diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml
index 9027da2f..96382d86 100644
--- a/.github/workflows/ci.yaml
+++ b/.github/workflows/ci.yaml
@@ -9,6 +9,10 @@ on:
branches:
- main
- develop
+ types:
+ - opened
+ - synchronize
+ - reopened
permissions:
contents: write
@@ -113,8 +117,13 @@ jobs:
fail_ci_if_error: true # Optional, ensures the CI fails if Codecov upload fails
docs:
- if: github.ref == 'refs/heads/main' || (github.event_name == 'pull_request' && github.event.pull_request.base.ref == 'main')
+ name: Docs
runs-on: ubuntu-latest
+ permissions:
+ contents: write # needed to push to gh-pages
+
+ # Only build+deploy docs when main is updated (i.e., after PR merge)
+ if: github.event_name == 'push' && github.ref == 'refs/heads/main'
strategy:
matrix:
@@ -124,7 +133,7 @@ jobs:
- name: Check out the repository
uses: actions/checkout@v4
with:
- fetch-depth: 0 # Fetch all history if necessary
+ fetch-depth: 0
- name: Set up Python
uses: actions/setup-python@v5
@@ -146,18 +155,20 @@ jobs:
- name: Generate API Documentation with Sphinx
run: |
source .venv/bin/activate
- sphinx-apidoc -o docs/ codes
+ mkdir -p docs/source/api
+ sphinx-apidoc -o docs/source/api codes
- name: Build HTML with Sphinx
run: |
source .venv/bin/activate
- sphinx-build -b html docs/ docs/_build
+ sphinx-build -b html docs/source docs/_build/html
- - name: Deploy Sphinx API docs to gh-pages
+ - name: Deploy docs to gh-pages
uses: peaceiris/actions-gh-pages@v4
with:
github_token: ${{ secrets.GITHUB_TOKEN }}
- publish_dir: docs/_build
+ publish_dir: docs/_build/html
publish_branch: gh-pages
user_name: "GitHub Actions"
user_email: "actions@github.com"
+ force_orphan: false
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
new file mode 100644
index 00000000..b366a311
--- /dev/null
+++ b/.pre-commit-config.yaml
@@ -0,0 +1,9 @@
+repos:
+ - repo: https://github.com/psf/black
+ rev: 26.1.0
+ hooks:
+ - id: black
+ - repo: https://github.com/pycqa/isort
+ rev: 7.0.0
+ hooks:
+ - id: isort
diff --git a/README.md b/README.md
index 18a785b8..ed1d78ff 100644
--- a/README.md
+++ b/README.md
@@ -1,176 +1,72 @@
# CODES Benchmark
-[](https://codecov.io/github/robin-janssen/CODES-Benchmark)
-
-
+[](https://codecov.io/github/robin-janssen/CODES-Benchmark)  
-🎉 CODES was accepted to the ML4PS workshop @ NeurIPS2024 🎉
+🎉 Accepted to the ML4PS workshop @ NeurIPS 2024
-## Benchmarking Coupled ODE Surrogates
+Benchmark coupled ODE surrogate models on curated datasets with reproducible training, evaluation, and visualization pipelines. CODES helps you answer: *Which surrogate architecture fits my data, accuracy target, and runtime budget?*
-CODES is a benchmark for coupled ODE surrogate models.
+## What you get
-
-
-
-
-
-
- 
- CODES paper on arXiV.
+- Baseline surrogates (MultiONet, FullyConnected, LatentNeuralODE, LatentPoly) with configurable hyperparameters
+- Rich datasets spanning chemistry, astrophysics, and dynamical systems
+- Optional studies for interpolation/extrapolation, sparse data regimes, uncertainty estimation, and batch scaling
+- Automated reporting: accuracy tables, resource usage, gradient analyses, and dozens of diagnostic plots
-
-
-
- The main documentation can be found on the CODES website.
+## Two-minute quickstart
-
-
-
-
-
-
-
- The technical API documentation is hosted on this GitHub Page.
+**uv (recommended)**
-## Motivation
-
-There are many efforts to use machine learning models ("surrogates") to replace the costly numerics involved in solving coupled ODEs. But for the end user, it is not obvious how to choose the right surrogate for a given task. Usually, the best choice depends on both the dataset and the target application.
-
-Dataset specifics - how "complex" is the dataset?
-
-- How many samples are there?
-- Are the trajectories very dynamic or are the developments rather slow?
-- How dense is the distribution of initial conditions?
-- Is the data domain of interest well-covered by the domain of the training set?
-
-Task requirements:
-
-- What is the required accuracy?
-- How important is inference time? Is the training time limited?
-- Are there computational constraints (memory or processing power)?
-- Is uncertainty estimation required (e.g. to replace uncertain predictions by numerics)?
-- How much predictive flexibility is required? Do we need to interpolate or extrapolate across time?
