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
20 changed files with 329 additions and 789 deletions
--- a/README.md
+++ b/README.md
@@ -67,8 +67,7 @@ sagemaker:
    hyperparameters: {}
 aihub:
-  device:
+  device: Samsung Galaxy S25 (Family)
    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
@@ -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
@@ -1,118 +0,0 @@
 # Qualcomm AI Hub Example
 This example takes the ONNX model produced by the SageMaker training example and runs the Qualcomm AI Hub upload workflow:
 1. Quantize
 2. Compile
 3. Validate
 4. Profile
 5. Download the compiled artifact
 ## Prerequisites
 Run the training example first and wait for it to complete:
 ```bash
 bash examples/training/run_training.sh --config config.yaml --wait
 ```
 If the dataset is already uploaded to S3, use:
 ```bash
 bash examples/training/run_training.sh --config config.yaml --skip-upload --wait
 ```
 The training artifact must contain a static-shape `model.onnx`. The training example exports an input named `input` with shape `1x3x160x160`.
 Your `config.yaml` must include AI Hub settings:
 ```yaml
 aihub:
  device:
    name: Samsung Galaxy S25 (Family)
  target_runtime: tflite
  input_specs:
    input: [[1, 3, 160, 160], float32]
  output_dir: build/qai-hub
 ```
 You also need local Qualcomm AI Hub SDK authentication configured.
 ## Prepare Inputs
 AI Hub does not consume the raw JPG training images directly. It needs NumPy tensors that match the ONNX model input shape and preprocessing.
 Generate calibration and validation inputs:
 ```bash
 uv run python examples/ai-hub/prepare_inputs.py
 ```
 This writes:
 ```text
 examples/training/data/aihub_calibration/*.npy
 examples/training/data/inputs.npz
 ```
 The script applies the same image preprocessing used by the training example:
 - resize to `160x160`
 - convert to channel-first `1x3x160x160`
 - normalize with ImageNet mean and standard deviation
 Useful options:
 ```bash
 uv run python examples/ai-hub/prepare_inputs.py \
  --dataset-dir examples/training/data/flower_photos_sagemaker \
  --calibration-dir examples/training/data/aihub_calibration \
  --input-file examples/training/data/inputs.npz \
  --samples 16
 ```
 ## Run AI Hub
 After training completes and inputs are prepared:
 ```bash
 bash examples/ai-hub/run_ai_hub.sh --config config.yaml
 ```
 By default, the script uses the last SageMaker training job recorded in `.qc-cli.json`. It downloads that job's `model.tar.gz`, extracts `model.onnx`, runs the AI Hub workflow, and downloads the compiled artifact.
 To use a specific training job:
 ```bash
 bash examples/ai-hub/run_ai_hub.sh \
  --config config.yaml \
  --from-job qc-cli-YYYYMMDD-HHMMSS
 ```
 To resume from a later Workbench step:
 ```bash
 bash examples/ai-hub/run_ai_hub.sh \
  --config config.yaml \
  --from-step validate
 ```
 To skip downloading the compiled artifact:
 ```bash
 bash examples/ai-hub/run_ai_hub.sh \
  --config config.yaml \
  --skip-download
 ```
 ## Troubleshooting
 If AI Hub reports dynamic input shapes, rerun training with the current training source. AI Hub quantization requires the exported ONNX model to use static input shapes.
 If `run_ai_hub.sh` reports missing calibration or input files, run:
 ```bash
 uv run python examples/ai-hub/prepare_inputs.py
 ```
 If validation fails with a missing input name, make sure `config.yaml` and the generated `.npz` both use `input` as the input name.
--- 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/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/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/aws/mlflow.py
+++ b/src/aws/mlflow.py
@@ -1,6 +1,3 @@
 import os
 from collections.abc import Generator
 from contextlib import contextmanager
 from typing import Any, cast
 import boto3
@@ -37,38 +34,3 @@ def create_presigned_tracking_server_url(region: str, profile: str, name: str) -
    client = boto3.Session(profile_name=profile, region_name=region).client("sagemaker")
    response = client.create_presigned_mlflow_tracking_server_url(TrackingServerName=name)
    return str(response["AuthorizedUrl"])
@contextmanager
 def tracking_auth_env(profile: str, region: str) -> Generator[None]:
    credentials = boto3.Session(profile_name=profile, region_name=region).get_credentials()
    if credentials is None:
        raise RuntimeError(f"AWS credentials could not be resolved for profile '{profile}'.")
    frozen_credentials = credentials.get_frozen_credentials()
    if not frozen_credentials.access_key or not frozen_credentials.secret_key:
        raise RuntimeError(f"AWS credentials are incomplete for profile '{profile}'.")
    env_updates = {
        "AWS_PROFILE": profile,
        "AWS_DEFAULT_REGION": region,
        "AWS_REGION": region,
        "AWS_ACCESS_KEY_ID": frozen_credentials.access_key,
        "AWS_SECRET_ACCESS_KEY": frozen_credentials.secret_key,
    }
    if frozen_credentials.token:
        env_updates["AWS_SESSION_TOKEN"] = frozen_credentials.token
    restore_keys = set(env_updates) | {"AWS_SESSION_TOKEN"}
    previous_env = {key: os.environ.get(key) for key in restore_keys}
    try:
        os.environ.update(env_updates)
        if not frozen_credentials.token:
            os.environ.pop("AWS_SESSION_TOKEN", None)
        yield
    finally:
        for key, value in previous_env.items():
            if value is None:
                os.environ.pop(key, None)
            else:
                os.environ[key] = value
--- a/src/cloud/init.py
+++ b/src/cloud/init.py
@@ -1 +0,0 @@
 """Cloud provider adapters."""
