update

examples/meter-detection/README.md

@@ -12,38 +12,51 @@ https://universe.roboflow.com/kemals-workspace-kbc8l/electric-meter-detection-o4
 
 ## Prerequisites
 
+- Install or sync the project dependencies: `uv sync`
+- The virtual environment is activated.
 - AWS credentials configured for the profile in `config.yaml`
-- Infrastructure already deployed with `uv run qc-cli infra setup`
+- Infrastructure already deployed with `qc-cli infra setup`
-- A Roboflow API key exported as `ROBOFLOW_API_KEY`
-- `curl` and `unzip` available locally
-
-Install or sync the project dependencies:
-
-```bash
-uv sync
-```
-
-Set the Roboflow API key for the current shell:
-
-```bash
-export ROBOFLOW_API_KEY=your-roboflow-api-key
-```
 
 ## 1. Download The Dataset
 
-Download version 1 of the dataset in YOLO format. The script uses the Roboflow REST API directly and does not require Python:
+Register or sign in to Roboflow, then open the dataset page:
 
-```bash
+```text
-bash examples/meter-detection/download_dataset.sh
+https://universe.roboflow.com/kemals-workspace-kbc8l/electric-meter-detection-o4tfi/dataset/1
 ```
 
-Confirm the extracted dataset has a YOLO data file and image splits:
+Download the dataset in YOLOv26 format from the Roboflow UI, then extract the downloaded archive into:
+
+```text
+examples/meter-detection/data/electric-meter-detection
+```
+
+The `data.yaml` file should be directly under that folder:
+
+```text
+examples/meter-detection/data/electric-meter-detection/data.yaml
+```
+
+Do not move `data.yaml` into the `train/` split folder.
+
+After extracting, confirm the dataset has a YOLO data file and image splits:
 
 ```bash
 find examples/meter-detection/data/electric-meter-detection -maxdepth 2 -type d | sort
 find examples/meter-detection/data/electric-meter-detection -name data.yaml -print
 ```
 
+Open `examples/meter-detection/data/electric-meter-detection/data.yaml` and make sure the split paths are relative to that folder:
+
+```yaml
+path: .
+train: train/images
+val: valid/images
+test: test/images
+```
+
+If your downloaded dataset does not include a `test/` folder, remove the `test:` line.
+
 The expected layout is similar to:
 
 ```text
@@ -54,8 +67,6 @@ examples/meter-detection/data/electric-meter-detection/
   test/
 ```
 
-The `test/` split may be absent depending on the exported dataset version.
-
 ## 2. Configure SageMaker Training
 
 Update `config.yaml` so the training section points at this example's source directory:
@@ -107,7 +118,7 @@ sagemaker:
 Confirm the CLI can see the configured SageMaker role and S3 bucket:
 
 ```bash
-uv run qc-cli infra status --config config.yaml
+qc-cli infra status --config config.yaml
 ```
 
 ## 4. Upload The Dataset
@@ -115,29 +126,31 @@ uv run qc-cli infra status --config config.yaml
 Upload the downloaded Roboflow dataset to the `s3.data_prefix` configured in `config.yaml`:
 
 ```bash
-uv run qc-cli upload examples/meter-detection/data/electric-meter-detection --config config.yaml
+qc-cli upload examples/meter-detection/data/electric-meter-detection
 ```
 
 Directory uploads preserve paths relative to the uploaded directory, so SageMaker receives the dataset root with `data.yaml` plus the split directories.
 
+In SageMaker, this uploaded dataset root is mounted at `/opt/ml/input/data/train`. That `train` path is the SageMaker channel name, not the YOLO `train/` split folder.
+
 ## 5. Start Training
 
 Submit the SageMaker training job:
 
 ```bash
-uv run qc-cli train start --config config.yaml
+qc-cli train start
 ```
 
 The command prints the submitted SageMaker job name. Check progress with:
 
 ```bash
-uv run qc-cli train status --config config.yaml
+qc-cli train status
 ```
 
 Or pass the job name explicitly:
 