-
-Besides these practical considerations, one overarching question is always: Does the model only learn the data, or does it "understand" something about the underlying dynamics?
-
-## Goals
-
-This benchmark aims to aid in choosing the best surrogate model for the task at hand and additionally to shed some light on the above questions.
-
-To achieve this, a selection of surrogate models are implemented in this repository. They can be trained on one of the included datasets or a custom dataset and then benchmarked on the corresponding test dataset.
-
-Some **metrics** included in the benchmark (but there is much more!):
-
-- Absolute and relative error of the models.
-- Inference time.
-- Number of trainable parameters.
-- Memory requirements (**WIP**).
-
-Besides this, there are plenty of **plots and visualisations** providing insights into the models behaviour:
-
-- Error distributions - per model, across time or per quantity.
-- Insights into interpolation and extrapolation across time.
-- Behaviour when training with sparse data or varying batch size.
-- Predictions with uncertainty and predictive uncertainty across time.
-- Correlations between the either predictive uncertainty or dynamics (gradients) of the data and the prediction error
-
-Some prime **use-cases** of the benchmark are:
-
-- Finding the best-performing surrogate on a dataset. Here, best-performing could mean high accuracy, low inference times or any other metric of interest (e.g. most accurate uncertainty estimates, ...).
-- Comparing performance of a novel surrogate architecture against the implemented baseline models.
-- Gaining insights into a dataset or comparing datasets using the built-in dataset insights.
-
-## Key Features
-
-
- Baseline Surrogates
-
-The following surrogate models are currently implemented to be benchmarked:
-
-- Fully Connected Neural Network:
- The vanilla neural network a.k.a. multilayer perceptron.
-- DeepONet:
- Two fully connected networks whose outputs are combined using a scalar product. In the current implementation, the surrogate comprises of only one DeepONet with multiple outputs (hence the name MultiONet).
-- Latent NeuralODE:
- NeuralODE combined with an autoencoder that reduces the dimensionality of the dataset before solving the dynamics in the resulting latent space.
-- Latent Polynomial:
- Uses an autoencoder similar to Latent NeuralODE, but fits a polynomial to the trajectories in the resulting latent space.
-
-
-
-
- Baseline Datasets
-
-The following datasets are currently included in the benchmark:
-
-
-
-
- Uncertainty Quantification (UQ)
-
-To give an uncertainty estimate that does not rely too much on the specifics of the surrogate architecture, we use DeepEnsemble for UQ.
-
-
-
-
- Parallel Training
-
-To gain insights into the surrogates behaviour, many models must be trained on varying subsets of the training data. This task is trivially parallelisable. In addition to utilising all specified devices, the benchmark features some nice progress bars to gain insights into the current status of the training.
-
-
-
-
- Plots, Plots, Plots
-
-While hard metrics are crucial to compare the surrogates, performance cannot always be broken down to a set of numbers. Running the benchmark creates many plots that serve to compare performance of surrogates or provide insights into the performance of each surrogate.
-
-
-
-
- Dataset Insights (WIP)
-
-"Know your data" is one of the most important rules in machine learning. To aid in this, the benchmark provides plots and visualisations that should help to understand the dataset better.
-
-
-
-
- Tabular Benchmark Results
+```bash
+git clone https://github.com/robin-janssen/CODES-Benchmark.git
+cd CODES-Benchmark
+uv sync # creates .venv from pyproject/uv.lock
+source .venv/bin/activate
+uv run python run_training.py --config configs/train_eval/config_minimal.yaml
+uv run python run_eval.py --config configs/train_eval/config_minimal.yaml
+```
-At the end of the benchmark, the most important metrics are displayed in a table, additionally, all metrics generated during the benchmark are provided as a csv file.
+**pip alternative**
-
+```bash
+git clone https://github.com/robin-janssen/CODES-Benchmark.git
+cd CODES-Benchmark
+python -m venv .venv && source .venv/bin/activate
+pip install -e .
+pip install -r requirements.txt
+python run_training.py --config configs/train_eval/config_minimal.yaml
+python run_eval.py --config configs/train_eval/config_minimal.yaml
+```
-
- Reproducibility
+Outputs land in `trained/`, `results/`, and `plots/`. The `configs/` folder contains ready-to-use templates (`train_eval/config_minimal.yaml`, `config_full.yaml`, etc.). Copy a file there and adjust datasets/surrogates/modalities before running the CLIs.
-Randomness is an important part of machine learning and even required in the context of UQ with DeepEnsemble, but reproducibility is key in benchmarking enterprises. The benchmark uses a custom seed that can be set by the user to ensure full reproducibility.
+## Documentation
-
+- [Main docs & tutorials](https://robin-janssen.github.io/CODES-Benchmark/)
+- [API reference (Sphinx)](https://robin-janssen.github.io/CODES-Benchmark/modules.html)
+- [Paper on arXiv](https://arxiv.org/abs/2410.20886)
-
- Custom Datasets and Own Models
+The GitHub Pages site now hosts the narrative guides, configuration reference, and interactive notebooks alongside the generated API docs.
