diff --git a/adapters/__init__.py b/adapters/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/adapters/__pycache__/__init__.cpython-310.pyc b/adapters/__pycache__/__init__.cpython-310.pyc deleted file mode 100644 index d9bb293639032e8034504a64dced7cf139249ebc..0000000000000000000000000000000000000000 Binary files a/adapters/__pycache__/__init__.cpython-310.pyc and /dev/null differ diff --git a/adapters/__pycache__/base.cpython-310.pyc b/adapters/__pycache__/base.cpython-310.pyc deleted file mode 100644 index aaf65acf1f8a217cd4bd8d587ba1ccb0882415d5..0000000000000000000000000000000000000000 Binary files a/adapters/__pycache__/base.cpython-310.pyc and /dev/null differ diff --git a/adapters/__pycache__/hf_adapter.cpython-310.pyc b/adapters/__pycache__/hf_adapter.cpython-310.pyc deleted file mode 100644 index 758bcc3b527bdfbf38fde4e68028803698efcdbe..0000000000000000000000000000000000000000 Binary files a/adapters/__pycache__/hf_adapter.cpython-310.pyc and /dev/null differ diff --git a/adapters/__pycache__/onnx_adapter.cpython-310.pyc b/adapters/__pycache__/onnx_adapter.cpython-310.pyc deleted file mode 100644 index 7c06e6a1f1f09945c85700de241fea7ef40caff3..0000000000000000000000000000000000000000 Binary files a/adapters/__pycache__/onnx_adapter.cpython-310.pyc and /dev/null differ diff --git a/adapters/__pycache__/roboflow_adapter.cpython-310.pyc b/adapters/__pycache__/roboflow_adapter.cpython-310.pyc deleted file mode 100644 index 0d566d82ef99ba9b8ed7838d00c9648d74efa5e2..0000000000000000000000000000000000000000 Binary files a/adapters/__pycache__/roboflow_adapter.cpython-310.pyc and /dev/null differ diff --git a/adapters/base.py b/adapters/base.py deleted file mode 100644 index a2644f64251c2da46205c9b297486df0c3d603e4..0000000000000000000000000000000000000000 --- a/adapters/base.py +++ /dev/null @@ -1,28 +0,0 @@ -""" -adapters/base.py — Abstract base class every source adapter must implement. -Enforces a stable contract so the registry never knows which adapter runs. -""" -from __future__ import annotations - -from abc import ABC, abstractmethod - -from models.model import Model - - -class BaseAdapter(ABC): - """Fetch models from an external source and normalize to the Model schema.""" - - source_name: str = "unknown" - - @abstractmethod - async def fetch_models(self) -> list[Model]: - """Return a list of normalized Model objects from the source.""" - ... - - def _format_size(self, bytes_: int) -> str: - """Human-readable file size.""" - for unit in ("B", "KB", "MB", "GB", "TB"): - if bytes_ < 1024: - return f"{bytes_:.1f} {unit}" - bytes_ //= 1024 - return f"{bytes_} PB" diff --git a/adapters/hf_adapter.py b/adapters/hf_adapter.py deleted file mode 100644 index 7dcf3232dfa9fbb35bb1f18edda53145c84147ad..0000000000000000000000000000000000000000 --- a/adapters/hf_adapter.py +++ /dev/null @@ -1,415 +0,0 @@ -""" -adapters/hf_adapter.py — Hugging Face Hub adapter. -Fetches real models via the public HF API and normalises them to our schema. - -Rate-limits respected via polite delays. Requires no authentication for -publicly accessible models; set HF_TOKEN env var for higher rate-limits. -""" -from __future__ import annotations - -import asyncio -import re -from typing import Any - - -def _is_shard_file(filename: str) -> bool: - """Return True for sharded weight files like model-00001-of-00003.safetensors.""" - return bool(re.search(r"-\d{5}-of-\d{5}\.", filename)) - -import httpx -from tenacity import retry, stop_after_attempt, wait_exponential - -from adapters.base import BaseAdapter -from config import settings -from models.model import Model, ModelMetrics, ModelVersion -from observability.logger import get_logger - -log = get_logger("hf_adapter") - -# ── Task mapping: HF pipeline_tag → our internal task ───────────────────────── -HF_TASK_MAP: dict[str, str] = { - "object-detection": "detection", - "image-classification": "classification", - "image-segmentation": "segmentation", - "text-to-image": "generation", - "image-to-image": "generation", - "image-feature-extraction": "embedding", -} - -# Tasks we actively fetch -FETCH_TASKS: list[str] = list(HF_TASK_MAP.keys()) - -# ── Framework detection ──────────────────────────────────────────────────────── -def _detect_framework(tags: list[str], model_id: str) -> str: - tag_str = " ".join(tags + [model_id]).lower() - if "onnx" in tag_str: return "onnx" - if "tflite" in tag_str: return "tflite" - if "coreml" in tag_str: return "coreml" - if "tensorflow" in tag_str or "tf" in tag_str: return "tensorflow" - return "pytorch" # HF default - -# ── Hardware detection ───────────────────────────────────────────────────────── -def _detect_hardware(tags: list[str]) -> list[str]: - hw: list[str] = [] - tag_str = " ".join(tags).lower() - if any(k in tag_str for k in ("cuda", "gpu")): hw.append("gpu") - if "edge" in tag_str or "mobile" in tag_str: hw.append("edge") - if "cpu" in tag_str: hw.append("cpu") - if not hw: hw.append("gpu") # safe default - return hw - -# ── Internal tag normalisation ───────────────────────────────────────────────── -QUALITY_TAG_MAP = { - "state-of-the-art": "sota", - "lightweight": "lightweight", - "tiny": "tiny", - "fast": "fastest", - "real-time": "real-time", - "accuracy": "high-accuracy", -} - -def _normalise_tags(raw_tags: list[str], pipeline: str) -> list[str]: - out: list[str] = [] - for t in raw_tags: - t_lower = t.lower() - for keyword, mapped in QUALITY_TAG_MAP.items(): - if keyword in t_lower: - out.append(mapped) - # keep relevant library / dataset tags - if any(t_lower.startswith(p) for p in ("dataset:", "license:", "language:")): - continue - out.append(t_lower) - # add pipeline as tag - if pipeline: - out.append(pipeline.replace("-", "_")) - return list(dict.fromkeys(out)) # deduplicate, preserve order - - -class HFAdapter(BaseAdapter): - source_name = "hf" - - def __init__(self) -> None: - headers = {"Accept": "application/json"} - if settings.hf_token: - headers["Authorization"] = f"Bearer {settings.hf_token}" - self._client = httpx.AsyncClient( - base_url=settings.hf_api_base, - headers=headers, - timeout=30, - ) - - @retry( - stop=stop_after_attempt(3), - wait=wait_exponential(multiplier=1, min=2, max=10), - reraise=True, - ) - async def _fetch_task_page( - self, pipeline_tag: str, limit: int = 100 - ) -> list[dict[str, Any]]: - params = { - "pipeline_tag": pipeline_tag, - "sort": "downloads", - "direction": -1, # descending - "limit": limit, - "full": "True", - } - log.info("hf_fetch_task", pipeline_tag=pipeline_tag, limit=limit) - resp = await self._client.get("/models", params=params) - resp.raise_for_status() - return resp.json() - - @retry( - stop=stop_after_attempt(3), - wait=wait_exponential(multiplier=1, min=2, max=10), - reraise=True, - ) - async def _fetch_model_detail(self, model_id: str) -> dict[str, Any]: - resp = await self._client.get(f"/models/{model_id}", params={"full": "True"}) - resp.raise_for_status() - raw = resp.json() - - siblings: list[dict[str, Any]] = raw.get("siblings") or [] - has_any_size = any(isinstance(s, dict) and s.get("size") for s in siblings) - if not has_any_size: - try: - tree = await self._fetch_model_tree(model_id, revision="main") - size_by_path: dict[str, int] = { - (t.get("path") or ""): int(t.get("size") or 0) - for t in (tree or []) - if isinstance(t, dict) - } - - patched: list[dict[str, Any]] = [] - for s in siblings: - if not isinstance(s, dict): - continue - fn = s.get("rfilename") or s.get("path") or "" - if fn and not s.get("size") and fn in size_by_path: - s = {**s, "size": size_by_path[fn]} - patched.append(s) - raw["siblings"] = patched - except Exception: - pass - - return raw - - @retry( - stop=stop_after_attempt(3), - wait=wait_exponential(multiplier=1, min=2, max=10), - reraise=True, - ) - async def _fetch_model_tree(self, model_id: str, *, revision: str = "main") -> list[dict[str, Any]]: - resp = await self._client.get(f"/models/{model_id}/tree/{revision}") - resp.raise_for_status() - data = resp.json() - if isinstance(data, list): - return data - return [] - - def _parse_safe_tensors_size(self, siblings: list[dict]) -> int: - """Estimate model size from sibling file list.""" - total = 0 - weight_exts = (".pt", ".pth", ".safetensors", ".bin", ".onnx", ".tflite", ".mlmodel") - for s in siblings or []: - filename = s.get("rfilename", "").lower() - if filename.endswith(weight_exts): - total += s.get("size", 0) - - if total > 0: - return total - - # If no size found in siblings, check if it's in the root dict (sometimes HF API does this) - return 0 # Return 0 if not found, we'll handle fallback in _make_model - - @retry( - stop=stop_after_attempt(3), - wait=wait_exponential(multiplier=1, min=2, max=10), - reraise=True, - ) - async def _fetch_model_card(self, model_id: str) -> str: - """Fetch model card (README.md) content for real-time description.""" - url = f"{settings.hf_hub_url}/{model_id}/raw/main/README.md" - try: - resp = await self._client.get(url) - if resp.status_code == 200: - return resp.text - except Exception: - pass - return "" - - def _extract_description(self, readme: str, raw: dict[str, Any]) -> str: - """Extract a clean description from README or card data.""" - if readme: - # Simple heuristic: take first paragraph that isn't frontmatter - lines = readme.split("\n") - in_frontmatter = False - for line in lines: - if line.strip() == "---": - in_frontmatter = not in_frontmatter - continue - if not in_frontmatter and line.strip() and not line.startswith("#"): - return line.strip()[:500] - - card_data = raw.get("cardData") or {} - description: str = ( - (card_data.get("summary") or "") - or (card_data.get("description") or "") - or (raw.get("description") or "") - ).strip() - return description - - def _estimate_metrics(self, model_id: str, task: str) -> ModelMetrics: - """ - Product-Grade Metrics Estimation. - Uses model name heuristics to provide realistic data for common architectures. - """ - metrics = ModelMetrics() - m_id = model_id.lower() - - # Base latency/vram estimates by architecture - if "vit" in m_id or "dinov2" in m_id: - metrics.latency_ms = 45.5 if "base" in m_id else 85.2 if "large" in m_id else 25.0 - metrics.vram_gb = 1.2 if "base" in m_id else 2.4 if "large" in m_id else 0.8 - metrics.accuracy = 82.4 if "base" in m_id else 84.5 - elif "segformer" in m_id: - # b0, b1, b2, b3, b4, b5 - if "b0" in m_id: metrics.latency_ms, metrics.vram_gb, metrics.accuracy = 12.0, 0.4, 35.0 - elif "b1" in m_id: metrics.latency_ms, metrics.vram_gb, metrics.accuracy = 18.0, 0.6, 40.0 - elif "b5" in m_id: metrics.latency_ms, metrics.vram_gb, metrics.accuracy = 45.0, 1.8, 50.0 - else: metrics.latency_ms, metrics.vram_gb, metrics.accuracy = 25.0, 1.0, 42.0 - elif "convnext" in m_id: - metrics.latency_ms = 15.0 if "tiny" in m_id else 30.0 - metrics.vram_gb = 0.5 if "tiny" in m_id else 1.2 - metrics.accuracy = 81.0 if "tiny" in m_id else 83.5 - elif "yolo" in m_id: - # n, s, m, l, x - if "yolov8n" in m_id: metrics.latency_ms, metrics.vram_gb, metrics.mAP = 1.5, 0.2, 37.3 - elif "yolov8s" in m_id: metrics.latency_ms, metrics.vram_gb, metrics.mAP = 2.8, 0.4, 44.9 - elif "yolov8m" in m_id: metrics.latency_ms, metrics.vram_gb, metrics.mAP = 6.2, 0.9, 50.2 - else: metrics.latency_ms, metrics.vram_gb, metrics.mAP = 10.0, 1.5, 52.0 - - # Generic task-based fallbacks if still empty - if metrics.latency_ms is None: - if task == "classification": metrics.latency_ms, metrics.accuracy = 20.0, 75.0 - elif task == "detection": metrics.latency_ms, metrics.mAP = 35.0, 45.0 - elif task == "embedding": metrics.latency_ms = 40.0 - elif task == "generation": metrics.latency_ms = 1500.0 - - return metrics - - def _make_model(self, raw: dict[str, Any], pipeline_tag: str) -> Model | None: - model_id: str = raw.get("id") or raw.get("modelId", "") - if not model_id: - return None - - task = HF_TASK_MAP.get(pipeline_tag) - if not task: - return None - tags_raw: list[str] = raw.get("tags") or [] - framework = _detect_framework(tags_raw, model_id) - hardware = _detect_hardware(tags_raw) - tags = _normalise_tags(tags_raw, pipeline_tag) - - # Size - siblings: list[dict] = raw.get("siblings") or [] - size = self._parse_safe_tensors_size(siblings) - if size == 0: - # Fallback based on model type if size not found - if "large" in model_id.lower(): size = 1_200_000_000 - elif "base" in model_id.lower(): size = 500_000_000 - elif "small" in model_id.lower() or "tiny" in model_id.lower(): size = 150_000_000 - else: size = 450_000_000 # More realistic general default than exactly 500MB - - # Provider — author part of model_id - provider = model_id.split("/")[0] if "/" in model_id else "community" - - # safe name - name = model_id.split("/")[-1] if "/" in model_id else model_id - # Clean ugly names - name = re.sub(r"[-_]+", "-", name).strip("-") - - downloads = raw.get("downloads") or 0 - likes = raw.get("likes") or 0 - - # Fabricate a sensible version from last modified - last_mod: str = raw.get("lastModified") or raw.get("createdAt") or "" - release_date = last_mod[:10] if last_mod else "2024-01-01" - sha8 = (raw.get("sha") or "main")[:8] - - # Build versions from weight files in the repo (one per distinct weight file) - weight_exts = (".pt", ".pth", ".safetensors", ".bin", ".onnx", ".tflite", ".mlmodel") - weight_files = [ - s for s in siblings - if s.get("rfilename", "").lower().endswith(weight_exts) - and not _is_shard_file(s.get("rfilename", "")) - ] - - if len(weight_files) > 1: - versions = [] - for s in weight_files[:15]: - filename = s["rfilename"] - # Detect variant from filename (n, s, m, l, x, or specific labels) - variant_label = "Stable" - fn_lower = filename.lower() - if any(x in fn_lower for x in ["-n.", "_n.", "nano"]): variant_label = "Nano" - elif any(x in fn_lower for x in ["-s.", "_s.", "small"]): variant_label = "Small" - elif any(x in fn_lower for x in ["-m.", "_m.", "medium"]): variant_label = "Medium" - elif any(x in fn_lower for x in ["-l.", "_l.", "large"]): variant_label = "Large" - elif any(x in fn_lower for x in ["-x.", "_x.", "xlarge", "huge"]): variant_label = "XLarge" - - versions.append(ModelVersion( - version=filename.replace(".", "_"), - label=variant_label, - description=f"Model variant: {filename}", - releaseDate=release_date, - changelog=None, - )) - else: - versions = [ - ModelVersion( - version=sha8, - label="Latest", - description="Primary model weight file.", - releaseDate=release_date, - changelog=None, - ) - ] - - # Description from card data - description = self._extract_description("", raw) - if not description: - description = f"{task.capitalize()} model by {provider}." - - # Metrics Estimation - metrics = self._estimate_metrics(model_id, task) - - return Model( - id = model_id.replace("/", "_").lower(), - name = name, - task = task, - framework = framework, - source = "hf", - provider = provider, - description = description, - download_url = f"https://huggingface.co/{model_id}", - size = size, - size_label = self._format_size(size), - tags = tags, - hardware = hardware, - status = "available", - downloaded = False, - downloads = downloads, - rating = min(5.0, (likes / 200) + 3.5) if likes else None, - liked = False, - metrics = metrics, - versions = versions, - ) - - async def fetch_models(self) -> list[Model]: - models: list[Model] = [] - seen_ids: set[str] = set() - - for pipeline_tag in FETCH_TASKS: - try: - raw_list = await self._fetch_task_page( - pipeline_tag, limit=settings.hf_models_per_task - ) - for idx, raw in enumerate(raw_list): - # Enrich top-N per task with full model detail so siblings include sizes. - if idx < 10: - original_id = raw.get("id") or raw.get("modelId") - if original_id: - try: - raw = await self._fetch_model_detail(original_id) - except Exception: - pass - - m = self._make_model(raw, pipeline_tag) - if m and m.id not in seen_ids: - # Try to fetch real-time description for the first 5 models of each task - if len([mod for mod in models if mod.task == m.task]) < 5: - original_id = raw.get("id") or raw.get("modelId") - if original_id: - readme = await self._fetch_model_card(original_id) - if readme: - m.description = self._extract_description(readme, raw) - - seen_ids.add(m.id) - models.append(m) - # Be polite to HF API - await asyncio.sleep(0.3) - except Exception as exc: - log.warning( - "hf_fetch_task_failed", - pipeline_tag=pipeline_tag, - error=str(exc), - ) - - log.info("hf_fetch_complete", total=len(models)) - return models - - async def __aenter__(self) -> "HFAdapter": - return self - - async def __aexit__(self, *_: Any) -> None: - await self._client.aclose() diff --git a/adapters/onnx_adapter.py b/adapters/onnx_adapter.py deleted file mode 100644 index b14d45c894997f4b8ac9f336cabfc3657e81704e..0000000000000000000000000000000000000000 --- a/adapters/onnx_adapter.py +++ /dev/null @@ -1,176 +0,0 @@ -""" -adapters/onnx_adapter.py — ONNX Model Zoo adapter. -Fetches the curated list of ONNX Zoo models from the GitHub API. -""" -from __future__ import annotations - -from typing import Any - -import httpx -from tenacity import retry, stop_after_attempt, wait_exponential - -from adapters.base import BaseAdapter -from models.model import Model, ModelMetrics, ModelVersion -from observability.logger import get_logger - -log = get_logger("onnx_adapter") - -# Curated ONNX Zoo models with metadata + download URLs (GitHub API is rate-limited without auth) -ONNX_CURATED: list[dict[str, Any]] = [ - { - "id": "onnx_resnet50", - "name": "ResNet-50", - "task": "classification", - "provider": "ONNX Zoo", - "description": "ResNet-50 v1 image classification model in ONNX format.", - "download_url": "https://github.com/onnx/models/raw/main/validated/vision/classification/resnet/model/resnet50-v2-7.onnx", - "size": 102_000_000, - "tags": ["resnet", "imagenet", "classification"], - "hardware": ["gpu", "cpu"], - "metrics": {"latency_ms": 14.2, "top1": 74.9}, - "downloads": 250_000, - "versions": [{"version": "1.0", "label": "Stable", "releaseDate": "2023-06-01"}], - }, - { - "id": "onnx_yolov8n", - "name": "YOLOv8n", - "task": "detection", - "provider": "Ultralytics", - "description": "Ultralytics YOLOv8 Nano — real-time object detection, ONNX export.", - "download_url": "https://github.com/ultralytics/yolov8/releases/download/v8.0.0/yolov8n.onnx", - "size": 6_200_000, - "tags": ["yolo", "real-time", "fastest", "edge"], - "hardware": ["gpu", "cpu", "edge"], - "metrics": {"latency_ms": 3.1, "mAP": 37.3}, - "downloads": 420_000, - "versions": [{"version": "8.0", "label": "Latest", "releaseDate": "2023-09-15"}], - }, - { - "id": "onnx_mobilenet_v3", - "name": "MobileNetV3-Large", - "task": "classification", - "provider": "Google", - "description": "MobileNetV3-Large for efficient on-device image classification.", - "download_url": "https://github.com/onnx/models/raw/main/validated/vision/classification/mobilenet/model/mobilenetv3-large-1.11.onnx", - "size": 22_000_000, - "tags": ["mobilenet", "lightweight", "edge", "efficient"], - "hardware": ["cpu", "edge"], - "metrics": {"latency_ms": 5.8, "top1": 75.2, "fps": 180}, - "downloads": 310_000, - "versions": [{"version": "3.0", "label": "Latest", "releaseDate": "2023-01-01"}], - }, - { - "id": "onnx_bert_base_uncased", - "name": "BERT-Base-Uncased", - "task": "nlp", - "provider": "Google", - "description": "BERT base model fine-tuned for NLP inference in ONNX format.", - "download_url": "https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2/resolve/main/onnx/model.onnx", - "size": 438_000_000, - "tags": ["bert", "nlp", "transformer"], - "hardware": ["gpu", "cpu"], - "metrics": {"latency_ms": 42.0}, - "downloads": 198_000, - "versions": [{"version": "1.0", "label": "Stable", "releaseDate": "2022-11-01"}], - }, - { - "id": "onnx_efficientnet_b0", - "name": "EfficientNet-B0", - "task": "classification", - "provider": "Google Brain", - "description": "EfficientNet-B0 for scalable image classification.", - "download_url": "https://github.com/onnx/models/raw/main/validated/vision/classification/efficientnet-lite/model/efficientnet-lite4-11.onnx", - "size": 20_000_000, - "tags": ["efficientnet", "efficient", "high-accuracy"], - "hardware": ["gpu", "cpu"], - "metrics": {"latency_ms": 10.4, "top1": 77.1}, - "downloads": 145_000, - "versions": [{"version": "1.0", "label": "Stable", "releaseDate": "2023-03-01"}], - }, - { - "id": "onnx_sam_vit_b", - "name": "SAM ViT-B", - "task": "segmentation", - "provider": "Meta AI", - "description": "Segment Anything Model (ViT-B) for universal image segmentation.", - "download_url": "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth", - "size": 375_000_000, - "tags": ["sam", "segmentation", "sota"], - "hardware": ["gpu"], - "metrics": {"latency_ms": 68.0}, - "downloads": 88_000, - "versions": [{"version": "1.0", "label": "Latest", "releaseDate": "2023-04-05"}], - }, - { - "id": "onnx_clip_vit_b32", - "name": "CLIP ViT-B/32", - "task": "embedding", - "provider": "OpenAI", - "description": "CLIP image + text embedding model for zero-shot classification.", - "download_url": "https://openaipublic.blob.core.windows.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba4f386/ViT-B-32.pt", - "size": 338_000_000, - "tags": ["clip", "embedding", "multimodal"], - "hardware": ["gpu", "cpu"], - "metrics": {"latency_ms": 25.0}, - "downloads": 275_000, - "versions": [{"version": "1.0", "label": "Stable", "releaseDate": "2023-01-01"}], - }, - { - "id": "onnx_whisper_tiny", - "name": "Whisper Tiny", - "task": "nlp", - "provider": "OpenAI", - "description": "Whisper Tiny speech-to-text model in ONNX format.", - "download_url": "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424930e36a852c0/tiny.pt", - "size": 39_000_000, - "tags": ["whisper", "speech", "lightweight"], - "hardware": ["cpu", "edge"], - "metrics": {"latency_ms": 100.0}, - "downloads": 167_000, - "versions": [{"version": "20231117", "label": "Latest", "releaseDate": "2023-11-17"}], - }, -] - - -class ONNXAdapter(BaseAdapter): - source_name = "onnx" - - async def fetch_models(self) -> list[Model]: - models: list[Model] = [] - for raw in ONNX_CURATED: - try: - versions = [ - ModelVersion( - version=v["version"], - label=v.get("label", "Stable"), - releaseDate=v.get("releaseDate", ""), - ) - for v in raw.get("versions", []) - ] - metrics_raw = raw.get("metrics", {}) - m = Model( - id = raw["id"], - name = raw["name"], - task = raw["task"], - framework = "onnx", - source = "onnx", - provider = raw.get("provider", "ONNX Zoo"), - description = raw.get("description", ""), - download_url = raw.get("download_url"), - size = raw.get("size", 0), - size_label = self._format_size(raw.get("size", 0)), - tags = raw.get("tags", []), - hardware = raw.get("hardware", ["gpu"]), - status = "available", - downloaded = False, - downloads = raw.get("downloads"), - rating = 4.2, - metrics = ModelMetrics(**metrics_raw), - versions = versions, - ) - models.append(m) - except Exception as exc: - log.warning("onnx_parse_failed", model_id=raw.get("id"), error=str(exc)) - - log.info("onnx_fetch_complete", total=len(models)) - return models diff --git a/adapters/roboflow_adapter.py b/adapters/roboflow_adapter.py deleted file mode 100644 index 74d4bb58fe2e0241a4c07bd80d20bea87308dcd5..0000000000000000000000000000000000000000 --- a/adapters/roboflow_adapter.py +++ /dev/null @@ -1,353 +0,0 @@ -""" -adapters/roboflow_adapter.py — Roboflow Universe API client. - -Responsibilities: - - Fetch dataset metadata (search, workspace listings, project details) - - Normalise responses → Dataset domain model - - Cache results in roboflow_cache table (TTL-aware) - - Handle pagination, rate limits, and errors robustly - -Roboflow API reference: https://docs.roboflow.com/api-reference/ -""" -from __future__ import annotations - -import hashlib -import json -import time -from typing import Any - -import httpx -from tenacity import retry, stop_after_attempt, wait_exponential - -from database.connection import get_db -from models.dataset import Dataset, DatasetFormat, DatasetSource, DatasetStatus, DatasetTask -from observability.logger import audit, get_logger - -log = get_logger("roboflow_adapter") - -_ROBOFLOW_BASE = "https://api.roboflow.com" -_UNIVERSE_BASE = "https://universe.roboflow.com" -_DEFAULT_TTL = 3600 # 1 hour - -# ── Task mapping from Roboflow annotation_type ─────────────────────────────── - -_TASK_MAP: dict[str, DatasetTask] = { - "object-detection": DatasetTask.detection, - "instance-segmentation": DatasetTask.segmentation, - "semantic-segmentation": DatasetTask.segmentation, - "classification": DatasetTask.classification, - "keypoint-detection": DatasetTask.keypoints, - "multiclass-classification": DatasetTask.classification, -} - -_FORMAT_MAP: dict[str, DatasetFormat] = { - "yolov5": DatasetFormat.yolo, - "yolov7": DatasetFormat.yolo, - "yolov8": DatasetFormat.yolo, - "yolov9": DatasetFormat.yolo, - "coco": DatasetFormat.coco, - "voc": DatasetFormat.voc, - "tfrecord": DatasetFormat.tfrecord, - "csv": DatasetFormat.csv, - "createml": DatasetFormat.json, - "multiclass": DatasetFormat.csv, -} - - -def _cache_key(parts: list[str]) -> str: - raw = "|".join(parts) - return hashlib.sha256(raw.encode()).hexdigest()[:32] - - -def _fmt_bytes(n: int) -> str: - for unit in ("B", "KB", "MB", "GB", "TB"): - if n < 1024: - return f"{n:.1f} {unit}" - n /= 1024 - return f"{n:.1f} PB" - - -# ── Cache helpers ───────────────────────────────────────────────────────────── - -async def _cache_get(key: str) -> dict[str, Any] | None: - db = await get_db() - async with db.execute( - "SELECT payload, fetched_at, ttl_secs FROM roboflow_cache WHERE cache_key = ?", - (key,), - ) as cur: - row = await cur.fetchone() - if row is None: - return None - fetched = time.mktime(time.strptime(row["fetched_at"], "%Y-%m-%d %H:%M:%S")) - if time.time() - fetched > row["ttl_secs"]: - return None # expired - return json.loads(row["payload"]) - - -async def _cache_set(key: str, payload: dict[str, Any], ttl: int = _DEFAULT_TTL) -> None: - db = await get_db() - await db.execute( - """INSERT OR REPLACE INTO roboflow_cache (cache_key, payload, ttl_secs) - VALUES (?, ?, ?)""", - (key, json.dumps(payload), ttl), - ) - await db.commit() - - -# ── HTTP client factory ─────────────────────────────────────────────────────── - -def _make_client(api_key: str) -> httpx.AsyncClient: - return httpx.AsyncClient( - base_url=_ROBOFLOW_BASE, - params={"api_key": api_key}, - timeout=30.0, - headers={"User-Agent": "MLForge/1.0"}, - ) - - -# ── Roboflow Adapter ────────────────────────────────────────────────────────── - -class RoboflowAdapter: - """ - Stateless adapter for the Roboflow API. - All methods accept api_key explicitly to support per-user keys. - """ - - # ── Search (Universe) ───────────────────────────────────────────────────── - - @staticmethod - @retry(stop=stop_after_attempt(3), wait=wait_exponential(min=1, max=8)) - async def search_datasets( - api_key: str, - query: str = "", - workspace: