Modifying package so we can start publishing project.

README.md

@@ -1,8 +1,8 @@
-# SIM: Self Improving Model framework
+# Apeiron
 [![Build Status](https://github.com/AI-ModCon/BaseSim_Framework/actions/workflows/build-test.yml/badge.svg)](https://github.com/AI-ModCon/BaseSim_Framework/actions/workflows/build-test.yml)
 [![Coverage Status](https://codecov.io/gh/AI-ModCon/BaseSim_Framework/badge.svg?branch=main)](https://codecov.io/gh/AI-ModCon/BaseSim_Framework?branch=main)
 
-A PyTorch framework for continuous learning that automatically detects concept drift in data streams and adapts models through JVP regularized retraining.
+A PyTorch framework for continual learning that automatically detects concept drift in data streams and adapts models through JVP regularized retraining.
 
 ## What This Repository Does
 
@@ -17,16 +17,35 @@ The pipeline runs on a changing data stream and loops through these stages:
 Core modules:
 
 - `src/main.py`: entry point
-- `src/config/configuration.py`: TOML/env/CLI config assembly
-- `src/driver/continuous_monitor.py`: monitoring + drift loop
-- `src/training/continuous_trainer.py`: CL training loop
-- `src/training/updater/`: CL update strategies
-- `src/drift_detection/`: detectors and detector factory
-- `examples/`: concrete model harness implementations
+- `src/apeiron/config/configuration.py`: TOML/env/CLI config assembly
+- `src/apeiron/driver/continuous_monitor.py`: monitoring + drift loop
+- `src/apeiron/training/continuous_trainer.py`: CL training loop
+- `src/apeiron/training/updater/`: CL update strategies
+- `src/apeiron/drift_detection/`: detectors and detector factory
+- `examples/`: standalone example projects (each declares `apeiron` as a dependency)
 
