update train loop eval logic

example/example.json

@@ -0,0 +1,29 @@
+{
+    "estimator_name": "mlp",
+    "dataset_name": "random_xy_dataset",
+    "dataloader_name": "supervised_data_loader",
+    "estimator_kwargs": {
+        "input_dim": 4,
+        "output_dim": 2
+    },
+    "dataset_kwargs": {
+        "num_samples": 100000,
+        "preload": true,
+        "input_dim": 4,
+        "output_dim": 2
+    },
+    "dataset_split_fracs": {
+        "train": 0.4,
+        "val": 0.3,
+        "aux": [0.3]
+    },
+    "dataloader_kwargs": {
+        "batch_size": 16
+    },
+    "train_kwargs": {
+        "summarize_every": 20,
+        "max_epochs": 100,
+        "stop_after_epochs": 100
+    },
+    "load_only": false
+}

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "trainlib"
-version = "0.2.0"
+version = "0.3.1"
 description = "Minimal framework for ML modeling. Supports advanced dataset operations and streamlined training."
 requires-python = ">=3.13"
 authors = [

trainlib/__main__.py

@@ -0,0 +1,18 @@
+import logging
+
+from trainlib.cli import create_parser
+
+
+def main() -> None:
+    parser = create_parser()
+    args = parser.parse_args()
+
+    # skim off log level to handle higher-level option
+    if hasattr(args, "log_level") and args.log_level is not None:
+        logging.basicConfig(level=args.log_level)
+
+    args.func(args) if "func" in args else parser.print_help()
+
+
+if __name__ == "__main__":
+    main()

trainlib/cli/__init__.py

@@ -0,0 +1,26 @@
+import logging
+import argparse
+
+from trainlib.cli import train
+
+logger: logging.Logger = logging.getLogger(__name__)
+
+
+def create_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(
+        description="trainlib cli",
+        # formatter_class=argparse.RawDescriptionHelpFormatter,
+    )
+    parser.add_argument(
+        "--log-level",
+        type=int,
+        metavar="int",
+        choices=[10, 20, 30, 40, 50],
+        help="Log level: 10=DEBUG, 20=INFO, 30=WARNING, 40=ERROR, 50=CRITICAL",
+    )
+
+    subparsers = parser.add_subparsers(help="subcommand help")
+    train.register_parser(subparsers)
+
+    return parser
+

