0ce879aa44
- Implemented HTML pages for datasets, models, training, testing, and results. - Created API endpoints for managing repositories, results, tests, and training sessions. - Added functionality for streaming training progress via Server-Sent Events (SSE). - Introduced a Dockerfile for the ML runner with necessary dependencies. - Developed an SDK for user code execution within the runner container. - Enhanced CSS styles for improved UI layout and navigation. - Established a layout template for consistent HTML structure across pages. - Added JavaScript for dynamic interactions on the models page. - Implemented WebSocket handling for real-time communication with kiosk devices and controllers. - Implemented model registration and management API at /api/models - Added Gitea proxy API for repository interactions at /api/repos - Created results API for listing and comparing training results at /api/results - Developed training management API for enqueueing and retrieving training jobs at /api/trainings - Introduced SSE endpoint for live training progress updates - Added HTML pages for models, datasets, and training management - Created a Dockerfile for the ML runner with necessary dependencies - Developed SDK for user code execution within the runner container - Enhanced CSS styles for improved UI/UX - Implemented WebSocket communication for real-time device and controller interactions in the kiosk system
91 lines
2.3 KiB
Python
91 lines
2.3 KiB
Python
"""Parse e validazione del contratto `model.yml` nelle repo utente.
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Schema sintetico (vedi piano):
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name, type, version, python
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train: {entrypoint, inputs, outputs, metrics}
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test: {entrypoint, io, input_schema[], output_schema[]}
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resources: {cpu, mem_mb, gpu}
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"""
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from __future__ import annotations
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from typing import Any, Optional
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import yaml
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from pydantic import BaseModel, ValidationError
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from core import gitea, redis_client
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class _FieldSpec(BaseModel):
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name: str
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dtype: str
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min: Optional[float] = None
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max: Optional[float] = None
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unit: Optional[str] = None
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class _Train(BaseModel):
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entrypoint: str
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inputs: dict = {}
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outputs: dict = {}
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metrics: dict = {}
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class _Test(BaseModel):
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entrypoint: str
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io: str = "stdio_json"
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input_schema: list[_FieldSpec] = []
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output_schema: list[_FieldSpec] = []
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class ModelSpec(BaseModel):
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name: str
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type: str
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version: str = "0.1.0"
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python: str = "3.11"
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train: _Train
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test: Optional[_Test] = None
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resources: dict = {}
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def parse_yaml(content: bytes | str) -> dict:
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"""Parsa stringa YAML → dict validato. Solleva ValueError su errore."""
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if isinstance(content, bytes):
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content = content.decode("utf-8")
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try:
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raw = yaml.safe_load(content) or {}
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spec = ModelSpec(**raw)
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return spec.model_dump()
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except (yaml.YAMLError, ValidationError) as e:
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raise ValueError(f"invalid model.yml: {e}") from e
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async def fetch_and_parse_spec(owner_repo: str, ref: str) -> Optional[dict]:
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"""Recupera model.yml dalla repo alla revisione e lo parsa.
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Cache Redis `ml:modelspec:{repo}:{ref}` TTL 1h.
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"""
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cache_key = f"ml:modelspec:{owner_repo}:{ref}"
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try:
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cached = await redis_client.client().get(cache_key)
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if cached:
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import json
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return json.loads(cached)
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except Exception:
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pass
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try:
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raw = await gitea.get_file_raw(owner_repo, ref, "model.yml")
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except Exception:
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try:
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raw = await gitea.get_file_raw(owner_repo, ref, "model.yaml")
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except Exception:
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return None
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spec = parse_yaml(raw)
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try:
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import json
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await redis_client.client().set(cache_key, json.dumps(spec), ex=3600)
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except Exception:
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pass
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return spec
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