unsloth/studio/backend/routes/training.py

# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0

"""
Training API routes
"""

import sys
from pathlib import Path
from fastapi import APIRouter, Depends, HTTPException, Request
from fastapi.responses import StreamingResponse
from typing import Dict, Optional, Any
import structlog
from loggers import get_logger
import asyncio
from datetime import datetime
import uuid as _uuid

# Add backend directory to path.
backend_path = Path(__file__).parent.parent.parent
if str(backend_path) not in sys.path:
    sys.path.insert(0, str(backend_path))

try:
    from core.training import get_training_backend
    from core.training.resume import (
        can_resume_run,
        get_resume_checkpoint_path,
        normalize_resume_output_dir,
    )
    from storage.studio_db import get_resumable_run_by_output_dir
    from utils.models.model_config import load_model_defaults
    from utils.paths import resolve_dataset_path
except ImportError:
    # Fallback: parent directory.
    parent_backend = backend_path.parent / "backend"
    if str(parent_backend) not in sys.path:
        sys.path.insert(0, str(parent_backend))
    from core.training import get_training_backend
    from core.training.resume import (
        can_resume_run,
        get_resume_checkpoint_path,
        normalize_resume_output_dir,
    )
    from storage.studio_db import get_resumable_run_by_output_dir
    from utils.models.model_config import load_model_defaults
    from utils.paths import resolve_dataset_path

# Auth
from auth.authentication import get_current_subject

from utils.utils import log_and_http_error

from models import (
    TrainingStartRequest,
    TrainingJobResponse,
    TrainingStatus,
    TrainingProgress,
)
from models.responses import TrainingStopResponse, TrainingMetricsResponse
from pydantic import BaseModel as PydanticBaseModel


class TrainingStopRequest(PydanticBaseModel):
    save: bool = True


router = APIRouter()
logger = get_logger(__name__)


def _validate_local_dataset_paths(paths: list[str], label: str = "Local dataset") -> list[str]:
    """Resolve and validate a list of local dataset paths. Returns validated absolute paths."""
    validated = []
    missing = []
    for dataset_path in paths:
        dataset_file = resolve_dataset_path(dataset_path)
        if not dataset_file.exists():
            missing.append(f"{dataset_path} (resolved: {dataset_file})")
            continue
        logger.info(f"Found {label.lower()} file: {dataset_file}")
        validated.append(str(dataset_file))

    if missing:
        missing_detail = "; ".join(missing[:3])
        raise HTTPException(
            status_code = 400,
            detail = f"{label} not found: {missing_detail}",
        )
    return validated


@router.get("/hardware")
async def get_hardware_utilization(current_subject: str = Depends(get_current_subject)):
    """
    Live snapshot of GPU hardware utilization for the active backend.

    Polled by the frontend during training.
    """
    from utils.hardware import get_gpu_utilization
    return get_gpu_utilization()


@router.get("/hardware/visible")
async def get_visible_hardware_utilization(current_subject: str = Depends(get_current_subject)):
    from utils.hardware import get_visible_gpu_utilization
    return get_visible_gpu_utilization()


@router.post("/start")
async def start_training(
    request: TrainingStartRequest, current_subject: str = Depends(get_current_subject)
):
    """
    Start a training job.

    Initiates training in the background and returns immediately. Use /status
    to check progress.
    """
    try:
        logger.info(f"Starting training job with model: {request.model_name}")

        # No in-process ensure_transformers_version(): the subprocess
        # (worker.py) activates the correct version before importing ML libs.

        backend = get_training_backend()

        # S3 dataset loading needs the optional boto3 dependency. Reject early
        # with a clear message so credentials are never accepted and then
        # silently dropped on a host without boto3 installed.
        if request.s3_config is not None:
            from core.training.s3_dataset import boto3_available
            if not boto3_available():
                raise HTTPException(
                    status_code = 501,
                    detail = "S3 dataset loading requires boto3. Install it with: pip install boto3",
                )