trainlib/cli/train.py

@@ -0,0 +1,164 @@
+import gc
+import json
+import argparse
+from typing import Any
+from argparse import _SubParsersAction
+
+import torch
+
+from trainlib.trainer import Trainer
+from trainlib.datasets import dataset_map
+from trainlib.estimator import Estimator
+from trainlib.estimators import estimator_map
+from trainlib.dataloaders import dataloader_map
+
+
+def prepare_run() -> None:
+    # prepare cuda memory
+    memory_allocated = torch.cuda.memory_allocated() / 1024**3  # GB
+    print(f"CUDA allocated: {memory_allocated}GB")
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+    gc.collect()
+
+
+def run(
+    estimator_name: str,
+    dataset_name: str,
+    dataloader_name: str,
+    estimator_kwargs: dict[str, Any] | None = None,
+    dataset_kwargs: dict[str, Any] | None = None,
+    dataset_split_fracs: dict[str, Any] | None = None,
+    dataset_split_kwargs: dict[str, Any] | None = None,
+    dataloader_kwargs: dict[str, Any] | None = None,
+    trainer_kwargs: dict[str, Any] | None = None,
+    train_kwargs: dict[str, Any] | None = None,
+    load_only: bool = False,
+) -> Trainer | Estimator:
+
+    try:
+        estimator_cls = estimator_map[estimator_name]
+    except KeyError as err:
+        raise ValueError(
+            f"Invalid estimator name '{estimator_name}',"
+            f"must be one of {estimator_map.keys()}"
+        ) from err
+
+    try:
+        dataset_cls = dataset_map[dataset_name]
+    except KeyError as err:
+        raise ValueError(
+            f"Invalid dataset name '{dataset_name}',"
+            f"must be one of {dataset_map.keys()}"
+        ) from err
+
+    try:
+        dataloader_cls = dataloader_map[dataloader_name]
+    except KeyError as err:
+        raise ValueError(
+            f"Invalid dataloader name '{dataloader_name}',"
+            f"must be one of {dataloader_map.keys()}"
+        ) from err
+
+    estimator_kwargs = estimator_kwargs or {}
+    dataset_kwargs = dataset_kwargs or {}
+    dataset_split_fracs = dataset_split_fracs or {}
+    dataset_split_kwargs = dataset_split_kwargs or {}
+    dataloader_kwargs = dataloader_kwargs or {}
+    trainer_kwargs = trainer_kwargs or {}
+    train_kwargs = train_kwargs or {}
+
+    default_estimator_kwargs = {}
+    default_dataset_kwargs = {}
+    default_dataset_split_kwargs = {}
+    default_dataset_split_fracs = {"train": 1.0, "val": 0.0, "aux": []}
+    default_dataloader_kwargs = {}
+    default_trainer_kwargs = {}
+    default_train_kwargs = {}
+
+    estimator_kwargs = {**default_estimator_kwargs, **estimator_kwargs}
+    dataset_kwargs = {**default_dataset_kwargs, **dataset_kwargs}
+    dataset_split_kwargs = {**default_dataset_split_kwargs, **dataset_split_kwargs}
+    dataset_split_fracs = {**default_dataset_split_fracs, **dataset_split_fracs}
+    dataloader_kwargs = {**default_dataloader_kwargs, **dataloader_kwargs}
+    trainer_kwargs = {**default_trainer_kwargs, **trainer_kwargs}
+    train_kwargs = {**default_train_kwargs, **train_kwargs}
+
+    estimator = estimator_cls(**estimator_kwargs)
+    dataset = dataset_cls(**dataset_kwargs)
+    train_dataset, val_dataset, *aux_datasets = dataset.split(
+        fracs=[
+            dataset_split_fracs["train"],
+            dataset_split_fracs["val"],
+            *dataset_split_fracs["aux"]
+        ],
+        **dataset_split_kwargs
+    )
+    train_loader = dataloader_cls(train_dataset, **dataloader_kwargs)
+    val_loader = dataloader_cls(val_dataset, **dataloader_kwargs)
+    aux_loaders = [
+        dataloader_cls(aux_dataset, **dataloader_kwargs)
+        for aux_dataset in aux_datasets
+    ]
+
+    trainer = Trainer(
+        estimator,
+        **trainer_kwargs,
+    )
+
+    if load_only:
+        return trainer
+
+    return trainer.train(
+        train_loader=train_loader,
+        val_loader=val_loader,
+        aux_loaders=aux_loaders,
+        **train_kwargs,
+    )
+
+
+def run_from_json(
+    parameters_json: str | None = None,
+    parameters_file: str | None = None,
+) -> Trainer | Estimator:
+    if not (parameters_json or parameters_file):
+        raise ValueError("parameter json or file required")
+
+    parameters: dict[str, Any]
+    if parameters_json:
+        try:
+            parameters = json.loads(parameters_json)
+        except json.JSONDecodeError as e:
+            raise ValueError(f"Invalid JSON format: {e}") from e
+        except Exception as e:
+            raise ValueError(f"Error loading JSON parameters: {e}") from e
+
+    elif parameters_file:
+        try:
+            with open(parameters_file, encoding="utf-8") as f:
+                parameters = json.load(f)
+        except FileNotFoundError as e:
+            raise ValueError(f"JSON file not found: {parameters_file}") from e
+        except Exception as e:
+            raise ValueError(f"Error loading JSON parameters: {e}") from e
+
+    return run(**parameters)
+
+
+def handle_train(args: argparse.Namespace) -> None:
+    run_from_json(args.parameters_json, args.parameters_file)
+
+
+def register_parser(subparsers: _SubParsersAction) -> None:
+    parser = subparsers.add_parser("train", help="run training loop")
+    parser.add_argument(
+        "--parameters-json",
+        type=str,
+        help="Raw JSON string with train parameters",
+    )
+    parser.add_argument(
+        "--parameters-file",
+        type=str,
+        help="Path to JSON file with train parameters",
+    )
+    parser.set_defaults(func=handle_train)

trainlib/dataloaders/__init__.py

@@ -0,0 +1,12 @@
+from trainlib.dataloader import EstimatorDataLoader
+from trainlib.utils.text import camel_to_snake
+from trainlib.dataloaders.memory import SupervisedDataLoader
+
+_dataloaders = [
+    SupervisedDataLoader,
+]
+dataloader_map: dict[str, type[EstimatorDataLoader]] = {
+    camel_to_snake(_dataloader.__name__): _dataloader
+    for _dataloader in _dataloaders
+}
+

