README.md

@@ -6,7 +6,7 @@ Code for detecting and classifying bat echolocation calls in high-frequency
 audio recordings.
 
 > [!WARNING]
-> `batdetect2` 2.0.0b3 is out.
+> `batdetect2` 2.0.0b2 is out.
 > This is a beta release and we are gathering user feedback.
 > If you run into issues or have feedback on the new workflows, please use the
 > GitHub issues page to let us know.
@@ -68,13 +68,13 @@ can try the following:
 ### Installing BatDetect2
 
 > [!NOTE]
-> `2.0.0b3` is a pre-release on PyPI.
+> `2.0.0b2` is a pre-release on PyPI.
 > You may need to request it explicitly by version, for example:
 >
 > ```bash
-> uvx --from batdetect2==2.0.0b3 batdetect2
+> uvx --from batdetect2==2.0.0b2 batdetect2
-> uv tool install batdetect2==2.0.0b3
+> uv tool install batdetect2==2.0.0b2
-> pip install batdetect2==2.0.0b3
+> pip install batdetect2==2.0.0b2
 > ```
 
 If you have `uv` installed (if not, we recommend it; follow the instructions

docs/source/reference/api.md

@@ -15,9 +15,6 @@ Defined in `batdetect2.api_v2`.
 - `BatDetect2API.from_config(model_config=..., targets_config=..., ...)`
   - build a full model stack from config objects.
 
-Both constructors accept `compile_model=True` to compile the detector after the
-API is built.
-
 ## Common tasks
 
 - Load a checkpoint and run prediction on one file.
@@ -25,8 +22,6 @@ API is built.
 - Save predictions in one of the supported output formats.
 - Evaluate a model on labelled data.
 - Fine-tune an existing checkpoint on new targets.
-- Compile the detector explicitly with `BatDetect2API.compile()` when you want
-  to opt into PyTorch runtime compilation from Python.
 
 ## Generated reference
 

docs/source/reference/configs/inference/inference-config.md

@@ -7,8 +7,6 @@ Defined in `batdetect2.inference.config`.
 
 ## Top-level fields
 
-- `compile_model`
-  - compile the detector before batch prediction. This is off by default.
 - `loader`
   - data-loader settings for inference.
 - `clipping`
@@ -36,19 +34,8 @@ Override `InferenceConfig` when:
 
 - long recordings need different clipping behavior,
 - you want to tune batch size for your hardware,
-- you want to opt into runtime model compilation for repeated predictions,
 - you need reproducible prediction settings across runs.
 
-## Runtime compilation
-
-Set `compile_model: true` to compile the detector before batch inference. This
-can help when you run repeated predictions with stable input shapes. For a
-single short run, the compile step can cost more time than it saves.
-
-In Python, you can also compile explicitly with `BatDetect2API.compile()` or by
-passing `compile_model=True` to `BatDetect2API.from_checkpoint(...)` or
-`BatDetect2API.from_config(...)`.
-
 ## Related pages
 
 - Tune inference clipping:

docs/source/reference/configs/training/training-config.md

@@ -7,8 +7,6 @@ Defined in `batdetect2.train.config`.
 
 ## Top-level fields
 
-- `compile_model`
-  - compile the detector before training starts. This is off by default.
 - `train_loader`
   - training data loading and clipping settings.
 - `val_loader`
@@ -35,18 +33,10 @@ Use `TrainingConfig` when you want to change things like:
 - batch size,
 - augmentation,
 - optimiser and scheduler settings,
-- runtime options such as model compilation,
 - number of epochs,
 - validation frequency,
 - checkpoint behaviour.
 