str | None = None, - page: int = 0, - page_size: int = 50, - ) -> list[Dataset]: - """ - Search Roboflow Universe for datasets. - Returns normalised Dataset objects. - """ - ck = _cache_key(["search", query, str(workspace), str(page), str(page_size)]) - cached = await _cache_get(ck) - if cached: - log.debug("roboflow_cache_hit", key=ck, query=query) - return [Dataset(**d) for d in cached] - - params: dict[str, Any] = { - "api_key": api_key, - "q": query or "*", - "from": page * page_size, - "size": page_size, - } - if workspace: - params["workspace"] = workspace - - async with _make_client(api_key) as client: - try: - resp = await client.get("/", params=params) - resp.raise_for_status() - data = resp.json() - except httpx.HTTPStatusError as e: - log.error("roboflow_api_error", status=e.response.status_code, query=query) - await audit("roboflow_error", {"query": query, "status": e.response.status_code}, level="error") - raise - - datasets = [] - for item in data.get("results", []): - try: - ds = RoboflowAdapter._normalise_search_result(item) - datasets.append(ds) - except Exception as exc: - log.warning("normalise_error", item_id=item.get("id"), error=str(exc)) - - await _cache_set(ck, [d.model_dump() for d in datasets]) - await audit("roboflow_search", {"query": query, "count": len(datasets)}) - return datasets - - # ── Workspace datasets listing ──────────────────────────────────────────── - - @staticmethod - @retry(stop=stop_after_attempt(3), wait=wait_exponential(min=1, max=8)) - async def list_workspace_datasets( - api_key: str, - workspace: str, - ) -> list[Dataset]: - """List all datasets in a Roboflow workspace.""" - ck = _cache_key(["workspace", workspace]) - cached = await _cache_get(ck) - if cached: - return [Dataset(**d) for d in cached] - - async with _make_client(api_key) as client: - try: - resp = await client.get(f"/{workspace}") - resp.raise_for_status() - data = resp.json() - except httpx.HTTPStatusError as e: - log.error("roboflow_workspace_error", workspace=workspace, status=e.response.status_code) - raise - - datasets = [] - for proj in data.get("workspace", {}).get("projects", []): - try: - ds = RoboflowAdapter._normalise_project(proj, workspace) - datasets.append(ds) - except Exception as exc: - log.warning("normalise_project_error", project=proj.get("id"), error=str(exc)) - - await _cache_set(ck, [d.model_dump() for d in datasets]) - return datasets - - # ── Single project detail ───────────────────────────────────────────────── - - @staticmethod - @retry(stop=stop_after_attempt(3), wait=wait_exponential(min=1, max=8)) - async def get_project( - api_key: str, - workspace: str, - project_id: str, - ) -> Dataset | None: - """Fetch full metadata for a single Roboflow project.""" - ck = _cache_key(["project", workspace, project_id]) - cached = await _cache_get(ck) - if cached: - return Dataset(**cached) - - async with _make_client(api_key) as client: - try: - resp = await client.get(f"/{workspace}/{project_id}") - resp.raise_for_status() - data = resp.json() - except httpx.HTTPStatusError as e: - if e.response.status_code == 404: - return None - raise - - proj_data = data.get("project", data) - ds = RoboflowAdapter._normalise_project(proj_data, workspace) - await _cache_set(ck, ds.model_dump()) - return ds - - # ── Download URL builder ────────────────────────────────────────────────── - - @staticmethod - async def get_download_url( - api_key: str, - workspace: str, - project_id: str, - version: int, - export_format: str = "yolov8", - ) -> str: - """ - Fetch the export download link from Roboflow for the specified format. - Uses the official Roboflow SDK to handle authentication and URL resolution. - """ - try: - from roboflow import Roboflow - rf = Roboflow(api_key=api_key) - project = rf.workspace(workspace).project(project_id) - version_obj = project.version(version) - - # The SDK's download method usually downloads to disk, - # but we can get the underlying export info. - # We'll use a thread to run the SDK call since it's blocking. - import asyncio - def _get_link(): - return version_obj.export(export_format).download_link - - link = await asyncio.to_thread(_get_link) - if not link: - raise ValueError(f"No download link returned for {workspace}/{project_id} v{version}") - return link - except Exception as e: - log.error("roboflow_sdk_error", error=str(e)) - # Fallback to manual API if SDK fails or isn't installed correctly - async with _make_client(api_key) as client: - resp = await client.get( - f"/{workspace}/{project_id}/{version}/{export_format}" - ) - resp.raise_for_status() - data = resp.json() - - link = export.get("link") or "" - if not link: - # If 'link' is missing, check if it's a Universe-style project and try to resolve manually - # Roboflow manual resolution often follows: universe.roboflow.com/ds/[id]?key=[api_key] - if "project" in data: - pid = data["project"].get("id") - if pid: - link = f"https://universe.roboflow.com/ds/{pid}?key={api_key}" - - if not link: - raise ValueError(f"No download link returned for {workspace}/{project_id} v{version}") - - # Ensure the link includes the API key correctly - if "universe.roboflow.com" in link: - if "key=" not in link: - separator = "&" if "?" in link else "?" - link = f"{link}{separator}key={api_key}" - elif f"key={api_key}" not in link: - # Replace old key if it exists but is wrong - import re - link = re.sub(r"key=[^&]+", f"key={api_key}", link) - - return link - - # ── Normalisation helpers ───────────────────────────────────────────────── - - @staticmethod - def _normalise_search_result(item: dict[str, Any]) -> Dataset: - """Map a Universe search result → Dataset.""" - ann_type = item.get("annotation", {}).get("type", "object-detection") - rf_task = _TASK_MAP.get(ann_type, DatasetTask.detection) - class_names = [c.get("name", "") for c in item.get("classes", [])] - images = item.get("images", 0) or 0 - - return Dataset( - id = item.get("id", "").replace("/", "__"), - name = item.get("name", "Unnamed"), - description = item.get("description", ""), - task = rf_task, - format = DatasetFormat.yolo, - source = DatasetSource.roboflow, - status = DatasetStatus.available, - images = images, - classes = len(class_names), - class_names = class_names, - size_bytes = 0, - size_label = "—", - tags = item.get("tags", []), - roboflow_id = item.get("id", ""), - created_at = item.get("created", ""), - updated_at = item.get("updated", ""), - ) - - @staticmethod - def _normalise_project(proj: dict[str, Any], workspace: str) -> Dataset: - """Map a workspace project → Dataset.""" - ann_type = proj.get("annotation", "object-detection") - rf_task = _TASK_MAP.get(ann_type, DatasetTask.detection) - class_names = [c.get("name", c) if isinstance(c, dict) else c - for c in proj.get("classes", [])] - project_id = proj.get("id", proj.get("name", "unknown")) - rf_id = f"{workspace}/{project_id}" - images = proj.get("images", 0) or 0 - - return Dataset( - id = rf_id.replace("/", "__"), - name = proj.get("name", project_id), - description = proj.get("description", ""), - task = rf_task, - format = DatasetFormat.yolo, - source = DatasetSource.roboflow, - status = DatasetStatus.available, - images = images, - classes = len(class_names), - class_names = class_names, - size_bytes = 0, - size_label = "—", - roboflow_id = rf_id, - created_at = proj.get("created", ""), - updated_at = proj.get("updated", ""), - ) diff --git a/api/routes/benchmark.py b/api/routes/benchmark.py deleted file mode 100644 index 63d8a1b9b078bafdc4e152c01cf823c4c7f85db1..0000000000000000000000000000000000000000 --- a/api/routes/benchmark.py +++ /dev/null @@ -1,238 +0,0 @@ -""" -api/routes/benchmark.py — Benchmark Bridge REST + WebSocket API. - -Routes: - POST /benchmark/validate — compatibility check (no job created) - POST /benchmark/run — validate + create + enqueue (202) - GET /benchmark/jobs — list jobs (filterable) - GET /benchmark/results/all — list all results - GET /benchmark/{job_id} — single job status + logs - GET /benchmark/{job_id}/result — metrics + telemetry for completed job - WS /benchmark/live/{job_id} — real-time progress stream -""" -from __future__ import annotations - -import asyncio -from typing import Any - -from fastapi import APIRouter, HTTPException, Query, WebSocket, WebSocketDisconnect - -import benchmark.orchestrator as orchestrator -import benchmark.registry as bench_reg -from models.benchmark import ( - BenchmarkContext, - BenchmarkJob, - BenchmarkResult, - BenchmarkRunResponse, - ValidationReport, -) -from observability.logger import get_logger - -log = get_logger("api.benchmark") - -router = APIRouter(prefix="/benchmark", tags=["benchmark"]) - - -# ── POST /benchmark/validate ────────────────────────────────────────────────── - -@router.post( - "/validate", - response_model = ValidationReport, - summary = "Validate model ↔ dataset compatibility", - description = ( - "Runs all 5 compatibility gates (task, format, framework×hardware, " - "VRAM, precision) and returns a structured report. " - "Does NOT create a benchmark job." - ), -) -async def validate_benchmark(ctx: BenchmarkContext) -> ValidationReport: - try: - return await orchestrator.validate_context(ctx) - except HTTPException: - raise - except Exception as exc: - log.exception("validate_error") - raise HTTPException(status_code=500, detail=str(exc)) from exc - - -# ── POST /benchmark/run ─────────────────────────────────────────────────────── - -@router.post( - "/run", - response_model = BenchmarkRunResponse, - status_code = 202, - summary = "Start a benchmark run", - description = ( - "Validates compatibility, creates a benchmark job, and starts async " - "execution. Returns job_id immediately — poll GET /benchmark/{job_id} " - "or connect to WS /benchmark/live/{job_id} for progress." - ), -) -async def run_benchmark(ctx: BenchmarkContext) -> BenchmarkRunResponse: - try: - job = await orchestrator.create_and_run(ctx) - return BenchmarkRunResponse( - job_id = job.id, - status = job.status, - message = f"Benchmark job {job.id} queued — connect to /benchmark/live/{job.id} for live telemetry", - ) - except HTTPException: - raise - except Exception as exc: - log.exception("run_benchmark_error") - raise HTTPException(status_code=500, detail=str(exc)) from exc - - -# ── POST /benchmark/sync ────────────────────────────────────────────────────────── - -@router.post( - "/sync", - summary = "Sync benchmarks from active project folder", - description = "Scans the active project's 'benchmarks' folder and ensures all JSON records are indexed in SQLite.", -) -async def sync_benchmarks() -> dict[str, Any]: - try: - count = await orchestrator.sync_project_benchmarks() - return {"status": "success", "count": count} - except Exception as exc: - log.exception("sync_error") - raise HTTPException(status_code=500, detail=str(exc)) from exc - - -# ── GET /benchmark/jobs ─────────────────────────────────────────────────────── - -@router.get( - "/jobs", - response_model = list[BenchmarkJob], - summary = "List benchmark jobs", -) -async def list_jobs( - status: str | None = Query(None, description="Filter by status (queued|running|completed|failed)"), - model_id: str | None = Query(None, description="Filter by model_id"), - limit: int = Query(100, ge=1, le=500), -) -> list[BenchmarkJob]: - return await bench_reg.list_jobs(status=status, model_id=model_id, limit=limit) - - -# ── GET /benchmark/results/all ──────────────────────────────────────────────── -# Must be declared BEFORE /{job_id} to avoid "results" being treated as a job_id - -@router.get( - "/results/all", - response_model = list[BenchmarkResult], - summary = "List all benchmark results (leaderboard feed)", -) -async def list_results( - limit: int = Query(100, ge=1, le=500), -) -> list[BenchmarkResult]: - return await bench_reg.list_results(limit=limit) - - -# ── GET /benchmark/{job_id} ─────────────────────────────────────────────────── - -@router.get( - "/{job_id}", - response_model = BenchmarkJob, - summary = "Get benchmark job status + logs", -) -async def get_job(job_id: str) -> BenchmarkJob: - job = await bench_reg.get_job(job_id) - if not job: - raise HTTPException(status_code=404, detail=f"Job '{job_id}' not found") - return job - - -# ── GET /benchmark/{job_id}/result ─────────────────────────────────────────── - -@router.get( - "/{job_id}/result", - response_model = BenchmarkResult, - summary = "Get final metrics + telemetry for a completed job", -) -async def get_result(job_id: str) -> BenchmarkResult: - result = await bench_reg.get_result(job_id) - if not result: - raise HTTPException( - status_code = 404, - detail = f"No result for job '{job_id}' — job may still be running", - ) - return result - - -# ── WS /benchmark/live/{job_id} ─────────────────────────────────────────────── - -@router.websocket("/live/{job_id}") -async def live_telemetry(websocket: WebSocket, job_id: str) -> None: - """ - WebSocket stream for real-time benchmark progress. - Streams incremental logs and high-frequency telemetry. - """ - await websocket.accept() - log.info("ws_connected", job_id=job_id) - - last_log_idx = 0 - - try: - while True: - job = await bench_reg.get_job(job_id) - - if not job: - await websocket.send_json( - {"error": f"Job '{job_id}' not found", "job_id": job_id} - ) - break - - # Only send new logs - new_logs = job.logs[last_log_idx:] - last_log_idx = len(job.logs) - - payload: dict[str, Any] = { - "job_id": job.id, - "status": job.status, - "progress": round(job.progress, 4), - "logs": new_logs, - "telemetry": job.last_telemetry.model_dump() if job.last_telemetry else None, - } - # Explicitly include detections for the UI visualizer if they exist - if job.last_telemetry and hasattr(job.last_telemetry, "detections"): - payload["detections"] = job.last_telemetry.detections - - await websocket.send_json(payload) - - if job.status == "completed": - result = await bench_reg.get_result(job_id) - if result: - await websocket.send_json( - { - "job_id": job_id, - "status": "completed", - "result": result.model_dump(), - } - ) - break - - if job.status == "failed": - await websocket.send_json( - { - "job_id": job_id, - "status": "failed", - "error": job.error or "Unknown error", - } - ) - break - - await asyncio.sleep(0.5) # poll at 2 Hz - - except WebSocketDisconnect: - log.info("ws_disconnected", job_id=job_id) - except Exception as exc: - log.exception("ws_error", job_id=job_id) - try: - await websocket.send_json({"error": str(exc), "job_id": job_id}) - except Exception: - pass - finally: - try: - await websocket.close() - except Exception: - pass diff --git a/api/routes/inference.py b/api/routes/inference.py deleted file mode 100644 index 9cb7039a84aa747d69a63ece340e94c4885b47af..0000000000000000000000000000000000000000 --- a/api/routes/inference.py +++ /dev/null @@ -1,168 +0,0 @@ -""" -api/routes/inference.py — Inference Engine endpoints. - -POST /inference/run — single synchronous inference pass -POST /inference/stream — SSE stream (stage-by-stage pipeline events) -GET /inference/history — session ledger -DELETE /inference/history — clear session ledger -GET /inference/cache — currently warm models in memory -DELETE /inference/cache/{model_id} — evict from cache -""" -from __future__ import annotations - -import asyncio -import json -import time - -from fastapi import APIRouter, HTTPException, Response -from fastapi.responses import StreamingResponse - -from inference.engine import InferenceEngine, evict_model, get_cache_status -from inference.session import clear_history, get_history, record -from models.inference import ( - InferenceHistoryEntry, - InferenceRequest, - InferenceResult, -) -from observability.logger import get_logger -from registry.registry import get_model - -log = get_logger("api.inference") -router = APIRouter(prefix="/inference", tags=["inference"]) - -_engine = InferenceEngine() - - -# ── Single run ─────────────────────────────────────────────────────────────── - -@router.post("/run", response_model=InferenceResult) -async def run_inference(body: InferenceRequest) -> InferenceResult: - """Execute one full inference pass and return the complete result.""" - model = await get_model(body.model_id) - if not model: - raise HTTPException(status_code=404, detail=f"Model '{body.model_id}' not found") - - result = await _engine.run(body, model) - - if result.status == "error": - raise HTTPException(status_code=500, detail=result.error or "Inference failed") - - await record(body, result, model.name) - return result - - -# ── SSE stream ─────────────────────────────────────────────────────────────── - -@router.post("/stream") -async def stream_inference(body: InferenceRequest) -> StreamingResponse: - """ - Server-Sent Events stream. - Emits one JSON event per pipeline stage as it completes, then a final - 'done' event with the full InferenceResult. - - Client usage: - const es = new EventSource('/inference/stream'); // POST via fetch + EventSource polyfill - es.onmessage = e => console.log(JSON.parse(e.data)); - """ - model = await get_model(body.model_id) - if not model: - raise HTTPException(status_code=404, detail=f"Model '{body.model_id}' not found") - - queue: asyncio.Queue[str | None] = asyncio.Queue() - - async def _producer() -> None: - """Run inference while pushing SSE events into the queue.""" - try: - # Patch engine to emit stage events - result = await _engine_stream(body, model, queue) - await record(body, result, model.name) - # Final complete event - await queue.put( - f"event: done\ndata: {result.model_dump_json()}\n\n" - ) - except Exception as exc: - await queue.put( - f"event: error\ndata: {json.dumps({'error': str(exc)})}\n\n" - ) - finally: - await queue.put(None) # sentinel - - asyncio.create_task(_producer()) - - async def _generator(): - while True: - msg = await queue.get() - if msg is None: - break - yield msg - - return StreamingResponse( - _generator(), - media_type="text/event-stream", - headers={ - "Cache-Control": "no-cache", - "X-Accel-Buffering": "no", - }, - ) - - -async def _engine_stream( - req: InferenceRequest, - model, - queue: asyncio.Queue, -) -> InferenceResult: - """ - Run inference and push a 'stage' SSE event for each PipelineStage. - Falls back to a simple full run if streaming is not distinguishable. - """ - # Run full pipeline - result = await _engine.run(req, model) - - # Emit one event per stage (replay after completion) - for stage in result.pipeline: - payload = json.dumps({ - "type": "stage", - "stage": stage.model_dump(), - "ts": time.time(), - }) - await queue.put(f"data: {payload}\n\n") - await asyncio.sleep(0) # yield - - # Emit vitals snapshot - vitals_payload = json.dumps({ - "type": "vitals", - "latency_ms": result.inference_ms, - "total_ms": result.total_ms, - "quality": result.quality_score, - }) - await queue.put(f"data: {vitals_payload}\n\n") - - return result - - -# ── History ────────────────────────────────────────────────────────────────── - -@router.get("/history", response_model=list[InferenceHistoryEntry]) -async def inference_history(limit: int = 50) -> list[InferenceHistoryEntry]: - return await get_history(limit=min(limit, 200)) - - -@router.delete("/history", status_code=204, response_model=None) -async def clear_inference_history(): - await clear_history() - return Response(status_code=204) - - -# ── Model cache ────────────────────────────────────────────────────────────── - -@router.get("/cache") -async def cache_status() -> dict[str, bool]: - return get_cache_status() - - -@router.delete("/cache/{model_id}", status_code=204, response_model=None) -async def evict_from_cache(model_id: str): - evicted = evict_model(model_id) - if not evicted: - raise HTTPException(status_code=404, detail="Model not in cache") - return Response(status_code=204) diff --git a/api/routes/jobs.py b/api/routes/jobs.py deleted file mode 100644 index 065f0e1ef1f075ec8d097ee56613eb0e0a5a3346..0000000000000000000000000000000000000000 --- a/api/routes/jobs.py +++ /dev/null @@ -1,56 +0,0 @@ -""" -api/routes/jobs.py — /jobs & /download endpoints. -""" -from __future__ import annotations - -from fastapi import APIRouter, HTTPException - -from download.manager import cancel_job, enqueue_download, get_job, list_jobs -from models.job import Job, JobCreate -from observability.logger import audit, get_logger -from registry.registry import get_model - -log = get_logger("api.jobs") -router = APIRouter(tags=["jobs"]) - - -@router.post("/download", response_model=Job, status_code=202) -async def trigger_download(body: JobCreate) -> Job: - """Enqueue a model download. Returns the created job immediately.""" - model = await get_model(body.model_id) - if not model: - raise HTTPException(status_code=404, detail=f"Model '{body.model_id}' not found") - if model.downloaded: - raise HTTPException(status_code=409, detail="Model is already cached locally") - - job_id = await enqueue_download( - model_id=body.model_id, - model_name=body.model_name, - version=body.version, - ) - job = await get_job(job_id) - if not job: - raise HTTPException(status_code=500, detail="Job creation failed") - - await audit("api_download_trigger", model_id=body.model_id, job_id=job_id) - return job - - -@router.get("/jobs", response_model=list[Job]) -async def jobs_list(status: str | None = None, limit: int = 50) -> list[Job]: - return await list_jobs(status=status, limit=limit) - - -@router.get("/jobs/{job_id}", response_model=Job) -async def job_detail(job_id: str) -> Job: - job = await get_job(job_id) - if not job: - raise HTTPException(status_code=404, detail=f"Job '{job_id}' not found") - return job - - -@router.delete("/jobs/{job_id}", status_code=204, response_model=None) -async def job_cancel(job_id: str) -> None: - success = await cancel_job(job_id) - if not success: - raise HTTPException(status_code=409, detail="Job cannot be cancelled") diff --git a/api/routes/system.py b/api/routes/system.py deleted file mode 100644 index 59191be55c260250f7ba18a2dd4fbf17859908e5..0000000000000000000000000000000000000000 --- a/api/routes/system.py +++ /dev/null @@ -1,97 +0,0 @@ -"""api/routes/system.py — System metrics endpoints.""" - -from __future__ import annotations - -import asyncio -import json - -from fastapi import APIRouter, Query -from fastapi.responses import StreamingResponse - -from models.system import SystemMetrics -from system.metrics import sample_metrics - -router = APIRouter(prefix="/system", tags=["system"]) - - -@router.get("/metrics", response_model=SystemMetrics) -async def get_metrics(gpu_index: int = Query(0, ge=0)) -> SystemMetrics: - payload = sample_metrics(gpu_index=gpu_index) - return SystemMetrics( - ts=payload["ts"], - cpu_pct=payload["cpu_pct"], - cpu_model=payload.get("cpu_model"), - cpu_freq_mhz=payload.get("cpu_freq_mhz"), - cpu_count=payload.get("cpu_count"), - ram_used_mb=payload["ram_used_mb"], - ram_total_mb=payload["ram_total_mb"], - gpu=payload.get("gpu"), - disks=payload.get("disks", []), - network=payload.get("network", []), - ) - - -@router.get("/metrics/stream") -async def stream_metrics( - gpu_index: int = Query(0, ge=0), - hz: float = Query(2.0, ge=0.2, le=20.0), -): - """Server-Sent Events stream of system metrics.""" - - interval = 1.0 / float(hz) - - async def gen(): - # Initial comment helps some proxies establish the stream - yield ": connected\n\n" - while True: - try: - payload = sample_metrics(gpu_index=gpu_index) - # Ensure the payload is valid JSON and wrapped in data: format - data = json.dumps(payload) - yield f"data: {data}\n\n" - except Exception as e: - # Log error but keep stream alive - print(f"Metrics streaming error: {e}") - await asyncio.sleep(interval) - - return StreamingResponse( - gen(), - media_type="text/event-stream", - headers={ - "Cache-Control": "no-cache", - "X-Accel-Buffering": "no", - "Connection": "keep-alive", - "Transfer-Encoding": "chunked", - }, - ) - - -@router.get("/logs/stream") -async def stream_system_logs(): - """SSE stream of global system and gateway logs.""" - from observability.logger import _sys_log_subs - - q: asyncio.Queue = asyncio.Queue() - _sys_log_subs.append(q) - - async def generator(): - yield ": connected\n\n" - try: - while True: - try: - entry = await asyncio.wait_for(q.get(), timeout=30.0) - except asyncio.TimeoutError: - yield ": heartbeat\n\n" - continue - if entry is None: - break - yield f"data: {json.dumps(entry)}\n\n" - finally: - if q in _sys_log_subs: - _sys_log_subs.remove(q) - - return StreamingResponse( - generator(), - media_type="text/event-stream", - headers={"Cache-Control": "no-cache", "X-Accel-Buffering": "no"}, - ) diff --git a/api/routes/training.py b/api/routes/training.py deleted file mode 100644 index b616144c0564c5902d14df56895522073e84d717..0000000000000000000000000000000000000000 --- a/api/routes/training.py +++ /dev/null @@ -1,428 +0,0 @@ -""" -api/routes/training.py — Training Engine REST + SSE endpoints. - -POST /train/start — create and launch a training run -POST /train/stop — cancel a running run -POST /train/pause — pause a running run -POST /train/resume — resume a paused run -GET /train/status — run status + progress snapshot -GET /train/runs — list all runs -GET /train/runs/{run_id} — single run detail -GET /train/schema — UI schema for task/model/dataset combo -GET /train/checkpoints — checkpoints for a run (stub) -POST /train/checkpoints/{id}/export — export a checkpoint (stub) -GET /train/metrics/stream — SSE: real-time metrics ticks -GET /train/logs/stream — SSE: real-time log entries -GET /train/resources/stream — SSE: real-time resource ticks -""" -from __future__ import annotations - -import asyncio -import json -import time -import os - -from fastapi import APIRouter, HTTPException, Query -from fastapi.responses import StreamingResponse - -from observability.logger import get_logger -from training import run_manager -from training.schema_engine import generate_schema -from training.schemas import ( - CheckpointOut, - PauseTrainRequest, - ResumeTrainRequest, - StartTrainRequest, - StartTrainResponse, - StopTrainRequest, - TrainRunOut, - TrainStatusResponse, - TrainingSchemaResponse, -) - -log = get_logger("api.training") -router = APIRouter(prefix="/train", tags=["training"]) - -# ── Helpers ──────────────────────────────────────────────────────────────────── - -def _format_duration(seconds: float) -> str: - h = int(seconds // 3600) - m = int((seconds % 3600) // 60) - s = int(seconds % 60) - return f"{h}h {m}m {s}s" - - -def _run_to_out(run: run_manager.TrainRun) -> TrainRunOut: - elapsed = (run.completed_at or time.time()) - run.created_at - return TrainRunOut( - id=run.run_id, - run_number=run.run_number, - model_id=run.model_id, - model_name=run.model_name, - dataset_id=run.dataset_id, - dataset_name=run.dataset_name, - task=run.task, - status=run.status, - epochs_done=run.epoch, - total_epochs=run.total_epochs, - best_metric=run.best_metric, - final_loss=run.final_loss, - duration=_format_duration(elapsed), - created_at=run.created_at, - completed_at=run.completed_at, - hyperparams=run.hyperparams, - ) - - -# ── Control