trainlib/dataloaders/memory.py

@@ -0,0 +1,17 @@
+from torch import Tensor
+
+from trainlib.estimator import SupervisedKwargs
+from trainlib.dataloader import EstimatorDataLoader
+
+
+class SupervisedDataLoader(
+    EstimatorDataLoader[tuple[Tensor, Tensor], SupervisedKwargs]
+):
+    def batch_to_est_kwargs(
+        self,
+        batch_data: tuple[Tensor, Tensor]
+    ) -> SupervisedKwargs:
+        return SupervisedKwargs(
+            inputs=batch_data[0],
+            labels=batch_data[1],
+        )

trainlib/datasets/__init__.py

@@ -0,0 +1,12 @@
+from trainlib.dataset import BatchedDataset
+from trainlib.utils.text import camel_to_snake
+from trainlib.datasets.memory import RandomXYDataset
+
+_datasets = [
+    RandomXYDataset,
+]
+dataset_map: dict[str, type[BatchedDataset]] = {
+    camel_to_snake(_dataset.__name__): _dataset
+    for _dataset in _datasets
+}
+

trainlib/datasets/memory.py

@@ -73,13 +73,33 @@ class RecordDataset[T: NamedTuple](HomogenousDataset[int, T, T]):
 
 from typing import Unpack
 
+import torch
 import torch.nn.functional as F
 from torch import Tensor
+from torch.utils.data import TensorDataset
 
 from trainlib.domain import SequenceDomain
 from trainlib.dataset import TupleDataset, DatasetKwargs
 
 
+class RandomXYDataset(TupleDataset[Tensor]):
+    def __init__(
+        self,
+        num_samples: int,
+        input_dim: int,
+        output_dim: int,
+        **kwargs: Unpack[DatasetKwargs],
+    ) -> None:
+        domain = SequenceDomain[tuple[Tensor, Tensor]](
+            TensorDataset(
+                torch.randn((num_samples, input_dim)),
+                torch.randn((num_samples, output_dim))
+            ),
+        )
+
+        super().__init__(domain, **kwargs)
+
+
 class SlidingWindowDataset(TupleDataset[Tensor]):
     def __init__(
         self,
@@ -88,12 +108,26 @@ class SlidingWindowDataset(TupleDataset[Tensor]):
         offset: int = 0,
         lookahead: int = 1,
         num_windows: int = 1,
+        pad_mode: str = "constant",
+        #fill_with: str = "zero",
         **kwargs: Unpack[DatasetKwargs],
     ) -> None:
+        """
+        Parameters:
+            TODO: implement options for `fill_with`; currently just passing
+            through a `pad_mode` the Functional call, which does the job
+
+            fill_with: strategy to use for padding values in windows
+              - `zero`: fill with zeros
+              - `left`: use nearest window column (repeat leftmost)
+              - `mean`: fill with the window mean
+        """
+
         self.lookback = lookback
         self.offset = offset
         self.lookahead = lookahead
         self.num_windows = num_windows
+        self.pad_mode = pad_mode
 
         super().__init__(domain, **kwargs)
 
@@ -103,7 +137,7 @@ class SlidingWindowDataset(TupleDataset[Tensor]):
         batch_index: int,
     ) -> list[tuple[Tensor, ...]]:
         """
-        Backward pads first sequence over (lookback-1) length, and steps the
+        Backward pads window sequences over (lookback-1) length, and steps the
         remaining items forward by the lookahead.
 
         Batch data:
@@ -146,7 +180,7 @@ class SlidingWindowDataset(TupleDataset[Tensor]):
           exceeds the offset.
 
         To get windows starting with the first index at the left: we first set
-        out window size (call it L), determined by `lookback`. Then the
+        our window size (call it L), determined by `lookback`. Then the
         rightmost index we want will be `L-1`, which determines our `offset`
         setting.
 