 ## Installation
 
-Requires Python `>=3.13,<3.15` and Poetry.
+### As a dependency in your project
+
+```toml
+# pyproject.toml
+[tool.poetry.dependencies]
+apeiron = "^0.1.0"  # once published to PyPI
+
+# Or as a path dependency during development
+apeiron = { path = "../apeiron/", develop = true }
+```
+
+```python
+from apeiron import BaseModelHarness, ContinuousMonitor, build_config
+from apeiron.drift_detection import ADWINDetector
+from apeiron.training.updater import BaseUpdater
+```
+
+### For development in this repo
+
+Requires Python `>=3.13,<3.14` and Poetry.
 
 ```bash
 poetry install
@@ -39,6 +58,7 @@ From the project root:
 ```bash
 poetry run python -m src.main --config examples/mnist/mnist.toml
 poetry run python -m src.main --config examples/cifar/cifar10_vit.toml
+poetry run python -m src.main --config examples/imagenet/imagenet_vit.toml  # requires ImageNet data at data.path
 ```
 
 ## Metrics Logging
@@ -105,7 +125,7 @@ poetry run mypy .
 
 Platform-specific deployment guides:
 
-- [NERSC Perlmutter](./src/deployment/perlmutter/README.md)
+- [NERSC Perlmutter](./src/apeiron/deployment/perlmutter/README.md)
 
 ## What `main.py` Does
 - Builds the `DummyCNN_MNIST` model defined in `src/model/DummyCNN_MNIST.py`, a cross-entropy loss, and an Adam optimizer.
@@ -127,4 +147,4 @@ Training logs report the task id, training/test accuracy, and replay-memory accu
 
 Platform-specific deployment guides:
 
-- [OLCF Frontier](./src/deployment/frontier/README.md)
+- [OLCF Frontier](./src/apeiron/deployment/frontier/README.md)

examples/cifar/cifar10_vgg11.toml

@@ -1,11 +1,11 @@
-# mnist.min.toml
-version = "1"
+# cifar10_vgg11.toml
 seed = 1337
 device = "auto"
 multi_gpu = false
+verbosity = "INFO"  # DEBUG, INFO, INFO:0-9, WARNING, ERROR, CRITICAL
 
 [model]
-name = "vgg11"  
+name = "vgg11"
 pretrained_path = "examples/cifar/cifar10_vgg11.pth"
 
 [data]
@@ -14,11 +14,38 @@ path = ""  # folder or file later for custom dataset
 
 [train]
 batch_size = 32
+grad_accumulation_steps = 1
 num_workers = 4
 init_lr = 0.001
+max_iter = 300
 
+[continual_learning]
+update_mode = "base"
+jvp_lambda = 10
+jvp_deltax_norm = 1
 
-[continuous_learning]
-jvp_reg = 0.001
-deltax_norm = 1
-max_iter = 300
+# ewc hyperparameters
+ewc_lambda = 1000.0
+ewc_ema_decay = 0.95
+
+# kfac hyperparameters
+kfac_lambda = 1e-2
+kfac_ema_decay = 0.95
+
+[drift_detection]
+detector_name = "ADWINDetector"
+detection_interval = 10  # Check drift every 10 batches
+aggregation = "mean"  # Average metric over 10 batches
+metric_index = 0  # Monitor accuracy (0=accuracy, 1=loss)
+reset_after_learning = false
+max_stream_updates = 20
+
+# ADWIN hyperparameters
+adwin_delta = 0.002
+adwin_minor_threshold = 0.3
+adwin_moderate_threshold = 0.6
+
+[visualization]
+baseline = 90.0
+input = "output/cifar.csv"
+output = "output/cifar10_vgg11_dashboard.png"

examples/cifar/model.py

@@ -5,8 +5,8 @@
 from torch.optim import Optimizer
 from torch.utils.data import DataLoader, ConcatDataset
 
-from model.torch_model_harness import BaseModelHarness
-from config.configuration import Config
+from apeiron.model.torch_model_harness import BaseModelHarness
+from apeiron.config.configuration import Config
 from examples.cifar.src.utils import (
     get_cifar_train,
     get_cifar_val,
@@ -16,7 +16,7 @@
     sample_aug,
 )
 from examples.cifar.src.utils import load_model
-from evaluation.metrics import accuracy
+from apeiron.evaluation.metrics import accuracy
 
 
 class VisionModelCifar(nn.Module):

examples/cifar/src/utils.py

@@ -6,7 +6,7 @@
 from torch.utils.data import Dataset, DataLoader
 from torchvision import datasets, transforms
 import torchvision.transforms.functional as TF
-from config.configuration import Config
+from apeiron.config.configuration import Config
 from examples.cifar.src import cnns, vision_transformers
 
 

examples/imagenet/model.py

@@ -5,8 +5,8 @@
 from torch.optim import Optimizer
 from torch.utils.data import DataLoader, ConcatDataset
 
-from model.torch_model_harness import BaseModelHarness
-from config.configuration import Config
+from apeiron.model.torch_model_harness import BaseModelHarness
+from apeiron.config.configuration import Config
 from examples.imagenet.src.utils import (
     get_imagenet_train,
     get_imagenet_val,
@@ -16,7 +16,7 @@
     sample_aug,
 )
 from examples.imagenet.src.utils import load_model
-from evaluation.metrics import accuracy
+from apeiron.evaluation.metrics import accuracy
 
 
 class VisionModelImageNet(nn.Module):

examples/imagenet/src/utils.py

@@ -9,7 +9,7 @@
 from torch.utils.data import Dataset, DataLoader
 from torchvision import datasets, transforms
 import torchvision.transforms.functional as TF
-from config.configuration import Config
+from apeiron.config.configuration import Config
 from examples.cifar.src import cnns, vision_transformers
 
 

