SIENTIAPDE-2072: bump sientia_do pin to 1.12.2

Picks up the notification timestamp -> native datetime fix; also
aligns requirements.txt's floor with requirements-local.txt's pin
(was >=1.11.0/@1.12.0, out of sync with the other repos).

.dockerignore

@@ -1,93 +0,0 @@
-# ============================================================================
-# WHITELIST APPROACH: Block everything by default, then allow only what's needed
-# ============================================================================
-
-# Block everything first
-*
-
-# ============================================================================
-# ALLOW: Application source code (model_manager package)
-# ============================================================================
-
-# Allow the main package directory and all Python files
-!model_manager/
-!model_manager/**/*.py
-!model_manager/**/__init__.py
-
-# Allow subdirectories structure
-!model_manager/activities/
-!model_manager/activities/**
-!model_manager/schedules/
-!model_manager/schedules/**
-!model_manager/sientia/
-!model_manager/sientia/**
-!model_manager/utils/
-!model_manager/utils/**
-!model_manager/utils/models/
-!model_manager/utils/models/**
-!model_manager/utils/repository/
-!model_manager/utils/repository/**
-!model_manager/worker/
-!model_manager/worker/**
-!model_manager/workflows/
-!model_manager/workflows/**
-
-# Allow reports directory with header.html
-!model_manager/reports/
-!model_manager/reports/header.html
-
-# Allow temp directory structure (but not its contents)
-!model_manager/reports/temp/
-
-# ============================================================================
-# ALLOW: Dependencies file (needed for pip install in Dockerfile)
-# ============================================================================
-!requirements.txt
-
-# ============================================================================
-# BLOCK: Explicitly block unwanted files even if they match above patterns
-# ============================================================================
-
-# Python cache and compiled files
-**/__pycache__/
-**/*.pyc
-**/*.pyo
-**/*.pyd
-**/.Python
-**/*.so
-**/*.egg
-**/*.egg-info/
-
-# Tests (not needed in production)
-model_manager/**/test_*.py
-model_manager/**/*_test.py
-
-# IDE and editor files
-**/.vscode/
-**/.idea/
-**/*.swp
-**/*.swo
-**/*~
-
-# OS files
-**/.DS_Store
-**/Thumbs.db
-
-# Logs and temporary files
-**/*.log
-**/*.tmp
-**/*.temp
-
-# Local configuration
-**/.env
-**/.env.local
-**/*.local
-
-# Documentation inside code
-**/*.md
-**/README*
-
-# Backup files
-**/*.bak
-**/*.backup
-**/*.old

.github/workflows/quality-gate.yml

@@ -1,6 +1,11 @@
 name: Quality gate
 
 on:
+  push:
+    branches:
+      - main
+      - 'release/**'
+      - 'feature/**'
   pull_request:
     branches:
       - main
@@ -8,10 +13,17 @@ on:
       - 'feature/**'
     types: [ opened, synchronize, reopened ]
 
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
 jobs:
   quality-gate:
     uses: Aignosi/github_workflow_templates/.github/workflows/python-quality-gate.yml@main
-    permissions: write-all
+    permissions:
+      contents: read
+      pull-requests: write
+      issues: write
     with:
       project_name: 'model_manager'
       repositories: 'sientia-dataops-library,sientia-model-library'