@@ -173,7 +207,7 @@ class SlidingWindowDataset(TupleDataset[Tensor]):
             # for window sized `lb`, we pad with `lb-1` zeros. We then take off
             # the amount of our offset, which in the extreme cases does no
             # padding.
-            xip = F.pad(t, ((lb-1) - off, 0))
+            xip = F.pad(t, ((lb-1) - off, 0), mode=self.pad_mode)
 
             # extract sliding windows over the padded tensor
             # unfold(-1, lb, 1) slides over the last dim, 1 step at a time, for

trainlib/estimator.py

@@ -35,6 +35,10 @@ class EstimatorKwargs(TypedDict):
     inputs: Tensor
 
 
+class SupervisedKwargs(EstimatorKwargs):
+    labels: Tensor
+
+
 class Estimator[Kw: EstimatorKwargs](nn.Module):
     """
     Estimator base class.

trainlib/estimators/__init__.py

@@ -0,0 +1,15 @@
+from trainlib.estimator import Estimator
+from trainlib.utils.text import camel_to_snake
+from trainlib.estimators.mlp import MLP
+from trainlib.estimators.rnn import LSTM, ConvGRU, MultiheadLSTM
+
+_estimators: list[type[Estimator]] = [
+    MLP,
+    LSTM,
+    MultiheadLSTM,
+    ConvGRU,
+]
+estimator_map: dict[str, type[Estimator]] = {
+    camel_to_snake(_estimator.__name__): _estimator
+    for _estimator in _estimators
+}

trainlib/estimators/rnn.py

@@ -8,6 +8,7 @@ from torch import nn, Tensor
 from torch.optim import Optimizer
 from torch.utils.tensorboard import SummaryWriter
 
+from trainlib.utils import op
 from trainlib.estimator import Estimator, EstimatorKwargs
 from trainlib.utils.type import OptimizerKwargs
 from trainlib.utils.module import get_grad_norm
@@ -102,6 +103,7 @@ class LSTM[Kw: RNNKwargs](Estimator[Kw]):
         labels = kwargs["labels"]
 
         yield F.mse_loss(predictions, labels)
+        #yield F.l1_loss(predictions, labels)
 
     def metrics(self, **kwargs: Unpack[Kw]) -> dict[str, float]:
         with torch.no_grad():
@@ -109,12 +111,16 @@ class LSTM[Kw: RNNKwargs](Estimator[Kw]):
 
             predictions = self(**kwargs)[0]
             labels = kwargs["labels"]
+
+            mse = F.mse_loss(predictions, labels).item()
             mae = F.l1_loss(predictions, labels).item()
+            r2 = op.r2_score(predictions, labels).item()
 
         return {
-            # "loss": loss,
-            "mse": loss,
+            "loss": loss,
+            "mse": mse,
             "mae": mae,
+            "r2": r2,
             "grad_norm": get_grad_norm(self)
         }
 
@@ -377,7 +383,8 @@ class ConvGRU[Kw: RNNKwargs](Estimator[Kw]):
         # will be (B, T, C), applies indep at each time step across channels
         # self.dense_z = nn.Linear(layer_in_dim, self.output_dim)
 
-        # will be (B, C, T), applies indep at each time step across channels
+        # will be (B, Co, 1), applies indep at each channel across temporal dim
+        # size time steps
         self.dense_z = TDNNLayer(
             layer_in_dim,
             self.output_dim,
@@ -438,6 +445,7 @@ class ConvGRU[Kw: RNNKwargs](Estimator[Kw]):
         predictions = predictions.squeeze(-1)
 
         yield F.mse_loss(predictions, labels, reduction="mean")
+        #yield F.l1_loss(predictions, labels)
 
     def metrics(self, **kwargs: Unpack[Kw]) -> dict[str, float]:
         with torch.no_grad():
@@ -445,11 +453,16 @@ class ConvGRU[Kw: RNNKwargs](Estimator[Kw]):
 
             predictions = self(**kwargs)[0].squeeze(-1)
             labels = kwargs["labels"]
+
+            mse = F.mse_loss(predictions, labels).item()
             mae = F.l1_loss(predictions, labels).item()
+            r2 = op.r2_score(predictions, labels).item()
 
         return {
-            "mse": loss,
+            "loss": loss,
+            "mse": mse,
             "mae": mae,
+            "r2": r2,
             "grad_norm": get_grad_norm(self)
         }
 

trainlib/plotter.py

@@ -64,14 +64,17 @@ class Plotter[Kw: EstimatorKwargs]:
         for i, loader in enumerate(self.dataloaders):
             label = self.dataloader_labels[i]
 