-## Runtime options
-
-Use `compile_model: true` to call `torch.compile` on the detector used during
-training. This can help on longer runs with stable tensor shapes, but it may be
-slower for short CPU-only experiments because PyTorch has to compile the graph
-before it can reuse it.
-
 Example files live under `example_data/configs/`, including
 `example_data/configs/training.yaml`.
 

justfile

@@ -136,7 +136,7 @@ clean: clean-build clean-pyc clean-test clean-docs
 
 # Train on example data.
 example-train OPTIONS="":
-    uv run batdetect2 -v train \
+    uv run batdetect2 train \
         --val-dataset example_data/dataset.yaml \
         --base-dir . \
         --targets example_data/targets.yaml \

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "batdetect2"
-version = "2.0.0b3"
+version = "2.0.0b2"
 description = "Deep learning model for detecting and classifying bat echolocation calls in high frequency audio recordings."
 authors = [
   { "name" = "Oisin Mac Aodha", "email" = "oisin.macaodha@ed.ac.uk" },
@@ -11,9 +11,10 @@ dependencies = [
   "deepmerge>=2.0",
   "hydra-core>=1.3.2",
   "librosa>=0.10.1",
-  "lightning==2.5.0",
+  "lightning[extra]==2.5.0",
   "loguru>=0.7.3",
   "matplotlib>=3.7.1",
+  "netcdf4>=1.6.5",
   "numpy>=1.23.5",
   "pandas>=1.5.3",
   "pydantic>=2.0.0",
@@ -23,10 +24,9 @@ dependencies = [
   "seaborn>=0.13.2",
   "soundevent[audio,geometry,plot]>=2.10.0",
   "soundfile>=0.12.1",
-  "tabulate>=0.10.0",
+  "tensorboard>=2.16.2",
   "torch>=2.0.0",
   "torchaudio>=2.0.0",
-  "tqdm>=4.70.0",
   "xarray>=2024.0.0",
 ]
 requires-python = ">=3.10,<3.14"
@@ -88,7 +88,6 @@ dev = [
   "deepdiff>=8.6.1",
   "pytest-xdist[psutil]>=3.8.0",
 ]
-tensorboard = ["tensorboard>=2.16.2"]
 dvclive = ["dvclive>=3.48.2"]
 mlflow = ["mlflow>=3.1.1"]
 gradio = [

src/batdetect2/api_v2.py

@@ -8,7 +8,6 @@ if TYPE_CHECKING:
 
     import numpy as np
     import torch
-    from lightning.pytorch.loggers import Logger
     from soundevent import data
 
     from batdetect2.audio import AudioConfig, AudioLoader
@@ -153,19 +152,6 @@ class BatDetect2API:
 
         self.model.eval()
 
-    def compile(self) -> "BatDetect2API":
-        """Compile the detector path used by inference.
-
-        Returns
-        -------
-        BatDetect2API
-            This API instance with the detector compiled.
-        """
-        from batdetect2.models import compile_model
-
-        compile_model(self.model)
-        return self
-
     def load_annotations(
         self,
         path: data.PathLike,
@@ -206,7 +192,6 @@ class BatDetect2API:
         train_config: TrainingConfig | None = None,
         logger_config: LoggerConfig | None = None,
         logging_callbacks: Sequence[LoggingCallback[TrainLoggingContext]] = (),
-        train_logger: Logger | None = None,
     ):
         """Train the current model on a set of annotations.
 
@@ -241,9 +226,6 @@ class BatDetect2API:
             Training logger config override.
         logging_callbacks : Sequence[LoggingCallback[TrainLoggingContext]], optional
             Extra logging callbacks to run during training setup.
-        train_logger : Logger | None, optional
-            Pre-built Lightning logger to use for training. If omitted, one is
-            built from ``logger_config``.
 
         Returns
         -------
@@ -273,7 +255,6 @@ class BatDetect2API:
             audio_config=audio_config or self.audio_config,
             logger_config=logger_config or self.logging_config.train,
             logging_callbacks=logging_callbacks,
-            train_logger=train_logger,
         )
         self.model.eval()
         return self
@@ -998,7 +979,6 @@ class BatDetect2API:
         inference_config: InferenceConfig | None = None,
         outputs_config: OutputsConfig | None = None,
         logging_config: AppLoggingConfig | None = None,
-        compile_model: bool = False,
     ) -> "BatDetect2API":
         """Build an API instance from config objects.
 
@@ -1024,8 +1004,6 @@ class BatDetect2API:
             Output config. If omitted, the default outputs config is used.
         logging_config : AppLoggingConfig | None, optional
             Logging config. If omitted, the default logging config is used.
-        compile_model : bool, optional
-            If ``True``, compile the detector path after building the API.
 
