fix trainer epcoh logging

README.md

@@ -1,5 +1,6 @@
 # Overview
-Package summary goes here, ideally with a diagram
+Minimal framework for ML modeling, supporting advanced dataset operations and
+streamlined training workflows.
 
 # Install
 The `trainlib` package can be installed from PyPI:
@@ -85,7 +86,7 @@ pip install trainlib
   class SequenceDataset[I, **P](HomogenousDataset[int, I, I, P]):
       ...
   
-  class TupleDataset[I](SequenceDataset[tuple[I, ...], ??]):
+  class TupleDataset[I](SequenceDataset[tuple[I, ...], "?"]):
       ...
   ```
 

TODO.md

@@ -0,0 +1,11 @@
+# Long-term
+- Implement a dataloader in-house, with a clear, lightweight mechanism for
+  collection-of-structures to structure-of-collections. For multi-proc handling
+  (happens in torch's dataloader, as well as the BatchedDataset for two
+  different purposes), we should rely on (a hopefully more stable) `execlib`.
+- `Domains` may be externalized (`co3` or `convlib`)
+- Up next: CLI, fully JSON-ification of model selection + train.
+- Consider a "multi-train" alternative (or arg support in `train()`) for
+  training many "rollouts" from the same base estimator (basically forks under
+  different seeds). For architecture benchmarking above all, seeing average
+  training behavior. Consider corresponding `Plotter` methods (error bars)

doc/conf.py

@@ -3,29 +3,64 @@
 # For the full list of built-in configuration values, see the documentation:
 # https://www.sphinx-doc.org/en/master/usage/configuration.html
 
+
+# -- Styling: type hints ------------------------------------------------------
+# There are several possible style combinations for rendering types, none of
+# which are optimal in my view. The main switches are:
+# 
+# - Parameter type hints in the signature vs in the separate parameter list
+# - Show type hints as plaintext vs rendered HTML elements
+#
+# The `sphinx_autodoc_typehints` extension enables more context-aware
+# rendering, but it's often way too explicit (e.g., unwrapping type variables)
+# and makes things difficult to read. It does, however, allow for automatic
+# inclusion of default values, which is nice.
+#
+# I'd like type hints to be rendered in an inline code element, but that
+# doesn't happen by default in either case unless you render them in the
+# signature. This is sloppy, however, often just a jumbled mess or parameter
+# names and types. The current preferred option is to just use the native
+# `autodoc` settings for rendering type hints, leaving them out of the
+# signature (for easy heading readability). Type hints in the parameter list
+# are also as short as possible, not rendered crazily (by default this is in
+# italics; not my favorite but it's what we have). No
+# `sphinx_autodoc_typehints` needed at this point; you can toggle it if you
+# want automatic default values or different formatting for type hints.
+
+
 # -- Project information ------------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
 
-project = "<package-name>"
+project = "trainlib"
-copyright = "2025, Sam Griesemer"
+copyright = "2026, Sam Griesemer"
 author = "Sam Griesemer"
 
+
 # -- General configuration ----------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
 
 extensions = [
     "sphinx.ext.autodoc",
+
     # enables a directive to be specified manually that gathers module/object
     # summary details in a table
     "sphinx.ext.autosummary",
+
     # allow viewing source in the HTML pages
     "sphinx.ext.viewcode",
+
     # only really applies to manual docs; docstrings still need RST-like
     "myst_parser",
+
     # enables Google-style docstring formats
     "sphinx.ext.napoleon",
-    # external extension that allows arg types to be inferred by type hints
+
-    "sphinx_autodoc_typehints",
+    # external extension that allows arg types to be inferred by type hints;
+    # without this, type hints show up inside method signatures as plaintext,
+    # but when enabled they are pulled into the parameter/description block and
+    # rendered as native nested markup. What's best for a given package may
+    # vary.
+    # "sphinx_autodoc_typehints",
 ]
 autosummary_generate = True
 autosummary_imported_members = True
@@ -39,11 +74,39 @@ templates_path = ["_templates"]
 exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
 
 
+# -- Options for autodoc ------------------------------------------------------
+# class signatures show up only in __init__ rather than at the class header;
+# generally cleaner, avoids redundancy
+autodoc_class_signature = "separated"
+
+# if `sphinx_autodoc_typehints` extension is enabled, this is redundant: type
+# hints are rendered natively and already show up in the parameter block. If
+# it's disabled, this setting will do the same job of moving the types to the
+# parameter block, but it renders them in plaintext (with links to in-package
+# type refs).
+autodoc_typehints = "description"  # "signature"
+autodoc_typehints_format = "short"
+autodoc_preserve_defaults = True
+autodoc_use_type_comments = False
+python_use_unqualified_type_names = True
+
+# push parameters to their own lines in the signature block
+# python_maximum_signature_line_length = 60
+
+
+# -- Options for autodoc_typehints --------------------------------------------
+# always_use_bars_union = True  # always on for Python 3.14+
+# typehints_defaults = "braces-after"  # render defaults in param block
+# typehints_use_signature = False  # False is default; enable if wanted in sig
+# always_document_param_types = True  # show types even when not in docstring
+
+
 # -- Options for HTML output --------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
 
-html_theme = "furo"
+html_theme = "furo"  # "pydata_sphinx_theme"
 html_static_path = ["_static"]
+
 # html_sidebars = {
 #    '**': ['/modules.html'],
 # }

doc/index.md

@@ -1,29 +1,37 @@
-# `<project-name>` package docs 
+# `trainlib` package docs 
+
 {ref}`genindex`
 {ref}`modindex`
-{ref}`search`
 
 ```{eval-rst}
 .. autosummary::
    :nosignatures:
+   :recursive:
+   :caption: Modules
 
-    # list modules here for quick links
+    trainlib.dataset
+    trainlib.domain
+    trainlib.estimator
+    trainlib.trainer
+    trainlib.transform
 ```
 
 ```{toctree}
 :maxdepth: 3
 :caption: Autoref
+:hidden:
 
-_autoref/<project-name>.rst
+_autoref/trainlib.rst
 ```
 
 ```{toctree}
 :maxdepth: 3
 :caption: Contents
+:hidden:
 
 reference/documentation/index
-reference/site/index
 ```
 
 ```{include} ../README.md
+:heading-offset: 1
 ```

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "trainlib"
-version = "0.1.1"
+version = "0.2.0"
 description = "Minimal framework for ML modeling. Supports advanced dataset operations and streamlined training."
 requires-python = ">=3.13"
 authors = [
@@ -24,12 +24,13 @@ classifiers = [
     "Intended Audience :: End Users/Desktop",
 ]
 dependencies = [
+    "torch",
     "colorama>=0.4.6",
     "matplotlib>=3.10.8",
     "numpy>=2.4.1",
     "tensorboard>=2.20.0",
-    "torch>=2.5.1",
     "tqdm>=4.67.1",
+    "setuptools<=81.0.0",  # here currently b/c tensorboard breaks @ v82.0.0
 ]
 
 [project.scripts]
@@ -41,6 +42,7 @@ dev = [
 ]
 doc = [
     "furo",
+    # "pydata-sphinx-theme",
     "myst-parser",
     "sphinx",
     "sphinx-togglebutton",
@@ -82,3 +84,11 @@ force-sort-within-sections = false
 quote-style = "double"
 indent-style = "space"
 docstring-code-format = true
+
+[tool.uv.sources]
+torch = { index = "pytorch" }
+
+[[tool.uv.index]]
+name = "pytorch"
+url = "https://download.pytorch.org/whl/cu128"
+explicit = true

trainlib/dataloader.py

@@ -0,0 +1,102 @@
+"""
+This class took me a long time to really settle into. It's a connector, and it
+feels redundant in many ways, so I've nearly deleted it several times while
+talking through the design. But in total, I think it serves a clear purpose.
+Reasons:
+
+- Need a typed dataloader, even if I know the type of my attached transform
+- Need a new scope that uses the same base dataset without interfering with the
+  transform attribute; a design that sets or relies on that is subject to
+  conflict
+
+- Why not just use vanilla DataLoaders?
+
+  I'd like to, but the two reasons above make it clear why this is challenging:
+  I don't get static checks on the structures returned during iteration, and
+  while you can control ad hoc data transforms via dataset ``post_transforms``,
+  things can get messy if you need to do that for many transforms using the
+  same dataset (without copying). Simplest way around this is just a new scope
+  with the same underlying dataset instance and a transform wrapper around the
+  iterator; no interference with object attributes.
+
+This is really just meant as the minimum viable logic needed to accomplish the
+above - it's a very lightweight wrapper on the base ``DataLoader`` object.
+There's an explicit type upper bound ``Kw: EstimatorKwargs``, but it is
+otherwise a completely general transform over dataloader batches, highlighting
+that it's *mostly* here to place nice with type checks.
+"""
+
+from typing import Unpack
+from collections.abc import Iterator
+
+from torch.utils.data import DataLoader
+
+from trainlib.dataset import BatchedDataset
+from trainlib.estimator import EstimatorKwargs
+from trainlib.utils.type import LoaderKwargs
+
+
+class EstimatorDataLoader[B, Kw: EstimatorKwargs]:
+    """
+    Data loaders for estimators.
+
+    This class exists to connect batched data from datasets to the expected
+    representation for estimator methods. Datasets may be developed
+    independently from a given model structures, and models should be trainable
+    under any such data. We need a way to ensure the batched groups of items we
+    get from dataloaders match on a type level, i.e., can be reshaped into the
+    expected ``Kw`` signature.
+
+    Note: batch structure ``B`` cannot be directly inferred from type variables
+    exposed by ``BatchedDatasets`` (namely ``R`` and ``I``). What's returned by
+    a data loader wrapping any such dataset can be arbitrary (depending on the
+    ``collate_fn``), with default behavior being fairly consistent under nested
+    collections but challenging to accurately type.
+
+    .. todo::
+
+        To log (have changed for Trainer):
+
+        - New compact eval pipeline for train/val/auxiliary dataloaders.
+          Somewhat annoying logic, but handled consistently
+        - Convergence tracker will dynamically use training loss (early
+          stopping) when a validation set isn't provided. Same mechanics for
+          stagnant epochs (although early stopping is generally a little more
+          nuanced, having a rate-based stopper, b/c train loss generally quite
+          monotonic). So that's to be updated, plus room for possible model
+          selection strategies later.
+        - Logging happens at each batch, but we append to an epoch-indexed list
+          and later average. There was a bug in the last round of testing that
+          I didn't pick up where I was just overwriting summaries using the
+          last seen batch.
+        - Reworked general dataset/dataloader handling for main train loop, now
+          accepting objects of this class to bridge estimator and dataset
+          communication. This cleans up the batch mapping model.
+        - TODO: implement a version of this that canonically works with the
+          device passing plus EstimatorKwargs input; this is the last fuzzy bit
+          I think.
+    """
+
+    def __init__(
+        self, 
+        dataset: BatchedDataset,
+        **dataloader_kwargs: Unpack[LoaderKwargs],
+    ) -> None:
+        self._dataloader = DataLoader(dataset, **dataloader_kwargs)
+
+    def batch_to_est_kwargs(self, batch_data: B) -> Kw:
+        """
+        .. note::
+
+            Even if we have a concrete shape for the output kwarg dict for base
+            estimators (requiring a tensor "inputs" attribute), we don't
+            presuppose how a given batch object will map into this dict
+            structure.
+
+            return EstimatorKwargs({"inputs":0})
+        """
+
+        raise NotImplementedError
+
+    def __iter__(self) -> Iterator[Kw]:
+        return map(self.batch_to_est_kwargs, self._dataloader)

trainlib/dataset.py

@@ -1,128 +1,62 @@
 """
-.. admonition:: Marginalizing out the modality layer
+Domain-generic dataset base with attribute-based splitting and balancing
 
-    With ``domain`` being an instance variable, one possible interpretation of
+**Marginalizing out the modality layer**
-    the object structures here is that one could completely abstract away
-    the domain model, defining only item structures and processing data. You
-    could have a single dataset definition for a particular concrete dataset,
-    and so long as we're talking about the same items, it can be instantiated
-    using *any domain*. You wouldn't need specific subclasses for disk or
-    network or in-memory; you can tell it directly at runtime.
 
-    That's an eventually possibility, anyway. As it stands, however, this is
+With ``domain`` being an instance variable, one possible interpretation of
-    effectively impossible:
+the object structures here is that one could completely abstract away
+the domain model, defining only item structures and processing data. You
+could have a single dataset definition for a particular concrete dataset,
+and so long as we're talking about the same items, it can be instantiated
+using *any domain*. You wouldn't need specific subclasses for disk or
+network or in-memory structures; you can tell it directly at runtime.