CLAUDE.md

@@ -0,0 +1,78 @@
+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## What this is
+
+Enterprise ML model training orchestration platform built on **Temporal**. Orchestrates training workflows (validation → train → save to MLFlow → cleanup), backed by PostgreSQL (experiment tracking), MinIO (data/artifacts), MongoDB (notifications), and a Git-based "Plugin Store" that supplies model runtimes/wrappers dynamically. Python 3.11+.
+
+## Commands
+
+```bash
+# Local run (loads .env, starts worker)
+./run_local.sh
+# or manually:
+python -m model_manager.worker.worker
+
+# Full validation (format check, lint, mypy, bandit, tests w/ coverage) — run before committing
+./validate.sh
+./validate.sh --fix          # auto-fix format/lint, no tests
+./validate.sh --no-tests     # skip tests
+./validate.sh --only-tests   # tests only
+
+# Individual tools
+ruff format --check model_manager/ tests/
+ruff format model_manager/ tests/          # auto-fix
+ruff check model_manager/ tests/
+ruff check --fix model_manager/ tests/
+mypy model_manager/
+bandit -r model_manager/ -ll
+
+# Tests
+pytest                                          # runs tests/ and e2e/ (see pyproject testpaths)
+pytest tests/                                   # unit tests only
+pytest tests/activities/test_training.py -v     # single file
+pytest tests/activities/test_training.py::test_name -v   # single test
+pytest tests/ --cov=model_manager --cov-report=term-missing --cov-report=html
+
+# Manual integration/E2E scripts (require live Temporal/Postgres/MinIO stack; run via IDE "Run Cell" or directly)
+python scripts/run_training_test.py     # supports --all for batch scenario execution
+python scripts/run_cleanup_test.py
+```
+
+Coverage gate in CI is 80% minimum; this repo targets 100% in practice — don't add uncovered code paths without tests.
+
+## Architecture
+
+### Temporal layering
+- **Worker** (`model_manager/worker/worker.py`): entrypoint. Builds config from env, connects Postgres/MLFlow/MinIO/Plugin Store, installs the `RUNTIME` model runtime via `PluginStore.install_runtime`, starts two Temporal workers (one per task queue) plus Prometheus metrics server and the cleanup schedule. All heavy imports are wrapped in `workflow.unsafe.imports_passed_through()` — required because Temporal workflow sandboxing forbids non-deterministic imports at module scope outside that block.
+- **Workflows** (`model_manager/workflows/`): pure orchestration, no I/O beyond `execute_activity_method` calls.
+  - `train_model.py` (`TrainModel`, workflow name `train_model`): validate `experiment_run_id` → `load_model_metadata` → `validate_train_params` → update DB status `ORCHESTRATOR_WAITING_PROC` → `train_model` activity → update DB status `TRAINING_SUCCESS`/`TRAINING_ERROR` → `cleanup_resources` (always runs in `finally` if a `run_dir` exists, even on failure). Uses distinct `RetryPolicy`s per operation class: `network_retry_policy` (transient I/O), `no_retry_policy` (training/validation — data errors are permanent, and retrying would duplicate MLflow writes), `database_retry_policy` (Postgres). Timeouts are env-driven (`TIMEOUT_*`), extend `TIMEOUT_TRAIN_MODEL` instead of adding retries for slow training.
+  - `cleanup_files.py` (`CleanupFiles`): scheduled (`cleanup_schedule.py`, default daily at midnight UTC via `CLEANUP_CRON`) deletion of stale local temp dirs matching `name_YYYYMMDD_HHMMSS_microseconds`, older than `CLEANUP_RETENTION_HOURS`.
+  - Task queues are derived from `RUNTIME` env var (default `single`) via `prepare_worker.build_queue_name`: `train_model-<runtime>-queue`, `cleanup_files-<runtime>-queue`. A Temporal client starting a workflow must target the matching queue.
+- **Activities** (`model_manager/activities/`): `Activities` (`activities.py`) combines `ExperimentTracking` + `Training` + `Cleanup` via multiple inheritance into one class registered with the worker — mind MRO and `__init__`/`__del__` ordering when touching this class (see the defensive `hasattr(self, 'engine')` check in `__del__`).
+  - `experiment_tracking.py`: single `update_experiment_run()` entrypoint handling three update kinds via `UpdateType`: `STATUS`, `STATUS_WITH_ERROR`, `MODEL_SAVED`. Error messages truncated to 1024 chars before persisting.
+  - `training.py`: single `train_model()` entrypoint. Receives pre-downloaded data as `BytesIO` (not re-downloaded per-activity) to avoid memory leaks; never raises on training failure — returns a result/error so the workflow can update DB status without an unhandled exception mid-activity.
+  - `cleanup.py`: local-filesystem-only cleanup (`cleanup_temp_directories`), no MinIO involved — MinIO artifacts are managed externally.
+
+### Data/config layer
+- `model_manager/utils/connectors_config.py`: single source of truth for env-var → config-dict builders (`build_postgres_config`, `build_mlflow_config`, `build_minio_config`, `build_mongodb_config`, `build_plugin_store_config`). Read this file (and `worker.py`) before trusting any env var default — the README table mirrors it but this file is authoritative.
+- `model_manager/utils/models/train_model_params.py`: `TrainModelParams` — the workflow's single input contract. `from_dict` does type coercion; `validate_business_rules()` runs after, enforcing 7 rules (train_size range, non-empty variable_columns, required model_metadata, JSON-Schema validation of `data_model_kwargs`/`model_kwargs`/`opt_params` against schemas in `model_metadata` via `Draft202012Validator`, required non-blank strings, allowed `date_format` values, numeric `experiment_run_id`). Extending training params means updating this file's validation, not just the workflow.
+- `model_manager/utils/repository/data_manager_repository.py`: data loading, feature prep, metrics, report generation — the actual ML/data logic activities delegate into.
+- `model_manager/sientia/`: RCE drift metrics (`metrics.py`), Evidently-based HTML report generation (`reports.py`), custom exceptions.
+- `model_manager/metrics.py`: Prometheus metric definitions (`app_up`, `workflow_execution_total`, `activity_execution_total`, training/cleanup/RCE-drift gauges). New metrics go here, not inline in activities.
+
+### External dependencies
+Two private Aignosi packages carry non-trivial logic: `sientia_do` (observability/logger, notification handler, metrics controller, Postgres/MinIO repositories) and `sientia_model` (Plugin Store, MLflow repository, model wrappers). Both are pinned in `requirements.txt`/`requirements-local.txt`; when behavior looks like it lives outside this repo, it's likely in one of these.
+
+### Test bootstrap quirk
+`tests/conftest.py` stubs `evidently` and parts of `sientia_do` at `pytest_configure` time so unit tests can import `model_manager.sientia.reports` without the full (heavy/optional) Evidently install. If you add new imports from these packages in production code paths that unit tests exercise, you likely need to extend the stubs here too.
+
+### Test layout
+- `tests/` — unit tests, mirrors `model_manager/` package structure (`tests/activities/`, `tests/schedules/`, `tests/sientia/`, etc.).
+- `e2e/` — integration tests against a real Temporal/Postgres/MinIO/MongoDB stack (via `testcontainers`), driven by JSON scenario files in `docs/test-scenarios/` (see `e2e/scenarios.md` for the scenario→test mapping). Scenario payloads are snake_case, shaped like `TrainModelParams`/`input-sample.json`.
+- New scenario: copy an existing `docs/test-scenarios/*.json`, add/extend a test in `e2e/test_train_model_workflow.py`, update `e2e/scenarios.md`.
+
+## Conventions
+- Ruff: line-length 100, single quotes, `select = [E,W,F,I,B,C4,UP,N,YTT,S,BLE,A,C90]`, max complexity 15. `N802`/`N806` ignored (Temporal decorators use non-lowercase names). `S101`/`S105`/`S106` ignored in tests.
+- mypy: `disallow_untyped_defs = false` but `check_untyped_defs = true` — existing untyped defs are tolerated, but code inside them is still checked. Several external packages (`temporalio`, `mlflow`, `sientia_model`, etc.) have `ignore_missing_imports`; `model_repository.py`/`data_manager_repository.py` have `ignore_errors = true` (legacy — don't extend that list without reason).

Dockerfile

@@ -1,78 +0,0 @@
-# Multi-stage build for optimized Python application
-FROM python:3.11-slim AS builder
-
-# Set build-time environment variables
-ENV PYTHONDONTWRITEBYTECODE=1 \
-    PYTHONUNBUFFERED=1 \
-    PIP_NO_CACHE_DIR=1 \
-    PIP_DISABLE_PIP_VERSION_CHECK=1
-
-# Install build dependencies only
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    git \
-    && rm -rf /var/lib/apt/lists/* && \
-    apt-get clean
-
-# Configure SSH to trust GitHub host key
-RUN mkdir -p ~/.ssh && \
-    ssh-keyscan -t rsa github.com >> ~/.ssh/known_hosts && \
-    chmod 600 ~/.ssh/known_hosts
-
-# Create virtual environment
-RUN python -m venv /opt/venv
-ENV PATH="/opt/venv/bin:$PATH"
-
-# Upgrade pip and wheel for better caching
-RUN pip install --upgrade pip setuptools wheel
-
-# Copy requirements files for better Docker layer caching
-COPY requirements.txt ./
-
-# Install only production dependencies with no cache
-RUN --mount=type=ssh echo "=== Installing dependencies ===" && \
-    pip install --no-cache-dir -r requirements.txt && \
-    echo "=== Dependencies installed successfully ===" && \
-    pip list | wc -l && \
-    echo "=== Cleaning cache files ===" && \
-    find /opt/venv -type d -name __pycache__ -exec rm -rf {} + 2>/dev/null || true && \
-    find /opt/venv -name "*.pyc" -delete 2>/dev/null || true && \
-    rm -rf /root/.cache/pip/* && \
-    echo "=== Cleaning venv site-packages ===" && \
-    find /opt/venv/lib/python3.11/site-packages/ -type f -name "*.md" -delete 2>/dev/null || true && \
-    echo "=== Stripping .so files ===" && \
-    find /opt/venv -name "*.so" -exec strip {} + 2>/dev/null || true
-
-# Production stage using python-slim for better functionality
-FROM python:3.11-slim AS production
-
-# Set environment variables
-ENV PYTHONDONTWRITEBYTECODE=1 \
-    PYTHONUNBUFFERED=1 \
-    PATH="/opt/venv/bin:$PATH" \
-    POD_ID=unknown \
-    HOME="/app"
-
-# Copy virtual environment from builder stage
-COPY --from=builder /opt/venv /opt/venv
-
-# Set working directory
-WORKDIR /app
-
-# Copy application code
-COPY . .
-
-# Create necessary directories for runtime file creation
-RUN mkdir -p /app/model_manager/reports /app/logs /app/temp /app/models /app/data && \
-    chmod 755 /app/model_manager/reports /app/logs /app/temp /app/models /app/data
-
-# Create non-root user
-RUN groupadd -r appuser && useradd -r -g appuser appuser && \
-    chown -R appuser:appuser /app
-
-# Switch to non-root user
-USER appuser
-
-# Set entrypoint for proper signal handling and PID 1
-ENTRYPOINT ["/opt/venv/bin/python", "-m", "model_manager.worker.worker"]