-To cover a wide variety of use-cases, the benchmark is designed such that adding own datasets and models is explicitly supported.
+## Repository map
-
+| Path | Purpose |
+| --- | --- |
+| `configs/` | Ready-to-edit benchmark configs (`train_eval/`, `tuning/`, etc.) |
+| `datasets/` | Bundled datasets + download helper (`data_sources.yaml`) |
+| `codes/` | Python package with surrogates, training, tuning, and benchmarking utilities |
+| `run_training.py`, `run_eval.py`, `run_tuning.py` | CLI entry points for the main workflows |
+| `docs/` | Sphinx project powering the GitHub Pages site (guides, tutorials, API reference) |
+| `scripts/` | Convenience tooling (dataset downloads, analysis utilities) |
-## Quickstart
+## Contributing
-First, clone the [GitHub Repository](https://github.com/robin-janssen/CODES-Benchmark) with
+Pull requests are welcome! Please include documentation updates, add or update tests when you touch executable code, and run:
+```bash
+uv pip install --group dev
+pytest
+sphinx-build -b html docs/source/ docs/_build/html
```
-git clone ssh://git@github.com/robin-janssen/CODES-Benchmark
-```
-
-Optionally, you can set up a [virtual environment](https://docs.python.org/3/library/venv.html) (recommended).
-
-Then, install the required packages with
-
-```
-pip install -r requirements.txt
-```
-
-The installation is now complete. To be able to run and evaluate the benchmark, you need to first set up a configuration YAML file. There is one provided, but it should be configured. For more information, check the [configuration page](https://robin-janssen.github.io/CODES-Benchmark/documentation.html#config). There, we also offer an interactive Config-Generator tool with some explanations to help you set up your benchmark.
-You can also add your own datasets and models to the benchmark to evaluate them against each other or some of our baseline models. For more information on how to do this, please refer to the [documentation](https://robin-janssen.github.io/CODES-Benchmark/documentation.html).
+If you publish a new surrogate or dataset, document it under `docs/guides` / `docs/reference` so users can adopt it quickly. For questions, open an issue on GitHub.
diff --git a/codes/benchmark/bench_fcts.py b/codes/benchmark/bench_fcts.py
index 265f7a39..da3346ca 100644
--- a/codes/benchmark/bench_fcts.py
+++ b/codes/benchmark/bench_fcts.py
@@ -1,8 +1,17 @@
+"""
+Utility functions that execute the different benchmark suites (accuracy, gradients,
+timing, sparsity studies, etc.) for every surrogate model.
+
+The functions in this module are invoked by `run_eval.py` and populate the metrics
+dictionary that later feeds the comparative plots and CSV exports.
+"""
+
import os
from contextlib import redirect_stdout
from typing import Any
import numpy as np
+import torch
from scipy.stats import pearsonr
from tabulate import tabulate
from torch.utils.data import DataLoader
@@ -80,6 +89,7 @@ def run_benchmark(surr_name: str, surrogate_class, conf: dict) -> dict[str, Any]
batch_size = conf["batch_size"]
# Load full data and parameters
+ dataset_conf = conf.get("dataset", {})
(
(train_data, test_data, val_data),
(train_params, test_params, val_params),
@@ -88,13 +98,13 @@ def run_benchmark(surr_name: str, surrogate_class, conf: dict) -> dict[str, Any]
_,
labels,
) = check_and_load_data(
- conf["dataset"]["name"],
+ dataset_conf["name"],
verbose=conf.get("verbose", False),
- log=conf["dataset"]["log10_transform"],
- log_params=conf.get("log10_transform_params", False),
- normalisation_mode=conf["dataset"]["normalise"],
- tolerance=conf["dataset"]["tolerance"],
- per_species=conf["dataset"].get("normalise_per_species", False),
+ log=dataset_conf.get("log10_transform", True),
+ log_params=dataset_conf.get("log10_transform_params", True),
+ normalisation_mode=dataset_conf.get("normalise", "minmax"),
+ tolerance=dataset_conf.get("tolerance", None),
+ per_species=dataset_conf.get("normalise_per_species", False),
)
model_config = get_model_config(surr_name, conf)
@@ -130,7 +140,7 @@ def run_benchmark(surr_name: str, surrogate_class, conf: dict) -> dict[str, Any]
)
# Plot training losses
- if conf["losses"]:
+ if conf.get("losses", False):
print("Loss plots...")
plot_surr_losses(model, surr_name, conf, timesteps, show_title=TITLE)
@@ -140,7 +150,7 @@ def run_benchmark(surr_name: str, surrogate_class, conf: dict) -> dict[str, Any]
model, surr_name, timesteps, val_loader, conf, labels
)
- if conf["iterative"]:
+ if conf.get("iterative", False):
# Iterative training benchmark
print("Running iterative training benchmark...")
metrics["iterative"] = evaluate_iterative_predictions(
@@ -148,53 +158,53 @@ def run_benchmark(surr_name: str, surrogate_class, conf: dict) -> dict[str, Any]
)
# Gradients benchmark
- if conf["gradients"]:
+ if conf.get("gradients", False):
print("Running gradients benchmark...")