         Returns
         -------
@@ -1108,7 +1086,7 @@ class BatDetect2API:
             ),
         )
 
-        api = cls(
+        return cls(
             model_config=model_config,
             audio_config=audio_config,
             train_config=train_config,
@@ -1127,11 +1105,6 @@ class BatDetect2API:
             output_transform=output_transform,
         )
 
-        if compile_model:
-            api.compile()
-
-        return api
-
     @classmethod
     def from_checkpoint(
         cls,
@@ -1142,7 +1115,6 @@ class BatDetect2API:
         inference_config: InferenceConfig | None = None,
         outputs_config: OutputsConfig | None = None,
         logging_config: AppLoggingConfig | None = None,
-        compile_model: bool = False,
     ) -> "BatDetect2API":
         """Build an API instance from a saved checkpoint.
 
@@ -1163,8 +1135,6 @@ class BatDetect2API:
             Output config override.
         logging_config : AppLoggingConfig | None, optional
             Logging config override.
-        compile_model : bool, optional
-            If ``True``, compile the detector path after building the API.
 
         Returns
         -------
@@ -1248,7 +1218,7 @@ class BatDetect2API:
             transform=output_transform,
         )
 
-        api = cls(
+        return cls(
             model_config=model_config,
             audio_config=audio_config,
             train_config=train_config,
@@ -1267,11 +1237,6 @@ class BatDetect2API:
             output_transform=output_transform,
         )
 
-        if compile_model:
-            api.compile()
-
-        return api
-
     def _set_trainable_parameters(
         self,
         trainable: Literal["all", "heads", "classifier_head", "size_head"],

src/batdetect2/inference/batch.py

@@ -10,7 +10,6 @@ from batdetect2.inference.clips import get_clips_from_files
 from batdetect2.inference.config import InferenceConfig
 from batdetect2.inference.dataset import build_inference_loader
 from batdetect2.inference.lightning import InferenceModule
-from batdetect2.models import compile_model
 from batdetect2.models.types import ModelProtocol
 from batdetect2.outputs import (
     OutputsConfig,
@@ -72,9 +71,6 @@ def run_batch_inference(
         batch_size=batch_size,
     )
 
-    if inference_config.compile_model:
-        compile_model(model)
-
     module = InferenceModule(
         model,
         output_transform=output_transform,

src/batdetect2/inference/config.py

@@ -15,7 +15,6 @@ class ClipingConfig(BaseConfig):
 
 
 class InferenceConfig(BaseConfig):
-    compile_model: bool = False
     loader: InferenceLoaderConfig = Field(
         default_factory=InferenceLoaderConfig
     )

src/batdetect2/logging.py

@@ -201,14 +201,7 @@ def create_tensorboard_logger(
     experiment_name: str | None = None,
     run_name: str | None = None,
 ) -> Logger:
-    try:
     from lightning.pytorch.loggers import TensorBoardLogger
-    except ImportError as error:
-        raise ValueError(
-            "TensorBoard is not installed and cannot be used for logging. "
-            "Make sure you have it installed by running `pip install tensorboard` "
-            "or `uv add tensorboard`"
-        ) from error
 
     if log_dir is None:
         log_dir = Path(config.log_dir)

src/batdetect2/models/__init__.py

@@ -100,7 +100,6 @@ __all__ = [
     "ModelConfig",
     "build_model",
     "build_model_with_new_targets",
-    "compile_model",
 ]
 
 
@@ -320,15 +319,3 @@ def build_model_with_new_targets(
         dimension_names=roi_mapper.dimension_names,
         config=model.get_config(),
     )
-
-
-def compile_model(model: ModelProtocol) -> ModelProtocol:
-    """Compile the detector path used by training and inference."""
-    if not isinstance(model.detector, torch.nn.Module):
-        raise TypeError("Detector must be a torch.nn.Module to compile.")
-
-    if getattr(model.detector, "_compiled_call_impl", None) is not None:
-        return model
-
-    model.detector.compile()
-    return model

src/batdetect2/outputs/formats/parquet.py

@@ -1,5 +1,5 @@
 from pathlib import Path
-from typing import List, Literal, Sequence, TypedDict
+from typing import List, Literal, Sequence
 from uuid import UUID
 
 import numpy as np
@@ -26,11 +26,6 @@ class ParquetOutputConfig(BaseConfig):
     include_geometry: bool = True
 