README.md

@@ -74,7 +74,7 @@ An enterprise-grade ML model training orchestration platform built on Temporal.
   - [Usage](#usage)
   - [Command Reference](#command-reference)
 - [Docker](#docker)
-- [Helm Chart](#helm-chart)
+- [Kubernetes Deployment](#kubernetes-deployment)
 
 ## Features
 
@@ -1186,8 +1186,6 @@ sientia-dataops-model-manager/
 ├── .github/workflows/       # CI/CD workflows
 │   ├── quality-gate.yml     # PR quality checks
 │   └── deploy.yml           # Deployment workflow
-├── Dockerfile               # Container image definition
-├── values.yaml              # Helm chart values
 ├── pyproject.toml           # Project configuration
 ├── requirements.txt         # Production dependencies
 ├── requirements-dev.txt     # Development dependencies
@@ -1475,79 +1473,17 @@ act -n
 
 ## Docker
 
-### Create image
+Model Manager runs on the shared **`sientia-module`** image — the application code is cloned from the internal Gitea at runtime, not baked into an image. The image is defined in the [Aignosi/sientia-container-images](https://github.com/Aignosi/sientia-container-images) repository (`templates/sientia-module/`) and published to GCP Artifact Registry (`southamerica-east1-docker.pkg.dev/sientia-dev/sientia/sientia-module`).
 
-```bash
+The legacy repo-specific Dockerfile was removed — no image is built from this repo.
-$ docker build --ssh default --no-cache --progress=plain -t aignosi.azurecr.io/sientia-dataops-model-manager:0.0.0 .
-```
 
-### Create container
+To run locally, use `./run_local.sh` (see [Local Execution](#local-execution)).
 
-```bash
+## Kubernetes Deployment
-$ docker run --env-file .env --network="host" --name sientia-dataops-model-manager -d aignosi.azurecr.io/sientia-dataops-model-manager:0.0.0
 
-$ docker logs -f sientia-dataops-model-manager
+Kubernetes deployment is managed by the [Aignosi/sientia-helm-chart](https://github.com/Aignosi/sientia-helm-chart) umbrella chart (`src/charts-internal/model-manager/`, which wraps the shared `sientia-module` chart) — the local `values.yaml` and the vendored `sientia-module/` chart copy were removed. Worker configuration, Gitea repo/branch, task queues, and sizing are all defined in the umbrella values.
-```
 
-### Login using access token
+The standalone `sientia-module` chart still lives in [sientia-dataops-helm-repo](https://github.com/Aignosi/sientia-dataops-helm-repo) for the legacy deploy flow (temporarily broken until the CI/CD redesign).
-
-```bash
-$ docker login -u <username> -p <access-token> aignosi.azurecr.io
-```
-
-### Push image to repository
-
-```bash
-$ docker push aignosi.azurecr.io/sientia-dataops-model-manager:0.0.0
-```
-
-## Helm Chart
-
-### Reference
-
-https://aignosi-wiki.atlassian.net/wiki/spaces/IT1/pages/274563074/Como+utilizar+o+Helm+Repo+Privado
-
-### Add Helm Chart repository
-
-```bash
-$ helm repo add sientia \
-    https://raw.githubusercontent.com/Aignosi/sientia-dataops-helm-repo/refs/heads/main/ \
-    --username $GITHUB_USER \
-    --password $GITHUB_PASS
-
-# Update repository
-$ helm repo update
-
-# List repositories
-$ helm repo list
-
-# List versions of a specific chart
-$ helm search repo sientia --versions
-
-# List all charts available
-$ helm search repo sientia
-
-# List chart details
-$ helm show all sientia/sientia-module
-
-# Download chart to current directory
-$ helm pull sientia/sientia-module --version 0.6.0 --untar
-
-# Remove chart directory
-$ rm -rf sientia-module
-```
-
-### Helm Install
-
-```shell
-$ helm upgrade --install sientia-dataops-model-manager sientia/sientia-module -n sientia --create-namespace -f ./values.yaml --version 0.6.0
-```
-
-### Uninstall Helm Chart
-
-```shell
-$ helm uninstall sientia-dataops-model-manager -n sientia
-```
 
 ---
 

model_manager/activities/cleanup.py

@@ -61,6 +61,87 @@ class Cleanup(SientiaMonitoring):
             r'^(.+)_(\d{8}_\d{6}_\d{6})$'
         )  # name_YYYYMMDD_HHMMSS_microseconds
 
+    def _process_directory_item(
+        self,
+        item_path: str,
+        item_name: str,
+        cutoff_time: datetime,
+        metadata: dict[str, Any],
+    ) -> tuple[bool, str | None]:
+        """
+        Handle a single temp-directory entry: skip if the name doesn't match the
+        timestamp pattern, otherwise delete (or dry-run log) it when stale.
+
+        Args:
+            item_path: Full path to the directory being evaluated
+            item_name: Directory name (used to extract the embedded timestamp)
+            cutoff_time: Directories older than this are considered stale
+            metadata: Workflow execution metadata for logging
+
+        Return:
+            tuple[bool, str | None]: (deleted, error_message). `deleted` is True
+            if the directory was removed (or would be, in dry-run mode).
+        """
+        match = self.dir_timestamp_pattern.match(item_name)
+        if not match:
+            self.debug(f'Skipping directory without timestamp pattern: {item_name}', metadata)
+            return False, None
+
+        timestamp_str = match.group(2)
+        try:
+            # Parse YYYYMMDD_HHMMSS_microseconds
+            dir_time = datetime.strptime(timestamp_str, '%Y%m%d_%H%M%S_%f')
+        except ValueError as e:
+            error_msg = f'Invalid timestamp format in directory {item_name}: {str(e)}'
+            self.error(error_msg, metadata)
+            return False, error_msg
+
+        return self._delete_if_stale(item_path, item_name, dir_time, cutoff_time, metadata)
+
+    def _delete_if_stale(
+        self,
+        item_path: str,
+        item_name: str,
+        dir_time: datetime,
+        cutoff_time: datetime,
+        metadata: dict[str, Any],
+    ) -> tuple[bool, str | None]:
+        """
+        Delete (or dry-run log) a directory whose embedded timestamp is older than
+        cutoff_time; otherwise leave it alone.
+
+        Args:
+            item_path: Full path to the directory
+            item_name: Directory name (for logging)
+            dir_time: Timestamp parsed from the directory name
+            cutoff_time: Directories older than this are considered stale
+            metadata: Workflow execution metadata for logging
+
+        Return:
+            tuple[bool, str | None]: (deleted, error_message)
+        """
+        if dir_time >= cutoff_time:
+            age_hours = (datetime.now() - dir_time).total_seconds() / 3600
+            self.debug(f'Keeping recent directory: {item_name} (age: {age_hours:.1f}h)', metadata)
+            return False, None
+
+        age_hours = (datetime.now() - dir_time).total_seconds() / 3600
+
+        if self.dry_run:
+            self.info(
+                f'[DRY RUN] Would delete directory: {item_name} (age: {age_hours:.1f}h)', metadata
+            )
+            return True, None
+
+        try:
+            shutil.rmtree(item_path)
+            self.info(f'Deleted stale directory: {item_name} (age: {age_hours:.1f}h)', metadata)
+            return True, None
+        except OSError as e:
+            error_msg = f'Failed to delete directory {item_name}: {str(e)}'
+            self.error(error_msg, metadata)
+            return False, error_msg
+
     @activity.defn(name='cleanup_temp_directories')
     def cleanup_temp_directories(self, input_data: dict[str, Any]) -> None:
         """
@@ -109,51 +190,13 @@ class Cleanup(SientiaMonitoring):
 