-            actual = torch.cat([
+            actual = [
                 self.kw_to_actual(batch_kwargs).detach().cpu()
                 for batch_kwargs in loader
-            ])
-            output = torch.cat([
+            ]
+            actual = torch.cat([ai.reshape(*([*ai.shape]+[1])[:2]) for ai in actual])
+
+            output = [
                 self.trainer.estimator(**batch_kwargs)[0].detach().cpu()
                 for batch_kwargs in loader
-            ])
+            ]
+            output = torch.cat([oi.reshape(*([*oi.shape]+[1])[:2]) for oi in output])
 
             data_tuples.append((actual, output, label))
 

trainlib/trainer.py

@@ -32,6 +32,7 @@ from trainlib.estimator import Estimator, EstimatorKwargs
 from trainlib.utils.map import nested_defaultdict
 from trainlib.dataloader import EstimatorDataLoader
 from trainlib.utils.module import ModelWrapper
+from trainlib.utils.session import ensure_same_device
 
 logger: logging.Logger = logging.getLogger(__name__)
 
@@ -103,24 +104,39 @@ class Trainer[I, Kw: EstimatorKwargs]:
 
         self.reset()
 
-    def reset(self) -> None:
+    def reset(self, resume: bool = False) -> None:
         """
         Set initial tracking parameters for the primary training loop.
+
+        Parameters:
+            resume: if ``True``, just resets the stagnant epoch counter, with
+                the aims of continuing any existing training state under
+                resumed ``train()`` call. This should likely only be set when
+                training is continued on the same dataset and the goal is to
+                resume convergence loss-based scoring for a fresh set of
+                epochs. If even that element of the training loop should resume
+                (which should only happen if a training loop was interrupted or
+                a max epoch limit was reached), then this method shouldn't be
+                called at all between ``train()`` invocations.
         """
 
-        self._epoch: int = 0
-        self._summary = defaultdict(lambda: defaultdict(list))
-
-        self._conv_loss = float("inf")
-        self._best_conv_loss = float("inf")
         self._stagnant_epochs = 0
-        self._best_model_state_dict: dict[str, Any] = {}
+
+        if not resume:
+            self._epoch: int = 0
+            self._summary = defaultdict(lambda: defaultdict(list))
+
+            self._conv_loss = float("inf")
+            self._best_conv_loss = float("inf")
+            self._best_conv_epoch = 0
+            self._best_model_state_dict: dict[str, Any] = {}
 
     def _train_epoch(
         self,
         loader: EstimatorDataLoader[Any, Kw],
         optimizers: tuple[Optimizer, ...],
         max_grad_norm: float | None = None,
+        progress_bar: tqdm | None = None,
     ) -> list[float]:
         """
         Train the estimator for a single epoch.
@@ -128,32 +144,40 @@ class Trainer[I, Kw: EstimatorKwargs]:
 
         loss_sums = []
         self.estimator.train()
-        with tqdm(loader, unit="batch") as batches:
-            for i, batch_kwargs in enumerate(batches):
-                losses = self.estimator.loss(**batch_kwargs)
+        for i, batch_kwargs in enumerate(loader):
+            batch_kwargs = ensure_same_device(batch_kwargs, self.device)
+            losses = self.estimator.loss(**batch_kwargs)
 
-                for o_idx, (loss, optimizer) in enumerate(
-                    zip(losses, optimizers, strict=True)
-                ):
-                    if len(loss_sums) <= o_idx:
-                        loss_sums.append(0.0)
-                    loss_sums[o_idx] += loss.item()
+            for o_idx, (loss, optimizer) in enumerate(
+                zip(losses, optimizers, strict=True)
+            ):
+                if len(loss_sums) <= o_idx:
+                    loss_sums.append(0.0)
+                loss_sums[o_idx] += loss.item()
 
-                    optimizer.zero_grad()
-                    loss.backward()
+                optimizer.zero_grad()
+                loss.backward()
 
-                    # clip gradients for optimizer's parameters
-                    if max_grad_norm is not None:
-                        clip_grad_norm_(
-                            self._get_optimizer_parameters(optimizer),
-                            max_norm=max_grad_norm
-                        )
+                # clip gradients for optimizer's parameters
+                if max_grad_norm is not None:
+                    clip_grad_norm_(
+                        self._get_optimizer_parameters(optimizer),
+                        max_norm=max_grad_norm
+                    )
 