 
-class ClipInfo(TypedDict):
-    clip: data.Clip
-    preds: list[Detection]
-
-
 class ParquetFormatter(OutputFormatterProtocol[ClipDetections]):
     def __init__(
         self,
@@ -125,7 +120,7 @@ class ParquetFormatter(OutputFormatterProtocol[ClipDetections]):
         else:
             df = pd.read_parquet(path)
 
-        predictions_by_clip: dict[UUID, ClipInfo] = {}
+        predictions_by_clip = {}
 
         for _, row in df.iterrows():
             clip_uuid = row["clip_uuid"]

src/batdetect2/plotting/detections.py

@@ -185,25 +185,25 @@ def plot_clip_evaluation(
                     label="found GT",
                     edgecolor=gt_color,
                     facecolor="none" if not fill else gt_color,
-                    linestyle=gt_linestyle,  # type: ignore
+                    linestyle=gt_linestyle,
                 ),
                 patches.Patch(
                     label="missed GT",
                     edgecolor=missed_gt_color,
                     facecolor="none" if not fill else missed_gt_color,
-                    linestyle=missed_gt_linestyle,  # type: ignore
+                    linestyle=missed_gt_linestyle,
                 ),
                 patches.Patch(
                     label="true Det",
                     edgecolor=true_pred_color,
                     facecolor="none" if not fill else true_pred_color,
-                    linestyle=true_pred_linestyle,  # type: ignore
+                    linestyle=true_pred_linestyle,
                 ),
                 patches.Patch(
                     label="false Det",
                     edgecolor=false_pred_color,
                     facecolor="none" if not fill else false_pred_color,
-                    linestyle=false_pred_linestyle,  # type: ignore
+                    linestyle=false_pred_linestyle,
                 ),
             ]
         )

src/batdetect2/plotting/heatmaps.py

@@ -1,9 +1,9 @@
 """Plot heatmaps."""
 
-import matplotlib.pyplot as plt
 import numpy as np
 import torch
 from matplotlib import axes, patches
+from matplotlib.cm import get_cmap
 from matplotlib.colors import Colormap, LinearSegmentedColormap, to_rgba
 
 from batdetect2.plotting.common import create_ax
@@ -80,7 +80,7 @@ def plot_classification_heatmap(
         raise ValueError("Inconsistent number of class names")
 
     if not isinstance(cmap, Colormap):
-        cmap = plt.get_cmap(cmap)
+        cmap = get_cmap(cmap)
 
     handles = []
 

src/batdetect2/train/config.py

@@ -39,7 +39,6 @@ class PLTrainerConfig(BaseConfig):
 
 
 class TrainingConfig(BaseConfig):
-    compile_model: bool = False
     train_loader: TrainLoaderConfig = Field(default_factory=TrainLoaderConfig)
     val_loader: ValLoaderConfig = Field(default_factory=ValLoaderConfig)
     optimizer: OptimizerConfig = Field(default_factory=AdamOptimizerConfig)

src/batdetect2/train/schedulers.py

@@ -23,6 +23,21 @@ __all__ = [
 ]
 
 
+class CosineAnnealingSchedulerConfig(BaseConfig):
+    """Configuration for ``CosineAnnealingLR``.
+
+    Attributes
+    ----------
+    name : Literal["cosine_annealing"]
+        Discriminator field used by the scheduler registry.
+    t_max : int
+        Number of epochs to complete one cosine cycle.
+    """
+
+    name: Literal["cosine_annealing"] = "cosine_annealing"
+    t_max: int = 200
+
+
 scheduler_registry: Registry[LRScheduler, [Optimizer]] = Registry("scheduler")
 
 
@@ -38,24 +53,6 @@ class SchedulerImportConfig(ImportConfig):
     name: Literal["import"] = "import"
 