                 directories_scanned += 1
 
-                # Extract timestamp from directory name
+                deleted, error = self._process_directory_item(
-                match = self.dir_timestamp_pattern.match(item_name)
+                    item_path, item_name, cutoff_time, metadata
-                if not match:
+                )
-                    self.debug(
+                if error:
-                        f'Skipping directory without timestamp pattern: {item_name}', metadata
+                    errors.append(error)
-                    )
+                if deleted:
-                    continue
+                    directories_deleted += 1
-
-                timestamp_str = match.group(2)
-                try:
-                    # Parse YYYYMMDD_HHMMSS_microseconds
-                    dir_time = datetime.strptime(timestamp_str, '%Y%m%d_%H%M%S_%f')
-
-                    if dir_time < cutoff_time:
-                        age_hours = (datetime.now() - dir_time).total_seconds() / 3600
-
-                        if self.dry_run:
-                            self.info(
-                                f'[DRY RUN] Would delete directory: {item_name} (age: {age_hours:.1f}h)',
-                                metadata,
-                            )
-                            directories_deleted += 1
-                        else:
-                            try:
-                                shutil.rmtree(item_path)
-                                self.info(
-                                    f'Deleted stale directory: {item_name} (age: {age_hours:.1f}h)',
-                                    metadata,
-                                )
-                                directories_deleted += 1
-                            except OSError as e:
-                                error_msg = f'Failed to delete directory {item_name}: {str(e)}'
-                                errors.append(error_msg)
-                                self.error(error_msg, metadata)
-                    else:
-                        age_hours = (datetime.now() - dir_time).total_seconds() / 3600
-                        self.debug(
-                            f'Keeping recent directory: {item_name} (age: {age_hours:.1f}h)',
-                            metadata,
-                        )
-
-                except ValueError as e:
-                    error_msg = f'Invalid timestamp format in directory {item_name}: {str(e)}'
-                    errors.append(error_msg)
-                    self.error(error_msg, metadata)
 
             self.info(
                 f'Directory cleanup completed - Scanned: {directories_scanned}, '

model_manager/activities/training.py

@@ -262,6 +262,18 @@ class Training(SientiaMonitoring):
                     train_result.r2_val
                 )
 
+            mm_metrics.SIENTIA_TRAINING_INFO.labels(
+                pod_id=labels['pod_id'],
+                model_name=train_params.model_name,
+                model_type=train_params.model_type,
+                dataset_train_rows=str(len(train_result.train_data)),
+                dataset_val_rows=str(len(train_result.val_data)),
+                feature_count=str(len(train_params.variable_columns)),
+                mse=str(train_result.mse_val) if train_result.mse_val is not None else '',
+                mae=str(train_result.mae_val) if train_result.mae_val is not None else '',
+                r2=str(train_result.r2_val) if train_result.r2_val is not None else '',
+            ).set(time.time() * 1000)
+
             self.info(f'Starting MLflow run for {train_params.model_type}', metadata)
             with self.mlflow_repository.start_run(
                 model_name=train_params.model_name,

model_manager/metrics.py

@@ -27,6 +27,22 @@ APP_UP = Gauge(
 
 _TRAINING_LABELS = ['pod_id', 'model_name', 'model_type']
 
+SIENTIA_TRAINING_INFO = Gauge(
+    'sientia_training_info',
+    'Metadata and execution timestamp (ms) of the last successful model training run',
+    [
+        'pod_id',
+        'model_name',
+        'model_type',
+        'dataset_train_rows',
+        'dataset_val_rows',
+        'feature_count',
+        'mse',
+        'mae',
+        'r2',
+    ],
+)
+
 SIENTIA_TRAINING_MODEL_TRAINED_TOTAL = Counter(
     'sientia_training_model_trained_total',
     'Number of successfully completed model training runs',

model_manager/utils/repository/data_manager_repository.py

@@ -42,15 +42,13 @@ def train_test_split(
     random_state: int | None = None,
     shuffle: bool = True,
 ) -> tuple[pd.DataFrame, pd.DataFrame]:
-    # 1. Definir a semente (seed) para reprodutibilidade
-    if random_state is not None:
-        np.random.seed(random_state)
-
     # 2. Gerar índices e embaralhar se necessário
     indices = np.arange(len(data))
 
     if shuffle:
-        np.random.shuffle(indices)
+        # 1. Generator local (em vez do estado global np.random) para reprodutibilidade
+        rng = np.random.default_rng(random_state)
+        rng.shuffle(indices)
 
     # 3. Calcular o ponto de corte (split point)
     # Cálculo: N_treino = tamanho_total * proporcao_treino

model_manager/workflows/train_model.py

@@ -136,8 +136,6 @@ class TrainModel:
                         run_dir=train_result.get('run_dir'),
                         metadata=metadata,
                     )
-                else:
-                    pass
             except Exception:  # noqa: BLE001
                 # If cleanup fails after training failed, there is nothing extra to log (DB not committed).
                 if training_succeeded:  # pragma: no branch

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "model-manager"
-version = "1.2.0"
+version = "2.0.0"
 description = "Sientia DataOps Model Manager - ML Model Orchestration System"
 readme = "README.md"
 requires-python = ">=3.11"

requirements-local.txt

@@ -3,8 +3,9 @@ psycopg2-binary==2.9.11
 sqlalchemy==2.0.49
 boto3==1.42.70
 botocore==1.42.70
-git+ssh://git@github.com/Aignosi/sientia-dataops-library.git@1.12.0
+git+ssh://git@github.com/Aignosi/sientia-dataops-library.git@1.12.2
 git+ssh://git@github.com/Aignosi/sientia-model-library.git@0.8.3
 prometheus-client==0.23.1
 beautifulsoup4==4.12.3
 evidently==0.6.7
+jsonschema==4.26.0

requirements.txt

@@ -3,7 +3,7 @@ psycopg2-binary==2.9.11
 sqlalchemy==2.0.50
 boto3==1.42.70
 botocore==1.42.70
-sientia_do>=1.11.0
+sientia_do>=1.12.2
 sientia_model>=0.8.1
 prometheus-client==0.23.1
 beautifulsoup4==4.12.3

sonar-project.properties

@@ -1,4 +1,4 @@
-sonar.projectKey=Aignosi_sientia-dataops-model-manager_dc6e051c-995a-4b57-9cde-f511018184c7
+sonar.projectKey=Aignosi_sientia-dataops-model-manager_ac6a7d4d-508d-4cbf-bd3c-1bb0d79db881
 sonar.projectName=sientia-dataops-model-manager
 sonar.sources=model_manager
 sonar.tests=tests

