inital ai hub implementation

rename and future steps
reorg
2026-06-01 15:14:10 -04:00 · 2026-05-29 15:40:38 -04:00 · 2026-05-29 14:52:34 -04:00 · 2026-05-29 14:36:57 -04:00 · 2026-05-29 14:33:17 -04:00 · 2026-05-29 14:31:36 -04:00
17 changed files with 260 additions and 419 deletions
--- a/README.md
+++ b/README.md
@@ -67,8 +67,7 @@ sagemaker:
    hyperparameters: {}

 aihub:
-  device:
-    name: Samsung Galaxy S25 (Family)
+  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
@@ -110,10 +109,10 @@ 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 mlflow open --config config.yaml
+qc-cli infra mlflow-url --config config.yaml
 ```

-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.
+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.

 ## Commands

@@ -125,12 +124,6 @@ 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`

 ```
@@ -138,6 +131,7 @@ 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
@@ -186,17 +180,6 @@ 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
@@ -1,79 +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
-examples/training/run_training.sh --wait
-```
-
-The `config.yaml` file 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
-```
-
-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.
-
-To generate calibration and validation inputs:
-
-```bash
-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
-
-## Upload Model to Qualcomm Workbench
-
-The model can be uploaded to Qualcomm Workbench using:
-
-```bash
-qc-cli ai-hub upload examples/training/data/aihub_calibration examples/training/data/inputs.npz
-```
-
-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.
-
-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
-qc-cli ai-hub download
-```
--- a/examples/ai-hub/prepare_inputs.py
+++ b/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()
--- a/examples/training/source/train.py
+++ b/examples/training/source/train.py
@@ -126,6 +126,10 @@ 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"},
+        },
    )


--- 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 training and deploying models for Qualcomm AI Hub"
+description = "CLI for SageMaker ONNX training and Qualcomm AI Hub optimization"
 requires-python = ">=3.13"
 dependencies = [
    "aws-cdk-lib>=2.180.0",
@@ -29,6 +29,8 @@ 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
@@ -4,9 +4,7 @@ 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
@@ -14,7 +12,7 @@ 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 Workbench")
+app = typer.Typer(help="Quantize, compile, validate, profile, and download models with Qualcomm AI Hub")

 _RUNTIME_EXTENSIONS = {
    "tflite": "tflite",
@@ -101,33 +99,6 @@ 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,
@@ -185,7 +156,6 @@ def _compile_step(
    prefer_quantized: bool,
 ) -> str:
    st = state_ops.store(config_path)
-    _validate_device(cfg)
    specs = _input_specs(cfg)

    model: Any
@@ -214,7 +184,7 @@ def _compile_step(
    try:
        result = aihub_jobs.submit_compile_job(
            model=model,
-            device=cfg.aihub.device,
+            device_name=cfg.aihub.device,
            input_specs=specs,
            target_runtime=cfg.aihub.target_runtime,
            options=cfg.aihub.compile_options,
@@ -244,7 +214,6 @@ 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:
@@ -278,7 +247,6 @@ 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
@@ -150,6 +150,35 @@ 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
@@ -1,40 +0,0 @@
-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
@@ -1,41 +0,0 @@
-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 mlflow open[/cyan]")
+        CONSOLE.print("Open MLflow: [cyan]qc-cli infra mlflow-url[/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 mlflow open[/cyan]")
+        CONSOLE.print("Open MLflow: [cyan]qc-cli infra mlflow-url[/cyan]")


@app.command(name="list")
--- a/src/commands/upload.py
+++ b/src/commands/upload.py
@@ -1,70 +0,0 @@
-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
@@ -4,8 +4,7 @@ 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, field_validator, model_validator
-from qai_hub.client import Device
+from pydantic import BaseModel, Field, model_validator


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


 class AIHubConfig(BaseModel):
-    device: Device = Field(default_factory=lambda: Device("Samsung Galaxy S25 (Family)"))
+    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
@@ -92,13 +91,6 @@ 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
@@ -1,14 +1,115 @@
-import typer
+import secrets
+from pathlib import Path

-from src.commands import ai_hub, infra, init, mlflow, train, upload
+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

 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
@@ -0,0 +1 @@
+
--- a/src/qualcomm/aihub_jobs.py
+++ b/src/qualcomm/aihub_jobs.py
@@ -1,26 +1,32 @@
 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
+from typing import Any


-class ModelJobResult(TypedDict):
-    job: CompileJob | QuantizeJob
-    job_id: str
-    model: Model
-    model_id: str
+def _hub() -> Any:
+    import qai_hub as hub
+
+    return hub


-class InferenceJobResult(TypedDict):
-    job: InferenceJob
-    job_id: str
-    outputs: Any
+def _id(obj: Any) -> str:
+    for attr in ("model_id", "job_id", "id"):
+        value = getattr(obj, attr, None)
+        if value:
+            return str(value)
+    return str(obj)