        # Check before mutating state.
        if backend.is_training_active():
            existing_job_id: Optional[str] = getattr(backend, "current_job_id", "")
            return TrainingJobResponse(
                job_id = existing_job_id or "",
                status = "error",
                message = (
                    "Training is already in progress. "
                    "Stop current training before starting a new one."
                ),
                error = "Training already active",
            )

        # Job ID; start_training() sets it on the backend only after the old
        # pump thread is dead.
        job_id = f"job_{datetime.now().strftime('%Y%m%d_%H%M%S')}_{_uuid.uuid4().hex[:8]}"

        # Validate dataset paths if provided.
        if request.local_datasets:
            request.local_datasets = _validate_local_dataset_paths(
                request.local_datasets, "Local dataset"
            )
        if request.local_eval_datasets and request.eval_steps > 0:
            request.local_eval_datasets = _validate_local_dataset_paths(
                request.local_eval_datasets, "Local eval dataset"
            )
        resume_output_dir: Optional[str] = None
        if request.resume_from_checkpoint:
            try:
                resume_output_dir = normalize_resume_output_dir(request.resume_from_checkpoint)
            except ValueError as e:
                # Deliberate user-facing validation message.
                validation_message = str(e)
                raise HTTPException(status_code = 400, detail = validation_message)

            resume_run = get_resumable_run_by_output_dir(resume_output_dir)
            if not resume_run or not can_resume_run(resume_run):
                raise HTTPException(
                    status_code = 400,
                    detail = "Resume checkpoint must belong to a stopped run with saved trainer state.",
                )
            resume_checkpoint = get_resume_checkpoint_path(resume_output_dir)
            if not resume_checkpoint:
                raise HTTPException(
                    status_code = 400,
                    detail = "Resume checkpoint must include saved trainer state.",
                )
            request.resume_from_checkpoint = resume_checkpoint

        # Convert request to backend kwargs.
        training_kwargs = {
            "model_name": request.model_name,
            "training_type": request.training_type,
            "hf_token": request.hf_token or "",
            "load_in_4bit": request.load_in_4bit,
            "max_seq_length": request.max_seq_length,
            "vision_image_size": request.vision_image_size,
            "hf_dataset": request.hf_dataset or "",
            "local_datasets": request.local_datasets,
            "local_eval_datasets": request.local_eval_datasets,
            "format_type": request.format_type,
            "subset": request.subset,
            "train_split": request.train_split,
            "eval_split": request.eval_split,
            "eval_steps": request.eval_steps,
            "dataset_slice_start": request.dataset_slice_start,
            "dataset_slice_end": request.dataset_slice_end,
            "custom_format_mapping": request.custom_format_mapping,
            "num_epochs": request.num_epochs,
            "learning_rate": request.learning_rate,
            "embedding_learning_rate": request.embedding_learning_rate,
            "batch_size": request.batch_size,
            "gradient_accumulation_steps": request.gradient_accumulation_steps,
            "warmup_steps": request.warmup_steps,
            "warmup_ratio": request.warmup_ratio,
            "max_steps": request.max_steps,
            "save_steps": request.save_steps,
            "weight_decay": request.weight_decay,
            "max_grad_norm": request.max_grad_norm,
            "max_grad_value": request.max_grad_value,
            "max_grad_leaf_norm": request.max_grad_leaf_norm,
            "cast_norm_output_to_input_dtype": request.cast_norm_output_to_input_dtype,
            "random_seed": request.random_seed,
            "packing": request.packing,
            "optim": request.optim,
            "lr_scheduler_type": request.lr_scheduler_type,
            "use_lora": request.use_lora,
            "lora_r": request.lora_r,
            "lora_alpha": request.lora_alpha,
            "lora_dropout": request.lora_dropout,
            "target_modules": request.target_modules if request.target_modules else None,
            "gradient_checkpointing": request.gradient_checkpointing.strip()
            if request.gradient_checkpointing and request.gradient_checkpointing.strip()
            else "unsloth",
            "use_rslora": request.use_rslora,
            "use_loftq": request.use_loftq,
            "train_on_completions": request.train_on_completions,
            "finetune_vision_layers": request.finetune_vision_layers,
            "finetune_language_layers": request.finetune_language_layers,
            "finetune_attention_modules": request.finetune_attention_modules,
            "finetune_mlp_modules": request.finetune_mlp_modules,
            "is_dataset_image": request.is_dataset_image,
            "is_dataset_audio": request.is_dataset_audio,
            "is_embedding": request.is_embedding,
            "enable_wandb": request.enable_wandb,
            "wandb_token": request.wandb_token or "",
            "wandb_project": request.wandb_project or "",
            "enable_tensorboard": request.enable_tensorboard,
            "tensorboard_dir": request.tensorboard_dir or "",
            "output_dir": resume_output_dir,
            "resume_from_checkpoint": request.resume_from_checkpoint,
            "trust_remote_code": request.trust_remote_code,
            "approved_remote_code_fingerprint": request.approved_remote_code_fingerprint,
            "gpu_ids": request.gpu_ids,
            "s3_config": request.s3_config.model_dump() if request.s3_config else None,
        }