# For this benchmark, we can also use the main model
metrics["gradients"] = evaluate_gradients(model, surr_name, val_loader, conf)
# Timing benchmark
- if conf["timing"]:
+ if conf.get("timing", False):
print("Running timing benchmark...")
metrics["timing"] = time_inference(
model, surr_name, val_loader, conf, n_test_samples
)
# Compute (resources) benchmark
- if conf["compute"]:
+ if conf.get("compute", False):
print("Running compute benchmark...")
metrics["compute"] = evaluate_compute(model, surr_name, val_loader, conf)
# Interpolation benchmark
- if conf["interpolation"]["enabled"]:
+ if conf.get("interpolation", {}).get("enabled", False):
print("Running interpolation benchmark...")
metrics["interpolation"] = evaluate_interpolation(
model, surr_name, val_loader, timesteps, conf, labels
)
# Extrapolation benchmark
- if conf["extrapolation"]["enabled"]:
+ if conf.get("extrapolation", {}).get("enabled", False):
print("Running extrapolation benchmark...")
metrics["extrapolation"] = evaluate_extrapolation(
model, surr_name, val_loader, timesteps, conf, labels
)
# Sparse data benchmark
- if conf["sparse"]["enabled"]:
+ if conf.get("sparse", {}).get("enabled", False):
print("Running sparse benchmark...")
metrics["sparse"] = evaluate_sparse(
model, surr_name, val_loader, timesteps, n_train_samples, conf
)
# Batch size benchmark
- if conf["batch_scaling"]["enabled"]:
+ if conf.get("batch_scaling", {}).get("enabled", False):
print("Running batch size benchmark...")
metrics["batch_size"] = evaluate_batchsize(
model, surr_name, val_loader, timesteps, conf
)
# Uncertainty Quantification (UQ) benchmark
- if conf["uncertainty"]["enabled"]:
+ if conf.get("uncertainty", {}).get("enabled", False):
print("Running UQ benchmark...")
metrics["UQ"] = evaluate_UQ(
model, surr_name, val_loader, timesteps, conf, labels
@@ -349,10 +359,26 @@ def evaluate_iterative_predictions(
labels: list | None = None,
) -> dict[str, Any]:
"""
- Evaluate the iterative predictions of the surrogate model.
+ Measure how prediction errors accumulate when the surrogate is rolled forward in
+ an auto-regressive fashion.
+
+ The model is fed with its own previous output (instead of ground truth) and
+ evaluated for each chunk of the trajectory. The same error metrics as
+ :func:`evaluate_accuracy` are reported so you can compare one-shot predictions
+ with long-horizon roll-outs.
+
+ Args:
+ model: Loaded surrogate instance.
+ surr_name (str): Surrogate identifier used to locate checkpoints.
+ timesteps (np.ndarray): Timeline associated with the validation data.
+ val_loader (DataLoader): Validation loader that provides initial states.
+ val_params (np.ndarray): Optional auxiliary parameters aligned with the data.
+ conf (dict): Benchmark configuration.
+ labels (list | None): Quantity names for labelling plots.
- Returns the same set of error metrics as evaluate_accuracy, but over the
- full trajectory built by re-feeding the last prediction as the next initial state.
+ Returns:
+ dict[str, Any]: Error statistics for iterative predictions together with the
+ generated trajectories.
"""
# load trained model
training_id = conf["training_id"]
@@ -434,7 +460,9 @@ def evaluate_iterative_predictions(
# We predict steps 1..(chunk_len-1) relative to the provided init state (index 0).
# Map these to global indices [start+1 .. end] inclusively.
if i == 0:
- iterative_preds[:, start : end + 1, :] = preds_chunk[:, : model.n_timesteps, :].detach().cpu().numpy()
+ iterative_preds[:, start : end + 1, :] = (
+ preds_chunk[:, : model.n_timesteps, :].detach().cpu().numpy()
+ )
iterative_preds[:, start + 1 : end + 1, :] = (
preds_chunk[:, 1 : model.n_timesteps, :].detach().cpu().numpy()
)
@@ -628,10 +656,35 @@ def evaluate_compute(
training_id = conf["training_id"]
model.load(training_id, surr_name, model_identifier=f"{surr_name.lower()}_main")
- # Get a sample input tensor from the test_loader
- inputs = next(iter(test_loader))
- # Measure the memory footprint during forward and backward pass
- memory_footprint, model = measure_memory_footprint(model, inputs)
+ device = torch.device(getattr(model, "device", torch.device("cpu")))
+ memory_footprint = {}
+
+ if torch.cuda.is_available():
+ try:
+ inputs = next(iter(test_loader))
+ except StopIteration:
+ inputs = None
+
+ if inputs is None:
+ print(
+ "Skipping GPU memory profiling for compute evaluation "
+ "(test loader provided no samples)."