 ```bash
-uv run qc-cli train status qc-cli-YYYYMMDD-HHMMSS --config config.yaml
+qc-cli train status qc-cli-YYYYMMDD-HHMMSS
 ```
 
 ## Outputs
@@ -167,6 +180,7 @@ Values under `sagemaker.training.hyperparameters` are passed to `source/train.py
 | `workers` | int | `2` | DataLoader worker count. |
 | `patience` | int | `20` | Early stopping patience. |
 | `device` | string | auto | Optional Ultralytics device value such as `0` or `cpu`. |
-| `data-yaml` | string | auto | Optional path to `data.yaml`; normally discovered from `SM_CHANNEL_TRAIN`. |
+| `data-yaml` | string | auto | Optional path to `data.yaml`; normally discovered from the uploaded dataset root. |
+| `dataset-dir` | string | `SM_CHANNEL_TRAIN` | Uploaded dataset root mounted by SageMaker. |
 
-Do not set `train-dir` or `model-dir` in normal SageMaker runs. SageMaker sets those automatically through `SM_CHANNEL_TRAIN` and `SM_MODEL_DIR`.
+Do not set `dataset-dir` or `model-dir` in normal SageMaker runs. SageMaker sets those automatically through `SM_CHANNEL_TRAIN` and `SM_MODEL_DIR`.

examples/meter-detection/download_dataset.sh

@@ -1,57 +0,0 @@
-#!/usr/bin/env bash
-set -euo pipefail
-
-WORKSPACE="kemals-workspace-kbc8l"
-PROJECT="electric-meter-detection-o4tfi"
-VERSION="1"
-FORMAT="yolov8"
-DATASET_DIR="examples/meter-detection/data/electric-meter-detection"
-
-if [[ -z "${ROBOFLOW_API_KEY:-}" ]]; then
-  echo "ROBOFLOW_API_KEY is required." >&2
-  echo "Run: export ROBOFLOW_API_KEY=your-roboflow-api-key" >&2
-  exit 1
-fi
-
-if ! command -v curl >/dev/null 2>&1; then
-  echo "curl is required." >&2
-  exit 1
-fi
-
-if ! command -v unzip >/dev/null 2>&1; then
-  echo "unzip is required." >&2
-  exit 1
-fi
-
-TMP_DIR="$(mktemp -d)"
-trap 'rm -rf "${TMP_DIR}"' EXIT
-
-API_URL="https://api.roboflow.com/${WORKSPACE}/${PROJECT}/${VERSION}/${FORMAT}?api_key=${ROBOFLOW_API_KEY}"
-RESPONSE_FILE="${TMP_DIR}/roboflow-export.json"
-ZIP_FILE="${TMP_DIR}/dataset.zip"
-
-echo "Requesting Roboflow export link..."
-curl -fsSL "${API_URL}" -o "${RESPONSE_FILE}"
-
-DOWNLOAD_URL="$(
-  sed -n 's/.*"link"[[:space:]]*:[[:space:]]*"\([^"]*\)".*/\1/p' "${RESPONSE_FILE}" \
-    | head -n 1 \
-    | sed 's#\\/#/#g; s#\\u0026#\&#g'
-)"
-
-if [[ -z "${DOWNLOAD_URL}" ]]; then
-  echo "Could not find export.link in Roboflow response." >&2
-  echo "Response:" >&2
-  cat "${RESPONSE_FILE}" >&2
-  exit 1
-fi
-
-mkdir -p "${DATASET_DIR}"
-
-echo "Downloading dataset ZIP..."
-curl -fL "${DOWNLOAD_URL}" -o "${ZIP_FILE}"
-
-echo "Extracting dataset..."
-unzip -q -o "${ZIP_FILE}" -d "${DATASET_DIR}"
-
-echo "Downloaded dataset to ${DATASET_DIR}"

examples/meter-detection/source/train.py

@@ -24,37 +24,29 @@ def parse_args() -> argparse.Namespace:
     parser.add_argument("--patience", type=int, default=20)
     parser.add_argument("--device", default=None)
     parser.add_argument("--data-yaml", default=None)
-    parser.add_argument("--train-dir", default=os.environ.get("SM_CHANNEL_TRAIN", "/opt/ml/input/data/train"))
+    parser.add_argument("--dataset-dir", default=os.environ.get("SM_CHANNEL_TRAIN", "/opt/ml/input/data/train"))
+    parser.add_argument("--train-dir", dest="dataset_dir", help=argparse.SUPPRESS)