tests/activities/test_training.py

@@ -369,7 +369,10 @@ def test_prepare_data_increments_error_counter_and_still_observes_lag_on_failure
     training.observe_lag_sync.assert_called_once()
     training.emit_metric_sync.assert_called_once()
     call_args = training.emit_metric_sync.call_args
-    assert call_args.kwargs['metric_object'] is mm_metrics.SIENTIA_TRAINING_DATA_PREPARATION_ERROR_COUNT_TOTAL
+    assert (
+        call_args.kwargs['metric_object']
+        is mm_metrics.SIENTIA_TRAINING_DATA_PREPARATION_ERROR_COUNT_TOTAL
+    )
 
 
 def test_fit_model_observes_lag_on_success(training):
@@ -419,12 +422,16 @@ def test_fit_model_increments_error_counter_on_failure(training):
     training.observe_lag_sync.assert_called_once()
     training.emit_metric_sync.assert_called_once()
     call_args = training.emit_metric_sync.call_args
-    assert call_args.kwargs['metric_object'] is mm_metrics.SIENTIA_TRAINING_MODEL_FIT_ERROR_COUNT_TOTAL
+    assert (
+        call_args.kwargs['metric_object'] is mm_metrics.SIENTIA_TRAINING_MODEL_FIT_ERROR_COUNT_TOTAL
+    )
 
 
 @patch('model_manager.activities.training.mm_metrics')
 @patch('model_manager.activities.training.mlflow')
-def test_train_model_sets_quality_gauges_after_compute_metrics(mock_mlflow, mock_mm_metrics, training):
+def test_train_model_sets_quality_gauges_after_compute_metrics(
+    mock_mlflow, mock_mm_metrics, training
+):
     tp = TrainModelParams.from_dict(
         {**_minimal_params_dict(), 'model_metadata': {'schemas': {'components': {'schemas': {}}}}}
     )
@@ -473,9 +480,15 @@ def test_train_model_sets_quality_gauges_after_compute_metrics(mock_mlflow, mock
 
     training.train_model({'metadata': {}, 'train_params': tp.to_dict()})
 
-    mock_mm_metrics.SIENTIA_TRAINING_MODEL_QUALITY_MSE.labels.return_value.set.assert_called_once_with(0.5)
+    mock_mm_metrics.SIENTIA_TRAINING_MODEL_QUALITY_MSE.labels.return_value.set.assert_called_once_with(
-    mock_mm_metrics.SIENTIA_TRAINING_MODEL_QUALITY_MAE.labels.return_value.set.assert_called_once_with(0.3)
+        0.5
-    mock_mm_metrics.SIENTIA_TRAINING_MODEL_QUALITY_R2.labels.return_value.set.assert_called_once_with(-0.1)
+    )
+    mock_mm_metrics.SIENTIA_TRAINING_MODEL_QUALITY_MAE.labels.return_value.set.assert_called_once_with(
+        0.3
+    )
+    mock_mm_metrics.SIENTIA_TRAINING_MODEL_QUALITY_R2.labels.return_value.set.assert_called_once_with(
+        -0.1
+    )
 
 
 @patch('model_manager.activities.training.mm_metrics')

tests/test_metrics.py

@@ -237,7 +237,10 @@ def test_sientia_training_data_preparation_error_count_total_is_counter():
     from model_manager.metrics import SIENTIA_TRAINING_DATA_PREPARATION_ERROR_COUNT_TOTAL
 
     assert isinstance(SIENTIA_TRAINING_DATA_PREPARATION_ERROR_COUNT_TOTAL, Counter)
-    assert 'sientia_training_data_preparation_error_count' in SIENTIA_TRAINING_DATA_PREPARATION_ERROR_COUNT_TOTAL._name
+    assert (
+        'sientia_training_data_preparation_error_count'
+        in SIENTIA_TRAINING_DATA_PREPARATION_ERROR_COUNT_TOTAL._name
+    )
     _assert_training_labels(SIENTIA_TRAINING_DATA_PREPARATION_ERROR_COUNT_TOTAL)
 
 
@@ -257,7 +260,10 @@ def test_sientia_training_model_fit_error_count_total_is_counter():
     from model_manager.metrics import SIENTIA_TRAINING_MODEL_FIT_ERROR_COUNT_TOTAL
 
     assert isinstance(SIENTIA_TRAINING_MODEL_FIT_ERROR_COUNT_TOTAL, Counter)
-    assert 'sientia_training_model_fit_error_count' in SIENTIA_TRAINING_MODEL_FIT_ERROR_COUNT_TOTAL._name
+    assert (
+        'sientia_training_model_fit_error_count'
+        in SIENTIA_TRAINING_MODEL_FIT_ERROR_COUNT_TOTAL._name
+    )
     _assert_training_labels(SIENTIA_TRAINING_MODEL_FIT_ERROR_COUNT_TOTAL)
 
 
@@ -329,3 +335,57 @@ def test_sientia_training_feature_count_is_gauge():
     assert isinstance(SIENTIA_TRAINING_FEATURE_COUNT, Gauge)
     assert SIENTIA_TRAINING_FEATURE_COUNT._name == 'sientia_training_feature_count'
     _assert_training_labels(SIENTIA_TRAINING_FEATURE_COUNT)
+
+
+def test_sientia_training_info_is_gauge():
+    from prometheus_client import Gauge
+
+    from model_manager.metrics import SIENTIA_TRAINING_INFO
+
+    assert isinstance(SIENTIA_TRAINING_INFO, Gauge)
+    assert SIENTIA_TRAINING_INFO._name == 'sientia_training_info'
+
+    expected_labels = {
+        'pod_id',
+        'model_name',
+        'model_type',
+        'dataset_train_rows',
+        'dataset_val_rows',
+        'feature_count',
+        'mse',
+        'mae',
+        'r2',
+    }
+    assert expected_labels == set(SIENTIA_TRAINING_INFO._labelnames)
+
+
+def test_sientia_training_info_set_value():
+    import time
+
+    from model_manager.metrics import SIENTIA_TRAINING_INFO
+
+    ts = time.time() * 1000
+    SIENTIA_TRAINING_INFO.labels(
+        pod_id='test-pod',
+        model_name='my_model',
+        model_type='linear',
+        dataset_train_rows='1000',
+        dataset_val_rows='200',
+        feature_count='5',
+        mse='0.01',
+        mae='0.08',
+        r2='0.95',
+    ).set(ts)
+
+    value = SIENTIA_TRAINING_INFO.labels(
+        pod_id='test-pod',
+        model_name='my_model',
+        model_type='linear',
+        dataset_train_rows='1000',
+        dataset_val_rows='200',
+        feature_count='5',
+        mse='0.01',
+        mae='0.08',
+        r2='0.95',
+    )._value._value
+    assert abs(value - ts) < 2000