+ )
+ else:
+ try:
+ memory_footprint, model = measure_memory_footprint(
+ model, inputs, device
+ )
+ except RuntimeError as exc:
+ print(
+ "Skipping GPU memory profiling for compute evaluation "
+ f"(reason: {exc})."
+ )
+ else:
+ print(
+ "Skipping GPU memory profiling for compute evaluation "
+ "(CUDA not available)."
+ )
# Count the number of trainable parameters
num_params = count_trainable_parameters(model)
@@ -1142,49 +1195,60 @@ def evaluate_UQ(
def compare_models(metrics: dict, config: dict):
+ """
+ Aggregate surrogate-level metrics into comparison plots and CSV summaries.
+
+ Args:
+ metrics (dict): Output of :func:`run_benchmark` for each surrogate.
+ config (dict): Benchmark configuration that determines which comparisons to run.
+
+ The function is intentionally side-effect heavy: it creates plots, writes tables,
+ and prints status messages. Nothing is returned; all artifacts are stored under
+ `plots/` and `results/`.
+ """
print("Making comparative plots... \n")
# Compare errors
compare_errors(metrics, config)
- if config["losses"]:
+ if config.get("losses", False):
compare_main_losses(metrics, config)
- if config["iterative"]:
+ if config.get("iterative", False):
compare_iterative(metrics, config)
- if config["gradients"]:
+ if config.get("gradients", False):
compare_gradients(metrics, config)
# Compare inference time
- if config["timing"]:
+ if config.get("timing", False):
compare_inference_time(metrics, config)
# Compare interpolation errors
- if config["interpolation"]["enabled"]:
+ if config.get("interpolation", {}).get("enabled", False):
compare_interpolation(metrics, config)
# Compare extrapolation errors
- if config["extrapolation"]["enabled"]:
+ if config.get("extrapolation", {}).get("enabled", False):
compare_extrapolation(metrics, config)
# Compare sparse training errors
- if config["sparse"]["enabled"]:
+ if config.get("sparse", {}).get("enabled", False):
compare_sparse(metrics, config)
if (
- config["interpolation"]["enabled"]
- and config["extrapolation"]["enabled"]
- and config["sparse"]["enabled"]
+ config.get("interpolation", {}).get("enabled", False)
+ and config.get("extrapolation", {}).get("enabled", False)
+ and config.get("sparse", {}).get("enabled", False)
):
plot_all_generalization_errors(metrics, config, show_title=TITLE)
# Compare batch size training errors
- if config["batch_scaling"]["enabled"]:
+ if config.get("batch_scaling", {}).get("enabled", False):
compare_batchsize(metrics, config)
# Compare UQ metrics
- if config["uncertainty"]["enabled"]:
+ if config.get("uncertainty", {}).get("enabled", False):
compare_UQ(metrics, config)
# rel_errors_and_uq(metrics, config)
diff --git a/codes/benchmark/bench_plots.py b/codes/benchmark/bench_plots.py
index f2804c45..f0948858 100644
--- a/codes/benchmark/bench_plots.py
+++ b/codes/benchmark/bench_plots.py
@@ -54,7 +54,7 @@ def save_plot(
# Call save_plot_counter with increase_count option
filepath = save_plot_counter(filename, plot_dir, increase_count=increase_count)
plt.savefig(filepath, dpi=dpi, bbox_inches="tight")
- if conf["verbose"]:
+ if conf.get("verbose", False):
print(f"Plot saved as: {filepath}")
@@ -213,7 +213,7 @@ def plot_error_percentiles_over_time(
plt.xlabel("Time")
plt.xlim(timesteps[0], timesteps[-1])
- if conf.get("dataset", {}).get("log_timesteps"):
+ if conf.get("dataset", {}).get("log_timesteps", False):
plt.xscale("log")
if show_title:
plt.title(title)
@@ -386,7 +386,7 @@ def plot_average_errors_over_time(
plt.xlim(timesteps[0], timesteps[-1])
plt.ylabel(r"Mean $\Delta dex$")
plt.yscale("log")
- if conf["dataset"]["log_timesteps"]:
+ if conf.get("dataset", {}).get("log_timesteps", False):
plt.xscale("log")
title = f"Mean Δdex Errors over Time ({mode.capitalize()}, {surr_name})"
filename = f"errors_over_time_{mode}.png"
@@ -485,7 +485,7 @@ def plot_example_mode_predictions(
ax.set_ylabel("log(Abundance)")
ax.set_xlim(timesteps.min(), timesteps.max())
- if conf["dataset"]["log_timesteps"]:
+ if conf.get("dataset", {}).get("log_timesteps", False):
ax.set_xscale("log")
if labels is not None:
@@ -578,7 +578,7 @@ def plot_example_iterative_predictions(
# retrigger lines
for t in timesteps[::iter_interval]:
ax.axvline(x=t, linestyle=":", linewidth=0.8, alpha=0.7)
- if conf.get("dataset", {}).get("log10_transform", False):
+ if conf.get("dataset", {}).get("log10_transform", True):
ax.set_yscale("log")
ax.set_xlim(timesteps.min(), timesteps.max())
if conf["dataset"].get("log_timesteps", False):
@@ -702,7 +702,7 @@ def plot_example_predictions_with_uncertainty(
)
ax.set_xlim(timesteps.min(), timesteps.max())
- if conf["dataset"]["log_timesteps"]:
+ if conf.get("dataset", {}).get("log_timesteps", False):
ax.set_xscale("log")
fig.text(0.5, 0.04, "Time", ha="center", va="center", fontsize=12)
@@ -776,7 +776,7 @@ def plot_average_uncertainty_over_time(
plt.xlabel("Time")
plt.ylabel(r"$\Delta dex$ / Log-Space Uncertainty")
plt.xlim(timesteps[0], timesteps[-1])
- if conf["dataset"]["log_timesteps"]:
+ if conf.get("dataset", {}).get("log_timesteps", False):
plt.xscale("log")
if show_title:
plt.title(r"Average Log-Space Uncertainty and Error ($\Delta dex$) Over Time")
@@ -863,7 +863,7 @@ def load_losses(model_identifier: str):
)
# Interpolation losses
- if conf["interpolation"]["enabled"]:
+ if conf.get("interpolation", {}).get("enabled", False):
intervals = conf["interpolation"]["intervals"]
interp_train_losses = [main_train_loss]
interp_test_losses = [main_test_loss]
@@ -886,7 +886,7 @@ def load_losses(model_identifier: str):
)
# Extrapolation losses
- if conf["extrapolation"]["enabled"]:
+ if conf.get("extrapolation", {}).get("enabled", False):
cutoffs = conf["extrapolation"]["cutoffs"]
extra_train_losses = [main_train_loss]
extra_test_losses = [main_test_loss]
@@ -909,7 +909,7 @@ def load_losses(model_identifier: str):
)
# Sparse losses
- if conf["sparse"]["enabled"]:
+ if conf.get("sparse", {}).get("enabled", False):
factors = conf["sparse"]["factors"]
sparse_train_losses = [main_train_loss]
sparse_test_losses = [main_test_loss]
@@ -932,7 +932,7 @@ def load_losses(model_identifier: str):
)
# UQ losses
- if conf["uncertainty"]["enabled"]:
+ if conf.get("uncertainty", {}).get("enabled", False):
n_models = conf["uncertainty"]["ensemble_size"]
uq_train_losses = [main_train_loss]
uq_test_losses = [main_test_loss]
@@ -955,7 +955,7 @@ def load_losses(model_identifier: str):
)
# Batchsize losses
- if conf["batch_scaling"]["enabled"]:
+ if conf.get("batch_scaling", {}).get("enabled", False):
batch_factors = conf["batch_scaling"]["sizes"]
batch_train_losses = []
batch_test_losses = []
@@ -1149,18 +1149,19 @@ def plot_losses(
show_title: bool = True,
) -> None:
"""
- Plot the loss trajectories for the training of multiple models.
+ Plot the loss trajectories for multiple models on a single axis.
- :param loss_histories: List of loss history arrays.
- :param epochs: Number of epochs.
- :param labels: List of labels for each loss history.
- :param title: Title of the plot.
- :param save: Whether to save the plot as an image file.
- :param conf: The configuration dictionary.
- :param surr_name: The name of the surrogate model.
- :param mode: The mode of the training.
- :param percentage: Percentage of initial values to exclude from min-max calculation.
- show_title (bool): Whether to show the title on the plot.
+ Args:
+ loss_histories (tuple[np.ndarray, ...]): Loss arrays for each model.
+ epochs (int): Number of epochs in the associated training run.
+ labels (tuple[str, ...]): Labels matching ``loss_histories``.
+ title (str): Plot title.
+ save (bool): Whether to save the figure.
+ conf (dict | None): Configuration dictionary used for saving.
+ surr_name (str | None): Surrogate identifier used in filenames.
+ mode (str): Training mode name appended to the filename.
+ percentage (float): Portion of early iterations ignored when computing y-limits.
+ show_title (bool): Whether to draw the title.
"""
# Determine start index based on percentage
@@ -1240,16 +1241,17 @@ def plot_losses_dual_axis(
show_title: bool = True,
) -> None:
"""
- Plot the training and test loss trajectories with dual y-axes.
+ Plot training and validation losses on dual y-axes.