 
-    You can't easily abstract the batch -> item splitting process, i.e.,
+That's an eventually possibility, anyway. As it stands, however, this is
-    ``_process_batch_data()``. A list-based version of the dataset you're
+effectively impossible:
-    trying to define might have an individual item tuple at every index,
-    whereas a disk-based version might have tuples batched across a few files.
-    This can't reliably be inferred, nor can it be pushed to the
-    ``Domain``-level without needing equal levels of specialization (you'd just
-    end up needing the exact same structural distinctions in the ``Domain``
-    hierarchy). So *somewhere* you need a batch splitting implementation that
-    is both item structure-dependent *and* domain-dependent...the question is
-    how dynamic you're willing to be about where it comes from. Right now, we
-    require this actually be defined in the ``_process_batch_data()`` method,
-    meaning you'll need a specific ``Dataset`` class for each domain you want
-    to support (e.g., ``MNISTDisk``, ``MNISTList``, ``MNISTNetwork``, etc), or
-    at least for each domain where "interpreting" a batch could possibly
-    differ. This is a case where the interface is all that enforces a
-    distinction: if you've got two domains that can be counted on to yield
-    batches in the exact same way and can use the same processing, then you
-    could feasibly provide ``Domain`` objects from either at runtime and have
-    no issues. We're "structurally blind" to any differentiation beyond the URI
-    and resource types by design, so two different domain implementations with
-    the same type signature ``Domain[U, R]`` should be expected to work fine at
-    runtime (again, so long as they don't also need different batch
-    processing), but that's not affording us much flexibility, i.e., most of
-    the time we'll still be defining new dataset classes for each domain.
 
-    I initially flagged this as feasible, however, because one could imagine
+You can't easily abstract the batch-to-item splitting process, i.e.,
-    accepting a batch processing method upon instantiation rather than
+``_process_batch_data()``. A list-based version of the dataset you're trying to
-    structurally bolting it into the ``Dataset`` definition. This would require
+define might have an individual item tuple at every index, whereas a disk-based
-    knowledge of the item structure ``I`` as well as the ``Domain[U, R]``, so
+version might have tuples batched across a few files. This can't reliably be
-    such a function will always have to be (I, U, R)-dependent. It nevertheless
+inferred, nor can it be pushed to the ``Domain``-level without needing equal
-    would take out some of the pain of having to define new dataset classes;
+levels of specialization (you'd just end up needing the exact same structural
-    instead, you'd just need to define the batch processing method. I see this
+distinctions in the ``Domain`` hierarchy). So *somewhere* you need a batch
-    as a worse alternative to just defining *inside* a safe context like a new
+splitting implementation that is both item structure-dependent *and*
-    dataset class: you know the types you have to respect, and you stick that
+domain-dependent...the question is how dynamic you're willing to be about where
-    method exactly in a context where it's understood. Freeing this up doesn't
+it comes from. Right now, we require this actually be defined in the
-    lighten the burden of processing logic, it just changes *when* it has to be
+``_process_batch_data()`` method, meaning you'll need a specific ``Dataset``
-    provided, and that's not worth much (to me) in this case given the bump in
+class for each domain you want to support (e.g., ``MNISTDisk``, ``MNISTList``,
-    complexity. (Taking this to the extreme: you could supply *all* of an
+``MNISTNetwork``, etc), or at least for each domain where "interpreting" a
-    object's methods "dynamically" and glue them together at runtime so long as
+batch could possibly differ. This is a case where the interface is all that
-    they all played nice. But wherever you were "laying them out" beforehand is
+enforces a distinction: if you've got two domains that can be counted on to
-    exactly the job of a class to begin with, so you don't end up with anything
+yield batches in the exact same way and can use the same processing, then you
-    more dynamic. All we're really discussing here is pushing around
+could feasibly provide ``Domain`` objects from either at runtime and have no
-    unavoidable complexity inside and outside of the "class walls," and in the
+issues. We're "structurally blind" to any differentiation beyond the URI and
-    particular case of ``_process_batch_data()``, it feels much better when
+resource types by design, so two different domain implementations with the same
-    it's on the inside.)
+type signature ``Domain[U, R]`` should be expected to work fine at runtime
+(again, so long as they don't also need different batch processing), but that's
+not affording us much flexibility, i.e., most of the time we'll still be
+defining new dataset classes for each domain.
 
-.. admonition:: Holding area
+I initially flagged this as feasible, however, because one could imagine
-
+accepting a batch processing method upon instantiation rather than structurally
-    .. code-block:: python
+bolting it into the ``Dataset`` definition. This would require knowledge of the
-
+item structure ``I`` as well as the ``Domain[U, R]``, so such a function will
-        @abstractmethod
+always have to be ``(I, U, R)``-dependent. It nevertheless would take out some
-        def _get_uri_groups(self) -> Iterable[tuple[U, ...]]:
+of the pain of having to define new dataset classes; instead, you'd just need
-            Get URI groups for each batch.
+to define the batch processing method. I see this as a worse alternative to
-
+just defining *inside* a safe context like a new dataset class: you know the
-            If there's more than one URI per batch (e.g., a data file and a
+types you have to respect, and you stick that method exactly in a context where
-            metadata file), zip the URIs such that we have a tuple of URIs per
+it's understood. Freeing this up doesn't lighten the burden of processing
-            batch.
+logic, it just changes *when* it has to be provided, and that's not worth much
-
+(to me) in this case given the bump in complexity. (Taking this to the extreme:
-            Note that this effectively defines the index style over batches in
+you could supply *all* of an object's methods "dynamically" and glue them
-            the attached domain. We get an ``int -> tuple[U, ...]`` map that
+together at runtime so long as they all played nice. But wherever you were
-            turns batch indices into URIs that can be read under the domain.
+"laying them out" beforehand is exactly the job of a class to begin with, so
-            ``get_batch()`` turns an integer index into its corresponding
+you don't end up with anything more dynamic. All we're really discussing here
-            ``tuple[U, ...]``, reading the resources with ``_read_resources()``
+is pushing around unavoidable complexity inside and outside of the "class
-            in the tuple, treating them as providers of batched data.
+walls," and in the particular case of ``_process_batch_data()``, it feels much
-            ``_read_resources()`` passes through to the attached domain logic,
+better when it's on the inside.)
-            which, although common, need not supply an explicit iterable of
-            batch items: we just access items with ``__getitem__()`` and may
-            ask for ``__len__``. So the returned URI group collection (this
-            method) does need to be iterable to measure the number of batches,
-            but the batch objects that are ultimately produced by these URI
-            groups need not be iterables themselves.
-
-            raise NotImplementedError
-
-        def _read_resources(
-            self,
-            uri_group: tuple[U, ...],
-            batch_index: int
-        ) -> tuple[R, ...]:
-            Read batch files at the provided paths.
-
-            This method should operate on a single tuple from the list of batch
-            tuples returned by the ``_get_uri_groups()`` method. That is, it
-            reads all of the resources for a single batch and returns a tuple
-            of the same size with their contents.
-
-            Note: the dependence on a batch index is mostly here to make
-            multi-dataset composition easier later. In-dataset, you don't need
-            to know the batch index to to simply process URIs, but across
-            datasets you need it to find out the origin of the batch (and
-            process those URIs accordingly).
-
-            return tuple(self.domain.read(uri) for uri in uri_group)
-
-    .. code-block:: python
-
-        # pulling the type variable out of the inline generic b/c `ty` has
-        # trouble understanding bound type variables in subclasses
-        # (specifically with Self@)
-
-        T = TypeVar("T", bound=NamedTuple)
-
-        class NamedTupleDataset[I](Dataset):
-            def __init__(self, data_list: list[I]) -> None:
-                self.data_list = data_list
-
-            def __len__(self) -> int:
-                return len(self.data_list)
-
-            def __getitem__(self, index: int) -> I:
-                return self.data_list[index]
 """
 
 import math
@@ -161,35 +95,69 @@ class BatchedDataset[U, R, I](Dataset):
 
     The class is generic over a URI type ``U``, a resource type ``R`` (both of
     which are used to concretize a domain ``Domain[U, R]``), and an item type
-    ``T`` (which has a ``tuple`` upper bound).
+    ``I``.
 
+    **Batch and item processing flow**
 
-    .. admonition:: Pipeline overview
+    .. code-block:: text
 
-        .. code-block:: python
+        Domain -> [U] :: self._batch_uris = list(domain)
 
-            Domain -> [U] (get _batch_uris)
+            Grab all URIs from Domain iterators. This is made concrete early to
-            U -> R (domain access ; Rs provide batches)
+            allow for Dataset sizing, and we need a Sequence representation to
-            R -> [I] (cache here ; _process_batch_data to use load_transform)
+            map integer batch indices into Domains, i.e., when getting the
-            [I] -> I (human item obj ; _get_item)
+            corresponding URI:
-            I -> **P (final packed item ; __getitem__ to use transform)
 
-        Note^1: as far as positioning, this class is meant to play nice with
+            batch_uri = self._batch_uris[batch_index]
-        PyTorch DataLoaders, hence the inheritance from ``torch.Dataset``. The
-        value add for this over the ``torch.Dataset`` base is almost entirely
-        in the logic it implements to map out of *batched resources* that are
-        holding data, and flattening it out into typical dataset items. There
-        are also some QoL items when it comes to splitting and balancing
-        samples. 
 
-        Note^2: even though ``Domains`` implement iterators over their URIs,
+            We let Domains implement iterators over their URIs, but explicitly
-        this doesn't imply a ``BatchedDataset`` is iterable. This just means we
+            exhaust when initializing Datasets.
-        can walk over the resources that provide data, but we don't necessarily
+
+        U -> R        :: batch_data = self.domain[batch_uri]
+
+            Retrieve resource from domain. Resources are viewed as batched
+            data, even if only wrapping single items (happens in trivial
+            settings).
+
+        R -> [I]      :: self._process_batch_data(batch_data, batch_index)
+
+            Possibly domain-specific batch processing of resource data into
+            explicit Sequence-like structures of items, each of which is
+            subject to the provided pre_transform. Processed batches at this
+            stage are cached (if enabled).
+
+        [I] -> I      :: self.get_batch(batch_index)[index_in_batch]
+
+            Select individual items from batches in _get_item. At this stage,
+            items are in intermediate states and pulled from the cached
+            batches.
+
+        I -> I        :: self._process_item_data(item_data, index)
+            
+            Produce final items with __getitem__, getting intermediate items
+            via _get_item and applying the provided post_transform.
+
+    .. note::
+
+        As far as positioning, this class is meant to play nice with PyTorch
+        DataLoaders, hence the inheritance from ``torch.Dataset``. The value
+        add for this over the ``torch.Dataset`` base is almost entirely in the
+        logic it implements to map out of *batched resources* that are holding
+        data, and flattening it out into typical dataset items. There are also
+        some QoL features when it comes to splitting and balancing samples.
+
+    .. note::
+
+        Even though ``Domains`` implement iterators over their URIs, this
+        doesn't imply a ``BatchedDataset`` is iterable. This just means we can
+        walk over the resources that provide data, but we don't necessarily
         presuppose an ordered walk over samples within batches. Point being:
         ``torch.Dataset``, not ``torch.IterableDataset``, is the appropriate
         superclass, even when we're working around iterable ``Domains``.
 
-        Note^3: transforms are expected to operate on ``I``-items and produce
+    .. note::
+
+        Transforms are expected to operate on ``I``-items and produce
         ``I``-items. They shouldn't be the "introducers" of ``I`` types from
         some other intermediate representation, nor should they map from ``I``
         to something else. Point being: the dataset definition should be able
@@ -211,6 +179,7 @@ class BatchedDataset[U, R, I](Dataset):
     ) -> None:
         """
         Parameters:
+            domain: ``Domain`` object providing access to batched data
             pre_transform: transform to apply over items during loading (in
                 ``_process_batch_data()``), i.e., *before* going into
                 persistent storage
@@ -220,6 +189,7 @@ class BatchedDataset[U, R, I](Dataset):
             batch_cache_limit: the max number of max batches to cache at any
                 one time
             preload: whether to load all data into memory during instantiation
+            num_workers: number of workers to use when preloading data
         """
 
         self.domain = domain
@@ -259,6 +229,9 @@ class BatchedDataset[U, R, I](Dataset):
         The behavior of this method can vary depending on what we know about
         batch sizes, and should therefore be implemented by inheriting classes.
 
+        Parameters:
+            item_index: index of item
+
         Returns:
             batch_index: int
             index_in_batch: int
@@ -302,6 +275,10 @@ class BatchedDataset[U, R, I](Dataset):
         place to use a provided ``post_transform``; items are pulled from the
         cache (if enabled) and processed before being returned as the final
         tuple outputs (so this processing is not persistent).
+
+        Parameters:
+            item_data: item data
+            item_index: index of item
         """
 
         raise NotImplementedError
@@ -317,6 +294,9 @@ class BatchedDataset[U, R, I](Dataset):
 
         Note that return values from `__getitem__()` are "cleaned up" versions
         of this representation, with minimal info needed for training.