endpoints ───────────────────────────────────────────────────────── - -@router.post("/start", response_model=StartTrainResponse) -async def start_training(body: StartTrainRequest) -> StartTrainResponse: - """Create and immediately launch a training run.""" - # Resolve friendly names (fall back to ids if registries unavailable) - model_name = body.model_id - dataset_name = body.dataset_id - try: - from registry.registry import get_model - m = await get_model(body.model_id) - if m: - model_name = m.name - except Exception: - pass - try: - from datasets.registry import get_dataset - d = await get_dataset(body.dataset_id) - if d: - dataset_name = d.get("name", body.dataset_id) if isinstance(d, dict) else getattr(d, "name", body.dataset_id) - except Exception: - pass - - run = run_manager.create_run( - model_id=body.model_id, - model_name=model_name, - dataset_id=body.dataset_id, - dataset_name=dataset_name, - task=body.task, - hyperparams=body.hyperparams, - augmentation=body.augmentation, - scheduler=body.scheduler, - project_id=body.project_id - ) - run_manager.start_run(run) - - log.info("training_started", run_id=run.run_id, model=body.model_id) - return StartTrainResponse( - run_id=run.run_id, - status=run.status, - message=f"Training run {run.run_id} started.", - ) - - -@router.post("/stop", status_code=200) -async def stop_training(body: StopTrainRequest) -> dict: - run = run_manager.get_run(body.run_id) - if not run: - raise HTTPException(status_code=404, detail=f"Run '{body.run_id}' not found") - run_manager.stop_run(run) - log.info("training_stopped", run_id=body.run_id) - return {"run_id": body.run_id, "status": run.status} - - -@router.post("/pause", status_code=200) -async def pause_training(body: PauseTrainRequest) -> dict: - run = run_manager.get_run(body.run_id) - if not run: - raise HTTPException(status_code=404, detail=f"Run '{body.run_id}' not found") - run_manager.pause_run(run) - return {"run_id": body.run_id, "status": run.status} - - -@router.post("/resume", status_code=200) -async def resume_training(body: ResumeTrainRequest) -> dict: - run = run_manager.get_run(body.run_id) - if not run: - raise HTTPException(status_code=404, detail=f"Run '{body.run_id}' not found") - run_manager.resume_run(run) - return {"run_id": body.run_id, "status": run.status} - - -@router.get("/status", response_model=TrainStatusResponse) -async def get_train_status(run_id: str = Query(...)) -> TrainStatusResponse: - run = run_manager.get_run(run_id) - if not run: - raise HTTPException(status_code=404, detail=f"Run '{run_id}' not found") - return TrainStatusResponse( - run_id=run.run_id, - status=run.status, - epoch=run.epoch, - total_epochs=run.total_epochs, - step=run.step, - total_steps=run.total_epochs * 100, - eta_seconds=run.eta_seconds, - elapsed_seconds=run.elapsed_seconds, - ) - - -# ── Run history ─────────────────────────────────────────────────────────────── - -@router.get("/runs", response_model=list[TrainRunOut]) -async def list_runs() -> list[TrainRunOut]: - return [_run_to_out(r) for r in reversed(run_manager.list_runs())] - - -@router.get("/runs/{run_id}", response_model=TrainRunOut) -async def get_run(run_id: str) -> TrainRunOut: - run = run_manager.get_run(run_id) - if not run: - raise HTTPException(status_code=404, detail=f"Run '{run_id}' not found") - return _run_to_out(run) - - -# ── Schema Engine ───────────────────────────────────────────────────────────── - -@router.get("/schema", response_model=TrainingSchemaResponse) -async def get_schema( - model_id: str = Query(""), - dataset_id: str = Query(""), - task: str = Query("detection"), -) -> TrainingSchemaResponse: - schema = generate_schema(task=task, model_id=model_id, dataset_id=dataset_id) - return TrainingSchemaResponse(**schema) - - -# ── Checkpoints (stub — extend when artifact storage is wired) ──────────────── - -@router.get("/checkpoints", response_model=list[CheckpointOut]) -async def list_checkpoints(run_id: str = Query(...)) -> list[CheckpointOut]: - """Returns an empty list until checkpoint persistence is implemented.""" - run = run_manager.get_run(run_id) - if not run: - raise HTTPException(status_code=404, detail=f"Run '{run_id}' not found") - return [] - - -@router.post("/checkpoints/{checkpoint_id}/export") -async def export_checkpoint(checkpoint_id: str, body: dict = {}) -> dict: - raise HTTPException(status_code=501, detail="Checkpoint export not yet implemented") - - -# ── SSE: Metrics stream ──────────────────────────────────────────────────────── - -@router.get("/metrics/stream") -async def stream_metrics(run_id: str = Query(...)) -> StreamingResponse: - """ - Server-Sent Events stream of TrainMetricsTick objects. - Connects to the run's metrics queue and forwards each tick as SSE. - Stream closes when the run finishes (sentinel None pushed by worker). - """ - run = run_manager.get_run(run_id) - if not run: - raise HTTPException(status_code=404, detail=f"Run '{run_id}' not found") - - q: asyncio.Queue = asyncio.Queue() - run.metrics_subs.append(q) - - async def generator(): - yield ": connected\n\n" - try: - while True: - try: - tick = await asyncio.wait_for(q.get(), timeout=30.0) - except asyncio.TimeoutError: - # Heartbeat to keep connection alive - yield ": heartbeat\n\n" - continue - if tick is None: - break - yield f"data: {json.dumps(tick)}\n\n" - finally: - if q in run.metrics_subs: - run.metrics_subs.remove(q) - - return StreamingResponse( - generator(), - media_type="text/event-stream", - headers={"Cache-Control": "no-cache", "X-Accel-Buffering": "no"}, - ) - - -# ── SSE: Logs stream ────────────────────────────────────────────────────────── - -@router.get("/logs/stream") -async def stream_logs(run_id: str = Query(...)) -> StreamingResponse: - """Server-Sent Events stream of LogEntry objects.""" - run = run_manager.get_run(run_id) - if not run: - raise HTTPException(status_code=404, detail=f"Run '{run_id}' not found") - - q: asyncio.Queue = asyncio.Queue() - run.log_subs.append(q) - - async def generator(): - yield ": connected\n\n" - try: - while True: - try: - entry = await asyncio.wait_for(q.get(), timeout=30.0) - except asyncio.TimeoutError: - yield ": heartbeat\n\n" - continue - if entry is None: - break - yield f"data: {json.dumps(entry)}\n\n" - finally: - if q in run.log_subs: - run.log_subs.remove(q) - - return StreamingResponse( - generator(), - media_type="text/event-stream", - headers={"Cache-Control": "no-cache", "X-Accel-Buffering": "no"}, - ) - - -@router.get("/runs/{run_id}/history") -async def get_run_history(run_id: str) -> list[dict]: - """Retrieves the full historical telemetry (metrics ticks) for a run.""" - run = run_manager.get_run(run_id) - if not run: - raise HTTPException(status_code=404, detail=f"Run '{run_id}' not found") - - from training.persistence import TrainingPersistence - run_dir = await TrainingPersistence.get_run_dir(run.project_id or "default", run_id) - telemetry_path = os.path.join(run_dir, "telemetry.jsonl") - - history = [] - if os.path.exists(telemetry_path): - try: - with open(telemetry_path, "r") as f: - for line in f: - if line.strip(): - history.append(json.loads(line)) - except Exception as e: - log.error("history_read_failed", run_id=run_id, error=str(e)) - raise HTTPException(status_code=500, detail="Failed to read telemetry history") - - return history - -@router.get("/runs/{run_id}/artifacts") -async def list_run_artifacts(run_id: str) -> dict: - """Lists available artifacts (images) for a specific run by scanning the directory.""" - run = run_manager.get_run(run_id) - if not run: - raise HTTPException(status_code=404, detail=f"Run '{run_id}' not found") - - from training.persistence import TrainingPersistence - run_dir = await TrainingPersistence.get_run_dir(run.project_id or "default", run_id) - - if not os.path.exists(run_dir): - return {"artifacts": [], "batches": []} - - artifacts = [] - batches = [] - - # Standard YOLO artifact mappings for better UI titles - titles = { - "confusion_matrix.png": "Confusion Matrix", - "confusion_matrix_normalized.png": "Confusion Matrix (Norm)", - "results.png": "Results Summary", - "F1_curve.png": "F1 Curve", - "PR_curve.png": "PR Curve", - "P_curve.png": "Precision Curve", - "R_curve.png": "Recall Curve", - "BoxF1_curve.png": "Box F1 Curve", - "BoxP_curve.png": "Box Precision Curve", - "BoxPR_curve.png": "Box PR Curve", - "BoxR_curve.png": "Box Recall Curve", - "labels.jpg": "Labels Distribution", - "labels_correlogram.jpg": "Labels Correlogram" - } - - for f in os.listdir(run_dir): - path = f"/train/runs/{run_id}/files/{f}" - if f.endswith(('.png', '.jpg', '.jpeg')): - item = { - "title": titles.get(f, f.replace('_', ' ').title().split('.')[0]), - "path": path, - "type": "Analysis" - } - - if "batch" in f.lower(): - item["type"] = "Batch Preview" if "val" in f.lower() else "Augmentation" - batches.append(item) - else: - if "curve" in f.lower(): - item["type"] = "Precision-Recall" - elif "confusion" in f.lower(): - item["type"] = "Analysis" - elif "results" in f.lower(): - item["type"] = "Overall" - artifacts.append(item) - - return { - "artifacts": sorted(artifacts, key=lambda x: x['title']), - "batches": sorted(batches, key=lambda x: x['title']) - } - -@router.get("/runs/{run_id}/files/{filename}") -async def get_run_file(run_id: str, filename: str): - """Serves a specific file from the run directory.""" - run = run_manager.get_run(run_id) - if not run: - raise HTTPException(status_code=404, detail="Run not found") - - # We need to find the project to get the run_dir - # Since run_manager doesn't easily expose the full path in memory, - # we recalculate it using persistence - from training.persistence import TrainingPersistence - run_dir = await TrainingPersistence.get_run_dir(run.project_id or "default", run_id) - file_path = os.path.join(run_dir, filename) - - if not os.path.exists(file_path): - raise HTTPException(status_code=404, detail="File not found") - - from fastapi.responses import FileResponse - return FileResponse(file_path) -# The frontend uses /system/metrics/stream for resources (already implemented). -# This alias exists for training-scoped resource monitoring. - -@router.get("/resources/stream") -async def stream_resources( - run_id: str = Query(...), - gpu_index: int = Query(0, ge=0), - hz: float = Query(1.0, ge=0.2, le=10.0), -) -> StreamingResponse: - """ - SSE stream of ResourceTick objects for a specific training run. - Forwards system metrics at the requested hz rate. - """ - run = run_manager.get_run(run_id) - if not run: - raise HTTPException(status_code=404, detail=f"Run '{run_id}' not found") - - q: asyncio.Queue = asyncio.Queue() - run.resource_subs.append(q) - - interval = 1.0 / hz - - async def generator(): - yield ": connected\n\n" - try: - while True: - try: - tick = await asyncio.wait_for(q.get(), timeout=30.0) - except asyncio.TimeoutError: - yield ": heartbeat\n\n" - continue - if tick is None: - break - yield f"data: {json.dumps(tick)}\n\n" - finally: - if q in run.resource_subs: - run.resource_subs.remove(q) - - return StreamingResponse( - generator(), - media_type="text/event-stream", - headers={"Cache-Control": "no-cache", "X-Accel-Buffering": "no"}, - ) diff --git a/benchmark/__init__.py b/benchmark/__init__.py deleted file mode 100644 index 639df04e71177c6512c122abf44e1f0138157e4b..0000000000000000000000000000000000000000 --- a/benchmark/__init__.py +++ /dev/null @@ -1 +0,0 @@ -# benchmark — Benchmark Bridge System for MLForge diff --git a/benchmark/__pycache__/__init__.cpython-310.pyc b/benchmark/__pycache__/__init__.cpython-310.pyc deleted file mode 100644 index 29c1709cbf123d14e003da151f564218de89bc90..0000000000000000000000000000000000000000 Binary files a/benchmark/__pycache__/__init__.cpython-310.pyc and /dev/null differ diff --git a/benchmark/__pycache__/compatibility.cpython-310.pyc b/benchmark/__pycache__/compatibility.cpython-310.pyc deleted file mode 100644 index a86a314341973d5f9dce083ff80ff7694d0af51e..0000000000000000000000000000000000000000 Binary files a/benchmark/__pycache__/compatibility.cpython-310.pyc and /dev/null differ diff --git a/benchmark/__pycache__/execution.cpython-310.pyc b/benchmark/__pycache__/execution.cpython-310.pyc deleted file mode 100644 index 512b964e89258d6d99ddc1c45301a9f5fee3d541..0000000000000000000000000000000000000000 Binary files a/benchmark/__pycache__/execution.cpython-310.pyc and /dev/null differ diff --git a/benchmark/__pycache__/metrics.cpython-310.pyc b/benchmark/__pycache__/metrics.cpython-310.pyc deleted file mode 100644 index fbf6b2f03c47aee2ac388d06b5a9f6ebac2910b5..0000000000000000000000000000000000000000 Binary files a/benchmark/__pycache__/metrics.cpython-310.pyc and /dev/null differ diff --git a/benchmark/__pycache__/orchestrator.cpython-310.pyc b/benchmark/__pycache__/orchestrator.cpython-310.pyc deleted file mode 100644 index 520d741deed4aa5b2849b79206aec911de4b573f..0000000000000000000000000000000000000000 Binary files a/benchmark/__pycache__/orchestrator.cpython-310.pyc and /dev/null differ diff --git a/benchmark/__pycache__/registry.cpython-310.pyc b/benchmark/__pycache__/registry.cpython-310.pyc deleted file mode 100644 index dd2a9a5f3a6cc0b3ef3ecf1cb6b5247b6acae876..0000000000000000000000000000000000000000 Binary files a/benchmark/__pycache__/registry.cpython-310.pyc and /dev/null differ diff --git a/benchmark/__pycache__/telemetry.cpython-310.pyc b/benchmark/__pycache__/telemetry.cpython-310.pyc deleted file mode 100644 index ea6f026b10c3f79d0825498114cd105b0dad4bae..0000000000000000000000000000000000000000 Binary files a/benchmark/__pycache__/telemetry.cpython-310.pyc and /dev/null differ diff --git a/benchmark/adapters/__pycache__/base.cpython-310.pyc b/benchmark/adapters/__pycache__/base.cpython-310.pyc deleted file mode 100644 index 24ff4a2890c8ba8d65b299c868e0717ca220c1e6..0000000000000000000000000000000000000000 Binary files a/benchmark/adapters/__pycache__/base.cpython-310.pyc and /dev/null differ diff --git a/benchmark/adapters/__pycache__/registry.cpython-310.pyc b/benchmark/adapters/__pycache__/registry.cpython-310.pyc deleted file mode 100644 index 6f78e04b2d65ecb46efde9b020774f2a0478d241..0000000000000000000000000000000000000000 Binary files a/benchmark/adapters/__pycache__/registry.cpython-310.pyc and /dev/null differ diff --git a/benchmark/adapters/__pycache__/torch_runner.cpython-310.pyc b/benchmark/adapters/__pycache__/torch_runner.cpython-310.pyc deleted file mode 100644 index 6d3bfaa256012b2af744526da42691e08b9af948..0000000000000000000000000000000000000000 Binary files a/benchmark/adapters/__pycache__/torch_runner.cpython-310.pyc and /dev/null differ diff --git a/benchmark/adapters/base.py b/benchmark/adapters/base.py deleted file mode 100644 index b3462388e9172ff98b98ab85d8a35d5a83fca1fa..0000000000000000000000000000000000000000 --- a/benchmark/adapters/base.py +++ /dev/null @@ -1,38 +0,0 @@ -""" -benchmark/adapters/base.py — Base class for all Benchmark Runners. -""" -from __future__ import annotations - -from abc import ABC, abstractmethod -from dataclasses import dataclass, field -from typing import Any, AsyncGenerator - -from models.benchmark import BenchmarkContext, TelemetrySample - - -@dataclass -class BatchResult: - """Result of a single batch execution.""" - latency_ms: float - vram_used_gb: float - task_scores: dict[str, float] = field(default_factory=dict) - metadata: dict[str, Any] = field(default_factory=dict) - - -class BaseRunner(ABC): - """Abstract interface for benchmark executors (Torch, Optimum, vLLM).""" - - @abstractmethod - async def initialize(self, ctx: BenchmarkContext, model_path: str) -> None: - """Load model and prepare environment.""" - pass - - @abstractmethod - async def run_batch(self, batch: Any) -> BatchResult: - """Execute a single batch of data.""" - pass - - @abstractmethod - async def shutdown(self) -> None: - """Release resources.""" - pass diff --git a/benchmark/adapters/optimum_runner.py b/benchmark/adapters/optimum_runner.py deleted file mode 100644 index 7aaf5d66f23033eb3be47b2fcbf667e5581d26c2..0000000000000000000000000000000000000000 --- a/benchmark/adapters/optimum_runner.py +++ /dev/null @@ -1,53 +0,0 @@ -""" -benchmark/adapters/optimum_runner.py — Hugging Face Optimum Adapter. -Supports ONNX, OpenVINO, and TensorRT acceleration. -""" -from __future__ import annotations - -import time -import asyncio -from typing import Any -from benchmark.adapters.base import BaseRunner, BatchResult -from models.benchmark import BenchmarkContext -from observability.logger import get_logger - -log = get_logger("benchmark.optimum") - -class OptimumRunner(BaseRunner): - def __init__(self): - self.session = None - self.device = "cpu" - - async def initialize(self, ctx: BenchmarkContext, model_path: str) -> None: - """ - Load model using Optimum's ORTModel or equivalent. - In a real implementation, this would detect the framework and use: - ORTModelForFeatureExtraction.from_pretrained(model_path, provider=...) - """ - log.info("optimum_init", model_path=model_path, hardware=ctx.hardware) - self.device = "cuda" if "gpu" in ctx.hardware.lower() or "rtx" in ctx.hardware.lower() else "cpu" - - # Simulate load time - await asyncio.sleep(1.5) - self.session = "active" # Placeholder for the real session object - - async def run_batch(self, batch: Any) -> BatchResult: - """Execute inference using the Optimum/ONNX Runtime session.""" - if not self.session: - raise RuntimeError("Optimum session not initialized") - - start_time = time.perf_counter() - # Mocking inference logic - # outputs = self.session(**batch) - await asyncio.sleep(0.01) # Simulated inference time - latency = (time.perf_counter() - start_time) * 1000 - - return BatchResult( - latency_ms=latency, - vram_used_gb=0.8, # Mocked - task_scores={"accuracy": 0.92} # Mocked - ) - - async def shutdown(self) -> None: - log.info("optimum_shutdown") - self.session = None diff --git a/benchmark/adapters/registry.py b/benchmark/adapters/registry.py deleted file mode 100644 index 877edaa8c05ee434e6780a58028c1e2471ace46e..0000000000000000000000000000000000000000 --- a/benchmark/adapters/registry.py +++ /dev/null @@ -1,44 +0,0 @@ -""" -benchmark/adapters/registry.py — Executor Registry for dynamic runner resolution. -""" -from __future__ import annotations - -from typing import Type -from benchmark.adapters.base import BaseRunner -from models.benchmark import BenchmarkContext -from models.model import Model - -class ExecutorRegistry: - _runners: dict[str, Type[BaseRunner]] = {} - - @classmethod - def register(cls, framework: str, runner_cls: Type[BaseRunner]): - cls._runners[framework.lower()] = runner_cls - - @classmethod - def get_runner(cls, framework: str) -> BaseRunner: - runner_cls = cls._runners.get(framework.lower()) - if not runner_cls: - # Fallback or default runner - from benchmark.adapters.torch_runner import TorchRunner - return TorchRunner() - return runner_cls() - -def get_executor(ctx: BenchmarkContext, model: Model) -> BaseRunner: - """Resolve the appropriate executor based on framework and task.""" - framework = model.framework.lower() - - # Special cases for optimized engines - if framework == "onnx" or framework == "openvino" or framework == "tensorrt": - from benchmark.adapters.optimum_runner import OptimumRunner - return OptimumRunner() - - if ctx.task in ("generation", "nlp") and framework == "pytorch": - # Potential for vLLM if configured - try: - from benchmark.adapters.vllm_runner import VLLMRunner - return VLLMRunner() - except ImportError: - pass - - return ExecutorRegistry.get_runner(framework) diff --git a/benchmark/adapters/torch_runner.py b/benchmark/adapters/torch_runner.py deleted file mode 100644 index 8a00dabb2bc0f86e692ebb721b0f0564e12ef228..0000000000000000000000000000000000000000 --- a/benchmark/adapters/torch_runner.py +++ /dev/null @@ -1,45 +0,0 @@ -""" -benchmark/adapters/torch_runner.py — PyTorch Runner Adapter. -Wraps standard PyTorch inference for Vision and NLP tasks. -""" -from __future__ import annotations - -import time -import asyncio -import random -from typing import Any -from benchmark.adapters.base import BaseRunner, BatchResult -from models.benchmark import BenchmarkContext -from observability.logger import get_logger - -log = get_logger("benchmark.torch") - -class TorchRunner(BaseRunner): - def __init__(self): - self.model = None - self.device = "cpu" - - async def initialize(self, ctx: BenchmarkContext, model_path: str) -> None: - log.info("torch_init", model_path=model_path, hardware=ctx.hardware) - # In production: self.model = torch.load(model_path).to(self.device) - await asyncio.sleep(1.0) - self.model = "active" - - async def run_batch(self, batch: Any) -> BatchResult: - if not self.model: - raise RuntimeError("Torch model not initialized") - - start_time = time.perf_counter() - # Mocking torch inference - await asyncio.sleep(0.02) - latency = (time.perf_counter() - start_time) * 1000 - - return BatchResult( - latency_ms=latency, - vram_used_gb=1.2, - task_scores={"mAP": 0.45} - ) - - async def shutdown(self) -> None: - log.info("torch_shutdown") - self.model = None diff --git a/benchmark/compatibility.py b/benchmark/compatibility.py deleted file mode 100644 index a299bff9ae4c5898a2bdebdd4d8457d26b3a56b9..0000000000000000000000000000000000000000 --- a/benchmark/compatibility.py +++ /dev/null @@ -1,360 +0,0 @@ -""" -benchmark/compatibility.py — Compatibility Validator (CRITICAL MODULE). - -Validates model ↔ dataset ↔ hardware compatibility before any benchmark -execution begins. Returns a structured ValidationReport — never raises. - -Five gates (all must pass): - A. Task compatibility — model.task matches dataset.task - B. Annotation format — dataset format supports the model's task - C. Framework × hardware — framework can run on the requested device - D. VRAM constraint — estimated memory fits available VRAM - E. Precision support — precision mode is valid for framework + hardware -""" -from __future__ import annotations - -from models.benchmark import BenchmarkContext, ValidationCheck, ValidationReport -from models.dataset import Dataset -from models.model import Model -from observability.logger import get_logger - -log = get_logger("benchmark.compatibility") - - -# ── Lookup tables ───────────────────────────────────────────────────────────── - -# Hardware → available VRAM in GB (normalized keys, no spaces/dashes) -HARDWARE_VRAM_GB: dict[str, float] = { - # NVIDIA consumer — Ampere / Ada - "rtx4090": 24.0, - "rtx4080": 16.0, - "rtx4070ti": 12.0, - "rtx4070": 12.0, - "rtx4060ti": 8.0, - "rtx4060": 8.0, - "rtx3090": 24.0, - "rtx3080": 10.0, - "rtx3070": 8.0, - "rtx3060": 12.0, - "rtx2080ti": 11.0, - "rtx2080": 8.0, - # NVIDIA datacenter - "a100": 80.0, - "a10040gb": 40.0, - "h100": 80.0, - "v100": 32.0, - "t4": 16.0, - "a10": 24.0, - # AMD - "rx7900xtx": 24.0, - "rx6800xt": 16.0, - # Generic fallbacks - "gpu": 8.0, - "cpu": 0.0, - "tpu": 0.0, - "edge": 0.0, -} - -# model.task → set of compatible dataset.task values -TASK_COMPAT: dict[str, set[str]] = { - "detection": {"detection"}, - "classification": {"classification"}, - "segmentation": {"segmentation"}, - "nlp": {"nlp"}, - "generation": {"generation"}, - "keypoints": {"keypoints", "detection"}, - "embedding": {"nlp", "classification"}, -} - -# dataset.format → set of model tasks it supports -FORMAT_TASK_COMPAT: dict[str, set[str]] = { - "yolo": {"detection", "segmentation", "keypoints"}, - "coco": {"detection", "segmentation", "keypoints"}, - "voc": {"detection"}, - "csv": {"classification"}, - "json": {"detection", "segmentation", "classification", "nlp", "generation"}, - "tfrecord": {"detection", "classification", "segmentation"}, - "custom": {"detection", "classification", "segmentation", "nlp", "generation", "keypoints"}, -} - -# model.framework → set of hardware targets (normalized) it can run on -FRAMEWORK_HARDWARE_COMPAT: dict[str, set[str]] = { - "pytorch": { - "cpu", "gpu", - "rtx4090", "rtx4080", "rtx4070ti", "rtx4070", "rtx4060ti", "rtx4060", - "rtx3090", "rtx3080", "rtx3070", "rtx3060", - "rtx2080ti", "rtx2080", - "a100", "a10040gb", "h100", "v100", "t4", "a10", - }, - "onnx": { - "cpu", "gpu", - "rtx4090", "rtx3090", "a100", "h100", "t4", "a10", - "edge", - }, - "tensorflow": { - "cpu", "gpu", - "rtx4090", "rtx3090", "a100", "h100", "v100", "t4", - "tpu", - }, - "tflite": {"cpu", "edge"}, - "coreml": {"cpu"}, -} - -# Precisions that require GPU -_GPU_ONLY_PRECISIONS = {"FP16", "BF16"} - -# Frameworks supporting INT8 quantization -_INT8_FRAMEWORKS = {"onnx", "tflite", "pytorch", "tensorflow"} - - -class CompatibilityValidator: - """ - Runs all compatibility gates before a benchmark job is created. - Returns a ValidationReport — never raises exceptions. - """ - - def validate( - self, - model: Model, - dataset: Dataset, - ctx: BenchmarkContext, - ) -> ValidationReport: - checks: list[ValidationCheck] = [ - self._check_task(model, dataset), - self._check_annotation_format(model, dataset), - self._check_framework_hardware(model, ctx), - self._check_vram(model, ctx), - self._check_precision(model, ctx), - ] - - errors = [c.detail for c in checks if not c.passed] - warnings: list[str] = [] - - log.info( - "compatibility_validated", - model_id = model.id, - dataset_id = dataset.id, - passed = len(errors) == 0, - error_count = len(errors), - ) - - return ValidationReport( - model_id = model.id, - dataset_id = dataset.id, - passed = len(errors) == 0, - checks = checks, - errors = errors, - warnings = warnings, - ) - - # ── Gate A: Task ────────────────────────────────────────────────────────── - - def _check_task(self, model: Model, dataset: Dataset) -> ValidationCheck: - model_task = model.task.lower().strip() - dataset_task = str(dataset.task).lower().strip() - - allowed = TASK_COMPAT.get(model_task, {model_task}) - if dataset_task in allowed: - return ValidationCheck( - name = "task_compatibility", - passed = True, - detail = ( - f"Model task '{model_task}' is compatible " - f"with dataset task '{dataset_task}'" - ), - ) - return ValidationCheck( - name = "task_compatibility", - passed = False, - detail = ( - f"Model task '{model_task}' cannot evaluate " - f"a '{dataset_task}' dataset" - ), - suggestion = ( - f"Select a model with task='{dataset_task}', " - f"or choose a dataset with task='{model_task}'" - ), - ) - - # ── Gate B: Annotation Format ───────────────────────────────────────────── - - def _check_annotation_format(self, model: Model, dataset: Dataset) -> ValidationCheck: - dataset_fmt = str(dataset.format).lower().strip() - model_task = model.task.lower().strip() - supported = FORMAT_TASK_COMPAT.get(dataset_fmt, set()) - - if model_task in supported: - return ValidationCheck( - name = "annotation_format", - passed = True, - detail = ( - f"Dataset format '{dataset_fmt}' supports " - f"model task '{model_task}'" - ), - ) - - if model_task in {"detection", "segmentation", "keypoints"}: - suggestion = ( - f"Convert dataset to YOLO or COCO format — both support '{model_task}'" - ) - elif model_task == "classification": - suggestion = "Convert dataset to CSV or JSON format for classification tasks" - else: - suggestion = f"Use a JSON or custom-format dataset for '{model_task}' tasks" - - return ValidationCheck( - name = "annotation_format", - passed = False, - detail = ( - f"Dataset format '{dataset_fmt}' does not support " - f"model task '{model_task}'" - ), - suggestion = suggestion, - ) - - # ── Gate C: Framework × Hardware ───────────────────────────────────────── - - def _check_framework_hardware( - self, model: Model, ctx: BenchmarkContext - ) -> ValidationCheck: - framework = model.framework.lower().strip() - hw_raw = ctx.hardware - hw_key = self._normalize_hw(hw_raw) - - supported_hw = FRAMEWORK_HARDWARE_COMPAT.get(framework, {"cpu"}) - - # Match: exact key, or generic "gpu" bucket covers any named GPU - hw_ok = ( - hw_key in supported_hw - or ("gpu" in supported_hw and hw_key not in {"cpu", "tpu", "edge"}) - ) - - if hw_ok: - return ValidationCheck( - name = "framework_hardware", - passed = True, - detail = f"Framework '{framework}' is supported on '{hw_raw}'", - ) - return ValidationCheck( - name = "framework_hardware", - passed = False, - detail = ( - f"Framework '{framework}' cannot run on '{hw_raw}'. " - f"Supported targets: {', '.join(sorted(supported_hw))}" - ), - suggestion = ( - "Use ONNX runtime for broadest hardware support, " - f"or pick a device from: {', '.join(sorted(supported_hw))}" - ), - ) - - # ── Gate D: VRAM Constraint ─────────────────────────────────────────────── - - def _check_vram(self, model: Model, ctx: BenchmarkContext) -> ValidationCheck: - hw_key = self._normalize_hw(ctx.hardware) - available = self._lookup_vram(hw_key) - - if available == 0.0: - return ValidationCheck( - name = "vram_constraint", - passed = True, - detail = f"Running on '{ctx.hardware}' (CPU/TPU/Edge) — no VRAM constraint", - ) - - # Estimate: weights at given precision + activations for one batch - model_gb = max(model.size, 1) / (1024 ** 3) - prec_map = {"FP16": 0.5, "BF16": 0.5, "INT8": 0.25, "FP32": 1.0} - prec_mult = prec_map.get(ctx.precision.upper(), 1.0) - # weights × precision + ~20% for optimizer/activation buffers + batch overhead - estimated = (model_gb * prec_mult * 1.2) + (ctx.batch_size * 0.05) - - if estimated <= available: - return ValidationCheck( - name = "vram_constraint", - passed = True, - detail = ( - f"Estimated VRAM {estimated:.2f} GB ≤ " - f"available {available:.1f} GB on '{ctx.hardware}'" - ), - ) - return ValidationCheck( - name = "vram_constraint", - passed = False, - detail = ( - f"Estimated VRAM {estimated:.2f} GB exceeds " - f"available {available:.1f} GB on '{ctx.hardware}'" - ), - suggestion = ( - f"Try: reduce batch_size (now {ctx.batch_size}), " - f"switch to FP16/INT8 precision, " - f"or use a GPU with ≥ {estimated:.1f} GB VRAM" - ), - ) - - # ── Gate E: Precision Support ───────────────────────────────────────────── - - def _check_precision(self, model: Model, ctx: BenchmarkContext) -> ValidationCheck: - precision = ctx.precision.upper() - framework = model.framework.lower().strip() - hw_key = self._normalize_hw(ctx.hardware) - is_gpu = hw_key not in {"cpu", "tpu", "edge"} - - if precision in _GPU_ONLY_PRECISIONS and not is_gpu: - return ValidationCheck( - name = "precision_support", - passed = False, - detail = ( - f"Precision '{precision}' requires a CUDA GPU; " - f"'{ctx.hardware}' does not support it" - ), - suggestion = "Use FP32 for CPU inference, or switch to a compatible GPU", - ) - - if precision == "INT8" and framework not in _INT8_FRAMEWORKS: - return ValidationCheck( - name = "precision_support", - passed = False, - detail = ( - f"Framework '{framework}' does not support INT8 quantization" - ), - suggestion = ( - "Convert model to ONNX or use PyTorch with torch.quantization" - ), - ) - - return ValidationCheck( - name = "precision_support", - passed = True, - detail = ( - f"Precision '{precision}' is valid for " - f"framework '{framework}' on '{ctx.hardware}'" - ), - ) - - # ── Helpers ─────────────────────────────────────────────────────────────── - - @staticmethod - def _normalize_hw(hardware: str) -> str: - """Lowercase, strip spaces/dashes/underscores for lookup.""" - return ( - hardware.lower() - .replace(" ", "") - .replace("-", "") - .replace("_", "") - .replace("nvidia", "") - .replace("geforce", "") - ) - - @staticmethod - def _lookup_vram(hw_key: str) -> float: - """Return VRAM GB for a normalized hardware key, with fallback matching.""" - if hw_key in HARDWARE_VRAM_GB: - return HARDWARE_VRAM_GB[hw_key] - # Partial match (e.g. "rtx4090laptop" → "rtx4090") - for key, vram in HARDWARE_VRAM_GB.items(): - if key and key in hw_key: - return vram - # Anything that looks like a GPU but isn't in the table - if "gpu" in hw_key or "rtx" in hw_key or "gtx" in hw_key or "cuda" in hw_key: - return HARDWARE_VRAM_GB["gpu"] - return 0.0 # CPU / unknown → no VRAM constraint diff --git a/benchmark/execution.py b/benchmark/execution.py deleted file mode 100644 index 3f7d76abec0189942414e930c10cf2600b70bae1..0000000000000000000000000000000000000000 --- a/benchmark/execution.py +++ /dev/null @@ -1,366 +0,0 @@ -""" -benchmark/execution.py — Benchmark Execution Engine. - -Drives the batch inference loop, collecting latencies and VRAM readings. -Calls TelemetryCollector in parallel with batch processing. -Yields progress callbacks so the orchestrator can persist real-time state. - -Adapter pattern: swap _run_single_batch() with a real inference call -(torch.cuda.synchronize + model(batch)) once GPU runtime is wired up. - -PRODUCTION SWAP POINTS are marked with # <<< REPLACE IN PRODUCTION >>> -""" -from __future__ import annotations - -import asyncio -import math -import random -from dataclasses import dataclass, field -from typing import Awaitable, Callable - -from benchmark.compatibility import HARDWARE_VRAM_GB -from benchmark.telemetry import TelemetryCollector -from models.benchmark import BenchmarkJob, LayerBreakdown, TelemetrySample, TelemetrySummary -from models.dataset import Dataset -from models.model import Model -from observability.logger import get_logger - -log = get_logger("benchmark.execution") - - -# ── Per-image latency profiles (ms at batch=1, fp32) ───────────────────────── -_LATENCY_MS_PER_IMAGE: dict[str, float] = { - "rtx4090": 1.8, - "rtx4080": 2.5, - "rtx4070ti": 3.2, - "rtx4070": 3.8, - "rtx3090": 3.0, - "rtx3080": 4.5, - "rtx3070": 6.5, - "rtx3060": 9.0, - "rtx2080ti": 5.0, - "rtx2080": 7.5, - "a100": 1.2, - "h100": 0.7, - "v100": 2.8, - "t4": 5.5, - "a10": 3.5, - "gpu": 8.0, - "cpu": 42.0, -} - -# Precision speedup multipliers (relative to FP32) -_PRECISION_SPEEDUP: dict[str, float] = { - "FP32": 1.0, - "FP16": 1.8, - "BF16": 1.7, - "INT8": 2.5, -} - -# Task-specific baseline metric scores (pre-jitter) -_TASK_BASELINES: dict[str, dict[str, float]] = { - "detection": {"mAP": 0.435, "mAP_50": 0.618, "mAP_50_95": 0.435}, - "classification": {"accuracy": 0.872, "top5": 0.968}, - "segmentation": {"mAP": 0.372, "iou_mean": 0.706}, - "keypoints": {"mAP": 0.641, "mAP_50": 0.860}, - "nlp": {"accuracy": 0.891}, - "generation": {"accuracy": 0.780}, -} - -# Cap simulated batches so large datasets don't stall the event loop -_MAX_SIMULATED_BATCHES = 250 - - -@dataclass -class ExecutionResult: - """Raw output from the execution engine, consumed by MetricsEngine.""" - latencies_ms: list[float] - total_images: int - vram_samples: list[float] - task_scores: dict[str, float] - telemetry_samples: list[TelemetrySample] = field(default_factory=list) - telemetry_summary: TelemetrySummary = field(default_factory=TelemetrySummary) - - -# Progress callback type: (progress_0_to_1, message, last_telemetry) → None -ProgressCallback = Callable[[float, str, TelemetrySample | None], Awaitable[None]] - - -class BenchmarkExecutor: - """ - Drives the benchmark execution loop. - Non-blocking: all sleeps are asyncio.sleep so other coroutines run freely. - """ - - async def execute( - self, - job: BenchmarkJob, - model: Model, - dataset: Dataset, - on_progress: ProgressCallback, - ) -> ExecutionResult: - hw = job.hardware - batch_sz = job.batch_size - - # Handle polymorphic input duration - is_live = getattr(job, "input_source", "dataset") in ("video", "live") - - if is_live: - # For live/video, we run for a fixed duration or until stopped - # Increase limit for a longer session (e.g., 10,000 batches) - total_img = 10000 * batch_sz - n_batches = 10000 - sim_batches = 10000 - else: - total_img = max(dataset.images, 100) # floor so simulation always runs - n_batches = math.ceil(total_img / batch_sz) - sim_batches = min(n_batches, _MAX_SIMULATED_BATCHES) - - vram_total = self._get_vram_gb(hw, model) - vram_frac = self._vram_usage_fraction(hw) - - telemetry = TelemetryCollector(hw, vram_total_gb=vram_total) - await telemetry.start() - - latencies: list[float] = [] - vram_samples: list[float] = [] - - base_lat_ms = self._base_batch_latency_ms(hw, model, batch_sz, job.precision) - - # Resolve real model path once (None → use simulation) - real_model_path = model.local_path if model.local_path and model.downloaded else None - use_real_inference = self._check_torch_available() and real_model_path is not None - loop = asyncio.get_event_loop() - - try: - for sim_idx in range(sim_batches): - # Map simulated index back to real batch index - real_idx = int(sim_idx * (n_batches / sim_batches)) - - if use_real_inference: - # Real GPU inference via torch_runner (runs in thread executor) - try: - from benchmark.torch_runner import run_torch_batch - batch_lat_ms = await loop.run_in_executor( - None, - run_torch_batch, - real_model_path, - batch_sz, - job.task, - ) - # Add a tiny sleep to prevent event loop starvation in live mode - if is_live: - await asyncio.sleep(0.001) - except Exception as exc: - log.warning("torch_inference_failed_fallback", error=str(exc)) - use_real_inference = False # fall back for remaining batches - batch_lat_ms = max( - 0.5, base_lat_ms + random.gauss(0, base_lat_ms * 0.07) - ) - else: - # Simulation path — non-blocking synthetic latency - batch_lat_ms = max( - 0.5, - base_lat_ms + random.gauss(0, base_lat_ms * 0.07), - ) - await asyncio.sleep(batch_lat_ms / 1000.0) # non-blocking - - latencies.append(batch_lat_ms) - vram_used = vram_total * random.uniform( - vram_frac - 0.05, vram_frac + 0.05 - ) - vram_samples.append(max(0.0, vram_used)) - - progress = (sim_idx + 1) / sim_batches - telemetry.record_batch_context(real_idx, progress) - - # Throttle callbacks: every 5 batches or first/last - if sim_idx % 5 == 0 or sim_idx == sim_batches - 1: - images_done = int(progress * total_img) - - # Generate simulated detection data for live preview if it's a vision task - live_data = {} - if job.task.lower() in ("detection", "segmentation"): - # Use provided bbox telemetry if available (e.g. from real inference) - # otherwise generate simulated ones - live_data["detections"] = [ - { - "x": random.uniform(0.1, 0.7), - "y": random.uniform(0.1, 0.7), - "width": random.uniform(0.1, 0.3), - "height": random.uniform(0.1, 0.3), - "label": random.choice(["person", "car", "bicycle", "dog"]), - "confidence": random.uniform(0.5, 0.99) - } - for _ in range(random.randint(1, 5)) - ] - - last_sample = telemetry.samples[-1] if telemetry.samples else None - if last_sample: - last_sample.live_data = live_data - # Explicitly broadcast detections for the visualizer - last_sample.detections = live_data.get("detections", []) - - await on_progress( - progress, - f"Batch {real_idx+1}/{n_batches} — " - f"{images_done}/{total_img} images processed", - last_sample, - ) - - finally: - telemetry_summary = await telemetry.stop() - # Attach simulated layer breakdown so Live Lab can display it - telemetry_summary.layer_breakdown = self._compute_layer_breakdown( - job.task, base_lat_ms - ) - - task_scores = self._simulate_task_scores(job.task, model, dataset) - - log.info( - "execution_complete", - job_id = job.id, - total_images = total_img, - sim_batches = sim_batches, - avg_lat_ms = round(sum(latencies) / len(latencies), 2) if latencies else 0, - ) - - return ExecutionResult( - latencies_ms = latencies, - total_images = total_img, - vram_samples = vram_samples, - task_scores = task_scores, - telemetry_samples = telemetry.samples, - telemetry_summary = telemetry_summary, - ) - - # ── Helpers ─────────────────────────────────────────────────────────────── - - def _base_batch_latency_ms( - self, - hardware: str, - model: Model, - batch_sz: int, - precision: str, - ) -> float: - """ - Estimate per-batch latency in ms. - Accounts for hardware tier, model size, batch size, and precision. - """ - hw_key = self._normalize_hw(hardware) - per_img = self._lookup_latency(hw_key) - - # Larger models are slower: +30% per GB of model weights - size_gb = max(model.size, 1) / (1024 ** 3) - size_factor = 1.0 + size_gb * 0.30 - - # Batch parallelism: ~65% linear efficiency on GPU, 90% on CPU - eff = 0.65 if "cpu" not in hw_key else 0.90 - batch_lat = per_img * size_factor * batch_sz * eff - - # Precision speedup - speedup = _PRECISION_SPEEDUP.get(precision.upper(), 1.0) - - return batch_lat / speedup - - def _get_vram_gb(self, hardware: str, model: Model) -> float: - hw_key = self._normalize_hw(hardware) - for key, vram in HARDWARE_VRAM_GB.items(): - if key and key in hw_key: - return vram - return 8.0 - - @staticmethod - def _vram_usage_fraction(hardware: str) -> float: - """Fraction of VRAM typically consumed during inference.""" - hw = hardware.lower() - if any(x in hw for x in ("4090", "3090", "a100", "h100")): - return 0.62 - if any(x in hw for x in ("4080", "3080", "v100", "a10")): - return 0.60 - if "cpu" in hw: - return 0.0 - return 0.55 - - @staticmethod - def _simulate_task_scores( - task: str, model: Model, dataset: Dataset - ) -> dict[str, float]: - """ - Produce realistic metric scores with small per-run variance. - - PRODUCTION SWAP: replace with actual metric computation: - from torchmetrics.detection import MeanAveragePrecision - metric = MeanAveragePrecision() - metric.update(predictions, targets) - return metric.compute() - """ - baselines = dict(_TASK_BASELINES.get(task.lower(), {"accuracy": 0.80})) - # Small Gaussian jitter simulates run-to-run variance - return { - k: float(max(0.0, min(1.0, v + random.gauss(0, 0.015)))) - for k, v in baselines.items() - } - - @staticmethod - def _check_torch_available() -> bool: - """Return True if PyTorch is installed and importable.""" - try: - import torch # noqa: F401 - return True - except ImportError: - return False - - @staticmethod - def _compute_layer_breakdown(task: str, base_lat_ms: float) -> list[LayerBreakdown]: - """Build a realistic layer breakdown for the given task. - - Splits total latency across architectural stages with small jitter. - PRODUCTION SWAP: replace with actual profiler data (e.g. torch.profiler). - """ - if task.lower() in ("detection", "segmentation"): - stages = [ - ("Backbone", 0.45), - ("Neck (FPN/PAFPN)", 0.30), - ("Detection Head", 0.20), - ("NMS Post-process", 0.05), - ] - elif task.lower() == "classification": - stages = [ - ("Feature Extractor", 0.70), - ("Classifier Head", 0.20), - ("Softmax", 0.10), - ] - else: - stages = [ - ("Encoder", 0.55), - ("Decoder / Head", 0.35), - ("Post-process", 0.10), - ] - - result: list[LayerBreakdown] = [] - remaining = base_lat_ms - for name, frac in stages: - t = round(base_lat_ms * frac + random.gauss(0, base_lat_ms * 0.01), 3) - result.append(LayerBreakdown(name=name, time_ms=t, percent=round(frac * 100, 1))) - return result - - @staticmethod - def _normalize_hw(hardware: str) -> str: - return ( - hardware.lower() - .replace(" ", "") - .replace("-", "") - .replace("_", "") - .replace("nvidia", "") - .replace("geforce", "") - ) - - @staticmethod - def _lookup_latency(hw_key: str) -> float: - for key, ms in _LATENCY_MS_PER_IMAGE.items(): - if key and key in hw_key: - return ms - if any(x in hw_key for x in ("gpu", "rtx", "gtx", "cuda")): - return _LATENCY_MS_PER_IMAGE["gpu"] - return _LATENCY_MS_PER_IMAGE["cpu"] diff --git a/benchmark/metrics.py b/benchmark/metrics.py deleted file mode 100644 index f80529e1cf8a754881bdadb3980f8e1915d336fb..0000000000000000000000000000000000000000 --- a/benchmark/metrics.py +++ /dev/null @@ -1,110 +0,0 @@ -""" -benchmark/metrics.py — Metrics Engine. - -Computes the final BenchmarkMetrics object from raw execution data: - - Latency statistics (mean, p95, p99) - - Throughput (FPS) - - VRAM statistics (avg, peak) - - Task-specific scores (mAP, accuracy, IoU) supplied by the executor - -In a production deployment the task_scores dict comes from actual -metric computation (e.g. pycocotools, torchmetrics). In this local-first -build the executor supplies realistic simulated scores. -""" -from __future__ import annotations - -import statistics - -from models.benchmark import BenchmarkMetrics, LayerBreakdown, TelemetrySummary -from observability.logger import get_logger - -log = get_logger("benchmark.metrics") - - -class MetricsEngine: - """Computes BenchmarkMetrics from raw benchmark execution data.""" - - def compute( - self, - *, - task: str, - latencies_ms: list[float], # per-batch latencies - total_images: int = 0, - total_tokens: int = 0, - batch_size: int, - vram_samples: list[float], # VRAM readings (GB) during run - task_scores: dict[str, float], # task-specific metric scores - ) -> BenchmarkMetrics: - if not latencies_ms: - return BenchmarkMetrics(total_images=total_images, total_tokens=total_tokens, batch_size=batch_size) - - total_time_s = sum(latencies_ms) / 1000.0 - fps = total_images / total_time_s if total_time_s > 0 and total_images > 0 else 0.0 - tps = total_tokens / total_time_s if total_time_s > 0 and total_tokens > 0 else 0.0 - - lat_mean = statistics.mean(latencies_ms) - lat_p95 = _percentile(latencies_ms, 0.95) - lat_p99 = _percentile(latencies_ms, 0.99) - - vram_peak = max(vram_samples) if vram_samples else 0.0 - vram_avg = statistics.mean(vram_samples) if vram_samples else 0.0 - - m = BenchmarkMetrics( - fps = round(fps, 2), - tokens_per_sec = round(tps, 2), - latency_mean_ms = round(lat_mean, 3), - latency_p95_ms = round(lat_p95, 3), - latency_p99_ms = round(lat_p99, 3), - vram_peak_gb = round(vram_peak, 3), - vram_avg_gb = round(vram_avg, 3), - total_images = total_images, - total_tokens = total_tokens, - batch_size = batch_size, - ) - - task_lower = task.lower() - - # CV Task Mapping - if task_lower in ("detection", "segmentation", "keypoints"): - m.mAP = _fmt(task_scores.get("mAP", 0.0)) - m.mAP_50 = _fmt(task_scores.get("mAP_50", 0.0)) - m.mAP_50_95 = _fmt(task_scores.get("mAP_50_95", 0.0)) - if task_lower == "segmentation": - m.iou_mean = _fmt(task_scores.get("iou_mean", 0.0)) - - elif task_lower == "classification": - m.accuracy = _fmt(task_scores.get("accuracy", 0.0)) - m.top1 = _fmt(task_scores.get("top1", 0.0)) - m.top5 = _fmt(task_scores.get("top5", 0.0)) - - # NLP Task Mapping (ROUGE, BLEU, Perplexity) - elif task_lower in ("nlp", "generation"): - m.accuracy = _fmt(task_scores.get("accuracy", 0.0)) - m.rouge_l = _fmt(task_scores.get("rouge_l", task_scores.get("rougeL", 0.0))) - m.bleu = _fmt(task_scores.get("bleu", 0.0)) - m.perplexity = task_scores.get("perplexity") - - log.info( - "metrics_computed", - task = task, - fps = m.fps, - tps = m.tokens_per_sec, - latency_ms = m.latency_mean_ms, - vram_peak = m.vram_peak_gb, - ) - return m - - -# ── Helpers ─────────────────────────────────────────────────────────────────── - -def _percentile(data: list[float], p: float) -> float: - if not data: - return 0.0 - s = sorted(data) - idx = min(int(len(s) * p), len(s) - 1) - return s[idx] - - -def _fmt(v: float) -> float: - """Round to 4dp and clamp to [0, 1].""" - return round(max(0.0, min(1.0, v)), 4) diff --git a/benchmark/orchestrator.py b/benchmark/orchestrator.py deleted file mode 100644 index b7fcef70f2d048a35db59e93ab98eb9703279f8e..0000000000000000000000000000000000000000 --- a/benchmark/orchestrator.py +++ /dev/null @@ -1,374 +0,0 @@ -""" -benchmark/orchestrator.py — Benchmark Orchestrator (Main Controller). - -Coordinates the full benchmark lifecycle: - 1. Resolve model + dataset from their registries - 2. Run all compatibility checks (gates A–E) - 3. If valid → create a BenchmarkJob in the DB - 4. Persist the validation audit log - 5. Enqueue async background task → execution → metrics → storage - 6. Return the job immediately so callers are non-blocking - -Public interface used by api/routes/benchmark.py: - validate_context(ctx) → ValidationReport (no job created) - create_and_run(ctx) → BenchmarkJob (job queued, execution in background) -""" -from __future__ import annotations - -import asyncio -from datetime import datetime, timezone - -from benchmark.adapters.registry import get_executor -from benchmark.compatibility import CompatibilityValidator -from benchmark.execution import BenchmarkExecutor -from benchmark.metrics import MetricsEngine -import benchmark.registry as bench_reg -from datasets.registry import get_dataset -from models.benchmark import ( - BenchmarkContext, - BenchmarkJob, - BenchmarkMetrics, - TelemetrySummary, - ValidationReport, -) -from models.dataset import Dataset -from models.model import Model -from observability.logger import audit, get_logger -from registry.registry import get_model - -log = get_logger("benchmark.orchestrator") - -# Module-level singletons — stateless, safe to share -_validator = CompatibilityValidator() -_metrics = MetricsEngine() - -# job_id → asyncio.Task (for future cancellation support) -_active_tasks: dict[str, asyncio.Task] = {} - - -# ── Public API ──────────────────────────────────────────────────────────────── - -async def sync_project_benchmarks() -> int: - """ - Sync benchmark jobs and results from the active project's 'benchmarks' folder. - This ensures that benchmarks created in different sessions or projects are indexed. - """ - from benchmark.registry import _get_active_project_benchmark_dir_sync - from projects.service import get_active_project_path - import json - import os - from database.connection import get_db - - project_path = await get_active_project_path() - benchmark_dir = _get_active_project_benchmark_dir_sync(project_path) - if not benchmark_dir or not benchmark_dir.exists(): - return 0 - - db = await get_db() - count = 0 - - for file_path in benchmark_dir.glob("*.json"): - try: - with open(file_path, "r") as f: - data = json.load(f) - - # Check if it's a job or a result - if file_path.name.startswith("job_"): - # Upsert into benchmark_jobs - await db.execute( - """INSERT OR IGNORE INTO benchmark_jobs - (id, model_id, dataset_id, task, framework, hardware, - precision, batch_size, config, status, progress, created_at, updated_at, started_at) - VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?,?)""", - ( - data["id"], data["model_id"], data["dataset_id"], - data["task"], data["framework"], data["hardware"], - data["precision"], data["batch_size"], - json.dumps(data["config"]), data["status"], - data.get("progress", 0.0), - data.get("created_at", datetime.now(timezone.utc).isoformat()), - data.get("updated_at", datetime.now(timezone.utc).isoformat()), - data.get("started_at") - ) - ) - count += 1 - elif file_path.name.startswith("result_"): - # Upsert into benchmark_results - await db.execute( - """INSERT OR IGNORE INTO benchmark_results - (id, job_id, metrics, telemetry_summary, created_at) - VALUES (?,?,?,?,?)""", - ( - data["id"], data["job_id"], - json.dumps(data["metrics"]), - json.dumps(data["telemetry_summary"]), - data.get("created_at", datetime.now(timezone.utc).isoformat()) - ) - ) - count += 1 - except Exception as e: - log.error("sync_file_failed", file=file_path.name, error=str(e)) - - await db.commit() - log.info("sync_complete", count=count) - return count - -async def validate_context(ctx: BenchmarkContext) -> ValidationReport: - """ - Validate model ↔ dataset ↔ hardware compatibility. - Does NOT create a job. Safe to call repeatedly from the UI. - """ - model = await _require_model(ctx.model_id) - - # ── Handle Polymorphic Input (Video/Live) ──────────────────────────────── - if ctx.input_source in ["video", "live"] or ctx.dataset_id == "none": - # Create a synthetic dataset object for non-dataset sources - now = datetime.now(timezone.utc).isoformat() - dataset = Dataset( - id="none", - name="Live/Video Stream", - task=model.task, # Match model task to pass task check - format="custom", - source="local", - status="imported", - images=0, - classes=0, - size_label="0 MB", - created_at=now, - updated_at=now - ) - else: - dataset = await _require_dataset(ctx.dataset_id) - - return _validator.validate(model, dataset, ctx) - - -async def create_and_run(ctx: BenchmarkContext) -> BenchmarkJob: - """ - Full benchmark initiation: - """ - model = await _require_model(ctx.model_id) - - # ── Handle Polymorphic Input (Video/Live) ──────────────────────────────── - if ctx.input_source in ["video", "live"] or ctx.dataset_id == "none": - now = datetime.now(timezone.utc).isoformat() - dataset = Dataset( - id="none", - name="Live/Video Stream", - task=model.task, - format="custom", - source="local", - status="imported", - images=0, - classes=0, - size_label="0 MB", - created_at=now, - updated_at=now - ) - else: - dataset = await _require_dataset(ctx.dataset_id) - - # ── Compatibility check ─────────────────────────────────────────────────── - report = _validator.validate(model, dataset, ctx) - - # Always persist the validation log (even for failures) - await bench_reg.save_validation_log( - job_id = "pre-check", - model_id = ctx.model_id, - dataset_id = ctx.dataset_id, - checks = report.checks, - passed = report.passed, - ) - - if not report.passed: - from fastapi import HTTPException - failed = [c for c in report.checks if not c.passed] - raise HTTPException( - status_code = 422, - detail = { - "error": "Compatibility validation failed", - "failed_checks": [ - { - "name": c.name, - "detail": c.detail, - "suggestion": c.suggestion, - } - for c in failed - ], - }, - ) - - # ── Create job ──────────────────────────────────────────────────────────── - job = await bench_reg.create_job(ctx) - - # Overwrite 'pre-check' validation log with the real job_id - await bench_reg.save_validation_log( - job_id = job.id, - model_id = ctx.model_id, - dataset_id = ctx.dataset_id, - checks = report.checks, - passed = True, - ) - - # ── Log the Polymorphic Input params ───────────────────────────────────── - if ctx.input_source or ctx.video_path or ctx.rtsp_url: - log.info("polymorphic_input_received", - job_id=job.id, - source=ctx.input_source, - video=ctx.video_path, - rtsp=ctx.rtsp_url) - - # ── Enqueue background execution ────────────────────────────────────────── - task = asyncio.create_task( - _execute_job(job.id, ctx, model, dataset), - name = f"benchmark_{job.id}", - ) - _active_tasks[job.id] = task - task.add_done_callback(lambda _t: _active_tasks.pop(job.id, None)) - - log.info("benchmark_enqueued", job_id=job.id, model=ctx.model_id) - return job - - -# ── Background execution ────────────────────────────────────────────────────── - -async def _execute_job( - job_id: str, - ctx: BenchmarkContext, - model: Model, - dataset: Dataset, -) -> None: - """Full benchmark lifecycle — runs in an asyncio background task.""" - now = datetime.now(timezone.utc).isoformat() - - # Transition → running - ts_color = "\x1b[36m" # Cyan - info_color = "\x1b[34m" # Blue - success_color = "\x1b[32m" # Green - reset = "\x1b[0m" - - await bench_reg.update_job( - job_id, - status = "running", - progress = 0.0, - started_at = now, - log_entry = f"{ts_color}[{now}]{reset} {info_color}Job started{reset} on {ctx.hardware} ({ctx.precision})", - ) - - runner = BenchmarkExecutor() - - try: - # ── Fetch the persisted job (for executor) ──────────────────────────── - job = await bench_reg.get_job(job_id) - assert job is not None, "Job disappeared from DB after creation" - - # ── Define Progress Callback ────────────────────────────────────────── - async def on_progress(progress: float, message: str, telemetry: Any | None): - await bench_reg.update_job( - job_id, - progress=progress, - log_entry=f"{ts_color}[{datetime.now(timezone.utc).isoformat()}]{reset} {info_color}{message}{reset}", - last_telemetry=telemetry.model_dump() if telemetry and hasattr(telemetry, "model_dump") else telemetry - ) - - # ── Execution Loop ──────────────────────────────────────────────────── - exec_result = await runner.execute( - job=job, - model=model, - dataset=dataset, - on_progress=on_progress - ) - - # ── Compute metrics ─────────────────────────────────────────────────── - metrics = _metrics.compute( - task = ctx.task, - latencies_ms = exec_result.latencies_ms, - total_images = exec_result.total_images, - batch_size = ctx.batch_size, - vram_samples = exec_result.vram_samples, - task_scores = exec_result.task_scores, - ) - - # ── Persist result ──────────────────────────────────────────────────── - await bench_reg.save_result( - job_id = job_id, - metrics = metrics, - telemetry_summary = exec_result.telemetry_summary, - ) - - ended = datetime.now(timezone.utc).isoformat() - await bench_reg.update_job( - job_id, - status = "completed", - progress = 1.0, - ended_at = ended, - log_entry = f"{ts_color}[{ended}]{reset} {success_color}Benchmark completed{reset} — {metrics.fps} FPS", - ) - - await audit( - "benchmark_completed", - job_id = job_id, - payload = {"model_id": ctx.model_id, "dataset_id": ctx.dataset_id}, - ) - log.info( - "benchmark_completed", - job_id = job_id, - fps = metrics.fps, - lat_ms = metrics.latency_mean_ms, - ) - - except asyncio.CancelledError: - # Task cancelled externally (e.g. server shutdown) — don't swallow - ended = datetime.now(timezone.utc).isoformat() - await bench_reg.update_job( - job_id, - status = "failed", - error = "Job cancelled", - ended_at = ended, - log_entry = f"{ts_color}[{ended}]{reset} \x1b[31mJob cancelled\x1b[0m", - ) - raise - - except Exception as exc: - ended = datetime.now(timezone.utc).isoformat() - err_msg = str(exc) - error_color = "\x1b[31m" # Red - await bench_reg.update_job( - job_id, - status = "failed", - error = err_msg, - ended_at = ended, - log_entry = f"{ts_color}[{ended}]{reset} {error_color}ERROR: {err_msg}{reset}", - ) - await audit( - "benchmark_failed", - job_id = job_id, - level = "error", - payload = {"error": err_msg, "model_id": ctx.model_id}, - ) - log.exception("benchmark_failed", job_id=job_id) - finally: - pass - -# ── Resource resolvers ──────────────────────────────────────────────────────── - -async def _require_model(model_id: str) -> Model: - model = await get_model(model_id) - if not model: - from fastapi import HTTPException - raise HTTPException( - status_code = 404, - detail = f"Model '{model_id}' not found in Model Zoo", - ) - return model - - -async def _require_dataset(dataset_id: str) -> Dataset: - dataset = await get_dataset(dataset_id) - if not dataset: - from fastapi import HTTPException - raise HTTPException( - status_code = 404, - detail = f"Dataset '{dataset_id}' not found in Dataset Manager", - ) - return dataset diff --git a/benchmark/registry.py b/benchmark/registry.py deleted file mode 100644 index ef02ea0cd927b2358611301aea64d9f7c4cd1746..0000000000000000000000000000000000000000 --- a/benchmark/registry.py +++ /dev/null @@ -1,302 +0,0 @@ -""" -benchmark/registry.py — Benchmark Registry. - -All DB interactions for: - • benchmark_jobs — job lifecycle state - • benchmark_results — final metrics + telemetry summary - • benchmark_validation_logs — immutable check audit trail - -Follows the same pattern as registry/registry.py and datasets/registry.py. -No direct DB access from other benchmark modules — everything routes here. -""" -from __future__ import annotations - -import json -import uuid -from datetime import datetime, timezone -from typing import Any -from pathlib import Path - -from database.connection import get_db -from models.benchmark import ( - BenchmarkContext, - BenchmarkJob, - BenchmarkMetrics, - BenchmarkResult, - TelemetrySummary, - ValidationCheck, - row_to_job, - row_to_result, -) -from observability.logger import get_logger - -log = get_logger("benchmark.registry") - - -def _get_active_project_benchmark_dir_sync(project_path: str | None) -> Path | None: - """Get the absolute path to the 'benchmarks' folder in a given project path.""" - if not project_path: - return None - - benchmark_dir = Path(project_path) / "benchmarks" - benchmark_dir.mkdir(parents=True, exist_ok=True) - return benchmark_dir - -async def _get_active_project_benchmark_dir() -> Path | None: - """Get the absolute path to the 'benchmarks' folder in the active project.""" - from projects.service import get_active_project_path - project_path = await get_active_project_path() - return _get_active_project_benchmark_dir_sync(project_path) - -async def _save_to_project(filename: str, data: dict) -> None: - """Save data to a JSON file in the active project's benchmark folder.""" - benchmark_dir = await _get_active_project_benchmark_dir() - if not benchmark_dir: - return - - file_path = benchmark_dir / filename - try: - with open(file_path, "w") as f: - json.dump(data, f, indent=2) - except Exception as e: - log.error("project_persistence_failed", error=str(e), file=filename) - -# ── Job CRUD ────────────────────────────────────────────────────────────────── - -async def create_job(ctx: BenchmarkContext) -> BenchmarkJob: - db = await get_db() - job_id = f"bmark-{uuid.uuid4().hex[:12]}" - now = datetime.now(timezone.utc).isoformat() - - # Create job object - job = BenchmarkJob( - id = job_id, - model_id = ctx.model_id, - dataset_id = ctx.dataset_id, - task = ctx.task, - framework = ctx.framework, - hardware = ctx.hardware, - precision = ctx.precision, - batch_size = ctx.batch_size, - config = ctx.model_dump(), - status = "queued", - progress = 0.0, - created_at = now, - updated_at = now, - ) - - # Persist to SQLite - await db.execute( - """INSERT INTO benchmark_jobs - (id, model_id, dataset_id, task, framework, hardware, - precision, batch_size, config, - status, progress, logs, created_at, updated_at) - VALUES (?,?,?,?,?,?,?,?,?,'queued',0.0,'[]',?,?)""", - ( - job_id, - ctx.model_id, ctx.dataset_id, - ctx.task, ctx.framework, ctx.hardware, - ctx.precision, ctx.batch_size, - json.dumps(ctx.model_dump()), - now, now, - ), - ) - await db.commit() - - # Persist to project folder - await _save_to_project(f"job_{job_id}.json", job.model_dump()) - - log.info("benchmark_job_created", job_id=job_id, model=ctx.model_id) - return job - - -async def get_job(job_id: str) -> BenchmarkJob | None: - db = await get_db() - async with db.execute( - "SELECT * FROM benchmark_jobs WHERE id = ?", (job_id,) - ) as cur: - row = await cur.fetchone() - return row_to_job(row) if row else None - - -async def list_jobs( - *, - status: str | None = None, - model_id: str | None = None, - limit: int = 100, -) -> list[BenchmarkJob]: - db = await get_db() - clauses: list[str] = [] - params: list[Any] = [] - - if status: - clauses.append("status = ?") - params.append(status) - if model_id: - clauses.append("model_id = ?") - params.append(model_id) - - where = f"WHERE {' AND '.join(clauses)}" if clauses else "" - params.append(limit) - - async with db.execute( - f"SELECT * FROM benchmark_jobs {where} ORDER BY created_at DESC LIMIT ?", - params, - ) as cur: - rows = await cur.fetchall() - return [row_to_job(r) for r in rows] - - -async def update_job( - job_id: str, - *, - status: str | None = None, - progress: float | None = None, - error: str | None = None, - started_at: str | None = None, - ended_at: str | None = None, - log_entry: str | None = None, - last_telemetry: dict | None = None, -) -> None: - """Update mutable fields on a benchmark job atomically.""" - db = await get_db() - now = datetime.now(timezone.utc).isoformat() - - sets: list[str] = ["updated_at = ?"] - vals: list[Any] = [now] - - if status is not None: - sets.append("status = ?"); vals.append(status) - if progress is not None: - sets.append("progress = ?"); vals.append(round(progress, 4)) - if error is not None: - sets.append("error = ?"); vals.append(error) - if started_at is not None: - sets.append("started_at = ?"); vals.append(started_at) - if ended_at is not None: - sets.append("ended_at = ?"); vals.append(ended_at) - if last_telemetry is not None: - sets.append("last_telemetry = ?"); vals.append(json.dumps(last_telemetry)) - - if log_entry is not None: - # Append new entry to the JSON log array (capped at 500 lines) - async with db.execute( - "SELECT logs FROM benchmark_jobs WHERE id = ?", (job_id,) - ) as cur: - row = await cur.fetchone() - existing = json.loads(row["logs"]) if row and row["logs"] else [] - existing.append(log_entry) - sets.append("logs = ?") - vals.append(json.dumps(existing[-500:])) - - vals.append(job_id) - # Persist to project folder if we have the job info - async with db.execute("SELECT * FROM benchmark_jobs WHERE id = ?", (job_id,)) as cur: - row = await cur.fetchone() - if row: - job = row_to_job(row) - if job: - await _save_to_project(f"job_{job_id}.json", job.model_dump()) - - await db.commit() - - -# ── Result CRUD ─────────────────────────────────────────────────────────────── - -async def save_result( - *, - job_id: str, - metrics: BenchmarkMetrics, - telemetry_summary: TelemetrySummary, -) -> BenchmarkResult: - db = await get_db() - result_id = f"bres-{uuid.uuid4().hex[:12]}" - now = datetime.now(timezone.utc).isoformat() - - # Persist result to SQLite - await db.execute( - """INSERT INTO benchmark_results - (id, job_id, metrics, telemetry_summary, created_at) - VALUES (?,?,?,?,?)""", - ( - result_id, - job_id, - json.dumps(metrics.model_dump(exclude_none=True)), - json.dumps(telemetry_summary.model_dump()), - now, - ), - ) - await db.commit() - - result = BenchmarkResult( - id = result_id, - job_id = job_id, - metrics = metrics, - telemetry_summary = telemetry_summary, - created_at = now, - ) - - # Persist result to project folder - await _save_to_project(f"result_{job_id}.json", result.model_dump()) - - log.info("benchmark_result_saved", job_id=job_id, result_id=result_id) - return result - - -async def get_result(job_id: str) -> BenchmarkResult | None: - db = await get_db() - async with db.execute( - """SELECT r.*, j.model_id, j.dataset_id, j.task, j.framework, j.hardware, j.precision - FROM benchmark_results r - JOIN benchmark_jobs j ON r.job_id = j.id - WHERE r.job_id = ?""", (job_id,) - ) as cur: - row = await cur.fetchone() - return row_to_result(row) if row else None - - -async def list_results(*, limit: int = 100) -> list[BenchmarkResult]: - db = await get_db() - async with db.execute( - """SELECT r.*, j.model_id, j.dataset_id, j.task, j.framework, j.hardware, j.precision - FROM benchmark_results r - JOIN benchmark_jobs j ON r.job_id = j.id - ORDER BY r.created_at DESC LIMIT ?""", (limit,) - ) as cur: - rows = await cur.fetchall() - return [row_to_result(r) for r in rows] - - -# ── Validation Log ──────────────────────────────────────────────────────────── - -async def save_validation_log( - *, - job_id: str, - model_id: str, - dataset_id: str, - checks: list[ValidationCheck], - passed: bool, -) -> None: - """Persist an immutable record of all compatibility checks.""" - db = await get_db() - log_id = f"bval-{uuid.uuid4().hex[:12]}" - now = datetime.now(timezone.utc).isoformat() - - await db.execute( - """INSERT INTO benchmark_validation_logs - (id, job_id, model_id, dataset_id, checks, passed, created_at) - VALUES (?,?,?,?,?,?,?)""", - ( - log_id, job_id, model_id, dataset_id, - json.dumps([c.model_dump() for c in checks]), - 1 if passed else 0, - now, - ), - ) - await db.commit() - log.info( - "validation_log_saved", - job_id = job_id, - passed = passed, - n_checks = len(checks), - ) diff --git a/benchmark/telemetry.py b/benchmark/telemetry.py deleted file mode 100644 index cee6cd1202b1d38fdb3de66903382fe7d9cac7b5..0000000000000000000000000000000000000000 --- a/benchmark/telemetry.py +++ /dev/null @@ -1,182 +0,0 @@ -""" -benchmark/telemetry.py — Real-time Telemetry Collector. - -Collects GPU/hardware metrics at 2 Hz during benchmark execution. -Designed as a drop-in adapter: - • Local dev → simulates realistic GPU readings based on hardware tier - • Production → replace _read_gpu_metrics() with pynvml calls: - nvmlDeviceGetUtilizationRates() - nvmlDeviceGetMemoryInfo() - nvmlDeviceGetTemperature() - nvmlDeviceGetPowerUsage() - -Usage (async context): - collector = TelemetryCollector("rtx4090", vram_total_gb=24.0) - await collector.start() - # ... run inference ... - summary = await collector.stop() - samples = collector.samples -""" -from __future__ import annotations - -import asyncio -import random -import statistics -import time - -from models.benchmark import TelemetrySample, TelemetrySummary -from observability.logger import get_logger - -log = get_logger("benchmark.telemetry") - -# ── Hardware simulation profiles ────────────────────────────────────────────── -# (base_util%, base_temp_C, base_power_W) -_HW_PROFILES: dict[str, tuple[float, float, float]] = { - "rtx4090": (88.0, 74.0, 380.0), - "rtx4080": (84.0, 70.0, 280.0), - "rtx4070": (80.0, 68.0, 200.0), - "rtx3090": (85.0, 72.0, 320.0), - "rtx3080": (82.0, 70.0, 250.0), - "rtx3070": (78.0, 66.0, 180.0), - "rtx3060": (74.0, 64.0, 150.0), - "a100": (90.0, 68.0, 350.0), - "h100": (92.0, 65.0, 550.0), - "v100": (87.0, 70.0, 280.0), - "t4": (75.0, 62.0, 60.0), - "gpu": (70.0, 65.0, 150.0), - "cpu": (0.0, 0.0, 0.0), -} - -_COLLECTION_INTERVAL_S = 0.5 # 2 Hz - - -class TelemetryCollector: - """ - Async telemetry collector. Call start() before inference, stop() after. - Thread-safe via asyncio (single-threaded event loop). - """ - - def __init__(self, hardware: str, vram_total_gb: float = 8.0) -> None: - self._hardware = hardware - self._vram_total = vram_total_gb - self._hw_profile = self._resolve_profile(hardware) - self._samples: list[TelemetrySample] = [] - self._running = False - self._task: asyncio.Task | None = None - - # ── Public API ──────────────────────────────────────────────────────────── - - async def start(self) -> None: - self._running = True - self._samples = [] - self._task = asyncio.create_task( - self._collect_loop(), name="telemetry_collector" - ) - log.debug("telemetry_started", hardware=self._hardware) - - async def stop(self) -> TelemetrySummary: - self._running = False - if self._task and not self._task.done(): - self._task.cancel() - try: - await self._task - except asyncio.CancelledError: - pass - log.debug( - "telemetry_stopped", - hardware = self._hardware, - samples = len(self._samples), - ) - return self._build_summary() - - def record_batch_context(self, batch_idx: int, progress: float) -> None: - """Annotate the most recent sample with the current batch context.""" - if self._samples: - self._samples[-1].batch_idx = batch_idx - self._samples[-1].progress = progress - - @property - def samples(self) -> list[TelemetrySample]: - return list(self._samples) - - # ── Internal ────────────────────────────────────────────────────────────── - - async def _collect_loop(self) -> None: - while self._running: - sample = self._read_gpu_metrics() - self._samples.append(sample) - await asyncio.sleep(_COLLECTION_INTERVAL_S) - - def _read_gpu_metrics(self) -> TelemetrySample: - """ - Returns a TelemetrySample for the current hardware state. - - PRODUCTION SWAP: Replace this body with pynvml calls: - handle = nvmlDeviceGetHandleByIndex(0) - util = nvmlDeviceGetUtilizationRates(handle) - mem = nvmlDeviceGetMemoryInfo(handle) - temp = nvmlDeviceGetTemperature(handle, NVML_TEMPERATURE_GPU) - power = nvmlDeviceGetPowerUsage(handle) / 1000 # mW → W - """ - base_util, base_temp, base_power = self._hw_profile - - if base_util == 0.0: # CPU path — no meaningful GPU readings - return TelemetrySample( - timestamp = time.time(), - gpu_util_pct = 0.0, - vram_used_gb = 0.0, - vram_total_gb = 0.0, - temp_c = 0.0, - power_w = 0.0, - ) - - # Simulate realistic jitter (±5% util, ±3°C, ±10W) - jitter_util = random.gauss(0, 3.0) - jitter_temp = random.gauss(0, 1.5) - jitter_power = random.gauss(0, 8.0) - vram_frac = random.uniform(0.58, 0.72) - - return TelemetrySample( - timestamp = time.time(), - gpu_util_pct = max(0.0, min(100.0, base_util + jitter_util)), - vram_used_gb = round( - max(0.0, min(self._vram_total, self._vram_total * vram_frac)), 3 - ), - vram_total_gb = self._vram_total, - temp_c = round(max(0.0, base_temp + jitter_temp), 1), - power_w = round(max(0.0, base_power + jitter_power), 1), - ) - - def _build_summary(self) -> TelemetrySummary: - if not self._samples: - return TelemetrySummary() - - utils = [s.gpu_util_pct for s in self._samples] - vrams = [s.vram_used_gb for s in self._samples] - temps = [s.temp_c for s in self._samples] - powers = [s.power_w for s in self._samples] - - def _safe_mean(lst: list[float]) -> float: - return statistics.mean(lst) if lst else 0.0 - - return TelemetrySummary( - gpu_util_avg = round(_safe_mean(utils), 2), - gpu_util_peak = round(max(utils), 2), - vram_avg_gb = round(_safe_mean(vrams), 3), - vram_peak_gb = round(max(vrams), 3), - temp_avg_c = round(_safe_mean(temps), 1), - temp_peak_c = round(max(temps), 1), - power_avg_w = round(_safe_mean(powers), 1), - power_peak_w = round(max(powers), 1), - ) - - @staticmethod - def _resolve_profile(hardware: str) -> tuple[float, float, float]: - hw = hardware.lower().replace(" ", "").replace("-", "") - for key, profile in _HW_PROFILES.items(): - if key in hw: - return profile - # Default for unknown GPU-class hardware - if any(x in hw for x in ("gpu", "rtx", "gtx", "cuda", "vram")): - return _HW_PROFILES["gpu"] - return _HW_PROFILES["cpu"] diff --git a/benchmark/torch_runner.py b/benchmark/torch_runner.py deleted file mode 100644 index 56ad3ca22d19e5497875d800f2a1bc243fe40df5..0000000000000000000000000000000000000000 --- a/benchmark/torch_runner.py +++ /dev/null @@ -1,142 +0,0 @@ -""" -benchmark/torch_runner.py — Synchronous GPU inference runner. - -Called from BenchmarkExecutor via asyncio.run_in_executor() so it never -blocks the event loop. PyTorch is an optional dependency — if it is not -installed the module raises ImportError and execution.py falls back to -the simulation path. - -Supported weight formats (detected by file extension): - .pt / .pth — torch.load (TorchScript or state-dict) - .safetensors — safetensors.torch.load_file - .onnx — onnxruntime InferenceSession - -PRODUCTION SWAP POINTS are marked with # <<< REPLACE IN PRODUCTION >>> -""" -from __future__ import annotations - -import time -from pathlib import Path -from typing import Any - -# ── Model cache (keyed by absolute path) ───────────────────────────────────── -_MODEL_CACHE: dict[str, Any] = {} - -# Standard input shapes per task (B, C, H, W) -_INPUT_SHAPES: dict[str, tuple[int, int, int]] = { - "detection": (3, 640, 640), - "segmentation": (3, 640, 640), - "classification": (3, 224, 224), - "generation": (3, 512, 512), - "embedding": (3, 224, 224), -} -_DEFAULT_SHAPE = (3, 640, 640) - - -def run_torch_batch(model_path: str, batch_size: int, task: str = "detection") -> float: - """Run one inference batch and return per-image latency in ms. - - Args: - model_path: Absolute path to the weight file. - batch_size: Number of images in the batch. - task: Model task (affects dummy input shape). - - Returns: - Latency per image in milliseconds. - """ - import torch # raises ImportError if not installed - - device = "cuda" if torch.cuda.is_available() else "cpu" - ext = Path(model_path).suffix.lower() - - model = _load_model(model_path, ext, device) - c, h, w = _INPUT_SHAPES.get(task, _DEFAULT_SHAPE) - dummy = torch.zeros(batch_size, c, h, w, device=device) - - # Warm-up pass (first call is slower due to CUDA kernel compilation) - if device == "cuda": - with torch.no_grad(): - _forward(model, dummy, ext, device) - torch.cuda.synchronize() - - # Timed pass - if device == "cuda": - torch.cuda.synchronize() - t0 = time.perf_counter() - with torch.no_grad(): - _forward(model, dummy, ext, device) - if device == "cuda": - torch.cuda.synchronize() - elapsed_ms = (time.perf_counter() - t0) * 1000 - - return elapsed_ms / batch_size - - -def _load_model(path: str, ext: str, device: str) -> Any: - """Load and cache the model by absolute path.""" - if path in _MODEL_CACHE: - return _MODEL_CACHE[path] - - model = _load_by_ext(path, ext, device) - _MODEL_CACHE[path] = model - return model - - -def _load_by_ext(path: str, ext: str, device: str) -> Any: - """Select loader based on file extension.""" - if ext in (".pt", ".pth"): - return _load_torch(path, device) - if ext == ".safetensors": - return _load_safetensors(path, device) - if ext == ".onnx": - return _load_onnx(path) - raise ValueError(f"Unsupported model format: {ext}") - - -def _load_torch(path: str, device: str) -> Any: - import torch - # <<< REPLACE IN PRODUCTION >>> with proper model class instantiation - # TorchScript models can be loaded directly; state-dict models need - # the model class to be imported separately. - try: - model = torch.jit.load(path, map_location=device) - model.eval() - return model - except RuntimeError: - # Not a TorchScript model — try loading as a full checkpoint - obj = torch.load(path, map_location=device, weights_only=False) - if hasattr(obj, "eval"): - obj.eval() - return obj - # It's a state-dict — we cannot run inference without knowing the arch - raise RuntimeError( - f"Model at {path} is a state-dict; cannot run inference without " - "the model class. Use a TorchScript-exported .pt file." - ) - - -def _load_safetensors(path: str, device: str) -> Any: - # <<< REPLACE IN PRODUCTION >>> safetensors gives tensors only; - # you still need the model class. This is intentionally left as a - # placeholder that raises a clear error rather than silently failing. - raise NotImplementedError( - "safetensors inference requires the model class to be registered. " - "Convert to TorchScript or ONNX for architecture-agnostic inference." - ) - - -def _load_onnx(path: str) -> Any: - import onnxruntime as ort # type: ignore[import] - providers = ["CUDAExecutionProvider", "CPUExecutionProvider"] - return ort.InferenceSession(path, providers=providers) - - -def _forward(model: Any, dummy: Any, ext: str, device: str) -> Any: - """Run a single forward pass, dispatching by model type.""" - if ext == ".onnx": - import numpy as np - np_input = dummy.cpu().numpy() - input_name = model.get_inputs()[0].name - return model.run(None, {input_name: np_input}) - # TorchScript / nn.Module - return model(dummy) diff --git a/config.py b/config.py index f632171ee65a51b00b76deaf8a4f2e24d63a5350..35f139c5e5f7cc9546e2344bfe9fbf32acde5b53 100644 --- a/config.py +++ b/config.py @@ -21,31 +21,15 @@ class Settings(BaseSettings): # ── API ─────────────────────────────────────────────────────────── host: str = "0.0.0.0" port: int = 7860 # Default for HF Spaces - cors_origins: list[str] = [ - "http://localhost:3000", - "http://127.0.0.1:3000", - "http://localhost:5173", - "http://127.0.0.1:5173", - "http://localhost:2000", - "http://127.0.0.1:2000", - ] + cors_origins: list[str] = ["*"] # ── Storage ─────────────────────────────────────────────────────── base_dir: Path = Path(__file__).resolve().parents[1] data_dir: Path = base_dir / "data" - models_dir: Path = data_dir / "models" - datasets_dir: Path = data_dir / "datasets" # root for imported datasets - logs_dir: Path = data_dir / "logs" - db_path: Path = data_dir / "modelzoo.db" - - # ── Download Manager ────────────────────────────────────────────── - max_concurrent_downloads: int = 5 - download_chunk_size: int = 1024 * 1024 # 1 MB - download_max_retries: int = 3 - download_retry_delay: float = 2.0 # seconds (base, exponential backoff) + db_path: Path = data_dir / "modelzoo.db" # ── Search ──────────────────────────────────────────────────────── - search_max_results: int = 500 + search_max_results: int = 1000 # ── Sync ────────────────────────────────────────────────────────── auto_sync_on_startup: bool = True @@ -54,30 +38,10 @@ class Settings(BaseSettings): hf_api_base: str = "https://huggingface.co/api" hf_hub_url: str = "https://huggingface.co" hf_token: str | None = None # Optional: HF_TOKEN env var - hf_models_per_task: int = 100 # How many to pull per task - - # ── ONNX Zoo ────────────────────────────────────────────────────── - onnx_models_url: str = ( - "https://raw.githubusercontent.com/onnx/models/main/README.md" - ) - - # ── Benchmark Bridge ────────────────────────────────────────────── - benchmark_max_concurrent: int = 3 # max parallel benchmark jobs - benchmark_max_log_lines: int = 500 # log entries kept per job - benchmark_ws_poll_hz: float = 2.0 # WebSocket telemetry poll rate - - # ── Dataset Manager ─────────────────────────────────────────────── - roboflow_api_base: str = "https://api.roboflow.com" - dataset_import_workers: int = 3 # max concurrent import jobs - dataset_chunk_size: int = 1024 * 1024 * 4 # 4 MB download chunk - roboflow_cache_ttl_secs: int = 3600 # 1 hour + hf_models_per_task: int = 200 # Discovery server pulls more per task def ensure_dirs(self) -> None: self.data_dir.mkdir(parents=True, exist_ok=True) - self.models_dir.mkdir(parents=True, exist_ok=True) - self.datasets_dir.mkdir(parents=True, exist_ok=True) - (self.datasets_dir / "_tmp").mkdir(parents=True, exist_ok=True) - self.logs_dir.mkdir(parents=True, exist_ok=True) settings = Settings() diff --git a/download/__init__.py b/download/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/download/__pycache__/__init__.cpython-310.pyc b/download/__pycache__/__init__.cpython-310.pyc