- :param train_loss: Training loss history array.
- :param test_loss: Test loss history array.
- :param labels: Labels for the losses (train and test).
- :param title: Title of the plot.
- :param save: Whether to save the plot as an image file.
- :param conf: The configuration dictionary.
- :param surr_name: The name of the surrogate model.
- show_title (bool): Whether to show the title on the plot.
+ Args:
+ train_loss (np.ndarray): Training loss curve.
+ test_loss (np.ndarray): Validation loss curve.
+ labels (tuple[str, str]): Axis labels for train/test.
+ title (str): Plot title.
+ save (bool): Whether to save the figure.
+ conf (dict | None): Configuration dictionary used for saving.
+ surr_name (str | None): Surrogate identifier used in filenames.
+ show_title (bool): Whether to draw the title.
"""
# Colormap
@@ -1576,7 +1578,7 @@ def plot_relative_errors(
plt.yscale("log")
if show_title:
plt.title("Comparison of Relative Errors Over Time")
- if config["dataset"]["log_timesteps"]:
+ if config.get("dataset", {}).get("log_timesteps", False):
plt.xscale("log")
plt.legend(loc="center left", bbox_to_anchor=(1, 0.5))
@@ -1597,21 +1599,18 @@ def plot_errors_over_time(
iter_interval: int | None = None,
) -> None:
"""
- Plot errors over time for different surrogate models (relative, Δdex, or iterative Δdex).
+ Plot error trajectories for each surrogate model.
Args:
- mean_errors (dict): Mean errors for each surrogate.
- median_errors (dict): Median errors for each surrogate.
- timesteps (np.ndarray): Array of timesteps.
- config (dict): Configuration dictionary.
- save (bool): Whether to save the figure.
- show_title (bool): Whether to add a title.
- mode (str):
- - "relative": percentage errors (y-axis log scale)
- - "deltadex": log-space absolute errors (Δdex)
- - "iterative": like "deltadex" but also draws dashed vertical lines at every
- n-th timestep to indicate the iterative retrigger interval.
- iter_interval (int | None): Interval for vertical guide lines when mode == "iterative".
+ mean_errors (dict[str, np.ndarray]): Mean error curves keyed by surrogate name.
+ median_errors (dict[str, np.ndarray]): Median error curves keyed by surrogate name.
+ timesteps (np.ndarray): Timeline in the same shape as the error curves.
+ config (dict): Benchmark configuration used for saving.
+ save (bool): Whether to write the figure to disk.
+ show_title (bool): Whether to display the figure title.
+ mode (str): ``\"relative\"`` for percentage errors, ``\"deltadex\"`` for log-space errors,
+ or ``\"iterative\"`` for chained Δdex errors with guide lines every ``iter_interval`` steps.
+ iter_interval (int | None): Interval for the dashed guide lines when ``mode == "iterative"``.
"""
plt.figure(figsize=(6, 4))
colors = plt.cm.viridis(np.linspace(0, 0.95, len(mean_errors)))
@@ -1673,7 +1672,7 @@ def plot_errors_over_time(
else:
raise ValueError(f"Unknown mode: {mode}")
- if config["dataset"]["log_timesteps"]:
+ if config.get("dataset", {}).get("log_timesteps", False):
plt.xscale("log")
if show_title:
plt.title(title)
@@ -1694,41 +1693,27 @@ def plot_uncertainty_confidence(
show_title: bool = True,
) -> dict[str, float]:
"""
- Plot a comparative grouped bar chart of catastrophic confidence measures and return a metric
- quantifying the net skew of over- versus underconfidence.
-
- For each surrogate model, the target-weighted difference is computed as:
- weighted_diff = (predicted uncertainty - |prediction - target|) / target.
- Negative values indicate overconfidence (i.e. the model's uncertainty is too low relative to its error),
- while positive values indicate underconfidence.
-
- Catastrophic events are defined as those samples in the lowest `percentile` (e.g. 2nd percentile)
- for overconfidence and in the highest `percentile` (i.e. 100 - percentile) for underconfidence.
+ Plot overconfidence/underconfidence statistics for each surrogate.
- For each surrogate, this function computes the mean and standard deviation of the weighted
- differences in both tails, then plots them as grouped bars (overconfidence bars on the left,
- underconfidence bars on the right) with standard error bars (thin, with capsize=3). The bar heights
- are expressed in percentages.
+ Each ``weighted_diff`` array stores ``(predicted_uncertainty - abs(prediction - target)) / target``.
+ Negative values indicate overconfidence (uncertainty too small), whereas positive values indicate
+ underconfidence. The function:
- The text labels for the bars are placed on the opposite side of the x-axis: for negative (overconfident)
- values the annotation is shown a few pixels above the x-axis, and for positive (underconfident) values
- it is shown a few pixels below the x-axis.
-
- The plot title includes the metric (mean ± std) and the number of samples (per tail).