+
+        Parameters:
+            item_index: index of item
         """
 
         if item_index >= len(self):
@@ -351,10 +331,13 @@ class BatchedDataset[U, R, I](Dataset):
         they're always connected, and nothing would notice if you waited
         between steps. The only way this could matter is if you split the
         resource reading and batch processing steps across methods, but when it
-        actually comes to accessing/caching the batch, you'd have to expand
+        actually comes to accessing/caching the batch, you'd have to expand any
-        any delayed reads here. There's no way around needing to see all batch
+        delayed reads here. There's no way around needing to see all batch data
-        data at once here, and we don't want to make that ambiguous: ``list``
+        at once here, and we don't want to make that ambiguous: ``list`` output
-        output type it is.
+        type it is.
+
+        Parameters:
+            batch_index: index of batch
         """
 
         logger.debug("Batch cache miss, reading from root...")
@@ -374,6 +357,9 @@ class BatchedDataset[U, R, I](Dataset):
         Can be useful when dynamically pulling data (as it's requested) isn't
         desired. Requires that `cache_sample_limit=None`, i.e., the cache won't
         continually remove previous batches as they're loaded.
+
+        Parameters:
+            num_workers: number of parallel workers to use for data loading
         """
 
         assert self.batch_cache_limit is None, "Preloading under cache limit"
@@ -406,36 +392,46 @@ class BatchedDataset[U, R, I](Dataset):
         """
         Split dataset into fractional pieces by data attribute.
 
-        If `by_attr` is None, recovers typical fractional splitting of dataset
+        If ``by_attr`` is None, recovers typical fractional splitting of
-        items, partitioning by size. Using None anywhere will index each item
+        dataset items, partitioning by size. Using None anywhere will index
-        into its own bucket, i.e., by its index. For instance,
+        each item into its own bucket, i.e., by its index. For instance:
 
-        - by_attr=["color"] -> {("red", 1), ("red", 2)},
+        - Splits on the attribute such that each subset contains entire strata
+          of the attribute. "Homogeneity within clusters:"
+
+          .. code-block::
+
+              by_attr=["color"] -> {("red", 1), ("red", 2)},
                                    {("blue", 1), ("blue", 2)}
 
-          Splits on the attribute such that each subset contains entire strata
+        - Stratifies by attribute and then splits "by index" within, uniformly
-          of the attribute. "Homogeneity within clusters"
-
-        - `by_attr=["color", None]` -> {("red", 1), ("blue", 1)},
-                                       {("red", 2), ("blue", 2)}
-
-          Stratifies by attribute and then splits "by index" within, uniformly
           grabbing samples across strata to form new clusters. "Homogeneity
           across clusters"
 
+          .. code-block::
+
+              by_attr=["color", None] -> {("red", 1), ("blue", 1)},
+                                         {("red", 2), ("blue", 2)}
+
         Note that the final list of Subsets returned are built from shallow
-        copies of the underlying dataset (i.e., `self`) to allow manual
+        copies of the underlying dataset (i.e., ``self``) to allow manual
         intervention with dataset attributes (e.g., setting the splits to have
-        different `transform`s). This is subject to possibly unexpected
+        different ``transforms``). This is subject to possibly unexpected
         behavior if re-caching data or you need a true copy of all data in
         memory, but should otherwise leave most interactions unchanged.
 
         Parameters:
+            frac: split fractions for datasets
+            dataset: dataset to split, defaults to ``self``. Facilitates
+                recursive splitting when multi-attribute splits are needed.
+            by_attr: attribute or attributes to use when grouping strata for
+                dataset splits. Defaults to ``None``, which will use item
+                indices.
             shuffle_strata: shuffle the strata order before split is drawn. We
-                parameterize this because a dataloader-level shuffle operation
+                parameterize this because a Dataloader-level shuffle operation
                 will only change the order of the indices in the resulting
-                splits; only a shuffle of items inside the strata can change
+                splits; only a shuffle of the strata order can change the
-                the actual content of the splits themselves.
+                actual content of the splits themselves. 
         """
 
         if by_attr == []:
@@ -544,6 +540,32 @@ class BatchedDataset[U, R, I](Dataset):
         split_max_sizes: list[int] | None = None,
         shuffle_strata: bool = True,
     ) -> None:
+        """
+        Balance the distribution of provided attributes over dataset items.
+
+        This method sets the indices over the dataset according to the result
+        of the rebalancing. The indices are produced by the recursive
+        ``_balance()`` method, which is necessarily separate due to the need
+        for a contained recursive approach that doesn't change the underlying
+        dataset during execution.
+
+        Parameters:
+            dataset: dataset to split, defaults to ``self``. Facilitates
+                recursive splitting when multi-attribute splits are needed.
+            by_attr: attribute or attributes to use when grouping strata for
+                dataset splits. Defaults to ``None``, which will use item
+                indices.
+            split_min_sizes: minimum allowed sizes of splits. Must have the
+                same length as ``by_attr``. 
+            split_max_sizes: maximum allowed sizes of splits. Must have the
+                same length as ``by_attr``. 
+            shuffle_strata: shuffle the strata order before split is drawn. We
+                parameterize this because a Dataloader-level shuffle operation
+                will only change the order of the indices in the resulting
+                splits; only a shuffle of the strata order can change the
+                actual content of the splits themselves. 
+        """
+
         self.indices = self._balance(
             dataset,
             by_attr,
@@ -561,9 +583,29 @@ class BatchedDataset[U, R, I](Dataset):
         shuffle_strata: bool = True,
     ) -> list[int]:
         """
-        Note: behavior is a little odd for nested behavior; not exactly
+        Recursive balancing of items by attribute.
-        perfectly uniform throughout.  This is a little difficult: you can't
+
-        exactly know ahead of time the size of the subgroups across splits
+        .. note::
+
+            Behavior is a little odd for nested behavior; not exactly perfectly
+            uniform throughout. This is a little difficult: you can't exactly
+            know ahead of time the size of the subgroups across splits
+
+        Parameters:
+            dataset: dataset to split, defaults to ``self``. Facilitates
+                recursive splitting when multi-attribute splits are needed.
+            by_attr: attribute or attributes to use when grouping strata for
+                dataset splits. Defaults to ``None``, which will use item
+                indices.
+            split_min_sizes: minimum allowed sizes of splits. Must have the
+                same length as ``by_attr``. 
+            split_max_sizes: maximum allowed sizes of splits. Must have the
+                same length as ``by_attr``. 
+            shuffle_strata: shuffle the strata order before split is drawn. We
+                parameterize this because a Dataloader-level shuffle operation
+                will only change the order of the indices in the resulting
+                splits; only a shuffle of the strata order can change the
+                actual content of the splits themselves. 
         """
 
         if by_attr == []:
@@ -653,6 +695,9 @@ class BatchedDataset[U, R, I](Dataset):
         dataset. The underlying data remain the same, but when indices get set,
         you're effectively applying a mask over any existing indices, always
         operating *relative* to the existing mask.
+
+        Parameters:
+            indices: list of indices to set
         """
 
         # manually set new size
@@ -680,6 +725,13 @@ class BatchedDataset[U, R, I](Dataset):
         return self._dataset_len
 
     def __getitem__(self, index: int) -> I:
+        """
+        Get the dataset item at the specified index.
+
+        Parameters:
+            index: index of item to retrieve
+        """
+
         item_data = self._get_item(index)
         index = self.indices[index]
 
@@ -701,9 +753,10 @@ class CompositeBatchedDataset[U, R, I](BatchedDataset[U, R, I]):
     """
     Dataset class for wrapping individual datasets.
 
-    Note: because this remains a valid ``BatchedDataset``, we re-thread the
+    .. note::
-    generic type variables through the set of composed datasets. That is, they
+        Because this remains a valid ``BatchedDataset``, we re-thread the
-    must have a common domain type ``Domain[U, R]``.
+        generic type variables through the set of composed datasets. That is,
+        they must have a common domain type ``Domain[U, R]``.
     """
 
     def __init__(
@@ -888,7 +941,7 @@ class HomogenousDataset[U, R, I](BatchedDataset[U, R, I]):
 
 class HeterogenousDataset[U, R, I](BatchedDataset[U, R, I]):
     """
-    Batched dataset where batches have arbitrary size.
+    Batched dataset where batches may have arbitrary size.
 
     Methods left for inheriting classes:
 

trainlib/datasets/disk.py

@@ -12,7 +12,7 @@ class DiskDataset[T: NamedTuple](HomogenousDataset[Path, bytes, T]):
     """
     The following line is to satisfy the type checker, which
 
-    1. Can't recognize an appropriately re-typed constructor arg like
+    1. Can't recognize an appropriately re-typed constructor arg like::
 
            def __init__(
                self,
@@ -20,7 +20,8 @@ class DiskDataset[T: NamedTuple](HomogenousDataset[Path, bytes, T]):
                ...
            ): ...
 
-       This *does* match the parent generic for the U=Path, R=bytes context
+       This *does* match the parent generic for the ``U=Path``, ``R=bytes``
+       context::
 
            def __init__(
                self,
@@ -32,19 +33,17 @@ class DiskDataset[T: NamedTuple](HomogenousDataset[Path, bytes, T]):
 
     2. "Lifted" type variables out of generics can't be used as upper bounds,
        at least not without throwing type checker warnings (thanks to PEP695).
-       So I'm not allowed to have
+       So I'm not allowed to have::
 
-       ```
            class BatchedDataset[U, R, D: Domain[U, R]]:
                ...
-       ```
 
-       which could bring appropriately dynamic typing for ``Domain``s, but is
+       which could bring appropriately dynamic typing for ``Domains``, but is
        not a sufficiently concrete upper bound.
 
     So: we settle for a class-level type declaration, which despite not being
     technically appropriately scoped, it's not harming anything and satisfies
-    ``ty`` type checks downstream (e.g., when we access ``DiskDomain.root``.
+    ``ty`` type checks downstream (e.g., when we access ``DiskDomain.root``).
     """
 
     domain: DiskDomain

trainlib/datasets/memory.py

@@ -80,10 +80,10 @@ from trainlib.domain import SequenceDomain
 from trainlib.dataset import TupleDataset, DatasetKwargs
 
 
-class SlidingWindowDataset[T: Tensor](TupleDataset[T]):
+class SlidingWindowDataset(TupleDataset[Tensor]):
     def __init__(
         self,
-        domain: SequenceDomain[tuple[T, ...]],
+        domain: SequenceDomain[tuple[Tensor, ...]],
         lookback: int,
         offset: int = 0,
         lookahead: int = 1,
@@ -99,9 +99,9 @@ class SlidingWindowDataset[T: Tensor](TupleDataset[T]):
 
     def _process_batch_data(
         self,
-        batch_data: tuple[T, ...],
+        batch_data: tuple[Tensor, ...],
         batch_index: int,
-    ) -> list[tuple[T, ...]]:
+    ) -> list[tuple[Tensor, ...]]:
         """
         Backward pads first sequence over (lookback-1) length, and steps the
         remaining items forward by the lookahead.

trainlib/domain.py

@@ -1,24 +1,5 @@
 """
-Defines a knowledge domain. Wraps a Dataset / Simulator / Knowledge
+Generic URI-resource mapping structure
-
-Downstream exploration might include
-
-- Calibrating Simulator / Knowledge with a Dataset
-- Amending Dataset with Simulator / Knowledge
-- Positioning Knowledge within Simulator context
-  * Where to replace Simulator subsystem with Knowledge?
-
-Other variations:
-
-- Multi-fidelity simulators
-- Multi-scale models
-- Multi-system
-- Incomplete knowledge / divergence among sources
-
-Questions:
-
-- Should Simulator / Knowledge be unified as one (e.g., "Expert")
-
 """
 
 from collections.abc import Mapping, Iterator, Sequence
@@ -83,3 +64,14 @@ class SequenceDomain[R](Domain[int, R]):
 
     def __len__(self) -> int:
         return len(self.sequence)
+
+
+class TupleDomain[T](SequenceDomain[tuple[T, ...]]):
+    """
+    Domain for homogenous tuples of the same type.
+
+    This class header exists primarily as typed alias that aligns with
+    TupleDataset.
+    """
+
+    ...

trainlib/domains/functional.py

@@ -8,10 +8,16 @@ class SimulatorDomain[P, R](Domain[int, R]):
     Base simulator domain, generic to arbitrary callables.
 
     Note: we don't store simulation results here; that's left to a downstream
-    object, like a `BatchedDataset`, to cache if needed. We also don't subclass
+    object, like a ``BatchedDataset``, to cache if needed. We also don't
-    `SequenceDataset` because the item getter type doesn't align: we accept an
+    subclass ``SequenceDataset`` because the item getter type doesn't align: we
-    `int` in the parameter list, but don't return the items directly from that
+    accept an ``int`` in the parameter list, but don't return the items
-    collection (we transform them first).