-class ProfileJobResult(TypedDict):
-    job: ProfileJob
-    job_id: str
+def _target_model(job: Any) -> Any:
+    if hasattr(job, "get_target_model"):
+        return job.get_target_model()
+    model = getattr(job, "target_model", None)
+    if model is not None:
+        return model
+    return job
+
+
+def get_model(model_id: str) -> Any:
+    return _hub().get_model(model_id)


 def _dataset_entries(inputs: dict[str, Any]) -> dict[str, list[Any]]:
@@ -29,13 +35,14 @@ def _dataset_entries(inputs: dict[str, Any]) -> dict[str, list[Any]]:

 def submit_compile_job(
    model: Any,
-    device: Device,
+    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:
+) -> dict[str, Any]:
+    hub = _hub()
    compile_options = f"--target_runtime {target_runtime}"
    if options:
        compile_options = f"{compile_options} {options}"
@@ -45,56 +52,58 @@ def submit_compile_job(
        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)
+        model_arg = model if candidate.exists() or candidate.suffix else 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=hub.Device(device_name),
        name=job_name,
        input_specs=input_specs,
        options=compile_options,
    )
-    target_model = job.get_target_model()
+    target_model = _target_model(job)
    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)}
+        raise RuntimeError(f"Compile job {_id(job)} did not produce a target model.")
+    return {"job": job, "job_id": _id(job), "model": target_model, "model_id": _id(target_model)}


 def submit_inference_job(
    model_id: str,
-    device: Device,
+    device_name: str,
    inputs: dict[str, Any],
    output_dir: str | Path,
    job_name: str | None = None,
-) -> InferenceJobResult:
+) -> dict[str, Any]:
+    hub = _hub()
    job = hub.submit_inference_job(
-        model=hub.get_model(model_id),
-        device=device,
+        model=get_model(model_id),
+        device=hub.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}
+    return {"job": job, "job_id": _id(job), "outputs": data}


 def submit_profile_job(
    model_id: str,
-    device: Device,
+    device_name: str,
    options: str | None = None,
    job_name: str | None = None,
-) -> ProfileJobResult:
+) -> dict[str, Any]:
+    hub = _hub()
    job = hub.submit_profile_job(
-        model=hub.get_model(model_id),
-        device=device,
+        model=get_model(model_id),
+        device=hub.Device(device_name),
        name=job_name,
        options=options or "",
    )
-    return {"job": job, "job_id": str(job.job_id)}
+    return {"job": job, "job_id": _id(job)}


 def submit_quantize_job(
@@ -103,27 +112,33 @@ def submit_quantize_job(
    options: str | None = None,
    job_name: str | None = None,
    model_name: str | None = None,
-) -> ModelJobResult:
+) -> dict[str, Any]:
+    hub = _hub()
    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,
+        weights_dtype=hub.QuantizeDtype.INT8,
+        activations_dtype=hub.QuantizeDtype.INT8,
        name=job_name,
        options=options or "",
    )
-    target_model = job.get_target_model()
+    target_model = _target_model(job)
    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)}
+        raise RuntimeError(f"Quantize job {_id(job)} did not produce a target model.")
+    return {"job": job, "job_id": _id(job), "model": target_model, "model_id": _id(target_model)}


 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)
+    model = get_model(model_id)
+    if hasattr(model, "download"):
        result = model.download(str(dest))
        return str(result or dest)
+    if hasattr(model, "download_model"):
+        result = model.download_model(str(dest))
+        return str(result or dest)
+    raise RuntimeError("AI Hub model object does not expose a download method.")
--- a/uv.lock
+++ b/uv.lock
@@ -1003,6 +1003,15 @@ 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"
@@ -1665,6 +1674,15 @@ 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"
@@ -1945,6 +1963,34 @@ 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"
@@ -2068,6 +2114,8 @@ dependencies = [
 dev = [
    { name = "boto3-stubs", extra = ["iam", "s3", "sagemaker"] },
    { name = "pyright" },
+    { name = "pytest" },
+    { name = "pytest-mock" },
    { name = "ruff" },
    { name = "types-pyyaml" },
 ]
@@ -2090,6 +2138,8 @@ 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
slalom	d3ebd2cc5f	inital ai hub implementation	2026-06-01 15:14:10 -04:00
slalom	57a8a0a9c4	rename and future steps	2026-05-29 15:40:38 -04:00
slalom	a43c792cfd	reorg	2026-05-29 14:52:34 -04:00
slalom	cf6a561e2f	clean	2026-05-29 14:36:57 -04:00
slalom	416e51901d	space	2026-05-29 14:33:17 -04:00
slalom	556797cf13	remove	2026-05-29 14:31:36 -04:00
slalom	19fef8638b	mlflow not being an optional lin	2026-05-29 14:29:05 -04:00
slalom	58681cef82	command to create presigned URL for MLFlow	2026-05-27 10:52:08 -04:00
slalom	e1c8d6574f	omit server name when created with config	2026-05-27 10:23:53 -04:00
slalom	35d25d8967	Merge branch 'main' into ml-flow	2026-05-27 08:58:46 -04:00
slalom	b907a74525	wip mlflow implementation	2026-05-26 15:03:53 -04:00