     parser.add_argument("--model-dir", default=os.environ.get("SM_MODEL_DIR", "/opt/ml/model"))
     return parser.parse_args()
 
 
-def find_data_yaml(train_dir: Path, explicit_path: str | None) -> Path:
+def find_data_yaml(dataset_dir: Path, explicit_path: str | None) -> Path:
     if explicit_path:
         data_yaml = Path(explicit_path)
         if data_yaml.is_file():
             return data_yaml
         raise FileNotFoundError(f"Configured data.yaml does not exist: {data_yaml}")
 
-    matches = sorted(train_dir.rglob("data.yaml"))
+    matches = sorted(dataset_dir.rglob("data.yaml"))
     if not matches:
-        raise FileNotFoundError(f"Could not find data.yaml under {train_dir}")
+        raise FileNotFoundError(f"Could not find data.yaml under {dataset_dir}")
     if len(matches) > 1:
         print(f"Found multiple data.yaml files; using {matches[0]}")
     return matches[0]
 
 
-def _split_exists(dataset_root: Path, value: Any) -> bool:
-    if value is None:
-        return False
-    split_path = Path(str(value))
-    if split_path.is_absolute():
-        return split_path.exists()
-    return (dataset_root / split_path).exists()
-
-
 def prepare_data_yaml(data_yaml: Path) -> Path:
-    """Write a SageMaker-local data file with absolute dataset paths."""
+    """Write a SageMaker-local data file rooted at the uploaded dataset."""
     dataset_root = data_yaml.parent
     data = yaml.safe_load(data_yaml.read_text(encoding="utf-8"))
     if not isinstance(data, dict):
@@ -62,24 +54,8 @@ def prepare_data_yaml(data_yaml: Path) -> Path:
 
     normalized = dict(data)
     normalized["path"] = str(dataset_root)
-
-    for split_name in ("train", "val", "valid", "test"):
-        split_value = normalized.get(split_name)
-        if split_value is None:
-            continue
-        split_path = Path(str(split_value))
-        if split_path.is_absolute():
-            normalized[split_name] = str(split_path)
-        else:
-            normalized[split_name] = str((dataset_root / split_path).resolve())
-
     if "val" not in normalized and "valid" in normalized:
-        normalized["val"] = normalized["valid"]
+        normalized["val"] = normalized.pop("valid")
-
-    if not _split_exists(dataset_root, normalized.get("train")):
-        raise FileNotFoundError(f"Could not resolve train split from {data_yaml}")
-    if not _split_exists(dataset_root, normalized.get("val")):
-        raise FileNotFoundError(f"Could not resolve validation split from {data_yaml}")
 
     prepared_path = dataset_root / "data.sagemaker.yaml"
     prepared_path.write_text(yaml.safe_dump(normalized, sort_keys=False), encoding="utf-8")
@@ -95,11 +71,11 @@ def copy_if_exists(source: Path, destination: Path) -> None:
 
 def main() -> None:
     args = parse_args()
-    train_dir = Path(args.train_dir)
+    dataset_dir = Path(args.dataset_dir)
     model_dir = Path(args.model_dir)
     model_dir.mkdir(parents=True, exist_ok=True)
 
-    data_yaml = prepare_data_yaml(find_data_yaml(train_dir, args.data_yaml))
+    data_yaml = prepare_data_yaml(find_data_yaml(dataset_dir, args.data_yaml))
     model = YOLO(args.model)
 
     train_kwargs: dict[str, Any] = {