+    directly from that collection (we transform them first).
+
+    Note: it's interesting to consider the idea of having parameters directly
+    act as URIs. There is, however, no obvious way to iterate over allowed
+    parameters (without additional components, like a prior or some other
+    generator), so we leave that outside the class scope and simply operate
+    over of a provided parameter sequence.
     """
 
     def __init__(

trainlib/estimator.py

@@ -1,4 +1,6 @@
 """
+Base class for trainable models
+
 Development note
 
 I'd rather lay out bare args and kwargs in the estimator methods, but the
@@ -162,7 +164,7 @@ class Estimator[Kw: EstimatorKwargs](nn.Module):
         self,
         writer: SummaryWriter,
         step: int | None = None,
-        val: bool = False,
+        group: str | None = None,
         **kwargs: Unpack[Kw],
     ) -> None:
         """

trainlib/estimators/mlp.py

@@ -0,0 +1,148 @@
+import logging
+from typing import Unpack, NotRequired
+from collections.abc import Callable, Generator
+
+import torch
+import torch.nn.functional as F
+from torch import nn, Tensor
+from torch.optim import Optimizer
+from torch.utils.tensorboard import SummaryWriter
+
+from trainlib.estimator import Estimator, EstimatorKwargs
+from trainlib.utils.type import OptimizerKwargs
+from trainlib.utils.module import get_grad_norm
+from trainlib.estimators.tdnn import TDNNLayer
+
+logger: logging.Logger = logging.getLogger(__name__)
+
+
+class MLPKwargs(EstimatorKwargs):
+    inputs: Tensor
+    labels: NotRequired[Tensor]
+
+
+class MLP[Kw: MLPKwargs](Estimator[Kw]):
+    """
+    Base MLP architecture.
+    """
+
+    def __init__(
+        self,
+        input_dim: int,
+        output_dim: int,
+        hidden_dims: list[int] | None = None,
+        norm_layer: Callable[..., nn.Module] | None = None,
+        activation_fn: nn.Module | None = None,
+        inplace: bool = False,
+        bias: bool = True,
+        dropout: float = 0.0,
+        verbose: bool = True,
+    ) -> None:
+        """
+        Parameters:
+            input_dim: dimensionality of the input
+            output_dim: dimensionality of the output
+            hidden_dims: dimensionalities of hidden layers
+        """
+
+        super().__init__()
+
+        self.input_dim = input_dim
+        self.output_dim = output_dim
+        self.hidden_dims = hidden_dims or []
+        self.norm_layer = norm_layer
+        self.activation_fn = activation_fn or nn.ReLU
+
+        # self._layers: nn.ModuleList = nn.ModuleList()
+        self._layers = []
+
+        layer_in_dim = input_dim
+        for layer_out_dim in self.hidden_dims:
+            hidden_layer = nn.Linear(layer_in_dim, layer_out_dim, bias=bias)
+            self._layers.append(hidden_layer)
+
+            if norm_layer is not None:
+                self._layers.append(norm_layer(layer_out_dim))
+
+            self._layers.append(self.activation_fn(inplace=inplace))
+            self._layers.append(nn.Dropout(dropout, inplace=inplace))
+
+            layer_in_dim = layer_out_dim
+
+        self._layers.append(nn.Linear(layer_in_dim, self.output_dim))
+        self._net = nn.Sequential(*self._layers)
+
+        if verbose:
+            self.log_arch()
+
+    def _clamp_rand(self, x: Tensor) -> Tensor:
+        return torch.clamp(
+            x + (1.0 / 127.0) * (torch.rand_like(x) - 0.5),
+            min=-1.0,
+            max=1.0,
+        )
+
+    def forward(self, **kwargs: Unpack[Kw]) -> tuple[Tensor, ...]:
+        inputs = kwargs["inputs"]
+        x = self._net(inputs)
+
+        return (x,)
+
+    def loss(self, **kwargs: Unpack[Kw]) -> Generator[Tensor]:
+        predictions = self(**kwargs)[0]
+        labels = kwargs["labels"]
+
+        yield F.mse_loss(predictions, labels)
+
+    def metrics(self, **kwargs: Unpack[Kw]) -> dict[str, float]:
+        with torch.no_grad():
+            loss = next(self.loss(**kwargs)).item()
+
+            predictions = self(**kwargs)[0]
+            labels = kwargs["labels"]
+            mae = F.l1_loss(predictions, labels).item()
+
+        return {
+            # "mse": loss,
+            "mae": mae,
+            "grad_norm": get_grad_norm(self)
+        }
+
+    def optimizers(
+        self,
+        **kwargs: Unpack[OptimizerKwargs],
+    ) -> tuple[Optimizer, ...]:
+        """
+        """
+
+        default_kwargs: Unpack[OptimizerKwargs] = {
+            "lr": 1e-3,
+            "eps": 1e-8,
+        }
+        opt_kwargs = {**default_kwargs, **kwargs}
+
+        optimizer = torch.optim.AdamW(
+            self.parameters(),
+            **opt_kwargs,
+        )
+
+        return (optimizer,)
+
+    def epoch_step(self) -> None:
+        return None
+
+    def epoch_write(
+        self,
+        writer: SummaryWriter,
+        step: int | None = None,
+        group: str | None = None,
+        **kwargs: Unpack[Kw],
+    ) -> None:
+        return None
+
+    def log_arch(self) -> None:
+        super().log_arch()
+
+        logger.info(f"| > {self.input_dim=}")
+        logger.info(f"| > {self.hidden_dims=}")
+        logger.info(f"| > {self.output_dim=}")

trainlib/estimators/rnn.py

@@ -21,7 +21,7 @@ class RNNKwargs(EstimatorKwargs):
     labels: NotRequired[Tensor]
 
 
-class LSTM[K: RNNKwargs](Estimator[K]):
+class LSTM[Kw: RNNKwargs](Estimator[Kw]):
     """
     Base RNN architecture.
     """
@@ -85,7 +85,7 @@ class LSTM[K: RNNKwargs](Estimator[K]):
             max=1.0,
         )
 
-    def forward(self, **kwargs: Unpack[K]) -> tuple[Tensor, ...]:
+    def forward(self, **kwargs: Unpack[Kw]) -> tuple[Tensor, ...]:
         inputs = kwargs["inputs"]
 
         # data shaped (B, C, T); map to (B, T, C)
@@ -97,18 +97,24 @@ class LSTM[K: RNNKwargs](Estimator[K]):
 
         return z[:, -1, :], hidden
 
-    def loss(self, **kwargs: Unpack[K]) -> Generator[Tensor]:
+    def loss(self, **kwargs: Unpack[Kw]) -> Generator[Tensor]:
         predictions = self(**kwargs)[0]
         labels = kwargs["labels"]
 
         yield F.mse_loss(predictions, labels)
 
-    def metrics(self, **kwargs: Unpack[K]) -> dict[str, float]:
+    def metrics(self, **kwargs: Unpack[Kw]) -> dict[str, float]:
         with torch.no_grad():
             loss = next(self.loss(**kwargs)).item()
 
+            predictions = self(**kwargs)[0]
+            labels = kwargs["labels"]
+            mae = F.l1_loss(predictions, labels).item()
+
         return {
-            "loss": loss,
+            # "loss": loss,
+            "mse": loss,
+            "mae": mae,
             "grad_norm": get_grad_norm(self)
         }
 
@@ -139,8 +145,8 @@ class LSTM[K: RNNKwargs](Estimator[K]):
         self,
         writer: SummaryWriter,
         step: int | None = None,
-        val: bool = False,
+        group: str | None = None,
-        **kwargs: Unpack[K],
+        **kwargs: Unpack[Kw],
     ) -> None:
         return None
 
@@ -159,7 +165,7 @@ class MultiheadLSTMKwargs(EstimatorKwargs):
     auxiliary: NotRequired[Tensor]
 
 
-class MultiheadLSTM[K: MultiheadLSTMKwargs](Estimator[K]):
+class MultiheadLSTM[Kw: MultiheadLSTMKwargs](Estimator[Kw]):
     def __init__(
         self,
         input_dim: int,
@@ -217,7 +223,7 @@ class MultiheadLSTM[K: MultiheadLSTMKwargs](Estimator[K]):
             max=1.0,
         )
 
-    def forward(self, **kwargs: Unpack[K]) -> tuple[Tensor, ...]:
+    def forward(self, **kwargs: Unpack[Kw]) -> tuple[Tensor, ...]:
         inputs = kwargs["inputs"]
 
         # data shaped (B, C, T); map to (B, T, C)
@@ -231,7 +237,7 @@ class MultiheadLSTM[K: MultiheadLSTMKwargs](Estimator[K]):
 
         return z[:, -1, :], zs[:, -1, :]
 
-    def loss(self, **kwargs: Unpack[K]) -> Generator[Tensor]:
+    def loss(self, **kwargs: Unpack[Kw]) -> Generator[Tensor]:
         pred, pred_aux = self(**kwargs)
         labels = kwargs["labels"]
         aux_labels = kwargs.get("auxiliary")
@@ -241,12 +247,8 @@ class MultiheadLSTM[K: MultiheadLSTMKwargs](Estimator[K]):
         else:
             yield F.mse_loss(pred, labels) + F.mse_loss(pred_aux, aux_labels)
 
-    def metrics(self, **kwargs: Unpack[K]) -> dict[str, float]:
+    def metrics(self, **kwargs: Unpack[Kw]) -> dict[str, float]:
-        with torch.no_grad():
-            loss = next(self.loss(**kwargs)).item()
-
         return {
-            "loss": loss,
             "grad_norm": get_grad_norm(self)
         }
 
@@ -277,8 +279,8 @@ class MultiheadLSTM[K: MultiheadLSTMKwargs](Estimator[K]):
         self,
         writer: SummaryWriter,
         step: int | None = None,
-        val: bool = False,
+        group: str | None = None,
-        **kwargs: Unpack[K],
+        **kwargs: Unpack[Kw],
     ) -> None:
         return None
 
@@ -291,7 +293,7 @@ class MultiheadLSTM[K: MultiheadLSTMKwargs](Estimator[K]):
         logger.info(f"| > {self.output_dim=}")
 
 
-class ConvRNN[K: RNNKwargs](Estimator[K]):
+class ConvGRU[Kw: RNNKwargs](Estimator[Kw]):
     """
     Base recurrent convolutional architecture.