        # Training page has no trust_remote_code toggle, so honor the YAML default
        # -- but only for genuine first-party (unsloth/nvidia) Hub repos, never a
        # local path or a name merely starting with "unsloth/".
        if not training_kwargs["trust_remote_code"]:
            from utils.security.trusted_org import is_trusted_org_repo

            model_defaults = load_model_defaults(request.model_name)
            yaml_trust = model_defaults.get("training", {}).get("trust_remote_code", False)
            if yaml_trust and is_trusted_org_repo(
                request.model_name, hf_token = request.hf_token or None
            ):
                logger.info(f"YAML config sets trust_remote_code=True for {request.model_name}")
                training_kwargs["trust_remote_code"] = True
            elif yaml_trust:
                logger.warning(
                    "YAML sets trust_remote_code=True for %s but it is not a trusted "
                    "first-party repo; leaving disabled (user can opt in explicitly).",
                    request.model_name,
                )

        # Free VRAM for training: stop export, unload chat unless it can coexist.
        # A before_spawn hook -> runs only after start_training's guards pass, so
        # we never tear down chat/export VRAM for a start that is then refused.
        def _free_vram_for_training() -> None:
            try:
                from core.export import get_export_backend
                exp_backend = get_export_backend()
                # Tear down the export subprocess whenever an export is in flight,
                # not just once a checkpoint is loaded: during the load phase
                # current_checkpoint is still unset while the worker is already
                # allocating GPU memory, so gate on is_export_active() too.
                if exp_backend.current_checkpoint or exp_backend.is_export_active():
                    logger.info("Shutting down export subprocess to free GPU memory for training")
                    exp_backend._shutdown_subprocess()
                    exp_backend.current_checkpoint = None
                    exp_backend.is_vision = False
                    exp_backend.is_peft = False
            except Exception as e:
                logger.warning("Could not shut down export subprocess: %s", e)

            try:
                from routes.training_vram import (
                    can_keep_chat_during_training,
                    free_chat_models_for_training,
                    summarize_resident_chat,
                )