examples/mnist/model.py

@@ -7,8 +7,8 @@
 from torch.optim import Optimizer
 from torch.utils.data import DataLoader, ConcatDataset
 
-from model.torch_model_harness import BaseModelHarness
-from config.configuration import Config
+from apeiron.model.torch_model_harness import BaseModelHarness
+from apeiron.config.configuration import Config
 from examples.mnist.utils import (
     get_mnist_train,
     get_mnist_val,
@@ -17,7 +17,7 @@
     make_loader,
     sample_aug,
 )
-from evaluation.metrics import accuracy
+from apeiron.evaluation.metrics import accuracy
 
 
 class Cnn(nn.Module):

examples/utils.py

@@ -1,5 +1,5 @@
-from config.configuration import Config
-from model.torch_model_harness import BaseModelHarness
+from apeiron.config.configuration import Config
+from apeiron.model.torch_model_harness import BaseModelHarness
 
 
 def get_example(cfg: Config) -> BaseModelHarness:

pyproject.toml

@@ -1,12 +1,12 @@
 [project]
-name = "basesim-framework"
+name = "apeiron"
 version = "0.1.0"
-description = ""
+description = "A PyTorch continual learning framework for real-time concept drift detection and model adaptation."
 authors = [
     {name = "Your Name",email = "you@example.com"}
 ]
 readme = "README.md"
-requires-python = ">=3.13,<3.15"
+requires-python = ">=3.13,<3.14"
 dependencies = [
     "torch (>=2.0)",
     "numpy (>=2.3.4,<3.0.0)",
@@ -41,7 +41,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 packages = [
-    { include = "*", from = "src" },
+    { include = "apeiron", from = "src" },
 ]
 
 [tool.ruff]

src/apeiron/__init__.py

@@ -0,0 +1,56 @@
+"""Apeiron: A PyTorch continual learning framework for real-time concept drift detection and model adaptation."""
+
+from apeiron.config.configuration import (
+    Config,
+    ModelCfg,
+    DataCfg,
+    TrainCfg,
+    ContinualLearningCfg,
+    DriftDetectionCfg,
+    VisualizationCfg,
+    LoggingCfg,
+    build_config,
+)
+from apeiron.model.torch_model_harness import BaseModelHarness
+from apeiron.driver.continuous_monitor import ContinuousMonitor
+from apeiron.drift_detection import (
+    BaseDriftDetector,
+    DriftSignal,
+    LearningRegime,
+    ADWINDetector,
+    KSWINDetector,
+    PageHinkleyDetector,
+    ModelPerformanceDetector,
+    EnsembleDetector,
+    ModelEvalDetector,
+)
+from apeiron.training import ContinuousTrainer
+from apeiron.training.updater import BaseUpdater
+from apeiron.logger import Logger, get_logger
+
+__all__ = [
+    "Config",
+    "ModelCfg",
+    "DataCfg",
+    "TrainCfg",
+    "ContinualLearningCfg",
+    "DriftDetectionCfg",
+    "VisualizationCfg",
+    "LoggingCfg",
+    "build_config",
+    "BaseModelHarness",
+    "ContinuousMonitor",
+    "BaseDriftDetector",
+    "DriftSignal",
+    "LearningRegime",
+    "ADWINDetector",
+    "KSWINDetector",
+    "PageHinkleyDetector",
+    "ModelPerformanceDetector",
+    "EnsembleDetector",
+    "ModelEvalDetector",
+    "ContinuousTrainer",
+    "BaseUpdater",
+    "Logger",
+    "get_logger",
+]

src/config/configuration.py → src/apeiron/config/configuration.py

@@ -27,7 +27,7 @@
 from typing import TYPE_CHECKING
 
 if TYPE_CHECKING:
-    from logger.logger import MetricsBackend
+    from apeiron.logger.logger import MetricsBackend
 
 
 def get_available_device(multi_gpu: bool = False) -> torch.device:

src/drift_detection/__init__.py → src/apeiron/drift_detection/__init__.py

@@ -15,17 +15,17 @@
 - EnsembleDetector: Combine multiple detectors
 """
 
-from drift_detection.detectors.base import (
+from apeiron.drift_detection.detectors.base import (
     BaseDriftDetector,
     DriftSignal,
     LearningRegime,
 )
-from drift_detection.detectors.statistical_detectors import (
+from apeiron.drift_detection.detectors.statistical_detectors import (
     ADWINDetector,
     KSWINDetector,
     PageHinkleyDetector,
 )
-from drift_detection.detectors.model_performance_detector import (
+from apeiron.drift_detection.detectors.model_performance_detector import (
     ModelPerformanceDetector,
     EnsembleDetector,
     ModelEvalDetector,

src/drift_detection/detectors/model_performance_detector.py → src/apeiron/drift_detection/detectors/model_performance_detector.py

@@ -12,7 +12,7 @@
 from typing import Optional, List
 from evidently import Report
 from evidently.presets import DataDriftPreset
-from drift_detection.detectors.base import (
+from apeiron.drift_detection.detectors.base import (
     BaseDriftDetector,
     DriftSignal,
     LearningRegime,

src/drift_detection/detectors/statistical_detectors.py → src/apeiron/drift_detection/detectors/statistical_detectors.py

@@ -10,7 +10,7 @@
 
 import numpy as np
 from river import drift as river_drift
-from drift_detection.detectors.base import (
+from apeiron.drift_detection.detectors.base import (
     BaseDriftDetector,
     DriftSignal,
     LearningRegime,