 
@@ -402,7 +404,7 @@ class ConvRNN[K: RNNKwargs](Estimator[K]):
             max=1.0,
         )
 
-    def forward(self, **kwargs: Unpack[K]) -> tuple[Tensor, ...]:
+    def forward(self, **kwargs: Unpack[Kw]) -> tuple[Tensor, ...]:
         inputs = kwargs["inputs"]
 
         # embedding shaped (B, C, T)
@@ -428,7 +430,7 @@ class ConvRNN[K: RNNKwargs](Estimator[K]):
 
         return (z,)
 
-    def loss(self, **kwargs: Unpack[K]) -> Generator[Tensor]:
+    def loss(self, **kwargs: Unpack[Kw]) -> Generator[Tensor]:
         predictions = self(**kwargs)[0]
         labels = kwargs["labels"]
 
@@ -437,12 +439,17 @@ class ConvRNN[K: RNNKwargs](Estimator[K]):
 
         yield F.mse_loss(predictions, labels, reduction="mean")
 
-    def metrics(self, **kwargs: Unpack[K]) -> dict[str, float]:
+    def metrics(self, **kwargs: Unpack[Kw]) -> dict[str, float]:
         with torch.no_grad():
             loss = next(self.loss(**kwargs)).item()
 
+            predictions = self(**kwargs)[0].squeeze(-1)
+            labels = kwargs["labels"]
+            mae = F.l1_loss(predictions, labels).item()
+
         return {
-            "loss": loss,
+            "mse": loss,
+            "mae": mae,
             "grad_norm": get_grad_norm(self)
         }
 
@@ -473,8 +480,8 @@ class ConvRNN[K: RNNKwargs](Estimator[K]):
         self,
         writer: SummaryWriter,
         step: int | None = None,
-        val: bool = False,
+        group: str | None = None,
-        **kwargs: Unpack[K],
+        **kwargs: Unpack[Kw],
     ) -> None:
         return None
 

trainlib/plotter.py

@@ -0,0 +1,528 @@
+from typing import Any
+from collections.abc import Callable
+
+import numpy as np
+import torch
+import matplotlib.pyplot as plt
+from torch import Tensor
+
+from trainlib.trainer import Trainer
+from trainlib.estimator import EstimatorKwargs
+from trainlib.dataloader import EstimatorDataLoader
+from trainlib.utils.type import AxesArray, SubplotsKwargs
+
+type SubplotFn = Callable[[plt.Axes, int, Tensor, Tensor], None]
+type ContextFn = Callable[[plt.Axes, str], None]
+
+
+class Plotter[Kw: EstimatorKwargs]:
+    """
+    TODOs:
+
+    - best fit lines for plots and residuals (compare to ideal lines in each
+      case)
+    - show val options across columns; preview how val is changing across
+      natural training, and what the best will look (so plot like uniform
+      intervals broken over the training epochs at 0, 50, 100, 150, ... and
+      highlight the best one, even if that's not actually the single best
+      epoch)
+    - Implement data and dimension limits; in the instance dataloaders have
+      huge numbers of samples or labels are high-dimensional
+    """
+
+    def __init__(
+        self,
+        trainer: Trainer[Any, Kw],
+        dataloaders: list[EstimatorDataLoader[Any, Kw]],
+        kw_to_actual: Callable[[Kw], Tensor],
+        dataloader_labels: list[str] | None = None,
+    ) -> None:
+        self.trainer = trainer
+        self.dataloaders = dataloaders
+        self.dataloader_labels = (
+            dataloader_labels or list(map(str, range(1, len(dataloaders)+1)))
+        )
+        self.kw_to_actual = kw_to_actual
+
+        self._outputs: list[list[Tensor]] | None = None
+        self._metrics: list[list[dict[str, float]]] | None = None
+
+        self._data_tuples = None
+
+    @property
+    def data_tuples(self) -> list[tuple[Tensor, Tensor, str]]:
+        """
+        Produce data items; to be cached. Zip later with axes
+        """
+
+        self.trainer.estimator.eval()
+
+        if self._data_tuples is not None:
+            return self._data_tuples
+
+        data_tuples = []
+        for i, loader in enumerate(self.dataloaders):
+            label = self.dataloader_labels[i]
+
+            actual = torch.cat([
+                self.kw_to_actual(batch_kwargs).detach().cpu()
+                for batch_kwargs in loader
+            ])
+            output = torch.cat([
+                self.trainer.estimator(**batch_kwargs)[0].detach().cpu()
+                for batch_kwargs in loader
+            ])
+
+            data_tuples.append((actual, output, label))
+
+        self._data_tuples = data_tuples
+
+        return self._data_tuples
+
+    def get_transposed_handles_labels(
+        self,
+        ax: plt.Axes
+    ) -> tuple[list, list]:
+        # transpose legend layout for more natural view
+        handles, labels = ax.get_legend_handles_labels()
+        handles = handles[::2] + handles[1::2]
+        labels = labels[::2] + labels[1::2]
+
+        return handles, labels
+
+    def _lstsq_dim(
+        self,
+        dim_actual: Tensor,
+        dim_output: Tensor
+    ) -> tuple[Tensor, Tensor]:
+        A = torch.stack(
+            [dim_actual, torch.ones_like(dim_actual)],
+            dim=1
+        )
+        m, b = torch.linalg.lstsq(A, dim_output).solution
+
+        return m, b
+
+    def _prepare_figure_kwargs(
+        self,
+        rows: int,
+        cols: int,
+        row_size: int | float = 2,
+        col_size: int | float = 4,
+        figure_kwargs: SubplotsKwargs | None = None,
+    ) -> SubplotsKwargs:
+        """
+        """
+
+        default_figure_kwargs = {
+            "sharex": True,
+            "figsize": (col_size*cols, row_size*rows),
+        }
+        figure_kwargs = {
+            **default_figure_kwargs,
+            **(figure_kwargs or {}),
+        }
+
+        return figure_kwargs
+
+    def _prepare_subplot_kwargs(
+        self,
+        subplot_kwargs: dict | None = None,
+    ) -> dict:
+        """
+        """
+
+        default_subplot_kwargs = {}
+        subplot_kwargs = {
+            **default_subplot_kwargs,
+            **(subplot_kwargs or {}),
+        }
+
+        return subplot_kwargs
+
+    def _prepare_gof_kwargs(
+        self,
+        gof_kwargs: dict | None = None,
+    ) -> dict:
+        """
+        """
+
+        default_gof_kwargs = {
+            "alpha": 0.5
+        }
+        gof_kwargs = {
+            **default_gof_kwargs,
+            **(gof_kwargs or {}),
+        }
+
+        return gof_kwargs
+
+    def _create_subplots(
+        self,
+        rows: int,
+        cols: int,
+        row_size: int | float = 2,
+        col_size: int | float = 4,
+        figure_kwargs: SubplotsKwargs | None = None,
+    ) -> tuple[plt.Figure, AxesArray]:
+        """
+        """
+
+        figure_kwargs: SubplotsKwargs = self._prepare_figure_kwargs(
+            rows,
+            cols,
+            row_size=row_size,
+            col_size=col_size,
+            figure_kwargs=figure_kwargs,
+        )
+        fig, axes = plt.subplots(
+            rows,
+            cols,
+            squeeze=False,
+            **figure_kwargs,
+        )  # ty:ignore[no-matching-overload]
+
+        return fig, axes
+
+    def _plot_base(
+        self,
+        subplot_fn: SubplotFn,
+        context_fn: ContextFn,
+        row_size: int | float = 2,
+        col_size: int | float = 4,
+        norm_samples: bool = False,
+        combine_dims: bool = True,
+        figure_kwargs: SubplotsKwargs | None = None,
+    ) -> tuple[plt.Figure, AxesArray]:
+        """
+        Note: transform samples in dataloader definitions beforehand if you
+        want to change data
+
+        .. todo::
+
+            Merge in logic from general diagnostics, allowing collapse from
+            either dim and transposing.
+
+            Later: multi-trial error bars, or at least the ability to pass that
+            downstream
+
+        Parameters:
+            row_size:
+            col_size:
+            figure_kwargs:
+            subplot_kwargs:
+            gof_kwargs:
+        """
+        
+        ndims = self.data_tuples[0][0].size(-1)
+        fig, axes = self._create_subplots(
+            rows=len(self.dataloaders),
+            cols=1 if (norm_samples or combine_dims) else ndims,
+            row_size=row_size,
+            col_size=col_size,
+            figure_kwargs=figure_kwargs,
+        )
+
+        for axes_row, data_tuple in zip(axes, self.data_tuples, strict=True):
+            actual, output, loader_label = data_tuple
+
+            if norm_samples:
+                actual = actual.norm(dim=1, keepdim=True)
+                output = output.norm(dim=1, keepdim=True)
+
+            for dim in range(ndims):
+                ax = axes_row[0 if combine_dims else dim]
+                subplot_fn(ax, dim, actual, output)
+
+                add_plot_context = (
+                    norm_samples  # dim=0 implicit b/c we break
+                    or not combine_dims  # add to every subplot across grid
+                    or combine_dims and dim == ndims-1  # wait for last dim
+                )
+
+                # always exec plot logic if not combining, o/w exec just once
+                if add_plot_context:
+                    context_fn(ax, loader_label)
+
+                    # transpose legend layout for more natural view
+                    if norm_samples or not combine_dims:
+                        ax.legend()
+                    else:
+                        handles, labels = self.get_transposed_handles_labels(ax)
+                        ax.legend(handles, labels, ncols=2)
+
+                # break dimension loop if collapsed by norm
+                if norm_samples:
+                    break
+
+        return fig, axes
+
+    # def plot_ordered(...): ...
+    #     """
+    #     Simple ordered view of output dimensions, with actual and output
+    #     overlaid.
+    #     """
+
+    def plot_actual_output(
+        self,
+        row_size: int | float = 2,
+        col_size: int | float = 4,
+        norm_samples: bool = False,
+        combine_dims: bool = True,
+        figure_kwargs: SubplotsKwargs | None = None,
+        subplot_kwargs: dict | None = None,
+        gof_kwargs: dict | None = None,
+    ) -> tuple[plt.Figure, AxesArray]:
+        """
+        Plot residual distribution.
+        """
+
+        subplot_kwargs = self._prepare_subplot_kwargs(subplot_kwargs)
+        gof_kwargs = self._prepare_gof_kwargs(gof_kwargs)
+        colors = plt.rcParams["axes.prop_cycle"].by_key()["color"]
+
+        def subplot_fn(
+            ax: plt.Axes, dim: int, actual: Tensor, output: Tensor
+        ) -> None:
+            dim_color = colors[dim % len(colors)]
+            group_name = "norm" if norm_samples else f"$d_{dim}$"
+
+            dim_actual = actual[:, dim]
+            dim_output = output[:, dim]
+
+            ax.scatter(
+                dim_actual, dim_output,
+                color=dim_color,
+                label=group_name,
+                **subplot_kwargs,
+            )
+
+            # plot goodness of fit line
+            m, b = self._lstsq_dim(dim_actual, dim_output)
+            ax.plot(
+                dim_actual, m * dim_actual + b,
+                color=dim_color,
+                label=f"GoF {group_name}",
+                **gof_kwargs,
+            )
+
+        def context_fn(ax: plt.Axes, loader_label: str) -> None:
+            # plot perfect prediction reference line, y=x
+            ax.plot(
+                [0, 1], [0, 1],
+                transform=ax.transAxes,
+                c="black",
+                alpha=0.2,
+            )
+
+            ax.set_title(
+                f"[{loader_label}] True labels vs Predictions"
+            )
+            ax.set_xlabel("actual")
+            ax.set_ylabel("output")
+
+        return self._plot_base(
+            subplot_fn, context_fn,
+            row_size=row_size,
+            col_size=col_size,
+            norm_samples=norm_samples,
+            combine_dims=combine_dims,
+            figure_kwargs=figure_kwargs,
+        )
+
+    def plot_actual_output_residual(
+        self,
+        row_size: int | float = 2,
+        col_size: int | float = 4,
+        order_residuals: bool = False,
+        norm_samples: bool = False,
+        combine_dims: bool = True,
+        figure_kwargs: SubplotsKwargs | None = None,
+        subplot_kwargs: dict | None = None,
+        gof_kwargs: dict | None = None,
+    ) -> tuple[plt.Figure, AxesArray]:
+        """
+        Plot prediction residuals.
+        """
+
+        subplot_kwargs = self._prepare_subplot_kwargs(subplot_kwargs)
+        gof_kwargs = self._prepare_gof_kwargs(gof_kwargs)
+        colors = plt.rcParams["axes.prop_cycle"].by_key()["color"]
+
+        def subplot_fn(
+            ax: plt.Axes, dim: int, actual: Tensor, output: Tensor
+        ) -> None:
+            dim_color = colors[dim % len(colors)]
+            group_name = "norm" if norm_samples else f"$d_{dim}$"
+
+            dim_actual = actual[:, dim]
+            dim_output = output[:, dim]
+
+            residuals = dim_actual - dim_output
+            X, Y = dim_actual, residuals
+
+            if order_residuals:
+                X = range(1, residuals.size(0)+1)
+                Y = residuals[residuals.argsort()]
+
+            ax.scatter(
+                X, Y,
+                color=dim_color,
+                label=group_name,
+                **subplot_kwargs,
+            )
+
+            # plot goodness of fit line
+            if not order_residuals:
+                m, b = self._lstsq_dim(dim_actual, residuals)
+                ax.plot(
+                    dim_actual, m * dim_actual + b,
+                    color=dim_color,
+                    label=f"GoF {group_name}",
+                    **gof_kwargs,
+                )
+
+        def context_fn(ax: plt.Axes, loader_label: str) -> None:
+            # compare residuals to y=0
+            ax.axhline(y=0, c="black", alpha=0.2)
+
+            ax.set_title(f"[{loader_label}] Prediction residuals")
+            ax.set_xlabel("actual")
+            ax.set_ylabel("residual")
+
+        return self._plot_base(
+            subplot_fn, context_fn,
+            row_size=row_size,
+            col_size=col_size,
+            norm_samples=norm_samples,
+            combine_dims=combine_dims,
+            figure_kwargs=figure_kwargs,
+        )
+ 
+    def plot_actual_output_residual_dist(
+        self,
+        row_size: int | float = 2,
+        col_size: int | float = 4,
+        norm_samples: bool = False,
+        combine_dims: bool = True,
+        figure_kwargs: SubplotsKwargs | None = None,
+        subplot_kwargs: dict | None = None,
+        gof_kwargs: dict | None = None,
+    ) -> tuple[plt.Figure, AxesArray]:
+        """
+        Plot residual distribution.