                resident = summarize_resident_chat()
                if not resident["any"]:
                    return
                if resident.get("loading"):
                    # In-flight load can't be sized -> free rather than risk OOM.
                    freed = free_chat_models_for_training(reason = "chat model still loading")
                    logger.info("Freed in-flight chat load for training: %s", freed)
                    return
                keep, info = can_keep_chat_during_training(
                    model_name = training_kwargs["model_name"],
                    hf_token = training_kwargs["hf_token"],
                    training_type = training_kwargs["training_type"],
                    load_in_4bit = training_kwargs["load_in_4bit"],
                    batch_size = training_kwargs["batch_size"],
                    max_seq_length = training_kwargs["max_seq_length"],
                    lora_rank = training_kwargs["lora_r"],
                    target_modules = training_kwargs["target_modules"],
                    gradient_checkpointing = training_kwargs["gradient_checkpointing"],
                    optimizer = training_kwargs["optim"],
                    gpu_ids = training_kwargs["gpu_ids"],
                )
                if keep:
                    logger.info(
                        "Keeping chat model(s) loaded during training "
                        "(free ~%s GB, needs ~%s GB): %s",
                        info.get("usable_gb"),
                        info.get("required_gb"),
                        resident,
                    )
                else:
                    freed = free_chat_models_for_training(
                        reason = "insufficient VRAM to run training alongside chat",
                    )
                    logger.info("Freed chat model(s) for training: %s", freed)
            except Exception as e:
                logger.warning("Chat/training VRAM coordination failed; proceeding: %s", e)

        # The hook runs only once start guards pass -> VRAM freed iff training starts.
        success = backend.start_training(
            job_id = job_id, before_spawn = _free_vram_for_training, **training_kwargs
        )

        if not success:
            progress_error = backend.trainer.training_progress.error
            return TrainingJobResponse(
                job_id = backend.current_job_id or "",
                status = "error",
                message = progress_error or "Failed to start training subprocess",
                error = progress_error or "subprocess_start_failed",
            )

        return TrainingJobResponse(
            job_id = job_id,
            status = "queued",
            message = "Training job queued and starting in subprocess",
            error = None,
        )

    except HTTPException:
        # Deliberate rejections (S3 not implemented, resume validation) must
        # reach the client with their original status, not a generic 500.
        raise
    except ValueError as e:
        logger.warning("Rejected training GPU selection: %s", e)
        # Deliberate user-facing GPU-selection validation message.
        validation_message = str(e)
        raise HTTPException(status_code = 400, detail = validation_message)
    except Exception as e:
        raise log_and_http_error(
            e,
            500,
            "Failed to start training",
            event = "training.start_failed",
            log = logger,
        )


@router.post("/stop", response_model = TrainingStopResponse)
async def stop_training(
    body: TrainingStopRequest = TrainingStopRequest(),
    current_subject: str = Depends(get_current_subject),
):
    """
    Stop the currently running training job.

    Body:
        save (bool): If True (default), save the model at the current checkpoint.
    """
    try:
        backend = get_training_backend()
        is_active = backend.is_training_active()
        logger.info("Stop requested: save=%s is_active=%s", body.save, is_active)

        if not is_active:
            return TrainingStopResponse(
                status = "idle", message = "No training job is currently running"
            )

        backend.stop_training(save = body.save)

        return TrainingStopResponse(
            status = "stopped",
            message = "Stop requested. Training will stop at the next safe step.",
        )

    except Exception as e:
        raise log_and_http_error(
            e,
            500,
            "Failed to stop training",
            event = "training.stop_failed",
            log = logger,
        )


@router.post("/reset")
async def reset_training(current_subject: str = Depends(get_current_subject)):
    """Reset training state so the user can return to configuration."""
    try:
        backend = get_training_backend()
        is_active = backend.is_training_active()

        if is_active:
            if backend._cancel_requested:
                # Cancel (save=False) requested — force-terminate to reset immediately.
                logger.info("Force-terminating subprocess for immediate reset (cancel path)")
                backend.force_terminate()
            else:
                logger.warning("Rejected reset while training active: is_active=%s", is_active)
                raise HTTPException(
                    status_code = 409,
                    detail = "Training is still running. Stop training and wait for it to finish before resetting.",
                )

        logger.info("Reset training state: clearing runtime + metric history")
        backend._should_stop = False  # Clear stop flag so status returns to idle
        backend.trainer._update_progress(
            is_training = False,
            is_completed = False,
            error = None,
            status_message = "Ready to train",
            step = 0,
            loss = None,
            epoch = 0,
            total_steps = 0,
        )
        backend.loss_history = []
        backend.lr_history = []
        backend.step_history = []
        backend.grad_norm_history = []
        backend.grad_norm_step_history = []
        return {"status": "ok"}
    except HTTPException:
        raise
    except Exception as e:
        raise log_and_http_error(
            e,
            500,
            "Failed to reset training",
            event = "training.reset_failed",
            log = logger,
        )