validate.sh

@@ -1,195 +0,0 @@
-#!/bin/bash
-# Model Manager Code Validation Script
-# This script runs all code quality checks before committing or deploying
-#
-# Usage:
-#   ./validate.sh              # Run all checks including tests (default)
-#   ./validate.sh --no-tests   # Skip unit tests
-#   ./validate.sh --skip-tests # Skip unit tests (alias)
-#   ./validate.sh --only-tests # Run only unit tests
-#   ./validate.sh --fix        # Auto-fix formatting and linting, then run validations (no tests)
-
-set -e  # Exit on any error
-
-# Parse command line arguments
-RUN_TESTS=true
-ONLY_TESTS=false
-FIX_MODE=false
-
-for arg in "$@"; do
-    case $arg in
-        --no-tests|--skip-tests)
-            RUN_TESTS=false
-            shift
-            ;;
-        --only-tests)
-            ONLY_TESTS=true
-            shift
-            ;;
-        --fix)
-            FIX_MODE=true
-            RUN_TESTS=false
-            shift
-            ;;
-        --help|-h)
-            echo "Usage: $0 [OPTIONS]"
-            echo ""
-            echo "Options:"
-            echo "  --no-tests, --skip-tests    Skip unit tests (default: run tests)"
-            echo "  --only-tests                Run only unit tests"
-            echo "  --fix                       Auto-fix formatting and linting, then run validations (no tests)"
-            echo "  --help, -h                  Show this help message"
-            echo ""
-            exit 0
-            ;;
-        *)
-            echo "Unknown option: $arg"
-            echo "Use --help for usage information"
-            exit 1
-            ;;
-    esac
-done
-
-# Colors for output
-RED='\033[0;31m'
-GREEN='\033[0;32m'
-YELLOW='\033[1;33m'
-BLUE='\033[0;34m'
-NC='\033[0m' # No Color
-
-echo -e "${BLUE}╔════════════════════════════════════════════════════════╗${NC}"
-echo -e "${BLUE}║  Model Manager - Code Validation Suite                ║${NC}"
-echo -e "${BLUE}╚════════════════════════════════════════════════════════╝${NC}"
-echo ""
-
-# Handle --fix mode
-if [ "$FIX_MODE" = true ]; then
-    echo -e "${BLUE}🔧 Running auto-fix mode...${NC}"
-    echo ""
-    
-    echo -e "${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}"
-    echo -e "${BLUE}▶ Auto-fixing code formatting (Ruff)${NC}"
-    echo -e "${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}"
-    ruff format model_manager/ tests/
-    echo -e "${GREEN}✅ Code formatting applied${NC}"
-    echo ""
-    
-    echo -e "${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}"
-    echo -e "${BLUE}▶ Auto-fixing linting issues (Ruff)${NC}"
-    echo -e "${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}"
-    ruff check --fix model_manager/ tests/
-    echo -e "${GREEN}✅ Linting fixes applied${NC}"
-    echo ""
-    
-    echo -e "${YELLOW}ℹ️  Now running validations (without tests)...${NC}"
-    echo ""
-fi
-
-# Handle --only-tests mode
-if [ "$ONLY_TESTS" = true ]; then
-    echo -e "${BLUE}🧪 Running only unit tests...${NC}"
-    echo ""
-fi
-
-if [ "$RUN_TESTS" = false ] && [ "$ONLY_TESTS" = false ]; then
-    echo -e "${YELLOW}ℹ️  Unit tests will be skipped${NC}"
-    echo ""
-fi
-
-# Check if virtual environment is activated
-if [[ -z "${VIRTUAL_ENV}" ]] && [[ -z "${CONDA_DEFAULT_ENV}" ]]; then
-    echo -e "${YELLOW}⚠️  Warning: No virtual environment detected${NC}"
-    echo -e "${YELLOW}   Consider activating your venv/conda environment${NC}"
-    echo ""
-fi
-
-# Function to run a validation step
-run_step() {
-    local step_name=$1
-    local step_command=$2
-    
-    echo -e "${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}"
-    echo -e "${BLUE}▶ ${step_name}${NC}"
-    echo -e "${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}"
-    
-    if eval "$step_command"; then
-        echo -e "${GREEN}✅ ${step_name} - PASSED${NC}"
-        echo ""
-        return 0
-    else
-        echo -e "${RED}❌ ${step_name} - FAILED${NC}"
-        echo ""
-        return 1
-    fi
-}
-
-# Track failures
-FAILED_STEPS=()
-
-# Handle --only-tests mode
-if [ "$ONLY_TESTS" = true ]; then
-    # Step 5: Unit Tests (pytest)
-    if ! run_step "Unit Tests (pytest)" "pytest tests/ --cov=model_manager --cov-report=term-missing --cov-report=xml --cov-report=html --cov-fail-under=80 -q"; then
-        FAILED_STEPS+=("Unit Tests")
-    fi
-else
-    # Step 1: Code Formatting Check (Ruff)
-    if ! run_step "1. Code Formatting (Ruff)" "ruff format --check model_manager/ tests/"; then
-        FAILED_STEPS+=("Code Formatting")
-    fi
-
-    # Step 2: Linting (Ruff)
-    if ! run_step "2. Code Linting (Ruff)" "ruff check model_manager/ tests/"; then
-        FAILED_STEPS+=("Linting")
-    fi
-
-    # Step 3: Type Checking (mypy)
-    if ! run_step "3. Type Checking (mypy)" "mypy model_manager/"; then
-        FAILED_STEPS+=("Type Checking")
-    fi
-
-    # Step 4: Security Analysis (Bandit)
-    if ! run_step "4. Security Analysis (Bandit)" "bandit -r model_manager/ -ll -q"; then
-        FAILED_STEPS+=("Security Analysis")
-    fi
-
-    # Step 5: Unit Tests (pytest)
-    if [ "$RUN_TESTS" = true ]; then
-        if ! run_step "5. Unit Tests (pytest)" "pytest tests/ --cov=model_manager --cov-report=term-missing --cov-report=xml --cov-report=html --cov-fail-under=80 -q"; then
-            FAILED_STEPS+=("Unit Tests")
-        fi
-    else
-        echo -e "${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}"
-        echo -e "${BLUE}▶ 5. Unit Tests (pytest)${NC}"
-        echo -e "${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}"
-        echo -e "${YELLOW}⏭️  Unit Tests - SKIPPED${NC}"
-        echo ""
-    fi
-fi
-
-# Summary
-echo -e "${BLUE}╔════════════════════════════════════════════════════════╗${NC}"
-echo -e "${BLUE}║  Validation Summary                                    ║${NC}"
-echo -e "${BLUE}╚════════════════════════════════════════════════════════╝${NC}"
-echo ""
-
-if [ ${#FAILED_STEPS[@]} -eq 0 ]; then
-    echo -e "${GREEN}✅ All validation checks passed!${NC}"
-    echo -e "${GREEN}   Your code is ready for commit/deployment.${NC}"
-    echo ""
-    exit 0
-else
-    echo -e "${RED}❌ Validation failed for the following steps:${NC}"
-    for step in "${FAILED_STEPS[@]}"; do
-        echo -e "${RED}   • ${step}${NC}"
-    done
-    echo ""
-    echo -e "${YELLOW}💡 Tips:${NC}"
-    echo -e "${YELLOW}   • Run 'ruff format model_manager/ tests/' to auto-fix formatting${NC}"
-    echo -e "${YELLOW}   • Run 'ruff check --fix model_manager/ tests/' to auto-fix linting issues${NC}"
-    echo -e "${YELLOW}   • Review mypy errors and add type hints where needed${NC}"
-    echo -e "${YELLOW}   • Check bandit warnings for security issues${NC}"
-    echo -e "${YELLOW}   • Fix failing tests or improve test coverage${NC}"
-    echo ""
-    exit 1
-fi

