")
-            raise typer.Exit(0)
-
-        prefix = cfg.s3.data_prefix
-        console.print(f"Uploading {len(files)} files to s3://{cfg.s3.bucket}/{prefix.rstrip('/')}/")
-        try:
-            with Progress(
-                SpinnerColumn(),
-                TextColumn("[progress.description]{task.description}"),
-                BarColumn(),
-                TaskProgressColumn(),
-                console=console,
-            ) as progress:
-                task = progress.add_task("Uploading...", total=len(files))
-                count = s3_ops.upload_dir(
-                    cfg.aws.region,
-                    cfg.aws.profile,
-                    cfg.s3.bucket,
-                    str(path),
-                    prefix,
-                    on_progress=lambda: progress.advance(task),
-                )
-        except Exception as e:
-            console.print(f"[red]Upload failed: {e}[/red]")
-            raise typer.Exit(1)
-
-        console.print(f"[green]✓[/green] Uploaded {count} files to s3://{cfg.s3.bucket}/{prefix.rstrip('/')}/")
-        return
-
-    console.print(f"[red]Path not found: {path}[/red]")
-    raise typer.Exit(1)
--- a/src/qualcomm/init.py
+++ b/src/qualcomm/init.py
@@ -1 +0,0 @@
-
--- a/src/qualcomm/aihub_jobs.py
+++ b/src/qualcomm/aihub_jobs.py
@@ -29,7 +29,7 @@ def _dataset_entries(inputs: dict[str, Any]) -> dict[str, list[Any]]:

 def submit_compile_job(
    model: Any,
-    device_name: str,
+    device: Device,
    input_specs: dict[str, tuple[tuple[int, ...], str]],
    target_runtime: str,
    options: str | None = None,
@@ -52,7 +52,7 @@ def submit_compile_job(

    job = hub.submit_compile_job(
        model=model_arg,
-        device=Device(device_name),
+        device=device,
        name=job_name,
        input_specs=input_specs,
        options=compile_options,
@@ -65,14 +65,14 @@ def submit_compile_job(

 def submit_inference_job(
    model_id: str,
-    device_name: str,
+    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),
-        device=Device(device_name),
+        device=device,
        inputs=_dataset_entries(inputs),
        name=job_name,
    )
@@ -84,13 +84,13 @@ def submit_inference_job(

 def submit_profile_job(
    model_id: str,
-    device_name: str,
+    device: Device,
    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),
+        device=device,
        name=job_name,
        options=options or "",
    )
--- a/uv.lock
+++ b/uv.lock
@@ -1003,15 +1003,6 @@ wheels = [
    { url = "https://files.pythonhosted.org/packages/8a/db/55a262f3606bebcae07cc14095338471ad7c0bbcaa37707e6f0ee49725b7/importlib_resources-7.1.0-py3-none-any.whl", hash = "sha256:1bd7b48b4088eddb2cd16382150bb515af0bd2c70128194392725f82ad2c96a1", size = 37232, upload-time = "2026-04-12T16:36:08.219Z" },
 ]

-[[package]]
-name = "iniconfig"
-version = "2.3.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/72/34/14ca021ce8e5dfedc35312d08ba8bf51fdd999c576889fc2c24cb97f4f10/iniconfig-2.3.0.tar.gz", hash = "sha256:c76315c77db068650d49c5b56314774a7804df16fee4402c1f19d6d15d8c4730", size = 20503, upload-time = "2025-10-18T21:55:43.219Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/cb/b1/3846dd7f199d53cb17f49cba7e651e9ce294d8497c8c150530ed11865bb8/iniconfig-2.3.0-py3-none-any.whl", hash = "sha256:f631c04d2c48c52b84d0d0549c99ff3859c98df65b3101406327ecc7d53fbf12", size = 7484, upload-time = "2025-10-18T21:55:41.639Z" },
-]
-
 [[package]]
 name = "itsdangerous"
 version = "2.2.0"
@@ -1674,15 +1665,6 @@ wheels = [
    { url = "https://files.pythonhosted.org/packages/ff/6e/cf826fae916b8658848d7b9f38d88da6396895c676e8086fc0988073aaf8/pillow-12.2.0-cp314-cp314t-win_arm64.whl", hash = "sha256:aa88ccfe4e32d362816319ed727a004423aab09c5cea43c01a4b435643fa34eb", size = 2556579, upload-time = "2026-04-01T14:45:52.529Z" },
 ]