--- a/src/cloud/mlflow.py
+++ b/src/cloud/mlflow.py
@@ -1,77 +0,0 @@
 from contextlib import AbstractContextManager
 from dataclasses import dataclass
 from typing import Any, Protocol
 from src.aws import mlflow as aws_mlflow
 from src.config import Config
 class MlflowTrackingBackend(Protocol):
    @property
    def provider_name(self) -> str: ...
    @property
    def profile(self) -> str: ...
    @property
    def region(self) -> str: ...
    def get_tracking_uri(self, tracking_server_name: str) -> str: ...
    def auth_env(self) -> AbstractContextManager[None]: ...
    def training_run_params(self, training_job: Any, *, region: str, profile: str, role_arn: str) -> dict[str, Any]: ...
    def training_run_tags(self, training_job: Any) -> dict[str, Any]: ...
    def training_status_params(self, training_job_status: Any) -> dict[str, Any]: ...
    def model_version_tags(self, training_job_status: Any) -> dict[str, Any]: ...
@dataclass(frozen=True)
 class AwsMlflowTrackingBackend:
    profile: str
    region: str
    provider_name: str = "aws"
    def get_tracking_uri(self, tracking_server_name: str) -> str:
        return aws_mlflow.get_tracking_server_arn(self.region, self.profile, tracking_server_name)
    def auth_env(self) -> AbstractContextManager[None]:
        return aws_mlflow.tracking_auth_env(self.profile, self.region)
    def training_run_params(self, training_job: Any, *, region: str, profile: str, role_arn: str) -> dict[str, Any]:
        return {
            "provider.name": self.provider_name,
            "provider.region": region,
            "provider.profile": profile,
            "sagemaker.role_arn": role_arn,
            "sagemaker.job_name": training_job.job_name,
            "sagemaker.training_image": training_job.image_uri,
            "sagemaker.instance_type": training_job.instance_type,
            "sagemaker.instance_count": training_job.instance_count,
            "sagemaker.s3_train_uri": training_job.s3_train_uri,
            "sagemaker.s3_output_path": training_job.s3_output_path,
            "sagemaker.entry_point": training_job.entry_point,
            "sagemaker.source_dir": training_job.source_dir,
        }
    def training_run_tags(self, training_job: Any) -> dict[str, Any]:
        return {"sagemaker.job_name": training_job.job_name}
    def training_status_params(self, training_job_status: Any) -> dict[str, Any]:
        return {
            "sagemaker.training_status": training_job_status.status,
            "sagemaker.created_at": training_job_status.created,
            "sagemaker.modified_at": training_job_status.modified,
            "sagemaker.model_artifacts": training_job_status.model_artifacts,
            "sagemaker.failure_reason": training_job_status.failure_reason,
        }
    def model_version_tags(self, training_job_status: Any) -> dict[str, Any]:
        return {"sagemaker.job_name": training_job_status.name}
 def mlflow_tracking_backend_from_config(cfg: Config) -> MlflowTrackingBackend:
    return AwsMlflowTrackingBackend(profile=cfg.aws.profile, region=cfg.aws.region)
--- 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
@@ -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 pydantic import BaseModel, Field, model_validator
 from qai_hub.client import Device
 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/aihub_jobs.py
+++ b/src/qualcomm/aihub_jobs.py
@@ -1,26 +1,32 @@
 from pathlib import Path
-from typing import Any, TypedDict
+from typing import Any
 import qai_hub.hub as hub
 from qai_hub.client import CompileJob, Device, InferenceJob, Model, ProfileJob, QuantizeDtype, QuantizeJob
-class ModelJobResult(TypedDict):
+def _hub() -> Any:
-    job: CompileJob | QuantizeJob
+    import qai_hub as hub
-    job_id: str
+
-    model: Model
+    return hub
    model_id: str
-class InferenceJobResult(TypedDict):
+def _id(obj: Any) -> str:
-    job: InferenceJob
+    for attr in ("model_id", "job_id", "id"):
-    job_id: str
+        value = getattr(obj, attr, None)
-    outputs: Any
+        if value:
            return str(value)
    return str(obj)
-class ProfileJobResult(TypedDict):
+def _target_model(job: Any) -> Any:
-    job: ProfileJob
+    if hasattr(job, "get_target_model"):
-    job_id: str
+        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.")
