fbeb6dfc6f
Make the SDXL LoRA trainer reachable from the app with a small, self-contained job service and JSON routes, deliberately separate from the LLM TrainingBackend (whose lifecycle -- LLM config build, per-run SQLite rows, matplotlib plots, transfer-to-chat- inference -- is text-training specific and would mis-handle a diffusion run). core/training/diffusion_training_service.py: DiffusionTrainingService runs one job at a time -- validate the config cheaply (before any spawn), spawn the trainer subprocess (spawn context, parent-lifetime bound), pump its events (model_load_* / progress / complete / error) into an in-memory status snapshot, and support a clean stop. The subprocess context and target are injectable so the full start -> pump -> status -> complete path is unit-tested without real multiprocessing or torch. routes/training.py: POST /api/train/diffusion/start (400 on a bad config, 409 when a job is already running), POST /api/train/diffusion/stop, GET /api/train/diffusion/status (JSON poll). models/training.py: DiffusionTrainingStartRequest + response schemas mirroring DiffusionLoraConfig, so model_dump() passes straight through. Tests: test_diffusion_training.py -- service happy path, bad-config-before-spawn, concurrent-job rejection, clean stop, crash-without-terminal-event, event transitions; plus route wiring via the FastAPI TestClient (start / 422 / 400 / 409 / status / stop) with a mocked service. The diffusion trainer's progress events already use the field names this path expects.
136 lines
3.2 KiB
Python
136 lines
3.2 KiB
Python
# SPDX-License-Identifier: AGPL-3.0-only
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# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
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"""Pydantic models for API request/response schemas."""
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from .training import (
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TrainingStartRequest,
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TrainingJobResponse,
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TrainingStatus,
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TrainingProgress,
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TrainingRunSummary,
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TrainingRunListResponse,
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TrainingRunMetrics,
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TrainingRunDetailResponse,
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TrainingRunDeleteResponse,
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TrainingRunUpdateRequest,
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DiffusionTrainingStartRequest,
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DiffusionTrainingStartResponse,
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DiffusionTrainingStatusResponse,
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)
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from .models import (
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CheckpointInfo,
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ModelCheckpoints,
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CheckpointListResponse,
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ModelDetails,
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LocalModelInfo,
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LocalModelListResponse,
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LoRAInfo,
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LoRAScanResponse,
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ModelListResponse,
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)
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from .auth import (
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AuthLoginRequest,
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RefreshTokenRequest,
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AuthStatusResponse,
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ChangePasswordRequest,
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)
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from .export import (
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LoadCheckpointRequest,
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ExportStatusResponse,
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ExportOperationResponse,
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ExportMergedModelRequest,
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ExportBaseModelRequest,
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ExportGGUFRequest,
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ExportLoRAAdapterRequest,
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)
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from .users import Token
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from .datasets import (
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CheckFormatRequest,
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CheckFormatResponse,
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)
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from .inference import (
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LoadRequest,
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UnloadRequest,
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GenerateRequest,
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LoadResponse,
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UnloadResponse,
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InferenceStatusResponse,
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)
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from .responses import (
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TrainingStopResponse,
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TrainingMetricsResponse,
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LoRABaseModelResponse,
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VisionCheckResponse,
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EmbeddingCheckResponse,
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)
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from .data_recipe import (
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RecipePayload,
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PreviewResponse,
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ValidateError,
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ValidateResponse,
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JobCreateResponse,
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)
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__all__ = [
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# Training schemas
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"TrainingStartRequest",
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"DiffusionTrainingStartRequest",
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"DiffusionTrainingStartResponse",
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"DiffusionTrainingStatusResponse",
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"TrainingJobResponse",
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"TrainingStatus",
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"TrainingProgress",
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"TrainingRunSummary",
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"TrainingRunListResponse",
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"TrainingRunMetrics",
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"TrainingRunDetailResponse",
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"TrainingRunDeleteResponse",
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"TrainingRunUpdateRequest",
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# Model management schemas
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"ModelDetails",
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"LocalModelInfo",
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"LocalModelListResponse",
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"LoRAInfo",
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"LoRAScanResponse",
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"ModelListResponse",
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# Auth schemas
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"AuthLoginRequest",
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"RefreshTokenRequest",
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"AuthStatusResponse",
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"ChangePasswordRequest",
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# Export schemas
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"CheckpointInfo",
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"ModelCheckpoints",
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"CheckpointListResponse",
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"LoadCheckpointRequest",
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"ExportStatusResponse",
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"ExportOperationResponse",
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"ExportMergedModelRequest",
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"ExportBaseModelRequest",
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"ExportGGUFRequest",
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"ExportLoRAAdapterRequest",
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"Token",
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# Dataset schemas
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"CheckFormatRequest",
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"CheckFormatResponse",
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# Inference schemas
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"LoadRequest",
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"UnloadRequest",
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"GenerateRequest",
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"LoadResponse",
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"UnloadResponse",
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"InferenceStatusResponse",
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# Response schemas
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"TrainingStopResponse",
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"TrainingMetricsResponse",
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"LoRABaseModelResponse",
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"VisionCheckResponse",
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"EmbeddingCheckResponse",
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# Data recipe
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"RecipePayload",
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"PreviewResponse",
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"ValidateError",
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"ValidateResponse",
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"JobCreateResponse",
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]
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