 
-class CosineAnnealingSchedulerConfig(BaseConfig):
-    """Configuration for ``CosineAnnealingLR``.
-
-    Attributes
-    ----------
-    name : Literal["cosine_annealing"]
-        Discriminator field used by the scheduler registry.
-    t_max : int
-        Number of epochs to complete one cosine cycle.
-    eta_min : float, optional
-        Minimum learning rate. Defaults to 0.
-    """
-
-    name: Literal["cosine_annealing"] = "cosine_annealing"
-    t_max: int = 200
-    eta_min: float = 0
-
-
 @scheduler_registry.register(CosineAnnealingSchedulerConfig)
 def build_cosine_scheduler(
     config: CosineAnnealingSchedulerConfig,
@@ -66,11 +63,7 @@ def build_cosine_scheduler(
     ``t_max`` is interpreted in epochs because Lightning steps the scheduler
     once per epoch when ``interval="epoch"`` is used.
     """
-    return CosineAnnealingLR(
+    return CosineAnnealingLR(optimizer, T_max=config.t_max)
-        optimizer,
-        T_max=config.t_max,
-        eta_min=config.eta_min,
-    )
 
 
 SchedulerConfig = Annotated[

src/batdetect2/train/train.py

@@ -15,7 +15,7 @@ from batdetect2.logging import (
     LoggingCallback,
     build_logger,
 )
-from batdetect2.models import ModelConfig, build_model, compile_model
+from batdetect2.models import ModelConfig, build_model
 from batdetect2.models.types import ModelProtocol
 from batdetect2.preprocess import PreprocessorProtocol, build_preprocessor
 from batdetect2.targets import (
@@ -71,7 +71,6 @@ def run_train(
     run_name: str | None = None,
     seed: int | None = None,
     logging_callbacks: Sequence[LoggingCallback[TrainLoggingContext]] = (),
-    train_logger: Logger | None = None,
 ):
     if seed is not None:
         seed_everything(seed)
@@ -165,7 +164,7 @@ def run_train(
         roi_mapper=roi_mapper,
     )
 
-    train_logger = train_logger or build_logger(
+    train_logger = build_logger(
         logger_config or CSVLoggerConfig(),
         log_dir=log_dir,
         experiment_name=experiment_name,
@@ -207,10 +206,6 @@ def run_train(
         run_name=run_name,
     )
 
-    if train_config.compile_model:
-        logger.info("Compiling detector...")
-        compile_model(module.model)
-
     logger.info("Starting main training loop...")
     trainer.fit(
         module,

tests/conftest.py

@@ -1,7 +1,6 @@
 import uuid
-from dataclasses import dataclass
 from pathlib import Path
-from typing import Any, Callable, List, Optional, cast
+from typing import Callable, List, Optional
 from uuid import uuid4
 
 import lightning as L
@@ -16,7 +15,6 @@ from batdetect2.audio.clips import build_clipper
 from batdetect2.audio.types import AudioLoader, ClipperProtocol
 from batdetect2.data import DatasetConfig, load_dataset
 from batdetect2.data.annotations.batdetect2 import BatDetect2FilesAnnotations
-from batdetect2.models.types import ModelProtocol
 from batdetect2.preprocess import build_preprocessor
 from batdetect2.preprocess.types import PreprocessorProtocol
 from batdetect2.targets import (
@@ -33,12 +31,6 @@ from batdetect2.train.lightning import build_training_module
 from batdetect2.train.types import ClipLabeller
 
 
-@dataclass
-class DetectorCompileRecorder:
-    compile_count: int = 0
-    call_count: int = 0
-
-
 @pytest.fixture
 def example_data_dir() -> Path:
     pkg_dir = Path(__file__).parent.parent
@@ -164,15 +156,12 @@ def generate_whistle(tmp_path: Path):
 
         offset = int((time - duration / 2) * samplerate)
         t = np.linspace(-duration / 2, duration / 2, frames, endpoint=False)
-        pulse = np.asarray(
+        data = signal.gausspulse(
-            signal.gausspulse(
             t,
             fc=frequency,
             bw=2 / (frequency * whistle_duration),
-            ),
-            dtype=np.float64,
         )
-        wave = (np.roll(pulse, offset) * np.iinfo(np.int16).max).astype(
+        wave = (np.roll(data, offset) * np.iinfo(np.int16).max).astype(
             np.int16
         )
         sf.write(str(path), wave, samplerate, subtype="PCM_16")
@@ -374,30 +363,6 @@ def sample_audio_loader() -> AudioLoader:
     return build_audio_loader()
 