+        """
+
+        subplot_kwargs = self._prepare_subplot_kwargs(subplot_kwargs)
+        gof_kwargs = self._prepare_gof_kwargs(gof_kwargs)
+        colors = plt.rcParams["axes.prop_cycle"].by_key()["color"]
+
+        def subplot_fn(
+            ax: plt.Axes, dim: int, actual: Tensor, output: Tensor
+        ) -> None:
+            dim_color = colors[dim % len(colors)]
+            group_name = "norm" if norm_samples else f"$d_{dim}$"
+
+            dim_actual = actual[:, dim]
+            dim_output = output[:, dim]
+
+            N = dim_actual.size(0)
+            residuals = dim_actual - dim_output
+
+            _, _, patches = ax.hist(
+                residuals.abs(),
+                bins=int(np.sqrt(N)),
+                density=True,
+                alpha=0.3,
+                color=dim_color,
+                label=group_name,
+                **subplot_kwargs
+            )
+
+            mu = residuals.abs().mean().item()
+            ax.axvline(mu, linestyle=":", c=dim_color, label=f"$\\mu_{dim}$")
+
+        def context_fn(ax: plt.Axes, loader_label: str) -> None:
+            ax.set_title(f"[{loader_label}] Residual distribution")
+            ax.set_xlabel("actual")
+            ax.set_ylabel("residual (density)")
+
+        return self._plot_base(
+            subplot_fn, context_fn,
+            row_size=row_size,
+            col_size=col_size,
+            norm_samples=norm_samples,
+            combine_dims=combine_dims,
+            figure_kwargs=figure_kwargs,
+        )
+
+    def estimator_diagnostic(
+        self,
+        row_size: int | float = 2,
+        col_size: int | float = 4,
+        session_name: str | None = None,
+        combine_groups: bool = False,
+        combine_metrics: bool = False,
+        transpose_layout: bool = False,
+        figure_kwargs: SubplotsKwargs | None = None,
+    ) -> tuple[plt.Figure, AxesArray]:
+        session_map = self.trainer._event_log
+        session_name = session_name or next(iter(session_map))
+        groups = session_map[session_name]
+        num_metrics = len(groups[next(iter(groups))])
+        # colors = plt.rcParams["axes.prop_cycle"].by_key()["color"]
+
+        rows = 1 if combine_groups else len(groups)
+        cols = 1 if combine_metrics else num_metrics
+        if transpose_layout:
+            rows, cols = cols, rows
+
+        fig, axes = self._create_subplots(
+            rows=rows,
+            cols=cols,
+            row_size=row_size,
+            col_size=col_size,
+            figure_kwargs=figure_kwargs,
+        )
+        if transpose_layout:
+            axes = axes.T
+
+        for i, group_name in enumerate(groups):
+            axes_row = axes[0 if combine_groups else i]
+            group_metrics = groups[group_name]
+
+            for j, metric_name in enumerate(group_metrics):
+                ax = axes_row[0 if combine_metrics else j]
+
+                metric_dict = group_metrics[metric_name]
+                metric_data = np.array([
+                    (k, np.mean(v)) for k, v in metric_dict.items()
+                ])
+
+                if combine_groups and combine_metrics:
+                    label = f"{group_name}-{metric_name}"
+                    title_prefix = "all"
+                elif combine_groups:
+                    label = group_name
+                    title_prefix = metric_name
+                # elif combine_metrics:
+                else:
+                    label = metric_name
+                    title_prefix = group_name
+                # else:
+                #     label = ""
+                #     title_prefix = f"{group_name},{metric_name}"
+
+                ax.plot(
+                    metric_data[:, 0],
+                    metric_data[:, 1],
+                    label=label,
+                    # color=colors[j],
+                )
+
+                ax.set_title(f"[{title_prefix}] Metrics over epochs")
+                ax.set_xlabel("epoch")
+                ax.set_ylabel("value")
+                ax.legend()
+
+        return fig, axes

trainlib/trainer.py

@@ -1,50 +1,67 @@
+"""
+Core interface for training ``Estimators`` with ``Datasets``
+
+.. admonition:: Design of preview ``get_dataloaders()``
+
+
+    Note how much this method is doing, and the positivity in letting that be
+    more explicit elsewhere. The assignment of transforms to datasets before
+    wrapping as loaders is chief among these items, alongside the balancing and
+    splitting; I think those are hamfisted here to make it work with the old
+    setup, but I generally it's not consistent with the name "get dataloaders"
+    (i.e., and also balance and split and set transforms)
+"""
+
 import os
 import time
 import logging
 from io import BytesIO
 from copy import deepcopy
-from typing import Any, Self
+from typing import Any
 from pathlib import Path
 from collections import defaultdict
-from collections.abc import Callable
 
 import torch
 from tqdm import tqdm
 from torch import cuda, Tensor
 from torch.optim import Optimizer
 from torch.nn.utils import clip_grad_norm_
-from torch.utils.data import Dataset, DataLoader
 from torch.utils.tensorboard import SummaryWriter
 
-from trainlib.dataset import BatchedDataset
 from trainlib.estimator import Estimator, EstimatorKwargs
-from trainlib.transform import Transform
+from trainlib.utils.map import nested_defaultdict
-from trainlib.utils.type import (
+from trainlib.dataloader import EstimatorDataLoader
-    SplitKwargs,
-    LoaderKwargs,
-    BalanceKwargs,
-)
 from trainlib.utils.module import ModelWrapper
 
 logger: logging.Logger = logging.getLogger(__name__)
 
 
-class Trainer[I, K: EstimatorKwargs]:
+class Trainer[I, Kw: EstimatorKwargs]:
     """
-    Training interface for updating ``Estimators`` with ``Datasets``.
+    Training interface for optimizing parameters of ``Estimators`` with
+    ``Datasets``.
+
+    This class is generic to a dataset item type ``I`` and an estimator kwarg
+    type ``Kw``. These are the two primary components ``Trainer`` objects need
+    to coordinate: they ultimately rely on a provided map to ensure data items
+    (type ``I``) from a dataset are appropriately routed as inputs to key
+    estimator methods (like ``forward()`` and ``loss()``), which accept inputs
+    of type ``Kw``.
     """
 
     def __init__(
         self,
-        estimator: Estimator[K],
+        estimator: Estimator[Kw],
         device: str | None = None,
         chkpt_dir: str = "chkpt/",
         tblog_dir: str = "tblog/",
     ) -> None:
         """
         Parameters:
-            estimator: `Estimator` model object
+            estimator: ``Estimator`` model object
             device: device on which to carry out training
+            chkpt_dir: directory to write model checkpoints
+            tblog_dir: directory to write TensorBoard logs
         """
 
         self.device: str
@@ -79,6 +96,7 @@ class Trainer[I, K: EstimatorKwargs]:
 
         self.estimator = estimator
         self.estimator.to(self.device)
+        self._event_log = nested_defaultdict(4, list)
 
         self.chkpt_dir = Path(chkpt_dir).resolve()
         self.tblog_dir = Path(tblog_dir).resolve()
@@ -87,67 +105,42 @@ class Trainer[I, K: EstimatorKwargs]:
 
     def reset(self) -> None:
         """
-        Set base tracking parameters.
+        Set initial tracking parameters for the primary training loop.
         """
 
-        self._step: int = 0
         self._epoch: int = 0
-        self._summary: dict[str, list[tuple[float, int]]] = defaultdict(list)
+        self._summary = defaultdict(lambda: defaultdict(list))
 
-        self._val_loss = float("inf")
+        self._conv_loss = float("inf")
-        self._best_val_loss = float("inf")
+        self._best_conv_loss = float("inf")
         self._stagnant_epochs = 0
         self._best_model_state_dict: dict[str, Any] = {}
 
     def _train_epoch(
         self,
-        train_loader: DataLoader,
+        loader: EstimatorDataLoader[Any, Kw],
-        batch_estimator_map: Callable[[I, Self], K],
         optimizers: tuple[Optimizer, ...],
-        writer: SummaryWriter,
         max_grad_norm: float | None = None,
     ) -> list[float]:
         """
         Train the estimator for a single epoch.
         """
 
-        train_loss_sums = []
+        loss_sums = []
         self.estimator.train()
-        with tqdm(train_loader, unit="batch") as train_epoch:
+        with tqdm(loader, unit="batch") as batches:
-            for i, batch_data in enumerate(train_epoch):
+            for i, batch_kwargs in enumerate(batches):
-                est_kwargs = batch_estimator_map(batch_data, self)
+                losses = self.estimator.loss(**batch_kwargs)
-                inputs = est_kwargs["inputs"]
 
-                # one-time logging
+                for o_idx, (loss, optimizer) in enumerate(
-                if self._step == 0:
+                    zip(losses, optimizers, strict=True)
-                    writer.add_graph(
+                ):
-                        ModelWrapper(self.estimator),
+                    if len(loss_sums) <= o_idx:
-                        est_kwargs
+                        loss_sums.append(0.0)
-                    )
+                    loss_sums[o_idx] += loss.item()
 
-                # once-per-epoch logging
-                if i == 0:
-                    self.estimator.epoch_write(
-                        writer,
-                        step=self._step,
-                        val=False,
-                        **est_kwargs
-                    )
-
-                train_losses = self.estimator.loss(**est_kwargs)
-                train_loss_items = []
-                for o_idx, optimizer in enumerate(optimizers):
                     optimizer.zero_grad()
-                    train_loss = next(train_losses)
+                    loss.backward()
-
-                    if len(train_loss_sums) <= o_idx:
-                        train_loss_sums.append(0.0)
-
-                    train_loss_item = train_loss.item()
-                    train_loss_sums[o_idx] += train_loss_item
-                    train_loss_items.append(train_loss_item)
-
-                    train_loss.backward()
 
                     # clip gradients for optimizer's parameters
                     if max_grad_norm is not None:
@@ -158,114 +151,174 @@ class Trainer[I, K: EstimatorKwargs]:
 
                     optimizer.step()
 
-                self._step += len(inputs)
+                # set loop loss to running average (reducing if multi-loss)
-
+                loss_avg = sum(loss_sums) / (len(loss_sums)*(i+1))
-                for train_loss_item, train_loss_sum in zip(
+                batches.set_postfix(loss=f"{loss_avg:8.2f}")
-                    train_loss_items,
-                    train_loss_sums,
-                    strict=True,
-                ):
-                    train_epoch.set_postfix(loss=f"{train_loss_sum/(i+1):8.2f}")
-                    self._add_summary_item("train_loss", train_loss_item)
-
-                estimator_metrics = self.estimator.metrics(**est_kwargs)
-                for metric_name, metric_value in estimator_metrics.items():
-                    self._add_summary_item(
-                        f"train_{metric_name}",
-                        metric_value
-                    )
 
+        # step estimator hyperparam schedules
         self.estimator.epoch_step()
 
-            for li, train_loss_sum in enumerate(train_loss_sums):
+        return loss_sums
-                self._add_summary_item(
-                    f"train_loss{li}_epoch", train_loss_sum / len(train_loader)
-                )
 
-        return train_loss_sums
+    def _eval_epoch(
-
-    def _val_epoch(
         self,
-        val_loader: DataLoader,
+        loader: EstimatorDataLoader[Any, Kw],
-        batch_estimator_map: Callable[[I, Self], K],
+        label: str
-        optimizers: tuple[Optimizer, ...],
-        writer: SummaryWriter,
     ) -> list[float]:
         """
         Perform and record validation scores for a single epoch.
+
+        .. On summary writers::
+
+            See the similarly titled note for ``_train_epoch()`` for general
+            remarks about optimizers and how we're recording losses/metrics.
+            The same mostly applies here in the validation setting, but we
+            crucially aren't stepping forward ``_step`` between batches. This
+            means that, even though we're writing losses and metrics once for
+            each val batch, those values are simply piling up under the same
+            summary item name. This is consistent with how we report training
+            items: the model isn't changing across val batches, so these don't
+            get plotted at different step points (which might be interpreted as
+            performance along model progression when it's actually variation
+            across batches). Currently, this "piling" means we actually write
+            batch values to the same event name at the same step rather than
+            manually averaging beforehand; we defer the handling to TB, and
+            although that may technically be discouraged, the val plots render
+            collections effectively as a vertical line between the min and max
+            value (which I find to be a satisfactory way to view cross-batch
+            variation during each val epoch).