deleted file mode 100644 index a282a59643aafb3112beb51caec1cb3e635b694b..0000000000000000000000000000000000000000 Binary files a/download/__pycache__/__init__.cpython-310.pyc and /dev/null differ diff --git a/download/__pycache__/manager.cpython-310.pyc b/download/__pycache__/manager.cpython-310.pyc deleted file mode 100644 index f01cd1376113a32b4920523bdf08796c359683d2..0000000000000000000000000000000000000000 Binary files a/download/__pycache__/manager.cpython-310.pyc and /dev/null differ diff --git a/download/manager.py b/download/manager.py deleted file mode 100644 index 0605d4b6ac6e2c73fa2d7ee4333ffa62983e364d..0000000000000000000000000000000000000000 --- a/download/manager.py +++ /dev/null @@ -1,366 +0,0 @@ -""" -download/manager.py — Async download manager. -Handles queueing, concurrency limiting, retry, resume, and progress tracking. -All state is persisted in the jobs table for crash recovery. -""" -from __future__ import annotations - -import asyncio -import json -import uuid -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - -import aiofiles -import httpx -from tenacity import retry, stop_after_attempt, wait_exponential - -from config import settings -from database.connection import get_db -from models.job import Job, row_to_job -from observability.logger import audit, get_logger -from registry.registry import get_model, update_model_status - -log = get_logger("download_manager") - -# ── Semaphore caps concurrent downloads ─────────────────────────────────────── -_download_sem: asyncio.Semaphore | None = None - - -def _get_sem() -> asyncio.Semaphore: - global _download_sem - if _download_sem is None: - _download_sem = asyncio.Semaphore(settings.max_concurrent_downloads) - return _download_sem - - -# ── Job CRUD ────────────────────────────────────────────────────────────────── - -async def _create_job( - job_type: str, - model_id: str, - model_name: str, - meta: dict | None = None, -) -> str: - job_id = str(uuid.uuid4()) - db = await get_db() - now = datetime.now(timezone.utc).isoformat() - await db.execute( - """INSERT INTO jobs (id, type, status, model_id, model_name, meta, created_at, updated_at) - VALUES (?,?,?,?,?,?,?,?)""", - (job_id, job_type, "queued", model_id, model_name, - json.dumps(meta or {}), now, now), - ) - await db.commit() - log.info("job_created", job_id=job_id, type=job_type, model_id=model_id) - await audit("job_created", model_id=model_id, job_id=job_id, - payload={"type": job_type, "model_name": model_name}) - return job_id - - -def _is_shard_file(filename: str) -> bool: - """Return True if the file is part of a sharded model (e.g. model-00001-of-00003.safetensors).""" - import re - return bool(re.search(r"-\d{5}-of-\d{5}\.", filename)) - - -async def _get_active_version(model_id: str) -> str: - """Return the active version string for a model, defaulting to 'v1'.""" - model = await get_model(model_id) - if model and model.active_version: - return model.active_version - return "v1" - - -@retry( - stop=stop_after_attempt(3), - wait=wait_exponential(multiplier=1, min=1, max=6), - reraise=True, -) -async def _resolve_hf_download_url(repo_id: str) -> str: - """Resolve a reliable download URL for a HF repo. - - Prefer safetensors over pytorch_model.bin; fall back to onnx if needed. - """ - async with httpx.AsyncClient(timeout=30, follow_redirects=True) as client: - resp = await client.get(f"{settings.hf_api_base}/models/{repo_id}") - resp.raise_for_status() - data = resp.json() - - siblings = data.get("siblings") or [] - filenames: list[str] = [] - for s in siblings: - fn = s.get("rfilename") or s.get("filename") - if fn: - filenames.append(fn) - - preferred_exact = [ - "model.safetensors", - "pytorch_model.bin", - "model.onnx", - ] - for fn in preferred_exact: - if fn in filenames: - return f"https://huggingface.co/{repo_id}/resolve/main/{fn}" - - preferred_suffix = [".safetensors", ".bin", ".onnx", ".pt", ".pth"] - for suffix in preferred_suffix: - for fn in filenames: - if fn.endswith(suffix) and not _is_shard_file(fn): - return f"https://huggingface.co/{repo_id}/resolve/main/{fn}" - - # Accept sharded files as a fallback (first shard of safetensors) - for fn in filenames: - if _is_shard_file(fn): - return f"https://huggingface.co/{repo_id}/resolve/main/{fn}" - - # Last resort: try the index file for sharded models - if "model.safetensors.index.json" in filenames: - # For sharded models without a single file, use the first shard - for fn in filenames: - if fn.startswith("model-") and fn.endswith(".safetensors"): - return f"https://huggingface.co/{repo_id}/resolve/main/{fn}" - - return f"https://huggingface.co/{repo_id}/resolve/main/pytorch_model.bin" - - -async def _update_job( - job_id: str, - status: str | None = None, - progress: float | None = None, - error: str | None = None, - started_at: str | None = None, - ended_at: str | None = None, -) -> None: - db = await get_db() - now = datetime.now(timezone.utc).isoformat() - parts: list[str] = ["updated_at = ?"] - vals: list[Any] = [now] - if status is not None: parts.append("status = ?"); vals.append(status) - if progress is not None: parts.append("progress = ?"); vals.append(progress) - if error is not None: parts.append("error = ?"); vals.append(error) - if started_at: parts.append("started_at = ?"); vals.append(started_at) - if ended_at: parts.append("ended_at = ?"); vals.append(ended_at) - vals.append(job_id) - await db.execute(f"UPDATE jobs SET {', '.join(parts)} WHERE id = ?", vals) - await db.commit() - - -# ── Download worker ─────────────────────────────────────────────────────────── - -async def _execute_download( - job_id: str, - model_id: str, - model_name: str, - download_url: str, - dest_path: Path, -) -> None: - now = datetime.now(timezone.utc).isoformat() - await _update_job(job_id, status="running", started_at=now) - - dest_path.parent.mkdir(parents=True, exist_ok=True) - tmp_path = dest_path.with_suffix(".tmp") - - # Determine resume offset - resume_offset = tmp_path.stat().st_size if tmp_path.exists() else 0 - - headers: dict[str, str] = {} - if resume_offset: - headers["Range"] = f"bytes={resume_offset}-" - log.info("download_resume", job_id=job_id, offset=resume_offset) - - try: - async with httpx.AsyncClient(timeout=120, follow_redirects=True) as client: - async with client.stream("GET", download_url, headers=headers) as resp: - resp.raise_for_status() - total = int(resp.headers.get("content-length", 0)) + resume_offset - downloaded = resume_offset - - async with aiofiles.open(tmp_path, "ab" if resume_offset else "wb") as fh: - async for chunk in resp.aiter_bytes(chunk_size=settings.download_chunk_size): - await fh.write(chunk) - downloaded += len(chunk) - progress = downloaded / total if total else 0 - await _update_job(job_id, progress=min(progress, 0.99)) - - # Rename tmp → final - tmp_path.rename(dest_path) - now_end = datetime.now(timezone.utc).isoformat() - await _update_job(job_id, status="completed", progress=1.0, ended_at=now_end) - await update_model_status( - model_id, - status="cached", - downloaded=True, - local_path=str(dest_path), - ) - # Copy into the active project's workspace models/ folder - from projects.service import link_model_to_active_project - await link_model_to_active_project(model_id, str(dest_path)) - log.info("download_complete", job_id=job_id, model_id=model_id, path=str(dest_path)) - await audit("download_complete", model_id=model_id, job_id=job_id, - payload={"path": str(dest_path)}) - - except Exception as exc: - now_end = datetime.now(timezone.utc).isoformat() - await _update_job(job_id, status="failed", error=str(exc), ended_at=now_end) - await update_model_status(model_id, status="error") - log.error("download_failed", job_id=job_id, error=str(exc)) - await audit("download_failed", model_id=model_id, job_id=job_id, - payload={"error": str(exc)}, level="error") - raise - - -# ── Public API ──────────────────────────────────────────────────────────────── - -async def enqueue_download( - model_id: str, - model_name: str, - download_url: str | None = None, - version: str | None = None, -) -> str: - """Create a download job and dispatch resolution+download in the background. - - This function should not perform network calls; otherwise /download can return 500 - on transient provider errors. - """ - job_id = await _create_job("download", model_id, model_name) - - asyncio.create_task( - _rate_limited_download_resolving(job_id, model_id, model_name, download_url, version) - ) - return job_id - - -async def _rate_limited_download_resolving( - job_id: str, - model_id: str, - model_name: str, - download_url: str | None, - version: str | None = None, -) -> None: - async with _get_sem(): - try: - resolved_url = await _resolve_download_url(model_id, download_url, version) - # Version folder: use explicit version label, else active_version from DB - folder = version or await _get_active_version(model_id) - ext = Path(resolved_url.split("?")[0]).suffix or ".bin" - dest_path = settings.models_dir / model_id / folder / f"model{ext}" - await _execute_download(job_id, model_id, model_name, resolved_url, dest_path) - except Exception as exc: - now_end = datetime.now(timezone.utc).isoformat() - await _update_job(job_id, status="failed", error=str(exc), ended_at=now_end) - await update_model_status(model_id, status="error") - log.error("download_failed", job_id=job_id, error=str(exc)) - await audit( - "download_failed", - model_id=model_id, - job_id=job_id, - payload={"error": str(exc)}, - level="error", - ) - - -async def _resolve_download_url( - model_id: str, - download_url: str | None, - version: str | None = None, -) -> str: - """Resolve the final download URL for a model. - - If `version` is provided and looks like a filename (e.g. 'yolov8n_pt'), - it was generated by hf_adapter from a sibling rfilename. Restore the - original filename (replace trailing _ext with .ext) and build a direct URL. - """ - repo_id: str | None = None - - if download_url and "huggingface.co" in download_url: - repo_id = download_url.replace("https://huggingface.co/", "").rstrip("/") - elif not download_url: - model = await get_model(model_id) - if model and model.download_url: - url = model.download_url - if "huggingface.co" in url: - repo_id = url.replace("https://huggingface.co/", "").rstrip("/") - else: - return url - else: - repo_id = model_id.replace("_", "/", 1) - else: - return download_url - - # If the caller specified a version that is a converted rfilename - # (dots replaced with underscores by hf_adapter), reconstruct the filename. - if version and repo_id: - filename = _version_to_filename(version) - if filename: - return f"https://huggingface.co/{repo_id}/resolve/main/{filename}" - - return await _resolve_hf_download_url(repo_id) - - -def _version_to_filename(version: str) -> str | None: - """Convert an hf_adapter version string back to a real filename. - - hf_adapter stores version as rfilename.replace('.', '_'), e.g.: - 'yolov8n_pt' → 'yolov8n.pt' - 'model_safetensors' → 'model.safetensors' - Only converts if the result ends with a known weight extension. - """ - weight_exts = (".pt", ".pth", ".safetensors", ".bin", ".onnx") - # Try replacing the last underscore with a dot - idx = version.rfind("_") - if idx == -1: - return None - candidate = version[:idx] + "." + version[idx + 1:] - if any(candidate.endswith(ext) for ext in weight_exts): - return candidate - return None - - -async def _rate_limited_download( - job_id: str, - model_id: str, - model_name: str, - download_url: str, - dest_path: Path, -) -> None: - async with _get_sem(): - try: - await _execute_download(job_id, model_id, model_name, download_url, dest_path) - except Exception: - pass # Already logged & stored in DB - - -async def get_job(job_id: str) -> Job | None: - db = await get_db() - async with db.execute("SELECT * FROM jobs WHERE id = ?", (job_id,)) as cur: - row = await cur.fetchone() - return row_to_job(row) if row else None - - -async def list_jobs( - status: str | None = None, - limit: int = 50, -) -> list[Job]: - db = await get_db() - if status: - sql = "SELECT * FROM jobs WHERE status = ? ORDER BY created_at DESC LIMIT ?" - params: tuple = (status, limit) - else: - sql = "SELECT * FROM jobs ORDER BY created_at DESC LIMIT ?" - params = (limit,) - async with db.execute(sql, params) as cur: - rows = await cur.fetchall() - return [row_to_job(r) for r in rows] - - -async def cancel_job(job_id: str) -> bool: - """Cancel a queued or running job (best-effort).""" - job = await get_job(job_id) - if not job or job.status not in ("queued", "running"): - return False - now = datetime.now(timezone.utc).isoformat() - await _update_job(job_id, status="cancelled", ended_at=now) - log.info("job_cancelled", job_id=job_id) - return True diff --git a/inference/__init__.py b/inference/__init__.py deleted file mode 100644 index a111050304c40dcb721f14fd1515588ba0cc0376..0000000000000000000000000000000000000000 --- a/inference/__init__.py +++ /dev/null @@ -1 +0,0 @@ -# inference package diff --git a/inference/__pycache__/__init__.cpython-310.pyc b/inference/__pycache__/__init__.cpython-310.pyc deleted file mode 100644 index 85505a07772b135ba47d22c8cf9200943fd2f983..0000000000000000000000000000000000000000 Binary files a/inference/__pycache__/__init__.cpython-310.pyc and /dev/null differ diff --git a/inference/__pycache__/engine.cpython-310.pyc b/inference/__pycache__/engine.cpython-310.pyc deleted file mode 100644 index 3b9abe12755a60406c2f1c9de2034e05a24ef885..0000000000000000000000000000000000000000 Binary files a/inference/__pycache__/engine.cpython-310.pyc and /dev/null differ diff --git a/inference/__pycache__/session.cpython-310.pyc b/inference/__pycache__/session.cpython-310.pyc deleted file mode 100644 index e2f1494ed9e28264fd4d522f56a20c20599fbce7..0000000000000000000000000000000000000000 Binary files a/inference/__pycache__/session.cpython-310.pyc and /dev/null differ diff --git a/inference/engine.py b/inference/engine.py deleted file mode 100644 index 61ff7c379a8ea7591abec730078418878239862a..0000000000000000000000000000000000000000 --- a/inference/engine.py +++ /dev/null @@ -1,447 +0,0 @@ -""" -inference/engine.py — MLForge Inference Engine. - -Dispatcher that routes each InferenceRequest to the correct adapter pipeline: - YOLO → YOLOInferencePipeline - TRANSFORMERS → TransformersPipeline - ONNX → ONNXPipeline - CUSTOM → CustomPipeline - -Each pipeline implements preprocess → inference_step → postprocess. -Simulation paths are used when real model weights are not loaded; -every # <<< REPLACE IN PRODUCTION >>> comment marks the exact swap point. - -Architecture follows the spec in infra_arch.md §4 (Adapter Protocol). -""" -from __future__ import annotations - -import asyncio -import base64 -import io -import random -import time -import uuid -from typing import Any - -from models.inference import ( - AdapterType, - Detection, - InferenceRequest, - InferenceResult, - PipelineStage, -) -from models.model import Model -from observability.logger import get_logger - -log = get_logger("inference.engine") - -# ── Model cache: model_id → loaded model object ────────────────────────────── -_MODEL_CACHE: dict[str, Any] = {} - - -def _now_ms() -> float: - return time.perf_counter() * 1000 - - -# ── YOLO Pipeline ───────────────────────────────────────────────────────────── - -class YOLOPipeline: - """ - YOLO inference pipeline. - Preprocess: letterbox resize → BGR→RGB → 1/255 normalise. - Postprocess: NMS → [{x1,y1,x2,y2,confidence,class_id,class_name}]. - """ - - async def run( - self, req: InferenceRequest, model: Model - ) -> tuple[list[PipelineStage], dict[str, Any]]: - cfg = req.yolo_config - conf = cfg.confidence if cfg else 0.25 - iou = cfg.iou_threshold if cfg else 0.45 - - stages: list[PipelineStage] = [] - - # — Stage 1: Preprocess ———————————————————————————— - t0 = _now_ms() - await asyncio.sleep(0) # yield control - if req.image_base64: - try: - raw_bytes = base64.b64decode(req.image_base64) - # <<< REPLACE IN PRODUCTION >>> - # img = cv2.imdecode(np.frombuffer(raw_bytes, np.uint8), cv2.IMREAD_COLOR) - # tensor = letterbox(img, 640) / 255.0 - _ = len(raw_bytes) # validate decode worked - except Exception as e: - return [PipelineStage(name="Preprocess", status="error", detail=str(e))], {} - pre_ms = _now_ms() - t0 + random.uniform(0.8, 2.5) - stages.append(PipelineStage(name="Preprocess", status="done", - latency_ms=round(pre_ms, 2), detail="Letterbox 640×640")) - - # — Stage 2: Engine Load ——————————————————————————— - t1 = _now_ms() - loaded = model.id in _MODEL_CACHE - load_ms = 0.0 if loaded else random.uniform(80, 220) - await asyncio.sleep(load_ms / 1000.0) - if not loaded: - _MODEL_CACHE[model.id] = object() # <<< REPLACE: load actual weights - stages.append(PipelineStage(name="Engine Load", status="done", - latency_ms=round(_now_ms() - t1, 2), - detail="Cache hit" if loaded else "Weights loaded")) - - # — Stage 3: Inference ———————————————————————————— - t2 = _now_ms() - size_gb = max(model.size, 1) / (1024 ** 3) - base_lat = 2.5 + size_gb * 1.5 - infer_ms = base_lat + random.gauss(0, base_lat * 0.07) - await asyncio.sleep(infer_ms / 1000.0) - # <<< REPLACE IN PRODUCTION >>> - # results = model_obj(tensor, conf=conf, iou=iou) - stages.append(PipelineStage(name="Inference", status="done", - latency_ms=round(infer_ms, 2), - detail=f"conf={conf} iou={iou}")) - - # — Stage 4: Post-process (NMS) —————————————————— - t3 = _now_ms() - detections = self._simulate_detections(conf, cfg.class_filter if cfg else []) - post_ms = random.uniform(0.3, 1.2) - await asyncio.sleep(post_ms / 1000.0) - stages.append(PipelineStage(name="NMS Post-process", status="done", - latency_ms=round(post_ms, 2), - detail=f"{len(detections)} detections")) - - output: dict[str, Any] = { - "detections": [d.model_dump() for d in detections], - "pre_ms": round(pre_ms, 2), - "infer_ms": round(infer_ms, 2), - "post_ms": round(post_ms, 2), - } - return stages, output - - @staticmethod - def _simulate_detections(conf_thresh: float, class_filter: list[str]) -> list[Detection]: - """Simulate bounding-box detections. <<< REPLACE with real NMS output.""" - CLASSES = ["person", "car", "truck", "bicycle", "dog", "cat", - "traffic light", "stop sign", "bench", "bird"] - n = random.randint(0, 8) - dets: list[Detection] = [] - for _ in range(n): - c = random.uniform(conf_thresh, 1.0) - cid = random.randint(0, len(CLASSES) - 1) - cname = CLASSES[cid] - if class_filter and cname not in class_filter: - continue - x1 = random.uniform(0, 0.7) - y1 = random.uniform(0, 0.7) - dets.append(Detection( - x1=round(x1 * 640, 1), y1=round(y1 * 640, 1), - x2=round((x1 + random.uniform(0.05, 0.3)) * 640, 1), - y2=round((y1 + random.uniform(0.05, 0.3)) * 640, 1), - confidence=round(c, 4), - class_id=cid, class_name=cname, - )) - return dets - - -# ── Transformers Pipeline ───────────────────────────────────────────────────── - -class TransformersPipeline: - """ - HuggingFace Transformers pipeline. - Preprocess: AutoTokenizer.encode. - Inference: model.generate with KV-cache. - Postprocess: decode + strip special tokens. - """ - - async def run( - self, req: InferenceRequest, model: Model - ) -> tuple[list[PipelineStage], dict[str, Any]]: - cfg = req.transformers_config - stages: list[PipelineStage] = [] - - # — Tokenize —————————————————————————————————————— - t0 = _now_ms() - txt = req.text_input or "Hello, world!" - tok_count = len(txt.split()) * 2 # rough BPE estimate - await asyncio.sleep(0.002) - pre_ms = _now_ms() - t0 + random.uniform(1, 4) - stages.append(PipelineStage(name="Tokenise", status="done", - latency_ms=round(pre_ms, 2), - detail=f"{tok_count} tokens")) - - # — Engine Load ————————————————————————————————— - t1 = _now_ms() - loaded = model.id in _MODEL_CACHE - load_ms = 0.0 if loaded else random.uniform(150, 400) - await asyncio.sleep(load_ms / 1000.0) - if not loaded: - _MODEL_CACHE[model.id] = object() - stages.append(PipelineStage(name="Engine Load", status="done", - latency_ms=round(_now_ms() - t1, 2), - detail="Cache hit" if loaded else "Model loaded")) - - # — Generate —————————————————————————————————————— - t2 = _now_ms() - max_tok = cfg.max_new_tokens if cfg else 256 - # Simulate token-by-token generation at ~20 tok/s - infer_ms = (max_tok / 20.0) * 1000 + random.gauss(0, 50) - await asyncio.sleep(min(infer_ms / 1000.0, 0.5)) # cap sim delay - # <<< REPLACE IN PRODUCTION >>> - # outputs = model_obj.generate(input_ids, max_new_tokens=max_tok, - # temperature=cfg.temperature, top_p=cfg.top_p, do_sample=cfg.do_sample) - stages.append(PipelineStage(name="Generate", status="done", - latency_ms=round(infer_ms, 2), - detail=f"~{max_tok} tokens @ fp16")) - - # — Decode ———————————————————————————————————————— - t3 = _now_ms() - text_output = self._simulate_text(txt, max_tok) - post_ms = random.uniform(0.5, 2.0) - stages.append(PipelineStage(name="Decode", status="done", - latency_ms=round(post_ms, 2), - detail="Special tokens stripped")) - - output: dict[str, Any] = { - "text_output": text_output, - "tokens_generated": max_tok, - "pre_ms": round(pre_ms, 2), - "infer_ms": round(infer_ms, 2), - "post_ms": round(post_ms, 2), - } - return stages, output - - @staticmethod - def _simulate_text(prompt: str, n_tokens: int) -> str: - """Placeholder generation. <<< REPLACE with model.generate.""" - lorem = ( - "The model processed your input and generated a response based on the " - "learned distribution of the training corpus. This output is a simulation " - "placeholder — replace with actual model.generate() in production. " - ) - # Repeat to roughly match token count - words = (lorem * (n_tokens // 20 + 1)).split()[:n_tokens] - return " ".join(words) - - -# ── ONNX Pipeline ───────────────────────────────────────────────────────────── - -class ONNXPipeline: - """ - ONNX Runtime pipeline. - Acts as universal wrapper for TF / sklearn / PyTorch exported models. - Dynamically maps input tensor names from model metadata. - """ - - async def run( - self, req: InferenceRequest, model: Model - ) -> tuple[list[PipelineStage], dict[str, Any]]: - cfg = req.onnx_config - stages: list[PipelineStage] = [] - provider = cfg.execution_provider if cfg else "CUDAExecutionProvider" - - # — Preprocess ———————————————————————————————————— - t0 = _now_ms() - pre_ms = random.uniform(1.0, 3.5) - await asyncio.sleep(pre_ms / 1000.0) - stages.append(PipelineStage(name="Preprocess", status="done", - latency_ms=round(pre_ms, 2), - detail="Normalise + reshape tensor")) - - # — ONNX Runtime —————————————————————————————————— - t1 = _now_ms() - loaded = model.id in _MODEL_CACHE - load_ms = 0.0 if loaded else random.uniform(50, 150) - await asyncio.sleep(load_ms / 1000.0) - if not loaded: - _MODEL_CACHE[model.id] = object() - # <<< REPLACE IN PRODUCTION >>> - # import onnxruntime as ort - # sess_opts = ort.SessionOptions() - # _MODEL_CACHE[model.id] = ort.InferenceSession( - # model.local_path, sess_options=sess_opts, - # providers=[provider]) - stages.append(PipelineStage(name="ONNX Runtime", status="done", - latency_ms=round(_now_ms() - t1, 2), - detail=provider.replace("ExecutionProvider", ""))) - - # — Inference ———————————————————————————————————— - t2 = _now_ms() - infer_ms = random.uniform(3.0, 12.0) - await asyncio.sleep(infer_ms / 1000.0) - # <<< REPLACE IN PRODUCTION >>> - # ort_inputs = {sess.get_inputs()[0].name: tensor.numpy()} - # raw = sess.run(None, ort_inputs) - stages.append(PipelineStage(name="Inference", status="done", - latency_ms=round(infer_ms, 2), - detail="session.run()")) - - # — Format Output ———————————————————————————————— - t3 = _now_ms() - post_ms = random.uniform(0.2, 0.8) - raw_out = {"output_0": [round(random.random(), 4) for _ in range(10)]} - stages.append(PipelineStage(name="Format Output", status="done", - latency_ms=round(post_ms, 2), - detail="Tensor → JSON")) - - output: dict[str, Any] = { - "raw_output": raw_out, - "pre_ms": round(pre_ms, 2), - "infer_ms": round(infer_ms, 2), - "post_ms": round(post_ms, 2), - } - return stages, output - - -# ── Custom Python Pipeline ──────────────────────────────────────────────────── - -class CustomPipeline: - """ - Sandboxed custom Python pipeline. - Executes user-supplied pre/postprocess scripts in a restricted namespace. - Only numpy, the input tensor, and the model's raw output are accessible. - """ - - FORBIDDEN = ("import os", "import sys", "subprocess", "open(", "__import__", - "eval(", "exec(", "globals(", "locals(") - - def _validate_script(self, script: str) -> str | None: - for tok in self.FORBIDDEN: - if tok in script: - return f"Forbidden token in script: {tok!r}" - return None - - async def run( - self, req: InferenceRequest, model: Model - ) -> tuple[list[PipelineStage], dict[str, Any]]: - cfg = req.custom_config - stages: list[PipelineStage] = [] - - # — Validate scripts —————————————————————————————— - if cfg: - for label, script in [("preprocess", cfg.preprocess_script), - ("postprocess", cfg.postprocess_script)]: - if script: - err = self._validate_script(script) - if err: - return [PipelineStage(name=label.capitalize(), - status="error", detail=err)], {} - - # — Transform Input ——————————————————————————————— - pre_ms = random.uniform(1.0, 5.0) - await asyncio.sleep(pre_ms / 1000.0) - stages.append(PipelineStage(name="Transform Input", status="done", - latency_ms=round(pre_ms, 2), - detail="Custom preprocess script")) - - # — Run Inference ———————————————————————————————— - infer_ms = random.uniform(5.0, 30.0) - await asyncio.sleep(infer_ms / 1000.0) - # <<< REPLACE IN PRODUCTION >>> - # namespace = {"input": tensor, "model": raw_model} - # exec(compile(cfg.preprocess_script, "
", "exec"), namespace)
-        # tensor = namespace.get("output", tensor)
-        stages.append(PipelineStage(name="Run Inference", status="done",
-                                    latency_ms=round(infer_ms, 2),
-                                    detail="Custom runtime"))
-
-        # — Format Result ————————————————————————————————
-        post_ms = random.uniform(0.5, 3.0)
-        stages.append(PipelineStage(name="Format Result", status="done",
-                                    latency_ms=round(post_ms, 2),
-                                    detail="Custom postprocess script"))
-
-        output: dict[str, Any] = {
-            "raw_output": {"custom_result": round(random.random(), 4)},
-            "pre_ms":   round(pre_ms, 2),
-            "infer_ms": round(infer_ms, 2),
-            "post_ms":  round(post_ms, 2),
-        }
-        return stages, output
-
-
-# ── Master Dispatcher ─────────────────────────────────────────────────────────
-
-_PIPELINE_MAP = {
-    AdapterType.YOLO:         YOLOPipeline,
-    AdapterType.TRANSFORMERS: TransformersPipeline,
-    AdapterType.ONNX:         ONNXPipeline,
-    AdapterType.CUSTOM:       CustomPipeline,
-}
-
-
-class InferenceEngine:
-    """
-    Central inference dispatcher.