+        raise RuntimeError(f"Compile job {_id(job)} 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)}
+    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),
+        model=get_model(model_id),
-        device=device,
+        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),
+        model=get_model(model_id),
-        device=device,
+        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,
+        weights_dtype=hub.QuantizeDtype.INT8,
-        activations_dtype=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.")
+        raise RuntimeError(f"Quantize job {_id(job)} 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)}
+    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/src/tracking/mlflow.py
+++ b/src/tracking/mlflow.py
@@ -5,7 +5,7 @@ from typing import Any, Protocol
 import mlflow
 from mlflow.tracking import MlflowClient
-from src.cloud.mlflow import MlflowTrackingBackend, mlflow_tracking_backend_from_config
+from src.aws import mlflow as aws_mlflow
 from src.config import Config, MlflowMode
@@ -30,7 +30,6 @@ class MlflowTracker:
    experiment_name: str
    registered_model_name: str
    register_trained_models: bool
    tracking_backend: MlflowTrackingBackend
    @classmethod
    def from_config(cls, cfg: Config) -> Tracker:
@@ -43,10 +42,11 @@ class MlflowTracker:
        if not tracking_server_name:
            raise RuntimeError("MLflow tracking server name could not be resolved.")
-        tracking_backend = mlflow_tracking_backend_from_config(cfg)
+        tracking_uri = aws_mlflow.get_tracking_server_arn(
-
+            cfg.aws.region,
-        tracking_uri = tracking_backend.get_tracking_uri(tracking_server_name)
+            cfg.aws.profile,
-        with tracking_backend.auth_env():
+            tracking_server_name,
        )
        mlflow.set_tracking_uri(tracking_uri)
        mlflow.set_experiment(cfg.mlflow.experiment_name)
@@ -55,30 +55,34 @@ class MlflowTracker:
            experiment_name=cfg.mlflow.experiment_name,
            registered_model_name=cfg.mlflow.registered_model_name,
            register_trained_models=cfg.mlflow.register_trained_models,
            tracking_backend=tracking_backend,
        )
    def start_training_run(self, training_job: Any, *, region: str, profile: str, role_arn: str) -> str | None:
        with self.tracking_backend.auth_env():
        run = mlflow.start_run(run_name=training_job.job_name)
        run_id = str(run.info.run_id)
-            self._log_params(
+        params = {
-                self.tracking_backend.training_run_params(
+            "aws.region": region,
-                    training_job,
+            "aws.profile": profile,
-                    region=region,
+            "sagemaker.role_arn": role_arn,
-                    profile=profile,
+            "sagemaker.job_name": training_job.job_name,
-                    role_arn=role_arn,
+            "sagemaker.training_image": training_job.image_uri,
-                )
+            "sagemaker.instance_type": training_job.instance_type,
-            )
+            "sagemaker.instance_count": training_job.instance_count,
            "sagemaker.s3_train_uri": training_job.s3_train_uri,
            "sagemaker.s3_output_path": training_job.s3_output_path,
            "sagemaker.entry_point": training_job.entry_point,
            "sagemaker.source_dir": training_job.source_dir,
        }
        self._log_params(params)
        self._log_params({f"hyperparameters.{key}": value for key, value in training_job.hyperparameters.items()})
        mlflow.set_tags(
            {
                "qc_cli.stage": "experiment",
                "qc_cli.artifact_kind": "trained_source",
-                    "qc_cli.source": self.tracking_backend.provider_name,
+                "qc_cli.source": "sagemaker",
                "qc_cli.command": "train start",
-                    **self.tracking_backend.training_run_tags(training_job),
+                "sagemaker.job_name": training_job.job_name,
            }
        )
        mlflow.end_run()
@@ -88,9 +92,16 @@ class MlflowTracker:
        if not run_id:
            return None
        with self.tracking_backend.auth_env():
        with mlflow.start_run(run_id=run_id):
-                self._log_params(self.tracking_backend.training_status_params(training_job_status))
+            self._log_params(
                {
                    "sagemaker.training_status": training_job_status.status,
                    "sagemaker.created_at": training_job_status.created,
                    "sagemaker.modified_at": training_job_status.modified,
                    "sagemaker.model_artifacts": training_job_status.model_artifacts,
                    "sagemaker.failure_reason": training_job_status.failure_reason,
                }
            )
            self._log_final_metrics(training_job_status.raw)
            mlflow.set_tag("qc_cli.command", "train status")
@@ -110,8 +121,8 @@ class MlflowTracker:
                tags={
                    "qc_cli.stage": "experiment",
                    "qc_cli.artifact_kind": "trained_source",
-                        "qc_cli.source": self.tracking_backend.provider_name,
+                    "qc_cli.source": "sagemaker",
-                        **self.tracking_backend.model_version_tags(training_job_status),
+                    "sagemaker.job_name": training_job_status.name,
                },
            )
            version_number = str(version.version)
--- 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" }
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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