 
-@pytest.fixture
-def record_detector_compilation(
-    monkeypatch: pytest.MonkeyPatch,
-) -> Callable[[ModelProtocol], DetectorCompileRecorder]:
-    def factory(model: ModelProtocol) -> DetectorCompileRecorder:
-        recorder = DetectorCompileRecorder()
-        detector = cast(Any, model.detector)
-        original_call_impl = detector._call_impl
-
-        def compile_detector() -> None:
-            recorder.compile_count += 1
-
-            def compiled_call(*args, **kwargs):
-                recorder.call_count += 1
-                return original_call_impl(*args, **kwargs)
-
-            detector._compiled_call_impl = compiled_call
-
-        monkeypatch.setattr(detector, "compile", compile_detector)
-        return recorder
-
-    return factory
-
-
 @pytest.fixture
 def bat_tag() -> data.Tag:
     return data.Tag(key="class", value="bat")

tests/test_api_v2/test_api_v2.py

@@ -153,61 +153,6 @@ def test_process_spectrogram_rejects_batched_input(
         api_v2.process_spectrogram(spec)
 
 
-def test_user_can_compile_api_detector(
-    api_v2: BatDetect2API,
-    example_audio_files: list[Path],
-    record_detector_compilation,
-) -> None:
-    recorder = record_detector_compilation(api_v2.model)
-    audio = api_v2.load_audio(example_audio_files[0])
-    spec = api_v2.generate_spectrogram(audio)
-
-    api_v2.compile()
-    api_v2.compile()
-    api_v2.process_spectrogram(spec)
-
-    assert recorder.compile_count == 1
-    assert recorder.call_count == 1
-
-
-def test_api_from_config_compiles_detector_when_requested(
-    monkeypatch: pytest.MonkeyPatch,
-) -> None:
-    compiled_models = []
-
-    def compile_model(model):
-        compiled_models.append(model)
-        return model
-
-    monkeypatch.setattr("batdetect2.models.compile_model", compile_model)
-
-    api = BatDetect2API.from_config(
-        compile_model=True,
-    )
-
-    assert compiled_models == [api.model]
-
-
-def test_api_from_checkpoint_compiles_detector_when_requested(
-    tiny_checkpoint_path: Path,
-    monkeypatch: pytest.MonkeyPatch,
-) -> None:
-    compiled_models = []
-
-    def compile_model(model):
-        compiled_models.append(model)
-        return model
-
-    monkeypatch.setattr("batdetect2.models.compile_model", compile_model)
-
-    api = BatDetect2API.from_checkpoint(
-        tiny_checkpoint_path,
-        compile_model=True,
-    )
-
-    assert compiled_models == [api.model]
-
-
 def test_user_can_read_top_class_and_other_class_scores(
     api_v2: BatDetect2API,
     example_audio_files: list[Path],

tests/test_inference/test_batch.py

@@ -3,8 +3,6 @@ from pathlib import Path
 import pytest
 from soundevent import data
 