-    Resolves the correct pipeline, executes it, and wraps the result
-    into a fully-populated InferenceResult.
-    """
-
-    async def run(self, req: InferenceRequest, model: Model) -> InferenceResult:
-        t_start = _now_ms()
-        pipeline_cls = _PIPELINE_MAP.get(req.adapter_type)
-        if pipeline_cls is None:
-            return InferenceResult(
-                request_id=str(uuid.uuid4()),
-                model_id=req.model_id,
-                adapter_type=req.adapter_type,
-                status="error",
-                error=f"Unknown adapter type: {req.adapter_type}",
-            )
-
-        try:
-            stages, output = await pipeline_cls().run(req, model)
-
-            total_ms = _now_ms() - t_start
-            pre_ms   = output.get("pre_ms", 0.0)
-            infer_ms = output.get("infer_ms", 0.0)
-            post_ms  = output.get("post_ms", 0.0)
-
-            # Quality score: mean confidence of detections (0–5 scale)
-            detections = [Detection(**d) for d in output.get("detections", [])]
-            if detections:
-                mean_conf = sum(d.confidence for d in detections) / len(detections)
-                quality   = round(mean_conf * 5.0, 2)
-            else:
-                quality = round(random.uniform(3.2, 4.8), 2)
-
-            result = InferenceResult(
-                model_id      = req.model_id,
-                adapter_type  = req.adapter_type,
-                preprocess_ms = pre_ms,
-                inference_ms  = infer_ms,
-                postprocess_ms= post_ms,
-                total_ms      = round(total_ms, 2),
-                pipeline      = stages,
-                detections    = detections,
-                text_output   = output.get("text_output"),
-                raw_output    = output.get("raw_output"),
-                quality_score = quality,
-                status        = "ok",
-            )
-
-            log.info("inference_complete",
-                     model_id=req.model_id,
-                     adapter=req.adapter_type,
-                     total_ms=round(total_ms, 2))
-            return result
-
-        except Exception as exc:
-            log.error("inference_error", model_id=req.model_id, error=str(exc))
-            return InferenceResult(
-                model_id=req.model_id,
-                adapter_type=req.adapter_type,
-                status="error",
-                error=str(exc),
-            )
-
-
-def get_cache_status() -> dict[str, bool]:
-    """Return which model IDs are currently warm in cache."""
-    return {k: True for k in _MODEL_CACHE}
-
-
-def evict_model(model_id: str) -> bool:
-    """Evict a model from the in-process cache (free VRAM sim)."""
-    if model_id in _MODEL_CACHE:
-        del _MODEL_CACHE[model_id]
-        return True
-    return False
diff --git a/inference/session.py b/inference/session.py
deleted file mode 100644
index 4e829443fddb311a8895e1feb183117d92e18c33..0000000000000000000000000000000000000000
--- a/inference/session.py
+++ /dev/null
@@ -1,80 +0,0 @@
-"""
-inference/session.py — In-memory inference session ledger.
-
-Keeps the last MAX_HISTORY inference results per process lifetime.
-Persisted to the SQLite `inference_history` table on each write
-(non-blocking via aiosqlite).
-"""
-from __future__ import annotations
-
-import asyncio
-import json
-import uuid
-from collections import deque
-from typing import Deque
-
-from models.inference import InferenceHistoryEntry, InferenceRequest, InferenceResult
-from observability.logger import get_logger
-
-log = get_logger("inference.session")
-
-MAX_HISTORY = 200
-
-_history: Deque[InferenceHistoryEntry] = deque(maxlen=MAX_HISTORY)
-_lock = asyncio.Lock()
-
-
-async def record(req: InferenceRequest, result: InferenceResult, model_name: str) -> None:
-    """Append a completed inference run to the ledger."""
-    entry = InferenceHistoryEntry(
-        model_id    = req.model_id,
-        model_name  = model_name,
-        adapter_type = req.adapter_type,
-        total_ms    = result.total_ms,
-        quality_score = result.quality_score,
-        status      = result.status,
-        request_snapshot = req.model_dump(exclude={"image_base64"}),
-    )
-    async with _lock:
-        _history.appendleft(entry)
-
-    # Persist to DB (fire-and-forget)
-    asyncio.create_task(_persist(entry))
-
-
-async def _persist(entry: InferenceHistoryEntry) -> None:
-    try:
-        from database.connection import get_db
-        async with get_db() as db:
-            await db.execute(
-                """
-                INSERT OR REPLACE INTO inference_history
-                  (id, model_id, model_name, adapter_type, timestamp,
-                   total_ms, quality_score, status, request_snapshot)
-                VALUES (?,?,?,?,?,?,?,?,?)
-                """,
-                (
-                    entry.id,
-                    entry.model_id,
-                    entry.model_name,
-                    entry.adapter_type.value,
-                    entry.timestamp,
-                    entry.total_ms,
-                    entry.quality_score,
-                    entry.status,
-                    json.dumps(entry.request_snapshot),
-                ),
-            )
-            await db.commit()
-    except Exception as exc:
-        log.warning("inference_persist_failed", error=str(exc))
-
-
-async def get_history(limit: int = 50) -> list[InferenceHistoryEntry]:
-    async with _lock:
-        return list(_history)[:limit]
-
-
-async def clear_history() -> None:
-    async with _lock:
-        _history.clear()
diff --git a/main.py b/main.py
index a25f68a44e0a261d25f1ee2182813a76f16fa578..8290baeaff5856951995c937a69573dbb00ca6d1 100644
--- a/main.py
+++ b/main.py
@@ -26,7 +26,6 @@ from fastapi.responses import JSONResponse
 from api.routes import models as models_router
 from api.routes import sync as sync_router
 from api.routes import datasets as datasets_router
-from api.routes import projects as projects_router
 from config import settings
 from database.connection import close_db, get_db
 from middleware.logging_middleware import RequestLoggingMiddleware
@@ -65,9 +64,9 @@ async def lifespan(app: FastAPI) -> AsyncIterator[None]:
 
 # ── Application ───────────────────────────────────────────────────────────────
 app = FastAPI(
-    title=settings.app_name,
+    title="MLForge Cloud Registry",
     version=settings.version,
-    description="Production ML Model Zoo backend — local-first, traceable, extensible.",
+    description="Global Model and Dataset Discovery Service — The Brain of MLForge.",
     docs_url="/docs",
     redoc_url="/redoc",
     lifespan=lifespan,
@@ -91,8 +90,7 @@ async def global_exception_handler(request: Request, exc: Exception):
 # ── Middleware ─────────────────────────────────────────────────────────────────
 app.add_middleware(
     CORSMiddleware,
-    allow_origins=settings.cors_origins,
-    allow_origin_regex=r"^https?://(localhost|127\\.0\\.0\\.1)(:\\d+)?$",
+    allow_origins=["*"],  # Allow all origins for the cloud registry to support SDK/CLI/UI
     allow_credentials=True,
     allow_methods=["*"],
     allow_headers=["*"],
@@ -103,7 +101,6 @@ app.add_middleware(RequestLoggingMiddleware)
 app.include_router(models_router.router)
 app.include_router(sync_router.router)
 app.include_router(datasets_router.router)
-app.include_router(projects_router.router)
 
 
 @app.get("/health", tags=["system"])
@@ -114,6 +111,7 @@ async def health() -> dict:
     n_datasets = await count_datasets()
     return {
         "status": "ok",
+        "service": "cloud_registry",
         "version": settings.version,
         "model_count": n_models,
         "dataset_count": n_datasets,
diff --git a/projects/__init__.py b/projects/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/projects/__pycache__/__init__.cpython-310.pyc b/projects/__pycache__/__init__.cpython-310.pyc
deleted file mode 100644
index 32012d58ef7624a78017691a0749ea3e665d812b..0000000000000000000000000000000000000000
Binary files a/projects/__pycache__/__init__.cpython-310.pyc and /dev/null differ
diff --git a/projects/__pycache__/registry.cpython-310.pyc b/projects/__pycache__/registry.cpython-310.pyc
deleted file mode 100644
index 67d54c8623bf91bec987d8cd7815bc46f2095fa8..0000000000000000000000000000000000000000
Binary files a/projects/__pycache__/registry.cpython-310.pyc and /dev/null differ
diff --git a/projects/__pycache__/service.cpython-310.pyc b/projects/__pycache__/service.cpython-310.pyc
deleted file mode 100644
index 001fc8db63f17e75d6ef644b43fe80388712d0f6..0000000000000000000000000000000000000000
Binary files a/projects/__pycache__/service.cpython-310.pyc and /dev/null differ
diff --git a/projects/registry.py b/projects/registry.py
deleted file mode 100644
index e541ae8fe4bdebbce6ce1a3b15f85aa61ddd0e4d..0000000000000000000000000000000000000000
--- a/projects/registry.py
+++ /dev/null
@@ -1,73 +0,0 @@
-"""projects/registry.py — Project persistence in SQLite."""
-
-from __future__ import annotations
-
-from datetime import datetime, timezone
-
-from database.connection import get_db
-from models.project import Project
-
-
-async def upsert_project(project: Project) -> None:
-    db = await get_db()
-    await db.execute(
-        """INSERT INTO projects (id, name, path, created_at, last_opened, status)
-           VALUES (?,?,?,?,?,?)
-           ON CONFLICT(id) DO UPDATE SET
-             name=excluded.name,
-             path=excluded.path,
-             last_opened=excluded.last_opened,
-             status=excluded.status
-        """,
-        (
-            project.id,
-            project.name,
-            project.path,
-            project.created_at,
-            project.last_opened,
-            project.status,
-        ),
-    )
-    await db.commit()
-
-
-async def list_projects(limit: int = 200, offset: int = 0) -> list[Project]:
-    db = await get_db()
-    async with db.execute(
-        """SELECT id, name, path, created_at, last_opened, status
-           FROM projects
-           ORDER BY datetime(last_opened) DESC
-           LIMIT ? OFFSET ?
-        """,
-        (limit, offset),
-    ) as cur:
-        rows = await cur.fetchall()
-    return [Project(**dict(r)) for r in rows]
-
-
-async def delete_project(project_id: str) -> bool:
-    db = await get_db()
-    cur = await db.execute("DELETE FROM projects WHERE id = ?", (project_id,))
-    await db.commit()
-    return (cur.rowcount or 0) > 0
-
-
-async def get_project(project_id: str) -> Project | None:
-    db = await get_db()
-    async with db.execute(
-        "SELECT id, name, path, created_at, last_opened, status FROM projects WHERE id = ?",
-        (project_id,),
-    ) as cur:
-        row = await cur.fetchone()
-    return Project(**dict(row)) if row else None
-
-
-async def touch_last_opened(project_id: str) -> None:
-    db = await get_db()
-    now = datetime.now(timezone.utc).isoformat()
-    await db.execute(
-        "UPDATE projects SET last_opened = ? WHERE id = ?",
-        (now, project_id),
-    )
-    await db.commit()
-
diff --git a/projects/service.py b/projects/service.py
deleted file mode 100644
index fba5632396b5f33091f45a5959ee3f01c7cd46d1..0000000000000000000000000000000000000000
--- a/projects/service.py
+++ /dev/null
@@ -1,186 +0,0 @@
-"""
-projects/service.py — Active project session + model workspace linking.
-
-Tracks which project is currently open (via the `session` DB table) and
-copies freshly-downloaded model files into the project's models/ folder
-so the benchmark engine and other workspaces can locate them.
-"""
-from __future__ import annotations
-
-import os
-import shutil
-import uuid
-from pathlib import Path
-from datetime import datetime, timezone
-
-from database.connection import get_db
-from observability.logger import get_logger, audit
-from models.model import Model, ModelMetrics
-from registry.registry import upsert_model
-
-log = get_logger("projects.service")
-
-
-async def import_local_model(
-    name: str,
-    task: str,
-    framework: str,
-    source_file_path: str
-) -> Model:
-    """Import a local model file into the active project."""
-    project_id = await get_active_project_id()
-    project_path = await get_active_project_path()
-    
-    if not project_id or not project_path:
-        raise ValueError("No active project found. Please open a project first.")
-
-    src = Path(source_file_path)
-    if not src.exists():
-        raise FileNotFoundError(f"Source model file not found: {source_file_path}")
-
-    # Create destination directory in project
-    model_id = f"local-{uuid.uuid4().hex[:12]}"
-    dest_dir = Path(project_path) / "models" / model_id
-    dest_dir.mkdir(parents=True, exist_ok=True)
-    
-    dest_path = dest_dir / src.name
-    shutil.copy2(src, dest_path)
-    
-    # Calculate size
-    size_bytes = dest_path.stat().st_size
-    size_label = f"{size_bytes / (1024*1024):.1f} MB" if size_bytes > 1024*1024 else f"{size_bytes / 1024:.1f} KB"
-
-    # Create model entry
-    now = datetime.now(timezone.utc).isoformat()
-    model = Model(
-        id=model_id,
-        name=name,
-        task=task,
-        framework=framework,
-        source="local",
-        provider="Local Import",
-        size=size_bytes,
-        size_label=size_label,
-        local_path=str(dest_path),
-        project_id=project_id,
-        downloaded=True,
-        status="cached",
-        created_at=now,
-        updated_at=now,
-        metrics=ModelMetrics()
-    )
-    
-    await upsert_model(model)
-    await audit("model_imported_locally", model_id=model_id, payload={"name": name, "project_id": project_id})
-    log.info("model_imported_locally", model_id=model_id, name=name, path=str(dest_path))
-    
-    return model
-
-
-# ── Session helpers ───────────────────────────────────────────────────────────
-
-async def set_active_project(project_id: str, project_path: str) -> None:
-    """Persist the currently open project in the session table."""
-    db = await get_db()
-    await db.execute(
-        "INSERT INTO session (key, value) VALUES ('active_project_id', ?)"
-        " ON CONFLICT(key) DO UPDATE SET value=excluded.value",
-        (project_id,),
-    )
-    await db.execute(
-        "INSERT INTO session (key, value) VALUES ('active_project_path', ?)"
-        " ON CONFLICT(key) DO UPDATE SET value=excluded.value",
-        (project_path,),
-    )
-    await db.commit()
-    log.info("active_project_set", project_id=project_id, path=project_path)
-
-
-async def get_active_project_id() -> str | None:
-    """Return the ID of the currently open project, or None."""
-    db = await get_db()
-    async with db.execute(
-        "SELECT value FROM session WHERE key = 'active_project_id'"
-    ) as cur:
-        row = await cur.fetchone()
-    return row["value"] if row else None
-
-
-async def get_active_project_path() -> str | None:
-    """Return the filesystem path of the currently open project, or None."""
-    db = await get_db()
-    async with db.execute(
-        "SELECT value FROM session WHERE key = 'active_project_path'"
-    ) as cur:
-        row = await cur.fetchone()
-    return row["value"] if row else None
-
-
-# ── Workspace model linking ───────────────────────────────────────────────────
-
-async def link_model_to_active_project(model_id: str, source_path: str) -> None:
-    """Copy the downloaded model file into the active project's models/ folder.
-
-    This is a best-effort operation — if no project is open, or if the copy
-    fails for any reason, we log and continue rather than failing the download.
-    """
-    project_path = await get_active_project_path()
-    if not project_path:
-        log.debug("link_model_skipped_no_project", model_id=model_id)
-        return
-
-    src = Path(source_path)
-    if not src.exists():
-        log.warning("link_model_source_missing", model_id=model_id, path=source_path)
-        return
-
-    dest_dir = Path(project_path) / "models" / model_id
-    dest_dir.mkdir(parents=True, exist_ok=True)
-    dest = dest_dir / src.name
-
-    if dest.exists():
-        log.debug("link_model_already_exists", model_id=model_id, dest=str(dest))
-        return
-
-    try:
-        shutil.copy2(src, dest)
-        log.info("model_linked_to_project", model_id=model_id, project=project_path, dest=str(dest))
-    except OSError as exc:
-        log.warning("link_model_copy_failed", model_id=model_id, error=str(exc))
-
-
-async def link_dataset_to_active_project(dataset_id: str, source_path: str) -> None:
-    """Copy the imported dataset folder into the active project's datasets/ folder.
-
-    This is a best-effort operation — if no project is open, or if the copy
-    fails for any reason, we log and continue rather than failing the import.
-    """
-    project_path = await get_active_project_path()
-    if not project_path:
-        log.debug("link_dataset_skipped_no_project", dataset_id=dataset_id)
-        return
-
-    src = Path(source_path)
-    if not src.exists():
-        log.warning("link_dataset_source_missing", dataset_id=dataset_id, path=source_path)
-        return
-
-    dest_dir = Path(project_path) / "datasets" / dataset_id
-    
-    if dest_dir.exists():
-        log.debug("link_dataset_already_exists", dataset_id=dataset_id, dest=str(dest_dir))
-        return
-
-    try:
-        if src.is_dir():
-            shutil.copytree(src, dest_dir, dirs_exist_ok=True)
-        else:
-            # If it's a file (e.g. zip that wasn't extracted yet), just copy it
-            dest_dir.parent.mkdir(parents=True, exist_ok=True)
-            shutil.copy2(src, dest_dir)
-            
-        log.info("dataset_linked_to_project", dataset_id=dataset_id, project=project_path, dest=str(dest_dir))
-        return dest_dir
-    except OSError as exc:
-        log.warning("link_dataset_copy_failed", dataset_id=dataset_id, error=str(exc))
-        return None
diff --git a/static/assets/index-CxHjXbkP.js b/static/assets/index-CxHjXbkP.js
deleted file mode 100644
index 6fe88e2ca75b131fe4e6a99866c9206a42b35957..0000000000000000000000000000000000000000
--- a/static/assets/index-CxHjXbkP.js
+++ /dev/null
@@ -1,136 +0,0 @@
-var e=Object.create,t=Object.defineProperty,n=Object.getOwnPropertyDescriptor,r=Object.getOwnPropertyNames,i=Object.getPrototypeOf,a=Object.prototype.hasOwnProperty,o=(e,t)=>()=>(t||(e((t={exports:{}}).exports,t),e=null),t.exports),s=(e,n)=>{let r={};for(var i in e)t(r,i,{get:e[i],enumerable:!0});return n||t(r,Symbol.toStringTag,{value:`Module`}),r},c=(e,i,o,s)=>{if(i&&typeof i==`object`||typeof i==`function`)for(var c=r(i),l=0,u=c.length,d;li[e]).bind(null,d),enumerable:!(s=n(i,d))||s.enumerable});return e},l=(n,r,a)=>(a=n==null?{}:e(i(n)),c(r||!n||!n.__esModule?t(a,`default`,{value:n,enumerable:!0}):a,n));(function(){let e=document.createElement(`link`).relList;if(e&&e.supports&&e.supports(`modulepreload`))return;for(let e of document.querySelectorAll(`link[rel="modulepreload"]`))n(e);new MutationObserver(e=>{for(let t of e)if(t.type===`childList`)for(let e of t.addedNodes)e.tagName===`LINK`&&e.rel===`modulepreload`&&n(e)}).observe(document,{childList:!0,subtree:!0});function 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diff --git a/static/favicon.svg b/static/favicon.svg
deleted file mode 100644
index 6893eb13237060adc0c968a690149a49faa2d7d3..0000000000000000000000000000000000000000
--- a/static/favicon.svg
+++ /dev/null
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diff --git a/static/icons.svg b/static/icons.svg
deleted file mode 100644
index e9522193d9f796a9748e9ad8c952a5df73c87db9..0000000000000000000000000000000000000000
--- a/static/icons.svg
+++ /dev/null
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diff --git a/static/index.html b/static/index.html
deleted file mode 100644
index 37e546f5750ddf04f07f3e9be27d7628dce6a816..0000000000000000000000000000000000000000
--- a/static/index.html
+++ /dev/null
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diff --git a/static/models_index.json b/static/models_index.json
deleted file mode 100644
index 2438e20a4aa81f59c1503aadf46d87a65a52d387..0000000000000000000000000000000000000000
--- a/static/models_index.json
+++ /dev/null
@@ -1,240 +0,0 @@
-{
-  "version": "1.0.0",
-  "updatedAt": "2024-01-15T12:00:00.000Z",
-  "models": [
-    {
-      "id": "yolov8-n",
-      "name": "YOLOv8-N",
-      "variant": "Nano",
-      "task": "detection",
-      "framework": "pytorch",
-      "size": 6300000,
-      "size_label": "6MB",
-      "tags": ["real-time", "edge", "nano", "best-for-edge", "yolo"],
-      "source": "hf",
-      "provider": "Ultralytics",
-      "description": "The smallest YOLOv8 variant optimized for edge deployment.",
-      "downloaded": true,
-      "status": "cached",
-      "hardware": ["cpu", "edge", "gpu"],
-      "metrics": { "latency_ms": 4.2, "mAP": 37.3, "fps": 238, "vram_gb": 0.5 },
-      "versions": [
-        { "version": "v8.2.0", "label": "Latest", "releaseDate": "2024-01-15" },
-        { "version": "v8.1.0", "label": "Stable", "releaseDate": "2023-09-20" },
-        { "version": "v8.0.0", "label": "Legacy", "releaseDate": "2023-03-10" }
-      ],
-      "rating": 4.6,
-      "downloads": 2340000
-    },
-    {
-      "id": "yolov8-s",
-      "name": "YOLOv8-S",
-      "variant": "Small",
-      "task": "detection",
-      "framework": "pytorch",
-      "size": 22000000,
-      "size_label": "22MB",
-      "tags": ["real-time", "small", "fastest", "yolo", "recommended"],
-      "source": "hf",
-      "provider": "Ultralytics",
-      "description": "Small YOLOv8 model striking the best balance between speed and accuracy.",
-      "downloaded": true,
-      "status": "cached",
-      "hardware": ["gpu", "cpu", "edge"],
-      "metrics": { "latency_ms": 8.4, "mAP": 44.9, "fps": 119, "vram_gb": 1.1 },
-      "versions": [
-        { "version": "v8.2.0", "label": "Latest", "releaseDate": "2024-01-15" },
-        { "version": "v8.1.0", "label": "Stable", "releaseDate": "2023-09-20" },
-        { "version": "v8.0.0", "label": "Legacy", "releaseDate": "2023-03-10" }
-      ],
-      "rating": 4.8,
-      "downloads": 4100000
-    },
-    {
-      "id": "efficientnet-b0",
-      "name": "EfficientNet-B0",
-      "task": "classification",
-      "framework": "pytorch",
-      "size": 21000000,
-      "size_label": "21MB",
-      "tags": ["efficiency", "lightweight", "imagenet", "mobile-friendly"],
-      "source": "hf",
-      "provider": "Google",
-      "description": "Baseline EfficientNet with excellent efficiency-accuracy tradeoff.",
-      "downloaded": true,
-      "status": "cached",
-      "hardware": ["gpu", "cpu", "edge"],
-      "metrics": { "latency_ms": 3.5, "top1": 77.1, "accuracy": 77.1, "vram_gb": 0.8 },
-      "versions": [
-        { "version": "v1.2.0", "label": "Latest", "releaseDate": "2024-01-15" },
-        { "version": "v1.1.0", "label": "Stable", "releaseDate": "2023-09-20" },
-        { "version": "v1.0.0", "label": "Legacy", "releaseDate": "2023-03-10" }
-      ],
-      "rating": 4.7,
-      "downloads": 3200000
-    },
-    {
-      "id": "mobilenetv3-small",
-      "name": "MobileNetV3-Small",
-      "task": "classification",
-      "framework": "tflite",
-      "size": 10000000,
-      "size_label": "10MB",
-      "tags": ["mobile", "edge", "tiny", "fast", "best-for-edge"],
-      "source": "local",
-      "provider": "Google",
-      "description": "Ultra-lightweight MobileNetV3 for on-device inference.",
-      "downloaded": true,
-      "status": "cached",
-      "hardware": ["edge", "cpu"],
-      "metrics": { "latency_ms": 1.8, "top1": 67.4, "accuracy": 67.4, "vram_gb": 0.1 },
-      "versions": [
-        { "version": "v3.1.0", "label": "Latest", "releaseDate": "2024-01-15" },
-        { "version": "v3.0.0", "label": "Stable", "releaseDate": "2023-09-20" },
-        { "version": "v2.0.0", "label": "Legacy", "releaseDate": "2023-03-10" }
-      ],
-      "rating": 4.8,
-      "downloads": 5600000
-    },
-    {
-      "id": "bert-base-uncased",
-      "name": "BERT-Base",
-      "variant": "Uncased",
-      "task": "nlp",
-      "framework": "pytorch",
-      "size": 440000000,
-      "size_label": "440MB",
-      "tags": ["nlp", "transformer", "embeddings", "bert", "text"],
-      "source": "hf",
-      "provider": "Google",
-      "description": "BERT base model pre-trained on Wikipedia and BookCorpus.",
-      "downloaded": false,
-      "status": "available",
-      "hardware": ["gpu", "cpu"],
-      "metrics": { "latency_ms": 45.2, "accuracy": 84.6, "vram_gb": 2.4 },
-      "versions": [
-        { "version": "v1.1.0", "label": "Latest", "releaseDate": "2024-01-15" },
-        { "version": "v1.0.0", "label": "Stable", "releaseDate": "2023-09-20" },
-        { "version": "v0.9.0", "label": "Legacy", "releaseDate": "2023-03-10" }
-      ],
-      "rating": 4.7,
-      "downloads": 12500000
-    },
-    {
-      "id": "clip-vit-b32",
-      "name": "CLIP-ViT-B/32",
-      "task": "embedding",
-      "framework": "pytorch",
-      "size": 354000000,
-      "size_label": "354MB",
-      "tags": ["clip", "multimodal", "embeddings", "vision-language", "zero-shot"],
-      "source": "hf",
-      "provider": "OpenAI",
-      "description": "CLIP vision-language model enabling zero-shot image classification.",
-      "downloaded": true,
-      "status": "cached",
-      "hardware": ["gpu", "cpu"],
-      "metrics": { "latency_ms": 18.4, "top1": 63.3, "accuracy": 63.3, "vram_gb": 2.0 },
-      "versions": [
-        { "version": "v1.0.0", "label": "Latest", "releaseDate": "2024-01-15" },
-        { "version": "v0.9.0", "label": "Stable", "releaseDate": "2023-09-20" },
-        { "version": "v0.8.0", "label": "Legacy", "releaseDate": "2023-03-10" }
-      ],
-      "rating": 4.8,
-      "downloads": 8700000
-    },
-    {
-      "id": "whisper-tiny",
-      "name": "Whisper-Tiny",
-      "task": "generation",
-      "framework": "pytorch",
-      "size": 39000000,
-      "size_label": "39MB",
-      "tags": ["asr", "audio", "speech", "tiny", "real-time", "edge"],
-      "source": "hf",
-      "provider": "OpenAI",
-      "description": "Smallest Whisper ASR model. Runs on CPU in real-time.",
-      "downloaded": true,
-      "status": "cached",
-      "hardware": ["cpu", "edge", "gpu"],
-      "metrics": { "latency_ms": 58.3, "accuracy": 81.4, "vram_gb": 0.5 },
-      "versions": [
-        { "version": "v20240101", "label": "Latest", "releaseDate": "2024-01-01" },
-        { "version": "v20231117", "label": "Stable", "releaseDate": "2023-11-17" },
-        { "version": "v20230314", "label": "Legacy", "releaseDate": "2023-03-14" }
-      ],
-      "rating": 4.5,
-      "downloads": 7600000
-    },
-    {
-      "id": "sam-vit-base",
-      "name": "SAM-ViT-Base",
-      "task": "segmentation",
-      "framework": "pytorch",
-      "size": 375000000,
-      "size_label": "375MB",
-      "tags": ["segmentation", "sam", "zero-shot", "foundational", "vit"],
-      "source": "hf",
-      "provider": "Meta AI",
-      "description": "Segment Anything Model with ViT-B backbone.",
-      "downloaded": false,
-      "status": "available",
-      "hardware": ["gpu"],
-      "metrics": { "latency_ms": 48.2, "mAP": 75.8, "vram_gb": 3.2 },
-      "versions": [
-        { "version": "v1.1.0", "label": "Latest", "releaseDate": "2024-01-15" },
-        { "version": "v1.0.0", "label": "Stable", "releaseDate": "2023-09-20" },
-        { "version": "v0.9.0", "label": "Legacy", "releaseDate": "2023-03-10" }
-      ],
-      "rating": 4.9,
-      "downloads": 2100000
-    },
-    {
-      "id": "resnet-50",
-      "name": "ResNet-50",
-      "task": "classification",
-      "framework": "pytorch",
-      "size": 98000000,
-      "size_label": "98MB",
-      "tags": ["backbone", "imagenet", "classic", "transfer-learning"],
-      "source": "hf",
-      "provider": "Microsoft Research",
-      "description": "Classic 50-layer ResNet. The gold standard backbone for transfer learning.",
-      "downloaded": false,
-      "status": "available",
-      "hardware": ["gpu", "cpu"],
-      "metrics": { "latency_ms": 12.4, "top1": 76.1, "accuracy": 76.1, "vram_gb": 2.0 },
-      "versions": [
-        { "version": "v2.0.0", "label": "Latest", "releaseDate": "2024-01-15" },
-        { "version": "v1.5.0", "label": "Stable", "releaseDate": "2023-09-20" },
-        { "version": "v1.0.0", "label": "Legacy", "releaseDate": "2023-03-10" }
-      ],
-      "rating": 4.5,
-      "downloads": 8200000
-    },
-    {
-      "id": "distilbert-base",
-      "name": "DistilBERT",
-      "variant": "Base",
-      "task": "nlp",
-      "framework": "pytorch",
-      "size": 268000000,
-      "size_label": "268MB",
-      "tags": ["nlp", "distilled", "fast", "lightweight", "bert", "efficient"],
-      "source": "hf",
-      "provider": "Hugging Face",
-      "description": "Distilled version of BERT — 40% smaller, 60% faster.",
-      "downloaded": true,
-      "status": "cached",
-      "hardware": ["gpu", "cpu", "edge"],
-      "metrics": { "latency_ms": 22.1, "accuracy": 82.2, "vram_gb": 1.2 },
-      "versions": [
-        { "version": "v1.1.0", "label": "Latest", "releaseDate": "2024-01-15" },
-        { "version": "v1.0.0", "label": "Stable", "releaseDate": "2023-09-20" },
-        { "version": "v0.9.0", "label": "Legacy", "releaseDate": "2023-03-10" }
-      ],
-      "rating": 4.8,
-      "downloads": 9800000
-    }
-  ]
-}
diff --git a/training/__pycache__/persistence.cpython-310.pyc b/training/__pycache__/persistence.cpython-310.pyc
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diff --git a/training/__pycache__/run_manager.cpython-310.pyc b/training/__pycache__/run_manager.cpython-310.pyc
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diff --git a/training/__pycache__/schema_engine.cpython-310.pyc b/training/__pycache__/schema_engine.cpython-310.pyc
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diff --git a/training/__pycache__/schemas.cpython-310.pyc b/training/__pycache__/schemas.cpython-310.pyc
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diff --git a/training/engines/__pycache__/base.cpython-310.pyc b/training/engines/__pycache__/base.cpython-310.pyc
deleted file mode 100644
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diff --git a/training/engines/base.py b/training/engines/base.py
deleted file mode 100644
index 5d704d4db4e4005f68b5db3edd6fa6b5fa26c199..0000000000000000000000000000000000000000
--- a/training/engines/base.py
+++ /dev/null
@@ -1,36 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import Dict, Any, Generator, Optional
-import asyncio
-
-class BaseTrainingEngine(ABC):
-    """Abstract base class for model-aware training engines."""
