208862218d* feat(db): add SQLite storage layer for training history * feat(api): add training history endpoints and response models * feat(training): integrate DB persistence into training event loop * feat(ui): add training history views and card grid * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix(studio): address review issues in training history persistence - Strip hf_token/wandb_token from config before SQLite storage - Add UUID suffix to job_id for collision resistance - Use isfinite() for 0.0 metric handling throughout - Respect _should_stop in error event finalization - Run schema DDL once per process, not per connection - Close connection on schema init failure - Guard cleanup_orphaned_runs at startup - Cap _metric_buffer at 500 entries - Make FLUSH_THRESHOLD a class constant - Map 'running' to 'training' phase in historical view - Derive LR/GradNorm from history arrays in historical view - Fix nested button with div[role=button] in history cards - Guard String(value) against null/undefined in config popover - Clear selectedHistoryRunId on auto tab switch * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix(studio): address round-2 review findings across training backend and frontend Backend (training.py): - Move state mutation after proc.start() so a failed spawn does not wedge the backend with is_training=True - Create DB run row eagerly after proc.start() so runs appear in history during model loading, not after first metric event - Rewrite _flush_metrics_to_db() with snapshot-before-insert pattern to preserve metrics arriving during the write and retain buffer on failure - Guard eval_loss with float() coercion and math.isfinite(), matching the existing grad_norm guard - Increase pump thread join timeout from 3s to 8s to cover SQLite's default 5s lock timeout Frontend (studio-page.tsx): - Fix history navigation: check isTrainingRunning instead of showTrainingView in onSelectRun so completed runs are not misrouted - Replace activeTab state + auto-switch useEffect with derived tab to eliminate react-hooks/set-state-in-effect lint violation Frontend (historical-training-view.tsx): - Add explicit "running" branch to message ternary so running runs no longer fall through to "Training errored" - Derive loading from detail/error state and move cleanup to effect return to eliminate react-hooks/set-state-in-effect lint violation Frontend (progress-section.tsx): - Derive stopRequested from isTrainingRunning && stopRequestedLocal to eliminate react-hooks/set-state-in-effect lint violation and remove unused useEffect import * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix(studio): resolve 3 remaining bugs from round-2 review 1. Stuck on Current Run tab [12/20]: Only force "current-run" tab when isTrainingRunning is true, not when stale completed-run data exists. After training ends, users can freely navigate to Configure. 2. Incomplete metric sanitization [7/20]: Apply float() coercion and isfinite() guards to loss and learning_rate, matching the existing pattern used by grad_norm and eval_loss. Prevents TypeError from string values and NaN leaks into history arrays. 3. Stop button state leak across runs [10/20]: Add key={runtime.jobId} to ProgressSection so React remounts it when a new run starts, resetting stopRequestedLocal state. * fix(studio): deduplicate loss/lr sanitization in training event handler Reuse _safe_loss/_safe_lr from the progress update block instead of re-sanitizing the same raw event values for metric history. * fix(studio): restore loss > 0 guard to prevent eval steps injecting 0.0 into metric histories Round-2/3 fixes relaxed the history append guard from `loss > 0` to `loss is not None`, which let eval-only log events (where loss defaults to 0.0) append fake zeros into loss_history and lr_history. Restore the `loss > 0` check to match the worker's own has_train_loss gate. The float() coercion and isfinite() sanitization from round-3 remain intact. * fix(studio): resolve training history bugs — nullable loss/lr, tab nav, sparkline * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Daniel Han <danielhanchen@gmail.com>
130 lines
2.9 KiB
Python
130 lines
2.9 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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"""
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Pydantic models for API request/response schemas
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"""
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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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)
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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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"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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# 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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