-from batdetect2.api_v2 import BatDetect2API
-from batdetect2.inference import InferenceConfig
 from batdetect2.inference.batch import run_batch_inference
 from batdetect2.targets import build_roi_mapping, build_targets
 from batdetect2.train import load_model_from_checkpoint
@@ -55,54 +53,3 @@ def test_run_batch_inference_matches_single_clip_inference(
         strict=True,
     ):
         assert_clip_detections_equal(batched, single)
-
-
-def test_run_batch_inference_compiles_detector_when_config_requests_compile(
-    example_annotations: list[data.ClipAnnotation],
-    record_detector_compilation,
-) -> None:
-    api = BatDetect2API.from_config()
-    recorder = record_detector_compilation(api.model)
-
-    predictions = run_batch_inference(
-        api.model,
-        [example_annotations[0].clip],
-        targets=api.targets,
-        roi_mapper=api.roi_mapper,
-        audio_loader=api.audio_loader,
-        preprocessor=api.preprocessor,
-        output_transform=api.output_transform,
-        inference_config=InferenceConfig(compile_model=True),
-        batch_size=1,
-        num_workers=0,
-    )
-
-    assert predictions
-    assert recorder.compile_count == 1
-    assert recorder.call_count > 0
-
-
-def test_run_batch_inference_does_not_recompile_compiled_detector(
-    example_annotations: list[data.ClipAnnotation],
-    record_detector_compilation,
-) -> None:
-    api = BatDetect2API.from_config()
-    recorder = record_detector_compilation(api.model)
-    api.compile()
-
-    predictions = run_batch_inference(
-        api.model,
-        [example_annotations[0].clip],
-        targets=api.targets,
-        roi_mapper=api.roi_mapper,
-        audio_loader=api.audio_loader,
-        preprocessor=api.preprocessor,
-        output_transform=api.output_transform,
-        inference_config=InferenceConfig(compile_model=True),
-        batch_size=1,
-        num_workers=0,
-    )
-
-    assert predictions
-    assert recorder.compile_count == 1
-    assert recorder.call_count > 0

tests/test_train/test_lightning.py

@@ -49,16 +49,6 @@ def build_default_module(
     )
 
 
-def build_fast_train_config() -> TrainingConfig:
-    train_config = TrainingConfig()
-    train_config.trainer.limit_train_batches = 1
-    train_config.trainer.limit_val_batches = 1
-    train_config.trainer.log_every_n_steps = 1
-    train_config.train_loader.batch_size = 1
-    train_config.train_loader.augmentations.enabled = False
-    return train_config
-
-
 def test_can_initialize_default_module():
     module = build_default_module()
     assert isinstance(module, L.LightningModule)
@@ -281,7 +271,19 @@ def test_train_smoke_produces_loadable_checkpoint(
     sample_audio_loader: AudioLoader,
 ):
     # Given
-    train_config = build_fast_train_config()
+    train_config = TrainingConfig.model_validate(
+        {
+            "trainer": {
+                "limit_train_batches": 1,
+                "limit_val_batches": 1,
+                "log_every_n_steps": 1,
+            },
+            "train_loader": {
+                "batch_size": 1,
+                "augmentations": {"enabled": False},
+            },
+        }
+    )
 
     # When
     run_train(
@@ -308,47 +310,6 @@ def test_train_smoke_produces_loadable_checkpoint(
     assert outputs is not None
 
 
-@pytest.mark.slow
-def test_run_train_compiles_detector_when_train_config_requests_compile(
-    tmp_path: Path,
-    example_annotations: list[data.ClipAnnotation],
-    record_detector_compilation,
-) -> None:
-    targets_config = TargetConfig()
-    targets = build_targets(targets_config)
-    roi_mapper = build_roi_mapping(targets_config.roi)
-    model = build_model(
-        ModelConfig(),
-        class_names=targets.class_names,
-        dimension_names=roi_mapper.dimension_names,
-    )
-    train_config = build_fast_train_config()
-    train_config.compile_model = True
-    recorder = record_detector_compilation(model)
-
-    module = run_train(
-        train_annotations=example_annotations[:1],
-        val_annotations=example_annotations[:1],
-        model=model,
-        targets=targets,
-        roi_mapper=roi_mapper,
-        targets_config=targets_config,
-        train_config=train_config,
-        num_epochs=1,
-        train_workers=0,
-        val_workers=0,
-        checkpoint_dir=tmp_path / "checkpoints",
-        log_dir=tmp_path / "logs",
-        seed=0,
-    )
-
-    assert (
-        getattr(module.model.detector, "_compiled_call_impl", None) is not None
-    )
-    assert recorder.compile_count == 1
-    assert recorder.call_count > 0
-
-
 def test_build_training_module_uses_provided_model() -> None:
     targets = build_targets(TargetConfig())
     roi_mapper = build_roi_mapping(TargetConfig().roi)