         """
 
-        val_loss_sums = []
+        loss_sums = []
         self.estimator.eval()
-        with tqdm(val_loader, unit="batch") as val_epoch:
+        with tqdm(loader, unit="batch") as batches:
-            for i, batch_data in enumerate(val_epoch):
+            for i, batch_kwargs in enumerate(batches):
-                est_kwargs = batch_estimator_map(batch_data, self)
+                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(
-                        writer,
+                        self._writer,
-                        step=self._step,
+                        step=self._epoch,
-                        val=True,
+                        group=label,
-                        **est_kwargs
+                        **batch_kwargs
                     )
 
-                val_losses = self.estimator.loss(**est_kwargs)
+                loss_items = []
-                val_loss_items = []
+                for o_idx, loss in enumerate(losses):
-                for o_idx in range(len(optimizers)):
+                    if len(loss_sums) <= o_idx:
-                    val_loss = next(val_losses)
+                        loss_sums.append(0.0)
 
-                    if len(val_loss_sums) <= o_idx:
+                    loss_item = loss.item()
-                        val_loss_sums.append(0.0)
+                    loss_sums[o_idx] += loss_item
+                    loss_items.append(loss_item)
 
-                    val_loss_item = val_loss.item()
+                # set loop loss to running average (reducing if multi-loss)
-                    val_loss_sums[o_idx] += val_loss_item
+                loss_avg = sum(loss_sums) / (len(loss_sums)*(i+1))
-                    val_loss_items.append(val_loss_item)
+                batches.set_postfix(loss=f"{loss_avg:8.2f}")
 
-                for val_loss_item, val_loss_sum in zip(
+                # log individual loss terms after each batch
-                    val_loss_items,
+                for o_idx, loss_item in enumerate(loss_items):
-                    val_loss_sums,
+                    self._log_event(label, f"loss_{o_idx}", loss_item)
-                    strict=True,
-                ):
-                    val_epoch.set_postfix(loss=f"{val_loss_sum/(i+1):8.2f}")
-                    self._add_summary_item("val_loss", val_loss_item)
 
-                estimator_metrics = self.estimator.metrics(**est_kwargs)
+                # log metrics for batch
+                estimator_metrics = self.estimator.metrics(**batch_kwargs)
                 for metric_name, metric_value in estimator_metrics.items():
-                    self._add_summary_item(f"val_{metric_name}", metric_value)
+                    self._log_event(label, metric_name, metric_value)
 
-            for li, val_loss_sum in enumerate(val_loss_sums):
+        avg_losses = [loss_sum / (i+1) for loss_sum in loss_sums]
-                self._add_summary_item(
-                    f"val_loss{li}_epoch", val_loss_sum / len(val_loader)
-                )
 
-            # convergence of multiple losses may be ambiguous
+        return avg_losses
-            self._val_loss = sum(val_loss_sums) / len(val_loader)
 
-        return val_loss_sums
+    def _eval_loaders(
+        self,
+        train_loader: EstimatorDataLoader[Any, Kw],
+        val_loader: EstimatorDataLoader[Any, Kw] | None = None,
+        aux_loaders: list[EstimatorDataLoader[Any, Kw]] | None = None,
+    ) -> tuple[list[float], list[float] | None, *list[float]]:
+        """
+        Evaluate estimator over each provided dataloader.
+
+        This streamlines the general train/val/etc evaluation pipeline during
+        training. This triggers logging of summary items, TensorBoard writes,
+        etc, and is therefore exclusively intended for use inside the primary
+        ``train()`` loop.
+
+        If looking to use this method publicly, you should instead work with
+        individual dataloaders and call helper methods like
+        ``get_batch_outputs()`` or ``get_batch_metrics()`` while iterating over
+        batches. This will have no internal side effects and provides much more
+        information (just aggregated losses are provided here).
+
+        .. admonition:: On epoch counting
+
+            Epoch counts start at 0 to allow for a sensible place to benchmark
+            the initial (potentially untrained/pre-trained) model before any
+            training data is seen. In the train loop, we increment the epoch
+            immediately, and all logging happens under the epoch value that's
+            set at the start of the iteration (rather than incrementing at the
+            end). Before beginning an additional training iteration, the
+            convergence condition in the ``while`` is effectively checking what
+            happened during the last epoch (the counter has not yet been
+            incremented); if no convergence, we begin again. (This is only
+            being noted because the epoch counting was previously quite
+            different: indexing started at ``1``, we incremented at the end of
+            the loop, and we didn't evaluate the model before the loop began.
+            This affects how we interpret plots and TensorBoard records, for
+            instance, so it's useful to spell out the approach clearly
+            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
+
+        aux_loaders = aux_loaders or []
+        aux_losses = [
+            self._eval_epoch(aux_loader, f"aux{i}")
+            for i, aux_loader in enumerate(aux_loaders)
+        ]
+        
+        return train_loss, val_loss, *aux_losses
 
     def train(
         self,
-        dataset: BatchedDataset[..., ..., I],
+        train_loader: EstimatorDataLoader[Any, Kw],
-        batch_estimator_map: Callable[[I, Self], K],
+        val_loader: EstimatorDataLoader[Any, Kw] | None = None,
+        aux_loaders: list[EstimatorDataLoader[Any, Kw]] | None = None,
+        *,
         lr: float = 1e-3,
         eps: float = 1e-8,
         max_grad_norm: float | None = None,
         max_epochs: int = 10,
         stop_after_epochs: int = 5,
-        batch_size: int = 256,
-        val_frac: float = 0.1,
-        train_transform: Transform | None = None,
-        val_transform: Transform | None = None,
-        dataset_split_kwargs: SplitKwargs | None = None,
-        dataset_balance_kwargs: BalanceKwargs | None = None,
-        dataloader_kwargs: LoaderKwargs | None = None,
         summarize_every: int = 1,
         chkpt_every: int = 1,
-        resume_latest: bool = False,
+        session_name: str | None = None,
         summary_writer: SummaryWriter | None = None,
     ) -> Estimator:
         """
+        .. todo::
+
+            - consider making the dataloader ``collate_fn`` an explicit
+              parameter with a type signature that reflects ``B``, connecting
+              the ``batch_estimator_map`` somewhere. Might also re-type a
+              ``DataLoader`` in-house to allow a generic around ``B``
+            - Rework the validation specification. Accept something like a
+              "validate_with" parameter, or perhaps just move entirely to
+              accepting a dataloader list, label list. You might then also need
+              a "train_with," and you could set up sensible defaults so you
+              basically have the same interaction as now. The "problem" is you
+              always need a train set, and there's some clearly dependent logic
+              on a val set, but you don't *need* val, so this should be
+              slightly reworked (and the more general, *probably* the better in
+              this case, given I want to plug into the Plotter with possibly
+              several purely eval sets over the model training lifetime).
+
         Note: this method attempts to implement a general scheme for passing
         needed items to the estimator's loss function from the dataloader. The
         abstract ``Estimator`` base only requires the model output be provided
@@ -276,13 +329,13 @@ class Trainer[I, K: EstimatorKwargs]:
         one should take care to synchronize the sample structure with `dataset`
         to match that expected by ``self.estimator.loss(...)``.
 
-        .. admonition:: On batch_estimator_map
+        .. admonition:: On ``batch_estimator_map``
 
             Dataloader collate functions are responsible for mapping a
             collection of items into an item of collections, roughly speaking.
             If items are tuples of tensors,
 
-            .. code-block::
+            .. code-block:: text
 
                 [
                     ( [1, 1], [1, 1] ),
@@ -293,7 +346,7 @@ class Trainer[I, K: EstimatorKwargs]:
             the collate function maps back into the item skeleton, producing a
             single tuple of (stacked) tensors
 
-            .. code-block::
+            .. code-block:: text
 
                 ( [[1, 1],
                    [2, 2],
@@ -303,14 +356,58 @@ class Trainer[I, K: EstimatorKwargs]:
                    [2, 2],
                    [3, 3]] )
 
-            This function should map from batches (which should be *item
+            This function should map from batches - which *may* be item
-            shaped*, i.e., have an ``I`` skeleton, even if stacked items may be
+            shaped, i.e., have an ``I`` skeleton, even if stacked items may be
-            different on the inside) into estimator keyword arguments (type
+            different on the inside - into estimator keyword arguments (type
-            ``K``).
+            ``Kw``). Collation behavior from a DataLoader (which can be
+            customized) doesn't consistently yield a known type shape, however,
+            so it's not appropriate to use ``I`` as the callable param type.
+
+        .. admonition:: On session management
+
+            This method works around an implicit notion of training sessions.
+            Estimators are set during instantiation and effectively coupled
+            with ``Trainer`` instances, but datasets can be supplied
+            dynamically here. One can, for instance, run under one condition
+            (specific dataset, number of epochs, etc), then resume later under
+            another. Relevant details persist across calls: the estimator is
+            still attached, best val scores stowed, current epoch tracked. By
+            default, new ``session_names`` are always generated, but you can
+            write to the same TB location if you using the same
+            ``session_name`` across calls; that's about as close to a direct
+            training resume as you could want.
+
+            If restarting training on new datasets, including short
+            fine-tuning on training-plus-validation data, it's often sensible
+            to call ``.reset()`` between ``.train()`` calls. While the same
+            estimator will be used, tracked variables will be wiped; subsequent
+            model updates take place under a fresh epoch, no val losses, and be
+            logged under a separate TB session. This is the general approach to
+            "piecemeal" training, i.e., incremental model updates under varying
+            conditions (often just data changes).
+
+        .. warning::
+
+            Validation convergence when there are multiple losses may be
+            ambiguous. These are cases where certain parameter sets are
+            optimized independently; the sum over these losses may not reflect
+            expected or consistent behavior. For instance, we may record a low
+            cumulative loss early with a small 1st loss and moderate 2nd loss,
+            while later encountering a moderate 1st lost and small 2nd loss. We
+            might prefer the latter case, while ``_converged()`` will stick to
+            the former -- we need to consider possible weighting across losses,
+            or storing possibly several best models (e.g., for each loss, the
+            model that scores best, plus the one scoring best cumulatively,
+            etc).
 
         Parameters:
+            dataset: dataset to train the estimator
+            batch_estimator_map: function mapping from batch data to expected
+                estimator kwargs
             lr: learning rate (default: 1e-3)
             eps: adam EPS (default: 1e-8)
+            max_grad_norm: upper bound to use when clipping gradients. If left
+                as ``None``, no gradient clipping is performed.
             max_epochs: maximum number of training epochs
             stop_after_epochs: number of epochs with stagnant validation losses
                 to allow before early stopping. If training stops earlier, the
@@ -323,93 +420,57 @@ class Trainer[I, K: EstimatorKwargs]:
                 dataset
             val_split_frac: fraction of dataset to use for validation
             chkpt_every: how often model checkpoints should be saved
-            resume_latest: resume training from the latest available checkpoint
-                in the `chkpt_dir`
         """
 
         logger.info("> Begin train loop:")
         logger.info(f"| > {lr=}")
         logger.info(f"| > {eps=}")
         logger.info(f"| > {max_epochs=}")
-        logger.info(f"| > {batch_size=}")
-        logger.info(f"| > {val_frac=}")
         logger.info(f"| > {chkpt_every=}")
-        logger.info(f"| > {resume_latest=}")
         logger.info(f"| > with device: {self.device}")
         logger.info(f"| > core count: {os.cpu_count()}")
 
-        writer: SummaryWriter
+        self._session_name = session_name or str(int(time.time()))
-        dir_prefix = str(int(time.time()))
+        tblog_path = Path(self.tblog_dir, self._session_name)
-        if summary_writer is None:
+        self._writer = summary_writer or SummaryWriter(f"{tblog_path}")
-            writer = SummaryWriter(f"{self.tblog_dir}")
-        else:
-            writer = summary_writer
-
-        train_loader, val_loader = self.get_dataloaders(
-            dataset,
-            batch_size,
-            val_frac=val_frac,
-            train_transform=train_transform,
-            val_transform=val_transform,
-            dataset_split_kwargs=dataset_split_kwargs,
-            dataset_balance_kwargs=dataset_balance_kwargs,
-            dataloader_kwargs=dataloader_kwargs,
-        )
 
+        # evaluate model on dataloaders once before training starts
+        self._eval_loaders(train_loader, val_loader, aux_loaders)
         optimizers = self.estimator.optimizers(lr=lr, eps=eps)
 
-        self._step = 0
+        while self._epoch < max_epochs and not self._converged(
-        self._epoch = 1  # start from 1 for logging convenience
-        while self._epoch <= max_epochs and not self._converged(
             self._epoch, 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}...")