values.yaml

@@ -1,388 +0,0 @@
-#
-# Default values for sientia-model-manager using the sientia-module chart (0.6.x).
-# This is a YAML-formatted file.
-# Declare variables to be passed into your templates.
-#
-
-projectName: &projectName "sientia-model-manager"
-
-# -----------------------------------------------------------------------------
-# Global configuration shared by all runtimes
-# -----------------------------------------------------------------------------
-global:
-  # Namespace used by the chart.
-  namespace: sientia
-
-  # -----------------------------------------------------------------------------
-  # Image configuration (chart-level)
-  # -----------------------------------------------------------------------------
-  # The sientia-module chart allows overriding the image used by all runtimes.
-  # Per requirement, we deploy using the sientia-module image v1.0.0.
-  image:
-    repository: aignosi.azurecr.io/sientia-module
-    pullPolicy: Always
-    tag: "1.0.2"
-
-  # Common labels applied to pods (can be extended per project).
-  commonLabels: {}
-
-  # Resources inherited by all runtimes unless overridden.
-  resources: {}
-    # We usually recommend not to specify default resources and to leave this as a conscious
-    # choice for the user. This also increases chances charts run on environments with little
-    # resources, such as Minikube. If you do want to specify resources, uncomment the following
-    # lines, adjust them as necessary, and remove the curly braces after 'resources:'.
-    # limits:
-    #   cpu: 100m
-    #   memory: 128Mi
-    # requests:
-    #   cpu: 100m
-    #   memory: 128Mi
-
-  # Probes inherited by all runtimes unless overridden.
-  # More information:
-  # https://kubernetes.io/docs/tasks/configure-pod-container/configure-liveness-readiness-startup-probes/
-  livenessProbe:
-    exec:
-      command:
-        - python3
-        - -c
-        - "import requests; requests.get('http://localhost:9090/metrics')"
-    initialDelaySeconds: 20
-    periodSeconds: 30
-
-  readinessProbe:
-    exec:
-      command:
-        - python3
-        - -c
-        - "import requests; requests.get('http://localhost:9090/metrics')"
-    initialDelaySeconds: 10
-    periodSeconds: 15
-
-  # Autoscaling configuration inherited by all runtimes unless overridden.
-  # More information:
-  # https://kubernetes.io/docs/concepts/workloads/autoscaling/
-  autoscaling:
-    enabled: false
-    minReplicas: 1
-    maxReplicas: 100
-    targetCPUUtilizationPercentage: 80
-    # targetMemoryUtilizationPercentage: 80
-
-  # Environment variables shared by all runtimes.
-  env:
-    # Entrypoint variables 
-    - name: GITHUB_REPO_URL
-      value: "git@github.com:Aignosi/sientia-dataops-model-manager.git"
-    - name: GITHUB_BRANCH
-      value: "release/SIENTIAPDE-1645"
-    - name: PYTHON_APP
-      value: "model_manager.worker.worker"
-    - name: PYPI_SERVER
-      value: "http://library-distribution-server.library.svc.cluster.local:5000"
-
-    - name: POSTGRES_HOST
-      value: "paradedb-rw.paradedb.svc.cluster.local"
-    - name: POSTGRES_PORT
-      value: "5432"
-    - name: POSTGRES_USER
-      value: "postgres"
-    - name: POSTGRES_PASSWORD
-      value: "nFqc81y6kwmr2zuAIx43DhiOosFCVPpeEfTtTWZflkNjB2j1KtEeIANkhFR9mAX3"
-    - name: POSTGRES_DBNAME
-      value: "sientia-core-mlops-bff"
-    - name: POSTGRES_MIN_CONNECTIONS
-      value: "10"
-    - name: POSTGRES_MAX_CONNECTIONS
-      value: "30"
-
-    - name: MLFLOW_URL
-      value: "http://sientia-tracker-mlflow-tracking.sientia-tracker.svc.cluster.local:80"
-    - name: MLFLOW_USERNAME
-      value: "aignosi"
-    - name: MLFLOW_PASSWORD
-      value: "1L0FP50j3ncp123"
-
-    - name: LOG_LEVEL
-      value: "DEBUG"
-    - name: HTTP_METRICS_PORT
-      value: "9090"
-    - name: HTTP_SDK_METRICS_PORT
-      value: "9091"
-
-    - name: TEMPORAL_HOST
-      value: "temporal-frontend.temporal.svc.cluster.local:7233"
-    - name: TEMPORAL_NAMESPACE
-      value: "model-manager"
-    - name: TRAIN_TASK_QUEUE
-      value: "train_model-queue"
-    - name: CLEANUP_TASK_QUEUE
-      value: "cleanup-queue"
-    - name: TEMPORAL_USE_TLS
-      value: "false"
-
-    - name: STORE_BASE_URL
-      value: "http://gitea-http.gitea.svc.cluster.local:3000"
-    - name: STORE_OWNER
-      value: "aignosi"
-    - name: STORE_REPO
-      value: "suse-model-store"
-    - name: STORE_USERNAME
-      valueFrom:
-        secretKeyRef:
-          name: sientia-plugin-store-credentials
-          key: username
-    - name: STORE_PASSWORD
-      valueFrom:
-        secretKeyRef:
-          name: sientia-plugin-store-credentials
-          key: password
-    - name: STORE_CACHE_TTL_SECONDS
-      value: "3600"
-
-    - name: MONGODB_USERNAME
-      value: "root"
-    - name: MONGODB_PASSWORD
-      value: "wKZDbMNU1c"
-    - name: MONGODB_URL
-      value: "my-release-mongodb.mongodb.svc.cluster.local:27017"
-    - name: MONGODB_DATABASE
-      value: "sientia"
-    - name: MONGODB_TTL_INDEX_HOURS
-      value: "1"
-
-    - name: MINIO_ENDPOINT_URL
-      value: "http://minio.minio.svc.cluster.local:9000"
-    - name: MINIO_ACCESS_KEY
-      value: "model-training-user"
-    - name: MINIO_SECRET_KEY
-      value: "modelTrainingUser123"
-    - name: MINIO_REGION
-      value: "us-east-1"
-    - name: MINIO_SECURE
-      value: "false"
-    - name: MINIO_MAX_RETRY_ATTEMPTS
-      value: "3"
-    - name: MINIO_RETRY_MODE
-      value: "adaptive"
-    - name: MINIO_CONNECT_TIMEOUT
-      value: "10"
-    - name: MINIO_READ_TIMEOUT
-      value: "60"
-    - name: MINIO_DEFAULT_BUCKET
-      value: "model-training"
-
-    - name: TIMEOUT_VALIDATE_PARAMS
-      value: "30"
-    - name: TIMEOUT_TRAIN_MODEL
-      value: "2700"
-    - name: TIMEOUT_DELETE_FILE
-      value: "120"
-    - name: TIMEOUT_UPDATE_DATABASE
-      value: "30"
-
-    - name: CLEANUP_RETENTION_HOURS
-      value: "24"
-    - name: CLEANUP_DRY_RUN
-      value: "false"
-    - name: TIMEOUT_CLEANUP_MINIO
-      value: "300"
-    - name: TIMEOUT_CLEANUP_LOCAL
-      value: "120"
-    - name: MAX_KEYS_CLEANUP
-      value: "1000"
-    - name: DEFAULT_CLEANUP_BUCKET
-      value: "model-training"
-
-    # Cleanup Schedule Configuration
-    - name: CLEANUP_SCHEDULE_ID
-      value: "cleanup-files-daily"