-[[package]]
-name = "pluggy"
-version = "1.6.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/f9/e2/3e91f31a7d2b083fe6ef3fa267035b518369d9511ffab804f839851d2779/pluggy-1.6.0.tar.gz", hash = "sha256:7dcc130b76258d33b90f61b658791dede3486c3e6bfb003ee5c9bfb396dd22f3", size = 69412, upload-time = "2025-05-15T12:30:07.975Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/54/20/4d324d65cc6d9205fabedc306948156824eb9f0ee1633355a8f7ec5c66bf/pluggy-1.6.0-py3-none-any.whl", hash = "sha256:e920276dd6813095e9377c0bc5566d94c932c33b27a3e3945d8389c374dd4746", size = 20538, upload-time = "2025-05-15T12:30:06.134Z" },
-]
-
 [[package]]
 name = "prettytable"
 version = "3.17.0"
@@ -1963,34 +1945,6 @@ wheels = [
    { url = "https://files.pythonhosted.org/packages/16/6b/330d8ebae582b30c2959a1ef4c3bc344ebde48c2ff0c3f113c4710735e11/pyright-1.1.409-py3-none-any.whl", hash = "sha256:aa3ea228cab90c845c7a60d28db7a844c04315356392aa09fafcee98c8c22fb3", size = 6438161, upload-time = "2026-04-23T11:02:01.309Z" },
 ]

-[[package]]
-name = "pytest"
-version = "9.0.3"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "colorama", marker = "sys_platform == 'win32'" },
-    { name = "iniconfig" },
-    { name = "packaging" },
-    { name = "pluggy" },
-    { name = "pygments" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/7d/0d/549bd94f1a0a402dc8cf64563a117c0f3765662e2e668477624baeec44d5/pytest-9.0.3.tar.gz", hash = "sha256:b86ada508af81d19edeb213c681b1d48246c1a91d304c6c81a427674c17eb91c", size = 1572165, upload-time = "2026-04-07T17:16:18.027Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/d4/24/a372aaf5c9b7208e7112038812994107bc65a84cd00e0354a88c2c77a617/pytest-9.0.3-py3-none-any.whl", hash = "sha256:2c5efc453d45394fdd706ade797c0a81091eccd1d6e4bccfcd476e2b8e0ab5d9", size = 375249, upload-time = "2026-04-07T17:16:16.13Z" },
-]
-
-[[package]]
-name = "pytest-mock"
-version = "3.15.1"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "pytest" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/68/14/eb014d26be205d38ad5ad20d9a80f7d201472e08167f0bb4361e251084a9/pytest_mock-3.15.1.tar.gz", hash = "sha256:1849a238f6f396da19762269de72cb1814ab44416fa73a8686deac10b0d87a0f", size = 34036, upload-time = "2025-09-16T16:37:27.081Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/5a/cc/06253936f4a7fa2e0f48dfe6d851d9c56df896a9ab09ac019d70b760619c/pytest_mock-3.15.1-py3-none-any.whl", hash = "sha256:0a25e2eb88fe5168d535041d09a4529a188176ae608a6d249ee65abc0949630d", size = 10095, upload-time = "2025-09-16T16:37:25.734Z" },
-]
-
 [[package]]
 name = "python-dateutil"
 version = "2.9.0.post0"
@@ -2114,8 +2068,6 @@ dependencies = [
 dev = [
    { name = "boto3-stubs", extra = ["iam", "s3", "sagemaker"] },
    { name = "pyright" },
-    { name = "pytest" },
-    { name = "pytest-mock" },
    { name = "ruff" },
    { name = "types-pyyaml" },
 ]
@@ -2138,8 +2090,6 @@ requires-dist = [
 dev = [
    { name = "boto3-stubs", extras = ["iam", "s3", "sagemaker"] },
    { name = "pyright", specifier = ">=1.1.409" },
-    { name = "pytest", specifier = ">=8.0" },
-    { name = "pytest-mock", specifier = ">=3.12" },
    { name = "ruff", specifier = ">=0.4" },
    { name = "types-pyyaml" },
 ]
Author	SHA1	Message	Date
samirodr	5360a482fc	update	2026-06-08 14:59:44 -04:00
samirodr	6a560a8610	match	2026-06-08 14:54:13 -04:00
slalom	d244150d98	move mlflow to its own command	2026-06-05 11:47:38 -04:00
slalom	d7c7158464	clean main file	2026-06-05 11:25:04 -04:00
slalom	6bc25dc183	restructure config to use Device class directly Also include device validation	2026-06-04 17:28:17 -04:00
samirodr	71a95aa3a7	update description	2026-06-03 17:13:00 -04:00
slalom	a3f3060e13	ai-hub (#3 ) Reviewed-on: #3	2026-06-03 21:06:06 +00:00
slalom	e9ada2612f	Mlflow implementation (#2 ) Reviewed-on: #2	2026-06-02 19:04:23 +00:00