-                    optimizer.step()
+                optimizer.step()
 
-                # set loop loss to running average (reducing if multi-loss)
-                loss_avg = sum(loss_sums) / (len(loss_sums)*(i+1))
-                batches.set_postfix(loss=f"{loss_avg:8.2f}")
+            # set loop loss to running average (reducing if multi-loss)
+            loss_avg = sum(loss_sums) / (len(loss_sums)*(i+1))
+
+            if progress_bar:
+                progress_bar.update(1)
+                progress_bar.set_postfix(
+                    epoch=self._epoch,
+                    mode="opt",
+                    data="train",
+                    loss=f"{loss_avg:8.2f}",
+                )
 
         # step estimator hyperparam schedules
         self.estimator.epoch_step()
@@ -163,7 +187,8 @@ class Trainer[I, Kw: EstimatorKwargs]:
     def _eval_epoch(
         self,
         loader: EstimatorDataLoader[Any, Kw],
-        label: str
+        label: str,
+        progress_bar: tqdm | None = None,
     ) -> list[float]:
         """
         Perform and record validation scores for a single epoch.
@@ -191,45 +216,53 @@ class Trainer[I, Kw: EstimatorKwargs]:
 
         loss_sums = []
         self.estimator.eval()
-        with tqdm(loader, unit="batch") as batches:
-            for i, batch_kwargs in enumerate(batches):
-                losses = self.estimator.loss(**batch_kwargs)
+        for i, batch_kwargs in enumerate(loader):
+            batch_kwargs = ensure_same_device(batch_kwargs, self.device)
+            losses = self.estimator.loss(**batch_kwargs)
 
-                # one-time logging
-                if self._epoch == 0:
-                    self._writer.add_graph(
-                        ModelWrapper(self.estimator), batch_kwargs
-                    )
-                # once-per-epoch logging
-                if i == 0:
-                    self.estimator.epoch_write(
-                        self._writer,
-                        step=self._epoch,
-                        group=label,
-                        **batch_kwargs
-                    )
+            # once-per-session logging
+            if self._epoch == 0 and i == 0:
+                self._writer.add_graph(
+                    ModelWrapper(self.estimator), batch_kwargs
+                )
+            # once-per-epoch logging
+            if i == 0:
+                self.estimator.epoch_write(
+                    self._writer,
+                    step=self._epoch,
+                    group=label,
+                    **batch_kwargs
+                )
 
-                loss_items = []
-                for o_idx, loss in enumerate(losses):
-                    if len(loss_sums) <= o_idx:
-                        loss_sums.append(0.0)
+            loss_items = []
+            for o_idx, loss in enumerate(losses):
+                if len(loss_sums) <= o_idx:
+                    loss_sums.append(0.0)
 
-                    loss_item = loss.item()
-                    loss_sums[o_idx] += loss_item
-                    loss_items.append(loss_item)
+                loss_item = loss.item()
+                loss_sums[o_idx] += loss_item
+                loss_items.append(loss_item)
 
-                # set loop loss to running average (reducing if multi-loss)
-                loss_avg = sum(loss_sums) / (len(loss_sums)*(i+1))
-                batches.set_postfix(loss=f"{loss_avg:8.2f}")
+            # set loop loss to running average (reducing if multi-loss)
+            loss_avg = sum(loss_sums) / (len(loss_sums)*(i+1))
 
-                # log individual loss terms after each batch
-                for o_idx, loss_item in enumerate(loss_items):
-                    self._log_event(label, f"loss_{o_idx}", loss_item)
+            if progress_bar:
+                progress_bar.update(1)
+                progress_bar.set_postfix(
+                    epoch=self._epoch,
+                    mode="eval",
+                    data=label,
+                    loss=f"{loss_avg:8.2f}",
+                )
 
-                # log metrics for batch
-                estimator_metrics = self.estimator.metrics(**batch_kwargs)
-                for metric_name, metric_value in estimator_metrics.items():
-                    self._log_event(label, metric_name, metric_value)
+            # log individual loss terms after each batch
+            for o_idx, loss_item in enumerate(loss_items):
+                self._log_event(label, f"loss_{o_idx}", loss_item)
+
+            # log metrics for batch
+            estimator_metrics = self.estimator.metrics(**batch_kwargs)
+            for metric_name, metric_value in estimator_metrics.items():
+                self._log_event(label, metric_name, metric_value)
 
         avg_losses = [loss_sum / (i+1) for loss_sum in loss_sums]
 