@router.get("/status")
async def get_training_status(current_subject: str = Depends(get_current_subject)):
    """
    Get the current training status.
    """
    try:
        backend = get_training_backend()
        job_id: str = getattr(backend, "current_job_id", "") or ""

        is_active = backend.is_training_active()

        try:
            progress = backend.trainer.get_training_progress()
        except Exception:
            progress = None

        status_message = (
            getattr(progress, "status_message", None) if progress else None
        ) or "Ready to train"
        error_message = getattr(progress, "error", None) if progress else None

        trainer_stopped = getattr(backend, "_should_stop", False)

        # Derive high-level phase
        if error_message:
            phase = "error"
        elif is_active:
            msg_lower = status_message.lower()
            if "loading" in msg_lower or "importing" in msg_lower:
                phase = "loading_model"
            elif any(k in msg_lower for k in ["preparing", "initializing", "configuring"]):
                phase = "configuring"
            else:
                phase = "training"
        elif trainer_stopped:
            phase = "stopped"
        elif progress and getattr(progress, "is_completed", False):
            phase = "completed"
        else:
            phase = "idle"

        details = None
        if progress:
            details = {
                "epoch": getattr(progress, "epoch", 0),
                "step": getattr(progress, "step", 0),
                "total_steps": getattr(progress, "total_steps", 0),
                "loss": getattr(progress, "loss", None),
                "learning_rate": getattr(progress, "learning_rate", None),
            }
            output_dir = getattr(backend, "_output_dir", None)
            if output_dir:
                details["output_dir"] = output_dir

        # Metric history for chart recovery after SSE reconnection.
        metric_history = None
        if backend.step_history:
            metric_history = {
                "steps": list(backend.step_history),
                "loss": list(backend.loss_history),
                "lr": list(backend.lr_history),
                "grad_norm": list(getattr(backend, "grad_norm_history", [])),
                "grad_norm_steps": list(getattr(backend, "grad_norm_step_history", [])),
                "eval_loss": list(backend.eval_loss_history),
                "eval_steps": list(backend.eval_step_history),
            }

        return TrainingStatus(
            job_id = job_id,
            phase = phase,
            is_training_running = is_active,
            eval_enabled = backend.eval_enabled,
            message = status_message,
            error = error_message,
            details = details,
            metric_history = metric_history,
        )

    except Exception as e:
        raise log_and_http_error(
            e,
            500,
            "Failed to get training status",
            event = "training.status_failed",
            log = logger,
        )


@router.get("/metrics", response_model = TrainingMetricsResponse)
async def get_training_metrics(current_subject: str = Depends(get_current_subject)):
    """
    Get training metrics (loss, learning rate, steps).
    """
    try:
        backend = get_training_backend()

        loss_history = backend.loss_history
        lr_history = backend.lr_history
        step_history = backend.step_history
        grad_norm_history = getattr(backend, "grad_norm_history", [])
        grad_norm_step_history = getattr(backend, "grad_norm_step_history", [])

        current_loss = loss_history[-1] if loss_history else None
        current_lr = lr_history[-1] if lr_history else None
        current_step = step_history[-1] if step_history else None

        return TrainingMetricsResponse(
            loss_history = loss_history,
            lr_history = lr_history,
            step_history = step_history,
            grad_norm_history = grad_norm_history,
            grad_norm_step_history = grad_norm_step_history,
            current_loss = current_loss,
            current_lr = current_lr,
            current_step = current_step,
        )

    except Exception as e:
        raise log_and_http_error(
            e,
            500,
            "Failed to get training metrics",
            event = "training.metrics_failed",
            log = logger,
        )


@router.get("/progress")
async def stream_training_progress(
    request: Request, current_subject: str = Depends(get_current_subject)
):
    """
    Stream training progress via Server-Sent Events (SSE).