 
             epoch_start_time = time.time()
-            self._train_epoch(
+            self._train_epoch(train_loader, optimizers, max_grad_norm)
-                train_loader,
+            epoch_end_time = time.time() - epoch_start_time
-                batch_estimator_map,
+            self._log_event("train", "epoch_duration", epoch_end_time)
-                optimizers,
-                writer,
-                max_grad_norm
-            )
 
-            if val_frac > 0:
+            train_loss, val_loss, _ = self._eval_loaders(
-                self._val_epoch(
+                train_loader, val_loader, aux_loaders
-                    val_loader,
-                    batch_estimator_map,
-                    optimizers,
-                    writer,
-                )
-
-            self._add_summary_item(
-                "epoch_time_sec",
-                time.time() - epoch_start_time
             )
+            # determine loss to use for measuring convergence
+            conv_loss = val_loss if val_loss else train_loss
+            self._conv_loss = sum(conv_loss) / len(conv_loss)
 
             if self._epoch % summarize_every == 0:
-                self._summarize(writer, self._epoch)
+                self._summarize()
-
-            # save checkpoint
             if self._epoch % chkpt_every == 0:
-                self.save_model(
+                self.save_model()
-                    self._epoch,
-                    self.chkpt_dir,
-                    dir_prefix
-                )
-
-            self._epoch += 1
 
         return self.estimator
 
     def _converged(self, epoch: int, stop_after_epochs: int) -> bool:
         converged = False
 
-        if epoch == 1 or self._val_loss < self._best_val_loss:
+        if epoch == 0 or self._conv_loss < self._best_val_loss:
-            self._best_val_loss = self._val_loss
+            self._best_val_loss = self._conv_loss
             self._stagnant_epochs = 0
             self._best_model_state_dict = deepcopy(self.estimator.state_dict())
         else:
@@ -421,94 +482,24 @@ class Trainer[I, K: EstimatorKwargs]:
 
         return converged
 
-    @staticmethod
+    def _summarize(self) -> None:
-    def get_dataloaders(
-        dataset: BatchedDataset,
-        batch_size: int,
-        val_frac: float = 0.1,
-        train_transform: Transform | None = None,
-        val_transform: Transform | None = None,
-        dataset_split_kwargs: SplitKwargs | None = None,
-        dataset_balance_kwargs: BalanceKwargs | None = None,
-        dataloader_kwargs: LoaderKwargs | None = None,
-    ) -> tuple[DataLoader, DataLoader]:
         """
-        Create training and validation dataloaders for the provided dataset.
+        Flush the training summary to the TensorBoard summary writer and print
+        metrics for the current epoch.
         """
 
-        if dataset_split_kwargs is None:
+        print(f"==== Epoch [{self._epoch}] summary ====")
-            dataset_split_kwargs = {}
+        for (group, name), epoch_map in self._summary.items():
-
+            for epoch, values in epoch_map.items():
-        if dataset_balance_kwargs is not None:
+                mean = torch.tensor(values).mean().item()
-            dataset.balance(**dataset_balance_kwargs)
+                self._writer.add_scalar(f"{group}-{name}", mean, epoch)
-
+                if epoch == self._epoch:
-        if val_frac <= 0:
+                    print(
-            dataset.post_transform = train_transform
+                        f"> ({len(values)}) [{group}] {name} :: {mean:.2f}"
-            train_loader_kwargs: LoaderKwargs = {
-                "batch_size": min(batch_size, len(dataset)),
-                "num_workers": 0,
-                "shuffle": True,
-            }
-            if dataloader_kwargs is not None:
-                train_loader_kwargs: LoaderKwargs = {
-                    **train_loader_kwargs,
-                    **dataloader_kwargs
-                }
-
-            return (
-                DataLoader(dataset, **train_loader_kwargs),
-                DataLoader(Dataset())
                     )
 
-        train_dataset, val_dataset = dataset.split(
+        self._writer.flush()
-            [1 - val_frac, val_frac],
+        self._summary = defaultdict(lambda: defaultdict(list))
-            **dataset_split_kwargs,
-        )
-
-        # Dataset.split() returns light Subset objects of shallow copies of the
-        # underlying dataset; can change the transform attribute of both splits
-        # w/o overwriting
-        train_dataset.post_transform = train_transform
-        val_dataset.post_transform = val_transform
-
-        train_loader_kwargs: LoaderKwargs = {
-            "batch_size": min(batch_size, len(train_dataset)),
-            "num_workers": 0,
-            "shuffle": True,
-        }
-        val_loader_kwargs: LoaderKwargs = {
-            "batch_size": min(batch_size, len(val_dataset)),
-            "num_workers": 0,
-            "shuffle": True,  # shuffle to prevent homogeneous val batches
-        }
-
-        if dataloader_kwargs is not None:
-            train_loader_kwargs = {**train_loader_kwargs, **dataloader_kwargs}
-            val_loader_kwargs = {**val_loader_kwargs, **dataloader_kwargs}
-
-        train_loader = DataLoader(train_dataset, **train_loader_kwargs)
-        val_loader = DataLoader(val_dataset, **val_loader_kwargs)
-
-        return train_loader, val_loader
-
-    def _summarize(self, writer: SummaryWriter, epoch: int) -> None:
-        """
-        Flush the training summary to the TB summary writer.
-        """
-
-        summary_values = defaultdict(list)
-        for name, records in self._summary.items():
-            for value, step in records:
-                writer.add_scalar(name, value, step)
-                summary_values[name].append(value)
-
-        print(f"==== Epoch [{epoch}] summary ====")
-        for name, values in summary_values.items():
-            mean_value = torch.tensor(values).mean().item()
-            print(f"> ({len(values)}) {name} :: {mean_value:.2f}")
-
-        writer.flush()
-        self._summary = defaultdict(list)
 
     def _get_optimizer_parameters(
         self,
@@ -521,15 +512,13 @@ class Trainer[I, K: EstimatorKwargs]:
             if param.grad is not None
         ]
 
-    def _add_summary_item(self, name: str, value: float) -> None:
+    def _log_event(self, group: str, name: str, value: float) -> None:
-        self._summary[name].append((value, self._step))
+        session, epoch = self._session_name, self._epoch
 
-    def save_model(
+        self._summary[group, name][epoch].append(value)
-        self,
+        self._event_log[session][group][name][epoch].append(value)
-        epoch: int,
+
-        chkpt_dir: str | Path,
+    def save_model(self) -> None:
-        dir_prefix: str,
-    ) -> None:
         """
         Save a model checkpoint.
         """
@@ -539,25 +528,26 @@ class Trainer[I, K: EstimatorKwargs]:
         model_buff.seek(0)
 
         model_class = self.estimator.__class__.__name__
-        chkpt_name = f"m_{model_class}-e_{epoch}.pth"
+        chkpt_name = f"m_{model_class}-e_{self._epoch}.pth"
 
-        chkpt_dir = Path(chkpt_dir, dir_prefix)
+        chkpt_dir = Path(self.chkpt_dir, self._session_name)
         chkpt_path = Path(chkpt_dir, chkpt_name)
 
         chkpt_dir.mkdir(parents=True, exist_ok=True)
         chkpt_path.write_bytes(model_buff.getvalue())
 
-    def load_model(
+    def load_model(self, chkpt_dir: str, epoch: int) -> None:
-        self,
-        epoch: int,
-        chkpt_dir: str,
-    ) -> None:
         """
         Load a model checkpoint from a given epoch.
 
-        Note that this assumes the model was saved via `Trainer.save_model()`,
+        Note that this assumes the model was saved via
-        and the estimator provided to this `Trainer` instance matches the
+        ``Trainer.save_model()``, and the estimator provided to this
-        architecture of the checkpoint model being loaded.
+        ``Trainer`` instance matches the architecture of the checkpoint model
+        being loaded.
+
+        Parameters:
+            epoch: epoch of saved model
+            chkpt_dir: 
         """
 
         model_class = self.estimator.__class__.__name__

trainlib/transform.py

@@ -1,3 +1,7 @@
+"""
+Transform base for dataset records
+"""
+
 class Transform[I]:
     """
     Dataset transform base class.
@@ -8,4 +12,14 @@ class Transform[I]:
     """
 
     def __call__(self, item: I) -> I:
+        """
+        Apply transform to item.
+
+        Parameters:
+            item: item object to transform
+
+        Returns:
+            transformed item (same type ``I`` as input)
+        """
+
         raise NotImplementedError

trainlib/utils/custom.mplstyle

@@ -0,0 +1,49 @@
+text.usetex                   : False
+mathtext.default              : regular
+
+# testing to prevent component overlap/clipping
+figure.constrained_layout.use : True
+
+font.family                   : sans-serif
+font.sans-serif               : DejaVu Sans
+font.serif                    : DejaVu Serif
+font.cursive                  : DejaVu Sans
+mathtext.fontset              : dejavuserif
+font.size                     : 9
+figure.titlesize              : 9
+legend.fontsize               : 9
+axes.titlesize                : 9
+axes.labelsize                : 9
+xtick.labelsize               : 9
+ytick.labelsize               : 9
+
+# monobiome -d 0.45 -l 22
+axes.prop_cycle               : cycler('color', ['5e8de4', 'c38141', '67a771', 'e15344', '9e9858', '41a6b0', 'a46fd7', 'c86d9a'])
+
+image.interpolation           : nearest
+image.resample                : False
+image.composite_image         : True
+
+axes.spines.left              : True
+axes.spines.bottom            : True
+axes.spines.top               : False
+axes.spines.right             : False
+
+axes.linewidth                : 1
+xtick.major.width             : 1
+xtick.minor.width             : 1
+ytick.major.width             : 1
+ytick.minor.width             : 1
+
+lines.linewidth               : 1
+lines.markersize              : 1
+
+savefig.dpi                   : 300
+savefig.format                : svg
+savefig.bbox                  : tight
+savefig.pad_inches            : 0.1
+
+svg.image_inline              : True
+svg.fonttype                  : none
+
+legend.frameon                : False

trainlib/utils/map.py

@@ -0,0 +1,10 @@
+from collections import defaultdict
+
+
+def nested_defaultdict(
+    depth: int,
+    final: type = dict,
+) -> defaultdict:
+    if depth == 1:
+        return defaultdict(final)
+    return defaultdict(lambda: nested_defaultdict(depth - 1, final))

trainlib/utils/plot.py

@@ -0,0 +1,38 @@
+from pathlib import Path
+
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+
+FILE = Path(__file__).parent.absolute()
+
+
+class use_style:
+    def __init__(
+        self,
+        style: list[str] | None = None,
+        **kwargs: str,
+    ) -> None:
+        super().__init__()
+
+        if style is None:
+            style = [str(Path(FILE, "custom.mplstyle"))]
+        self.style = style + [kwargs]
+        self.previous_style = {}
+
+    def __enter__(self) -> None:
+        self.previous_style = mpl.rcParams.copy()
+        if self.style is not None:
+            plt.style.use(self.style)
+
+    def __exit__(self, *args: str, **kwargs: str) -> None:
+        mpl.rcParams.update(self.previous_style)
+
+
+def set_style(
+    style: list[str] | None = None,
+    **kwargs: str,
+) -> None:
+    if style is None:
+        style = [str(Path(FILE, "custom.mplstyle"))]
+
+    plt.style.use(style + [kwargs])

trainlib/utils/session.py

@@ -0,0 +1,21 @@
+import random
+
+import numpy as np
+import torch
+from torch import Tensor
+
+
+def seed_all_backends(seed: int | Tensor | None = None) -> None:
+    """Sets all python, numpy and pytorch seeds."""
+
+    if seed is None:
+        seed = int(torch.randint(1000000, size=(1,)))
+    else:
+        seed = int(seed)
+
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False

trainlib/utils/type.py

@@ -1,14 +1,17 @@
 from typing import Any, TypedDict
-from collections.abc import Callable, Iterable
+from collections.abc import Callable, Iterable, Sequence
 
+import numpy as np
 from torch import Tensor
 from torch.utils.data.sampler import Sampler
 
 from trainlib.dataset import BatchedDataset
 
+# need b/c matplotlib axes are insanely stupid
+type AxesArray = np.ndarray[tuple[int, int], np.dtype[np.object_]]
 
 class LoaderKwargs(TypedDict, total=False):
-    batch_size: int
+    batch_size: int | None
     shuffle: bool
     sampler: Sampler | Iterable | None
     batch_sampler: Sampler[list] | Iterable[list] | None
@@ -50,3 +53,17 @@ class OptimizerKwargs(TypedDict, total=False):
     capturable: bool
     differentiable: bool
     fused: bool | None
+
+
+class SubplotsKwargs(TypedDict, total=False):
+    sharex: bool | str
+    sharey: bool | str
+    squeeze: bool
+    width_ratios: Sequence[float]
+    height_ratios: Sequence[float]
+    subplot_kw: dict[str, ...]
+    gridspec_kw: dict[str, ...]
+    figsize: tuple[float, float]
+    dpi: float
+    layout: str
+    constrained_layout: bool

uv.lock

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 [[package]]
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+version = "3.4.6"
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 source = { editable = "." }
 dependencies = [
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     { name = "matplotlib" },
     { name = "numpy" },
+    { name = "setuptools" },
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     { name = "torch" },
     { name = "tqdm" },
@@ -1658,11 +1695,12 @@ requires-dist = [
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     { name = "numpy", specifier = ">=2.4.1" },
     { name = "pytest", marker = "extra == 'test'" },
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     { name = "sphinx", marker = "extra == 'doc'" },
     { name = "sphinx-autodoc-typehints", marker = "extra == 'doc'" },
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     { name = "tensorboard", specifier = ">=2.20.0" },
-    { name = "torch", specifier = ">=2.5.1" },
+    { name = "torch", index = "https://download.pytorch.org/whl/cu128" },
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