@@ -240,6 +273,7 @@ class Trainer[I, Kw: EstimatorKwargs]:
         train_loader: EstimatorDataLoader[Any, Kw],
         val_loader: EstimatorDataLoader[Any, Kw] | None = None,
         aux_loaders: list[EstimatorDataLoader[Any, Kw]] | None = None,
+        progress_bar: tqdm | None = None,
     ) -> tuple[list[float], list[float] | None, *list[float]]:
         """
         Evaluate estimator over each provided dataloader.
@@ -274,12 +308,15 @@ class Trainer[I, Kw: EstimatorKwargs]:
             somewhere given the many possible design choices here.)
         """
 
-        train_loss = self._eval_epoch(train_loader, "train")
-        val_loss = self._eval_epoch(val_loader, "val") if val_loader else None
+        train_loss = self._eval_epoch(train_loader, "train", progress_bar)
+
+        val_loss = None
+        if val_loader is not None:
+            val_loss = self._eval_epoch(val_loader, "val", progress_bar) 
 
         aux_loaders = aux_loaders or []
         aux_losses = [
-            self._eval_epoch(aux_loader, f"aux{i}")
+            self._eval_epoch(aux_loader, f"aux{i}", progress_bar)
             for i, aux_loader in enumerate(aux_loaders)
         ]
         
@@ -433,27 +470,36 @@ class Trainer[I, Kw: EstimatorKwargs]:
         self._session_name = session_name or str(int(time.time()))
         tblog_path = Path(self.tblog_dir, self._session_name)
         self._writer = summary_writer or SummaryWriter(f"{tblog_path}")
+        progress_bar = tqdm(train_loader, unit="batch")
 
         # evaluate model on dataloaders once before training starts
-        self._eval_loaders(train_loader, val_loader, aux_loaders)
+        train_loss, val_loss, *_ = self._eval_loaders(
+            train_loader, val_loader, aux_loaders, progress_bar
+        )
+        conv_loss = val_loss if val_loss else train_loss
+        self._conv_loss = sum(conv_loss) / len(conv_loss)
+
         optimizers = self.estimator.optimizers(lr=lr, eps=eps)
 
-        while self._epoch < max_epochs and not self._converged(
-            self._epoch, stop_after_epochs
-        ):
+        while self._epoch < max_epochs and not self._converged(stop_after_epochs):
             self._epoch += 1
-            train_frac = f"{self._epoch}/{max_epochs}"
-            stag_frac = f"{self._stagnant_epochs}/{stop_after_epochs}"
-            print(f"Training epoch {train_frac}...")
-            print(f"Stagnant epochs {stag_frac}...")
+            #train_frac = f"{self._epoch}/{max_epochs}"
+            #stag_frac = f"{self._stagnant_epochs}/{stop_after_epochs}"
+            #print(f"Training epoch {train_frac}...")
+            #print(f"Stagnant epochs {stag_frac}...")
 
             epoch_start_time = time.time()
-            self._train_epoch(train_loader, optimizers, max_grad_norm)
+            self._train_epoch(
+                train_loader,
+                optimizers,
+                max_grad_norm,
+                progress_bar=progress_bar
+            )
             epoch_end_time = time.time() - epoch_start_time
             self._log_event("train", "epoch_duration", epoch_end_time)
 
-            train_loss, val_loss, _ = self._eval_loaders(
-                train_loader, val_loader, aux_loaders
+            train_loss, val_loss, *_ = self._eval_loaders(
+                train_loader, val_loader, aux_loaders, progress_bar
             )
             # determine loss to use for measuring convergence
             conv_loss = val_loss if val_loss else train_loss
@@ -466,12 +512,43 @@ class Trainer[I, Kw: EstimatorKwargs]:
 