    Real-time progress with reconnection support per the SSE spec:
      - `id:` per event so the browser tracks position.
      - `retry:` to control reconnection interval.
      - Named `event:` types (progress, heartbeat, complete, error).
      - Reads `Last-Event-ID` on reconnect to replay missed steps.
    """
    # Read Last-Event-ID header for reconnection resume.
    last_event_id = request.headers.get("last-event-id")
    resume_from_step: Optional[int] = None
    if last_event_id is not None:
        try:
            resume_from_step = int(last_event_id)
            logger.info(f"SSE reconnect: resuming from step {resume_from_step}")
        except ValueError:
            logger.warning(f"Invalid Last-Event-ID: {last_event_id}")

    async def event_generator():
        backend = get_training_backend()
        job_id: str = getattr(backend, "current_job_id", "") or ""

        # ── Helpers ──────────────────────────────────────────────
        def build_progress(
            step: int,
            loss: Optional[float],
            learning_rate: Optional[float],
            total_steps: int,
            epoch: Optional[float] = None,
            progress: Optional[Any] = None,
            grad_norm_override: Optional[float] = None,
            eval_loss_override: Optional[float] = None,
        ) -> TrainingProgress:
            total = max(total_steps, 0)
            if step < 0 or total == 0:
                progress_percent = 0.0
            else:
                progress_percent = float(step) / float(total) * 100.0 if total > 0 else 0.0

            # Pull values from the progress object if available.
            elapsed_seconds = getattr(progress, "elapsed_seconds", None) if progress else None
            eta_seconds = getattr(progress, "eta_seconds", None) if progress else None
            grad_norm = grad_norm_override
            if grad_norm is None and progress:
                grad_norm = getattr(progress, "grad_norm", None)
            num_tokens = getattr(progress, "num_tokens", None) if progress else None
            eval_loss = eval_loss_override
            if eval_loss is None and progress:
                eval_loss = getattr(progress, "eval_loss", None)

            return TrainingProgress(
                job_id = job_id,
                step = step,
                total_steps = total,
                loss = loss,
                learning_rate = learning_rate,
                progress_percent = progress_percent,
                epoch = epoch,
                elapsed_seconds = elapsed_seconds,
                eta_seconds = eta_seconds,
                grad_norm = grad_norm,
                num_tokens = num_tokens,
                eval_loss = eval_loss,
            )

        def format_sse(
            data: str,
            event: str = "progress",
            event_id: Optional[int] = None,
        ) -> str:
            """Format a single SSE message with id/event/data fields."""
            lines = []
            if event_id is not None:
                lines.append(f"id: {event_id}")
            lines.append(f"event: {event}")
            lines.append(f"data: {data}")
            lines.append("")  # trailing blank line
            lines.append("")  # double newline terminates the event
            return "\n".join(lines)

        # ── Retry directive ──────────────────────────────────────
        # Reconnect after 3 seconds if the connection drops.
        yield "retry: 3000\n\n"