-    - name: CLEANUP_CRON
-      value: "0 0 * * *"  # Midnight UTC
-    - name: CLEANUP_TIMEZONE
-      value: "UTC"
-    - name: CLEANUP_EXECUTION_TIMEOUT_HOURS
-      value: "1"
-
-# -----------------------------------------------------------------------------
-# Runtimes configuration
-# -----------------------------------------------------------------------------
-# Each runtime inherits settings from `global` (resources, env, probes, autoscaling)
-# unless overridden here.
-runtimes:
-  basic:
-    # Replicas for this runtime. Replaces the old replicaCount.
-    replicas: 1
-  xgboost:
-    replicas: 1
-
-# -----------------------------------------------------------------------------
-# Chart-level configuration (applies to all runtimes)
-# -----------------------------------------------------------------------------
-
-# This is for the secrets for pulling an image from a private repository more information can be found here: https://kubernetes.io/docs/tasks/configure-pod-container/pull-image-private-registry/
-imagePullSecrets:
-  - name: docker-hub-secret
-
-# This is to override the chart name.
-nameOverride: *projectName
-fullnameOverride: *projectName
-
-# This section builds out the service account more information can be found here: https://kubernetes.io/docs/concepts/security/service-accounts/
-serviceAccount:
-  # Specifies whether a service account should be created
-  create: true
-  # Automatically mount a ServiceAccount's API credentials?
-  automount: true
-  # Annotations to add to the service account
-  annotations: {}
-  # The name of the service account to use.
-  # If not set and create is true, a name is generated using the fullname template
-  name: *projectName
-
-# This is for setting Kubernetes Annotations to a Pod.
-# For more information checkout: https://kubernetes.io/docs/concepts/overview/working-with-objects/annotations/
-podAnnotations: {}
-# This is for setting Kubernetes Labels to a Pod.
-# For more information checkout: https://kubernetes.io/docs/concepts/overview/working-with-objects/labels/
-podLabels: {}
-
-podSecurityContext: {}
-  # fsGroup: 2000
-
-securityContext: {}
-  # capabilities:
-  #   drop:
-  #   - ALL
-  # readOnlyRootFilesystem: true
-  # runAsNonRoot: true
-  # runAsUser: 1000
-
-# Additional volumes on the output Deployment definition.
-volumes:
-  - name: model-manager-runtime
-    emptyDir:
-      sizeLimit: 1Gi
-
-# Additional volumeMounts on the output Deployment definition.
-volumeMounts:
-  - name: model-manager-runtime
-    mountPath: "/var/lib/model-manager"
-
-# Deployment strategy configuration
-# More information: https://kubernetes.io/docs/concepts/workloads/controllers/deployment/#strategy
-deploymentStrategy:
-  type: Recreate
-  # rollingUpdate:
-  #   maxSurge: 0
-  #   maxUnavailable: 1
-
-# Number of old ReplicaSets to retain
-revisionHistoryLimit: 2
-
-nodeSelector: {}
-
-tolerations: []
-
-affinity: {}
-
-services:
-  sdk-metrics:
-    enabled: true
-    type: ClusterIP
-    port: 9091
-    targetPort: 9091
-    name: sdk-metrics
-  metrics:
-    enabled: true
-    type: ClusterIP
-    port: 9090
-    targetPort: 9090
-    name: metrics
-
-# Configuração do ServiceMonitor para o Prometheus Operator
-# ref: https://github.com/prometheus-operator/prometheus-operator
-serviceMonitor:
-  # Se true, um recurso ServiceMonitor será criado.
-  enabled: true
-  # O intervalo no qual as métricas devem ser coletadas (ex: 30s, 1m).
-  endpoints:
-  - port: metrics
-    path: /metrics
-    interval: 30s
-    relabelings: []
-  - port: sdk-metrics
-    path: /metrics
-    interval: 30s
-    relabelings: []
-  additionalLabels:
-    release: kube-prometheus-stack
-
-ssh:
-  enabled: true
-  secretName: git-ssh-key-sientia-model-manager-worker
-  sshPath: /mnt/.ssh
-  knownHostsPath: /mnt/known_hosts
-
-# Configuração para dashboards do Grafana
-grafanaDashboard:
-  # Habilita a criação de ConfigMaps para dashboards
-  enabled: true
-  # Namespace onde o Grafana está instalado (ajuste conforme seu ambiente)
-  namespace: monitoring
-  # Labels para que o sidecar do Grafana encontre os dashboards
-  labels:
-    grafana_dashboard: "1"
-  # Lista de dashboards para importar
-  dashboards:
-    - name: sientia-dataops-model-manager
-      title: "Sientia DataOps Model Manager"
-      uid: "sientia-dataops-model-manager"
-      folder: "Sientia"
-      jsonFile: "dashboards/sientia-dataops-model-manager.json"
-      overwrite: true          # Sobrescreve dashboard se já existir
-      version: "1.0.0"         # Version inicial do dashboard
-
-# Configuração para datasources do Grafana
-grafanaDatasource:
-  # Habilita a criação de ConfigMap para datasources
-  enabled: false
-  # Namespace onde o Grafana está instalado
-  namespace: monitoring
-  # Labels para que o sidecar do Grafana encontre os datasources
-  labels:
-    grafana_datasource: "1"
-  # Lista de datasources para configurar
-  datasources: []
-  # Exemplo de datasource:
-  # - name: Prometheus
-  #   type: prometheus
-  #   url: http://prometheus-server.monitoring.svc.cluster.local
-  #   isDefault: true
-  #   jsonData:
-  #     timeInterval: "5s"
-
-# -----------------------------------------------------------------------------
-# Helm usage examples
-# -----------------------------------------------------------------------------
-# kubectl create secret docker-registry docker-hub-secret --namespace sientia --docker-server=http://aignosi.azurecr.io --docker-username=aignosi --docker-password=<pwd>
-#
-# helm upgrade --install sientia-model-manager sientia/sientia-module -n sientia --create-namespace -f ./values.yaml --version 0.6.1
-#
-# Global/runtimes layout note:
-# - Shared configuration lives under `global` (env, probes, autoscaling, namespace).
-# - Individual runtimes are defined under `runtimes`, each with its own `name` and `replicas`.
-# - Runtimes inherit `global` settings unless overridden at the runtime level.
-#
-# kubectl create secret generic git-ssh-key-sientia-model-manager-worker \
-#   --namespace sientia \
-#   --from-file=ssh-privatekey=git_key \
-#   --type=kubernetes.io/ssh-auth
-
-# kubectl create secret generic sientia-plugin-store-credentials \
-#   --namespace sientia \
-#   --from-literal=username=<username> \
-#   --from-literal=password=<password>
-
-