         return self.estimator
 
-    def _converged(self, epoch: int, stop_after_epochs: int) -> bool:
+    def _converged(self, stop_after_epochs: int) -> bool:
+        """
+        Check if model has converged.
+
+        This method looks at the current "convergence loss" (validation-based
+        if a val set is provided to ``train()``, otherwise the training loss is
+        used), checking if it's the best yet recorded, incrementing the
+        stagnancy count if not. Convergence is asserted only if the number of
+        stagnant epochs exceeds ``stop_after_epochs``.
+
+        .. admonition:: Evaluation order
+
+            Convergence losses are recorded before the first training update,
+            so initial model states are appropriately benchmarked by the time
+            ``_converged()`` is invoked.
+
+            If resuming training on the same dataset, one might expect only to
+            reset the stagnant epoch counter: you'll resume from the last
+            epoch, estimator state, and best seen loss, while allowed
+            ``stop_after_epochs`` more chances for better validation.
+
+            If picking up training on a new dataset, even a training+validation
+            setting, resetting the best seen loss and best model state is
+            needed: you can't reliably compare the existing stats under new
+            data. It's somewhat ambiguous whether ``epoch`` absolutely must be
+            reset; you could continue logging metrics under the same named
+            session. But best practices would suggest restarting the epoch
+            count and have events logged under a new session heading when data
+            change.
+        """
+
         converged = False
 
-        if epoch == 0 or self._conv_loss < self._best_val_loss:
-            self._best_val_loss = self._conv_loss
+        if self._conv_loss < self._best_conv_loss:
             self._stagnant_epochs = 0
+            self._best_conv_loss = self._conv_loss
+            self._best_conv_epoch = self._epoch
             self._best_model_state_dict = deepcopy(self.estimator.state_dict())
         else:
             self._stagnant_epochs += 1
@@ -491,6 +568,7 @@ class Trainer[I, Kw: EstimatorKwargs]:
         print(f"==== Epoch [{self._epoch}] summary ====")
         for (group, name), epoch_map in self._summary.items():
             for epoch, values in epoch_map.items():
+                # compute average over batch items recorded for the epoch
                 mean = torch.tensor(values).mean().item()
                 self._writer.add_scalar(f"{group}-{name}", mean, epoch)
                 if epoch == self._epoch:

trainlib/utils/op.py

@@ -0,0 +1,9 @@
+from torch import Tensor
+
+
+def r2_score(y: Tensor, y_hat: Tensor) -> Tensor:
+    ss_res = ((y - y_hat)**2).sum()
+    ss_tot = ((y - y.mean())**2).sum()
+    r2 = 1 - ss_res / ss_tot
+
+    return r2

trainlib/utils/session.py

@@ -3,6 +3,7 @@ import random
 import numpy as np
 import torch
 from torch import Tensor
+from torch.utils import _pytree as pytree
 
 
 def seed_all_backends(seed: int | Tensor | None = None) -> None:
@@ -19,3 +20,9 @@ def seed_all_backends(seed: int | Tensor | None = None) -> None:
     torch.cuda.manual_seed(seed)
     torch.backends.cudnn.deterministic = True
     torch.backends.cudnn.benchmark = False
+
+def ensure_same_device[T](tree: T, device: str) -> T:
+    return pytree.tree_map(
+        lambda x: x.to(device) if isinstance(x, torch.Tensor) else x,
+        tree,
+    )

trainlib/utils/text.py

@@ -1,8 +1,14 @@
+import re
 from typing import Any
 
 from colorama import Style
 
+camel2snake_regex: re.Pattern[str] = re.compile(
+    r"(?<!^)(?=[A-Z][a-z])|(?<=[a-z])(?=[A-Z])"
+)
+
+def camel_to_snake(text: str) -> str:
+    return camel2snake_regex.sub("_", text).lower()
 
 def color_text(text: str, *colorama_args: Any) -> str:
     return f"{''.join(colorama_args)}{text}{Style.RESET_ALL}"
-

trainlib/utils/type.py

@@ -61,8 +61,8 @@ class SubplotsKwargs(TypedDict, total=False):
     squeeze: bool
     width_ratios: Sequence[float]
     height_ratios: Sequence[float]
-    subplot_kw: dict[str, ...]
-    gridspec_kw: dict[str, ...]
+    subplot_kw: dict[str, Any]
+    gridspec_kw: dict[str, Any]
     figsize: tuple[float, float]
     dpi: float
     layout: str

uv.lock

@@ -1659,7 +1659,7 @@ wheels = [
 
 [[package]]
 name = "trainlib"
-version = "0.1.2"
+version = "0.3.1"
 source = { editable = "." }
 dependencies = [
     { name = "colorama" },