        # ── Replay missed steps on reconnect ─────────────────────
        if resume_from_step is not None and backend.step_history:
            replayed = 0
            grad_norm_by_step = {
                step_val: grad_val
                for step_val, grad_val in zip(
                    getattr(backend, "grad_norm_step_history", []),
                    getattr(backend, "grad_norm_history", []),
                )
            }
            for i, step_val in enumerate(backend.step_history):
                if step_val > resume_from_step:
                    loss_val = backend.loss_history[i] if i < len(backend.loss_history) else None
                    lr_val = backend.lr_history[i] if i < len(backend.lr_history) else None
                    tp_replay = getattr(
                        getattr(backend, "trainer", None), "training_progress", None
                    )
                    total_replay = (
                        getattr(tp_replay, "total_steps", step_val) if tp_replay else step_val
                    )
                    epoch_replay = getattr(tp_replay, "epoch", None) if tp_replay else None
                    payload = build_progress(
                        step_val,
                        loss_val,
                        lr_val,
                        total_replay,
                        epoch_replay,
                        progress = tp_replay,
                        grad_norm_override = grad_norm_by_step.get(step_val),
                    )
                    yield format_sse(payload.model_dump_json(), event = "progress", event_id = step_val)
                    replayed += 1
            if replayed:
                logger.info(f"SSE reconnect: replayed {replayed} missed steps")

        # ── Initial status (only on fresh connections) ───────────
        if resume_from_step is None:
            is_active = backend.is_training_active()
            tp = getattr(getattr(backend, "trainer", None), "training_progress", None)
            initial_total_steps = getattr(tp, "total_steps", 0) if tp else 0
            initial_epoch = getattr(tp, "epoch", None) if tp else None

            initial_progress = build_progress(
                step = 0,
                loss = None,
                learning_rate = None,
                total_steps = initial_total_steps,
                epoch = initial_epoch,
                progress = tp,
            )
            yield format_sse(initial_progress.model_dump_json(), event = "progress", event_id = 0)

            # If not active, send final state and exit
            if not is_active:
                _live = (getattr(tp, "step", 0) or 0) if tp else 0
                if backend.step_history or _live > 0:
                    final_step = backend.step_history[-1] if backend.step_history else 0
                    final_loss = backend.loss_history[-1] if backend.loss_history else None
                    final_lr = backend.lr_history[-1] if backend.lr_history else None
                    # Histories skip non-finite steps; report the live step with
                    # loss=None instead of the last finite pair.
                    if _live > final_step:
                        final_step = _live
                        final_loss = getattr(tp, "loss", None)
                        final_lr = getattr(tp, "learning_rate", final_lr)
                    final_total_steps = getattr(tp, "total_steps", final_step) if tp else final_step
                    final_epoch = getattr(tp, "epoch", None) if tp else None
                    payload = build_progress(
                        final_step,
                        final_loss,
                        final_lr,
                        final_total_steps,
                        final_epoch,
                        progress = tp,
                    )
                    yield format_sse(
                        payload.model_dump_json(), event = "complete", event_id = final_step
                    )
                else:
                    yield format_sse(
                        build_progress(-1, None, None, 0, progress = tp).model_dump_json(),
                        event = "complete",
                        event_id = 0,
                    )
                return

        # ── Live polling loop ────────────────────────────────────
        last_step = resume_from_step if resume_from_step is not None else -1
        no_update_count = 0
        max_no_updates = 1800  # Timeout after 30 min (large models need compile time)

        while backend.is_training_active():
            try:
                tp_inner = getattr(getattr(backend, "trainer", None), "training_progress", None)
                live_step = (getattr(tp_inner, "step", 0) or 0) if tp_inner else 0
                if backend.step_history or live_step > 0:
                    current_step = backend.step_history[-1] if backend.step_history else 0
                    current_loss = backend.loss_history[-1] if backend.loss_history else None
                    current_lr = backend.lr_history[-1] if backend.lr_history else None
                    # Histories skip non-finite steps; follow the live progress
                    # step and report its loss (None until it recovers).
                    if live_step > current_step:
                        current_step = live_step
                        current_loss = getattr(tp_inner, "loss", None)
                        current_lr = getattr(tp_inner, "learning_rate", current_lr)
                    current_total_steps = (
                        getattr(tp_inner, "total_steps", current_step) if tp_inner else current_step
                    )
                    current_epoch = getattr(tp_inner, "epoch", None) if tp_inner else None