-    
-    @abstractmethod
-    async def train(self, config: Dict[str, Any], run_id: str, project_id: str, dataset_id: str) -> Generator[Dict[str, Any], None, None]:
-        """
-        Executes the training loop.
-        Yields metrics and progress updates.
-        """
-        pass
-
-    def stop(self):
-        """Signals the engine to stop the current training process."""
-        pass
-
-    @abstractmethod
-    def get_framework_info(self) -> Dict[str, str]:
-        """Returns metadata about the engine (name, version, supported tasks)."""
-        pass
-
-class EngineSelector:
-    """Factory to select the appropriate training engine based on model and task."""
-    
-    @staticmethod
-    def get_engine(model_id: str, task: str) -> BaseTrainingEngine:
-        # For now, we prioritize YOLO for object detection
-        if task == "detection" or "yolo" in model_id.lower():
-            from .object_detection.yolo_engine import YOLOEngine
-            return YOLOEngine()
-        
-        # Fallback or other engines can be added here
-        raise ValueError(f"No suitable engine found for model {model_id} and task {task}")
diff --git a/training/engines/object_detection/__pycache__/yolo_engine.cpython-310.pyc b/training/engines/object_detection/__pycache__/yolo_engine.cpython-310.pyc
deleted file mode 100644
index b6d9e12f4b6ffe16ce1346aff33d9748dd03f4a6..0000000000000000000000000000000000000000
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diff --git a/training/engines/object_detection/yolo_engine.py b/training/engines/object_detection/yolo_engine.py
deleted file mode 100644
index 3ef227f0f9774c4f4c7113dfa024732c52249655..0000000000000000000000000000000000000000
--- a/training/engines/object_detection/yolo_engine.py
+++ /dev/null
@@ -1,263 +0,0 @@
-import json
-import asyncio
-import time
-import os
-from typing import Dict, Any, Generator, List
-from pathlib import Path
-from ..base import BaseTrainingEngine
-from ...persistence import TrainingPersistence
-from observability.logger import get_logger
-
-log = get_logger("training.engines.object_detection.yolo")
-
-class YOLOEngine(BaseTrainingEngine):
-    """
-    YOLO-specific training engine implementation.
-    Integrates with Ultralytics YOLOv8/v10/v11 for real training.
-    """
-
-    def get_framework_info(self) -> Dict[str, str]:
-        return {
-            "name": "Ultralytics YOLO",
-            "version": "8.x",
-            "task": "object_detection"
-        }
-
-    def __init__(self):
-        super().__init__()
-        self._stop_requested = False
-
-    def stop(self):
-        """Signals the engine to stop the current training process."""
-        self._stop_requested = True
-        log.info("yolo_engine_stop_signaled", stop_requested=self._stop_requested)
-
-    async def train(self, config: Dict[str, Any], run_id: str, project_id: str, dataset_id: str):
-        """
-        Executes the YOLO training pipeline using the ultralytics package.
-        """
-        try:
-            from ultralytics import YOLO
-        except ImportError:
-            log.error("ultralytics_not_installed")
-            yield {
-                "event": "log",
-                "data": {"level": "ERROR", "message": "Ultralytics package not installed. Run 'pip install ultralytics'", "timestamp": time.strftime("%H:%M:%S")}
-            }
-            return
-
-        run_dir = await TrainingPersistence.get_run_dir(project_id, run_id)
-        
-        from datasets.registry import get_dataset
-        dataset = await get_dataset(dataset_id)
-        if not dataset:
-            raise ValueError(f"Dataset {dataset_id} not found")
-
-        dataset_path = dataset.local_path
-        
-        # ── Dataset Config Resolution ──────────────────────────────────────────────
-        data_yaml = None
-        for yaml_name in ["data.yaml", "dataset.yaml"]:
-            p = os.path.join(dataset_path, yaml_name)
-            if os.path.exists(p):
-                data_yaml = p
-                break
-        
-        if not data_yaml:
-            log.error("yolo_config_missing", path=dataset_path)
-            yield {
-                "event": "log",
-                "data": {"level": "ERROR", "message": f"YOLO config (data.yaml) missing in {dataset_path}", "timestamp": time.strftime("%H:%M:%S")}
-            }
-            return
-
-        # ── Start Training ────────────────────────────────────────────────────────
-        log.info("yolo_training_start", run_id=run_id, project_id=project_id, data_yaml=data_yaml)
-        
-        # Training Parameters
-        epochs = config.get('epochs', 100)
-        batch_size = config.get('batchSize', 16)
-        imgsz = config.get('imgSize', 640)
-        lr0 = config.get('lr', 0.01)
-        optimizer = config.get('optimizer', 'auto')
-        patience = config.get('patience', 50)
-        
-        # Extract params
-        model_name = config.get("model_id", "yolov8n.pt")
-        if not model_name.endswith(".pt"):
-            model_name += ".pt"
-
-        # Initialize model
-        model = YOLO(model_name)
-        
-        # Setup Queue for metrics/logs from callbacks
-        event_queue = asyncio.Queue()
-
-        # Callbacks for stopping and batch metrics
-        loop = asyncio.get_running_loop()
-
-        # Advanced: HPO & Early Stopping
-        hpo_config = config.get('hpo', {})
-        early_stop_config = config.get('early_stopping', {})
-        min_delta = early_stop_config.get('min_delta', 0.01)
-        
-        # DVC Meta
-        dvc_version = config.get('dvc_version', 'v1.0.0')
-
-        # Check for HPO (Hyperparameter Optimization)
-        if hpo_config.get('enabled', False):
-            trials = hpo_config.get('trials', 10)
-            log.info("yolo_hpo_started", trials=trials, run_id=run_id)
-            # In production, we'd trigger yolo.tune() or a custom Optuna loop
-            # For now, we'll simulate the HPO start in the log
-            loop.call_soon_threadsafe(
-                event_queue.put_nowait,
-                {
-                    "event": "log",
-                    "data": {
-                        "level": "INFO",
-                        "message": f"🚀 Starting HPO with {trials} trials (Optuna Engine)...",
-                        "timestamp": time.strftime("%H:%M:%S")
-                    }
-                }
-            )
-        
-        def run_yolo_train():
-            # This function runs in a background thread
-            try:
-                log.info("yolo_thread_start", stop_flag=self._stop_requested)
-                model.train(
-                    data=data_yaml,
-                    epochs=epochs,
-                    imgsz=imgsz,
-                    batch=batch_size,
-                    project=os.path.join(run_dir, ".."), # persistence/training/runs
-                    name=run_id,
-                    exist_ok=True,
-                    save=True,
-                    device=config.get("device", "cpu")
-                )
-            except Exception as e:
-                if "TRAINING_STOPPED_BY_USER" in str(e):
-                    log.info("yolo_train_thread_terminated_safely")
-                else:
-                    raise e
-
-        def on_train_epoch_end(trainer):
-            # Extract metrics from trainer
-            metrics = {}
-            try:
-                # Get training losses
-                tloss = trainer.label_loss_items(trainer.tloss, prefix='train')
-                metrics.update(tloss)
-                
-                # Get validation metrics
-                if hasattr(trainer, 'validator') and hasattr(trainer.validator, 'metrics'):
-                    metrics.update(trainer.validator.metrics.results_dict)
-            except Exception as e:
-                log.error("metrics_extraction_failed", error=str(e))
-
-            # --- Telemetry Logging Implementation ---
-            tick_data = {
-                "run_id": run_id,
-                "epoch": trainer.epoch + 1,
-                "progress": round((trainer.epoch + 1) / trainer.epochs, 4),
-                "task_metrics": metrics,
-                "timestamp": time.time()
-            }
-            
-            # Save to telemetry.jsonl for historical reconstruction
-            try:
-                telemetry_path = os.path.join(run_dir, "telemetry.jsonl")
-                with open(telemetry_path, "a") as f:
-                    f.write(json.dumps(tick_data) + "\n")
-            except Exception as e:
-                log.error("telemetry_log_failed", error=str(e))
-
-            # Put into queue using loop.call_soon_threadsafe for async-safe execution from another thread
-            loop.call_soon_threadsafe(
-                event_queue.put_nowait,
-                {
-                    "event": "metrics",
-                    "data": tick_data
-                }
-            )
-
-        def on_train_batch_end(trainer):
-            # Check flag directly on self
-            if getattr(self, '_stop_requested', False):
-                log.info("yolo_engine_stop_callback_invoked", 
-                         epoch=trainer.epoch, 
-                         batch=getattr(trainer, 'batch', 0))
-                raise Exception("TRAINING_STOPPED_BY_USER")
-
-            # Log current batch loss
-            batch_idx = getattr(trainer, 'batch', 0)
-            nb_batches = getattr(trainer, 'nb', 0)
-            
-            tloss = trainer.tloss
-            if hasattr(tloss, 'tolist'):
-                loss_val = sum(tloss.tolist()) / len(tloss.tolist()) if len(tloss.tolist()) > 0 else 0
-            else:
-                loss_val = float(tloss)
-
-            msg = f"Epoch {trainer.epoch + 1}/{trainer.epochs} - Batch {batch_idx}/{nb_batches} - Loss: {loss_val:.4f}"
-            
-            # Send live batch progress
-            loop.call_soon_threadsafe(
-                event_queue.put_nowait,
-                {
-                    "event": "metrics",
-                    "data": {
-                        "run_id": run_id,
-                        "epoch": trainer.epoch + 1,
-                        "progress": round((trainer.epoch + (batch_idx / max(1, nb_batches))) / trainer.epochs, 4),
-                        "batch": batch_idx,
-                        "total_batches": nb_batches,
-                        "timestamp": time.time()
-                    }
-                }
-            )
-
-            loop.call_soon_threadsafe(
-                event_queue.put_nowait,
-                {
-                    "event": "log",
-                    "data": {
-                        "level": "INFO",
-                        "message": msg,
-                        "timestamp": time.strftime("%H:%M:%S")
-                    }
-                }
-            )
-
-        # Add callbacks
-        model.add_callback("on_train_epoch_end", on_train_epoch_end)
-        model.add_callback("on_train_batch_end", on_train_batch_end)
-
-        train_task = loop.run_in_executor(None, run_yolo_train)
-
-        # Yield events as they come
-        while not train_task.done() or not event_queue.empty():
-            if self._stop_requested:
-                log.info("yolo_engine_stopping", run_id=run_id)
-                # Note: Ultralytics doesn't have a direct 'stop' method on the model instance 
-                # that works across threads easily without custom trainers, 
-                # but canceling the task and exiting the loop handles the cleanup.
-                break
-            try:
-                # Wait for an event or check if task is done
-                event = await asyncio.wait_for(event_queue.get(), timeout=1.0)
-                yield event
-            except asyncio.TimeoutError:
-                if train_task.done():
-                    break
-                continue
-
-        await train_task # Ensure it's fully done
-        
-        log.info("yolo_training_complete", run_id=run_id, path=run_dir)
-        yield {
-            "event": "status",
-            "data": {"status": "completed", "run_id": run_id}
-        }
diff --git a/training/persistence.py b/training/persistence.py
deleted file mode 100644
index 0a469c10ff028ab384b671b45dcc18b662e9bf61..0000000000000000000000000000000000000000
--- a/training/persistence.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import os
-import json
-import asyncio
-from typing import Dict, Any, Optional
-from projects.registry import get_project
-from models.project import Project
-from observability.logger import get_logger
-
-log = get_logger("training.persistence")
-
-class TrainingPersistence:
-    """Manages project-aware persistence for training runs and models."""
-    
-    @staticmethod
-    async def get_training_dir(project_id: str) -> str:
-        """Returns the absolute path to the training directory within the active project."""
-        project = await get_project(project_id)
-        if not project:
-            raise ValueError(f"Project {project_id} not found")
-        
-        training_dir = os.path.join(project.path, "training")
-        os.makedirs(training_dir, exist_ok=True)
-        return training_dir
-
-    @staticmethod
-    async def get_run_dir(project_id: str, run_id: str) -> str:
-        """Returns the run-specific directory within the project's training folder."""
-        training_dir = await TrainingPersistence.get_training_dir(project_id)
-        run_dir = os.path.join(training_dir, "runs", run_id)
-        os.makedirs(run_dir, exist_ok=True)
-        
-        # Subdirectories for artifacts
-        os.makedirs(os.path.join(run_dir, "weights"), exist_ok=True)
-        os.makedirs(os.path.join(run_dir, "logs"), exist_ok=True)
-        os.makedirs(os.path.join(run_dir, "previews"), exist_ok=True)
-        
-        return run_dir
-
-    @staticmethod
-    async def save_run_metadata(project_id: str, run_id: str, metadata: Dict[str, Any]):
-        """Saves run metadata (hyperparams, config) to the project folder."""
-        run_dir = await TrainingPersistence.get_run_dir(project_id, run_id)
-        meta_path = os.path.join(run_dir, "metadata.json")
-        
-        with open(meta_path, "w") as f:
-            json.dump(metadata, f, indent=2)
-        
-        log.info("run_metadata_saved", project_id=project_id, run_id=run_id, path=meta_path)
diff --git a/training/run_manager.py b/training/run_manager.py
deleted file mode 100644
index 1cfb813296c73a9c19dfa1968bf567be27aba32e..0000000000000000000000000000000000000000
--- a/training/run_manager.py
+++ /dev/null
@@ -1,196 +0,0 @@
-import asyncio
-import json
-import time
-import os
-import uuid
-from dataclasses import dataclass, field
-from typing import Any, Dict, List, Optional, Union
-
-from observability.logger import get_logger
-from .persistence import TrainingPersistence
-from .engines.base import EngineSelector
-
-log = get_logger("training.run_manager")
-
-# Maximum items kept in each per-run queue before old entries are dropped
-_QUEUE_CAP = 2000
-
-@dataclass
-class TrainRun:
-    run_id:       str
-    run_number:   int
-    model_id:     str
-    model_name:   str
-    dataset_id:   str
-    dataset_name: str
-    task:         str
-    hyperparams:  Dict[str, Any]
-    augmentation: Dict[str, Any]
-    scheduler:    Dict[str, Any]
-    project_id:   Optional[str] = None
-
-    # Mutable state
-    status:          str = "queued"       # queued|running|paused|completed|failed|cancelled
-    epoch:           int = 0
-    total_epochs:    int = 50
-    step:            int = 0
-    total_steps:     int = 0
-    eta_seconds:     float = 0.0
-    elapsed_seconds: float = 0.0
-    final_loss:      float = 0.0
-    best_metric:     Dict[str, float] = field(default_factory=dict)
-    created_at:      float = field(default_factory=time.time)
-    completed_at:    Optional[float] = None
-
-    # SSE subscriber queues
-    metrics_subs:   List[asyncio.Queue] = field(default_factory=list)
-    log_subs:       List[asyncio.Queue] = field(default_factory=list)
-    resource_subs:  List[asyncio.Queue] = field(default_factory=list)
-
-    # Background worker task
-    _task: Optional[asyncio.Task] = field(default=None, repr=False)
-    _engine: Optional[Any] = field(default=None, repr=False)
-
-_runs: Dict[str, TrainRun] = {}
-_run_counter = 0
-
-def _next_run_number() -> int:
-    global _run_counter
-    _run_counter += 1
-    return _run_counter
-
-def create_run(
-    model_id: str,
-    model_name: str,
-    dataset_id: str,
-    dataset_name: str,
-    task: str,
-    hyperparams: Dict[str, Any],
-    augmentation: Dict[str, Any],
-    scheduler: Dict[str, Any],
-    project_id: Optional[str] = None
-) -> TrainRun:
-    run_id = str(uuid.uuid4())
-    total_epochs = int(hyperparams.get("epochs", 50))
-    run = TrainRun(
-        run_id=run_id,
-        run_number=_next_run_number(),
-        model_id=model_id,
-        model_name=model_name,
-        dataset_id=dataset_id,
-        dataset_name=dataset_name,
-        task=task,
-        hyperparams=hyperparams,
-        augmentation=augmentation,
-        scheduler=scheduler,
-        total_epochs=total_epochs,
-        project_id=project_id
-    )
-    _runs[run_id] = run
-    log.info("run_created", run_id=run_id, model=model_id, project=project_id)
-    return run
-
-def get_run(run_id: str) -> Optional[TrainRun]:
-    return _runs.get(run_id)
-
-def list_runs() -> List[TrainRun]:
-    return list(_runs.values())
-
-def _broadcast(subs: List[asyncio.Queue], payload: Any) -> None:
-    dead: List[asyncio.Queue] = []
-    for q in subs:
-        if q.qsize() >= _QUEUE_CAP:
-            try: q.get_nowait()
-            except asyncio.QueueEmpty: pass
-        try:
-            q.put_nowait(payload)
-        except Exception:
-            dead.append(q)
-    for d in dead:
-        if d in subs:
-            subs.remove(d)
-
-async def _run_training_pipeline(run: TrainRun):
-    """Orchestrates the actual training using the selected engine."""
-    try:
-        run.status = "running"
-        engine = EngineSelector.get_engine(run.model_id, run.task)
-        run._engine = engine
-        
-        # Ensure project-aware directories exist
-        if run.project_id:
-            await TrainingPersistence.save_run_metadata(run.project_id, run.run_id, {
-                "model_id": run.model_id,
-                "dataset_id": run.dataset_id,
-                "hyperparams": run.hyperparams,
-                "augmentation": run.augmentation
-            })
-
-        async for update in engine.train(run.hyperparams, run.run_id, run.project_id or "default", run.dataset_id):
-            event_type = update.get("event")
-            data = update.get("data")
-            
-            if event_type == "metrics":
-                run.epoch = data.get("epoch", run.epoch)
-                run.step = data.get("step", run.step)
-                # Ensure the data structure matches what the frontend expects
-                broadcast_data = {
-                    "run_id": run.run_id,
-                    "epoch": run.epoch,
-                    "step": run.step,
-                    "epoch_progress": data.get("progress", 0),
-                    "eta_seconds": data.get("eta_seconds", 0),
-                    "elapsed_seconds": data.get("elapsed_seconds", 0),
-                    "task_metrics": data.get("task_metrics", {})
-                }
-                _broadcast(run.metrics_subs, broadcast_data)
-            elif event_type == "log":
-                # Ensure logs have the required format for the frontend log panel
-                log_entry = {
-                    "id": f"log-{run.run_id}-{time.time()}",
-                    "run_id": run.run_id,
-                    "timestamp": data.get("timestamp"),
-                    "level": data.get("level", "INFO"),
-                    "message": data.get("message"),
-                    "source": "trainer"
-                }
-                _broadcast(run.log_subs, log_entry)
-            elif event_type == "status":
-                run.status = data.get("status", run.status)
-
-        if run.status not in ("cancelled", "failed"):
-            run.status = "completed"
-        run.completed_at = time.time()
-        
-    except Exception as e:
-        run.status = "failed"
-        run.completed_at = time.time()
-        error_log = {
-            "level": "ERROR",
-            "message": f"Training failed: {str(e)}",
-            "timestamp": time.strftime("%H:%M:%S")
-        }
-        _broadcast(run.log_subs, error_log)
-        log.error("training_failed", run_id=run.run_id, error=str(e))
-    finally:
-        # Sentinel for SSE streams
-        for subs in [run.metrics_subs, run.log_subs, run.resource_subs]:
-            _broadcast(subs, None)
-
-def start_run(run: TrainRun) -> None:
-    run._task = asyncio.create_task(_run_training_pipeline(run))
-
-def stop_run(run: TrainRun) -> None:
-    run.status = "cancelled"
-    if run._engine and hasattr(run._engine, 'stop'):
-        run._engine.stop()
-    if run._task and not run._task.done():
-        run._task.cancel()
-
-def pause_run(run: TrainRun) -> None:
-    if run.status == "running":
-        run.status = "paused"
-
-def resume_run(run: TrainRun) -> None:
-    if run.status == "paused":
-        run.status = "running"
diff --git a/training/schema_engine.py b/training/schema_engine.py
deleted file mode 100644
index c77e5f6c00623c56a106822f692a21dcee018d8c..0000000000000000000000000000000000000000
--- a/training/schema_engine.py
+++ /dev/null
@@ -1,40 +0,0 @@
-from typing import Dict, Any
-
-def generate_schema(task: str, model_id: str, dataset_id: str) -> Dict[str, Any]:
-    """
-    Generates a UI schema for the training dashboard based on the task, model, and dataset.
-    This schema defines what parameters and panels are shown in the frontend.
-    """
-    # Default schema structure
-    schema = {
-        "task_type": task,
-        "panels": [
-            {
-                "type": "hyperparams",
-                "title": "Hyperparameters",
-                "config": {}
-            },
-            {
-                "type": "metrics",
-                "title": "Training Metrics",
-                "config": {}
-            }
-        ],
-        "metric_defs": [
-            {"key": "train/box_loss", "label": "Box Loss", "unit": "", "higher_better": False, "color": "#ef4444"},
-            {"key": "train/cls_loss", "label": "Class Loss", "unit": "", "higher_better": False, "color": "#f97316"},
-            {"key": "metrics/mAP50(B)", "label": "mAP@50", "unit": "", "higher_better": True, "color": "#10b981"}
-        ],
-        "param_defs": [
-            {"key": "epochs", "label": "Epochs", "type": "number", "default": 50},
-            {"key": "batchSize", "label": "Batch Size", "type": "number", "default": 16},
-            {"key": "imgSize", "label": "Image Size", "type": "number", "default": 640},
-            {"key": "learning_rate", "label": "Learning Rate", "type": "number", "default": 0.01}
-        ]
-    }
-    
-    # Task-specific overrides can be added here
-    if task == "detection":
-        pass
-        
-    return schema
diff --git a/training/schemas.py b/training/schemas.py
deleted file mode 100644
index bad7f4fd88fc726eda7bdc18b18cb787a200d60f..0000000000000000000000000000000000000000
--- a/training/schemas.py
+++ /dev/null
@@ -1,65 +0,0 @@
-from pydantic import BaseModel
-from typing import Dict, Any, Optional, List
-
-class StartTrainRequest(BaseModel):
-    model_id:      str
-    dataset_id:    str
-    task:          str
-    hyperparams:   Dict[str, Any]
-    augmentation:  Dict[str, Any]
-    scheduler:     Dict[str, Any]
-    project_id:    Optional[str] = None
-
-class StartTrainResponse(BaseModel):
-    run_id:   str
-    status:   str
-    message:  str
-
-class TrainStatusResponse(BaseModel):
-    run_id:       str
-    status:       str
-    epoch:        int
-    total_epochs: int
-    step:         int
-    total_steps:  int
-    eta_seconds:  float
-    elapsed_seconds: float
-
-class CheckpointOut(BaseModel):
-    id: str
-    epoch: int
-    path: str
-    metrics: Dict[str, float]
-
-class TrainRunOut(BaseModel):
-    id: str
-    run_number: int
-    model_id: str
-    model_name: str
-    dataset_id: str
-    dataset_name: str
-    task: str
-    status: str
-    epochs_done: int
-    total_epochs: int
-    best_metric: Dict[str, float]
-    final_loss: float
-    duration: str
-    created_at: float
-    completed_at: Optional[float]
-    hyperparams: Dict[str, Any]
-
-class TrainingSchemaResponse(BaseModel):
-    task_type: str
-    panels: List[Dict[str, Any]]
-    metric_defs: List[Dict[str, Any]]
-    param_defs: List[Dict[str, Any]]
-
-class StopTrainRequest(BaseModel):
-    run_id: str
-
-class PauseTrainRequest(BaseModel):
-    run_id: str
-
-class ResumeTrainRequest(BaseModel):
-    run_id: str