                    # Only send if the step changed.
                    if current_step != last_step:
                        progress_payload = build_progress(
                            current_step,
                            current_loss,
                            current_lr,
                            current_total_steps,
                            current_epoch,
                            progress = tp_inner,
                        )
                        yield format_sse(
                            progress_payload.model_dump_json(),
                            event = "progress",
                            event_id = current_step,
                        )
                        last_step = current_step
                        no_update_count = 0
                    else:
                        no_update_count += 1
                        # Heartbeat every 10 seconds.
                        if no_update_count % 10 == 0:
                            heartbeat_payload = build_progress(
                                current_step,
                                current_loss,
                                current_lr,
                                current_total_steps,
                                current_epoch,
                                progress = tp_inner,
                            )
                            yield format_sse(
                                heartbeat_payload.model_dump_json(),
                                event = "heartbeat",
                                event_id = current_step,
                            )
                else:
                    # No steps yet, but training is active (model loading, etc.).
                    no_update_count += 1
                    if no_update_count % 5 == 0:
                        # Pull total_steps + status so the frontend can show
                        # "Tokenizing…" etc.
                        tp_prep = getattr(
                            getattr(backend, "trainer", None),
                            "training_progress",
                            None,
                        )
                        prep_total = getattr(tp_prep, "total_steps", 0) if tp_prep else 0
                        preparing_payload = build_progress(
                            0,
                            None,
                            None,
                            prep_total,
                            progress = tp_prep,
                        )
                        yield format_sse(
                            preparing_payload.model_dump_json(),
                            event = "heartbeat",
                            event_id = 0,
                        )

                # Timeout check
                if no_update_count > max_no_updates:
                    logger.warning("Progress stream timeout - no updates received")
                    tp_timeout = getattr(
                        getattr(backend, "trainer", None), "training_progress", None
                    )
                    timeout_payload = build_progress(last_step, None, None, 0, progress = tp_timeout)
                    yield format_sse(
                        timeout_payload.model_dump_json(),
                        event = "error",
                        event_id = last_step if last_step >= 0 else 0,
                    )
                    break

                await asyncio.sleep(1)  # Poll every second

            except Exception as e:
                logger.error(f"Error in progress stream: {e}", exc_info = True)
                tp_error = getattr(getattr(backend, "trainer", None), "training_progress", None)
                error_payload = build_progress(0, None, None, 0, progress = tp_error)
                yield format_sse(
                    error_payload.model_dump_json(),
                    event = "error",
                    event_id = last_step if last_step >= 0 else 0,
                )
                break

        # ── Final "complete" event ───────────────────────────────
        final_step = backend.step_history[-1] if backend.step_history else last_step
        final_loss = backend.loss_history[-1] if backend.loss_history else None
        final_lr = backend.lr_history[-1] if backend.lr_history else None
        final_tp = getattr(getattr(backend, "trainer", None), "training_progress", None)
        # If the run ended on a non-finite stretch, report the live step with
        # loss=None instead of rolling back to the last finite pair.
        _final_live_step = (getattr(final_tp, "step", 0) or 0) if final_tp else 0
        if _final_live_step > (final_step if final_step is not None else -1):
            final_step = _final_live_step
            final_loss = getattr(final_tp, "loss", None)
            final_lr = getattr(final_tp, "learning_rate", final_lr)
        final_total_steps = getattr(final_tp, "total_steps", final_step) if final_tp else final_step
        final_epoch = getattr(final_tp, "epoch", None) if final_tp else None
        final_payload = build_progress(
            final_step,
            final_loss,
            final_lr,
            final_total_steps,
            final_epoch,
            progress = final_tp,
        )
        yield format_sse(
            final_payload.model_dump_json(),
            event = "complete",
            event_id = final_step if final_step >= 0 else 0,
        )

    return StreamingResponse(
        event_generator(),
        media_type = "text/event-stream",
        headers = {
            "Cache-Control": "no-cache",
            "Connection": "keep-alive",
            "X-Accel-Buffering": "no",
        },
    )