-
- Additionally, if the range between the smallest and largest bar is more than two orders of magnitude,
- the y-axis is set to a symmetric log scale.
+ 1. Selects catastrophic events in the lower ``percentile`` tail (overconfidence) and upper tail
+ (underconfidence).
+ 2. Computes the mean and standard deviation of each tail.
+ 3. Draws grouped bars with error caps so the tails are easy to compare visually.
Args:
- weighted_diffs (dict[str, np.ndarray]): Dictionary of weighted_diff arrays for each surrogate model.
- conf (dict): The configuration dictionary.
- save (bool, optional): Whether to save the plot.
- percentile (float, optional): Percentile threshold for defining catastrophic events (default is 2).
- summary_stat (str, optional): Currently only "mean" is implemented.
- show_title (bool): Whether to show the title on the plot.
+ weighted_diffs (dict[str, np.ndarray]): Weighted-difference arrays keyed by surrogate name.
+ conf (dict): Configuration dictionary used for saving.
+ save (bool): Whether to save the figure.
+ percentile (float): Tail percentile for defining catastrophic events.
+ summary_stat (str): Currently unused hook for other aggregations (defaults to ``\"mean\"``).
+ show_title (bool): Whether to draw the title.
Returns:
- dict[str, float]: A dictionary mapping surrogate names to the net difference (over_summary + under_summary).
+ dict[str, float]: Net overconfidence/underconfidence score for each surrogate.
"""
surrogate_names = list(weighted_diffs.keys())
num_surrogates = len(surrogate_names)
@@ -1936,7 +1921,7 @@ def plot_mean_deltadex_over_time_main_vs_ensemble(
plt.xlabel("Time")
plt.ylabel(r"Log-MAE ($\Delta dex$)")
plt.xlim(timesteps[0], timesteps[-1])
- if config["dataset"]["log_timesteps"]:
+ if config.get("dataset", {}).get("log_timesteps", False):
plt.xscale("log")
if show_title:
plt.title("Mean Δdex Over Time: Main vs Ensemble")
@@ -1993,7 +1978,7 @@ def plot_uncertainty_over_time_comparison(
plt.xlabel("Time")
plt.ylabel("Log-Space Uncertainty / Log-Space MAE")
plt.xlim(timesteps[0], timesteps[-1])
- if config["dataset"]["log_timesteps"]:
+ if config.get("dataset", {}).get("log_timesteps", False):
plt.xscale("log")
if show_title:
plt.title("Comparison of Log-Space Uncertainty and Log-MAE Over Time")
@@ -2451,31 +2436,27 @@ def plot_catastrophic_detection_curves(
show_title: bool = True,
) -> dict[str, dict[float, dict[str, float]]]:
"""
- Plot catastrophic error recall (Δdex) vs fraction flagged by uncertainty,
- across multiple catastrophic percentiles, plus performance improvement curves.
+ Plot recall vs. flagged fraction curves for catastrophic errors.
- Catastrophic errors are those with Δdex >= P_cat (e.g. 99th percentile).
- For each flag fraction f, the uncertainty threshold is the (1 - f) quantile of std.
- We then flag samples with std >= threshold and measure recall among catastrophic samples.
+ For each surrogate and percentile threshold ``p`` we:
- Additionally, computes the effective log-space MAE if flagged samples are replaced
- by solver outputs (0 error). This measures how much UQ-guided deferral improves performance.
+ - Treat samples with ``Δdex >= percentile(p)`` as catastrophic.
+ - Sweep the requested ``flag_fractions`` to determine which proportion of points would be
+ deferred based on predictive uncertainty.
+ - Report both the catastrophic recall and the hypothetical MAE if flagged samples were replaced
+ by exact solver outputs.
Args:
- errors_log (dict): Δdex arrays [N, T, Q] per surrogate.
- std_log (dict): Log-space std arrays [N, T, Q] per surrogate.
+ errors_log (dict[str, np.ndarray]): Log-space errors per surrogate.
+ std_log (dict[str, np.ndarray]): Log-space predictive standard deviations.
conf (dict): Configuration dictionary.
- percentiles (tuple): Catastrophic percentiles to evaluate (default: (90, 95, 99)).
- flag_fractions (tuple): Fractions of predictions to flag (includes 0 for baseline).
- save (bool): Save figure.
- show_title (bool): Add title.
+ percentiles (tuple[float, ...]): Catastrophic thresholds to evaluate.
+ flag_fractions (tuple[float, ...]): Fractions of samples to flag at each step.
+ save (bool): Whether to save the figure.
+ show_title (bool): Whether to draw the title.
Returns:
- dict: Nested dict of results:
- summary[surrogate][percentile] = {
- 'flag_fraction': f, 'recall': r, 'cat_threshold': thr,
- 'mae_curve': [(f, mae), ...]
- }
+ dict[str, dict[float, dict[str, float]]]: Recall/threshold summary per surrogate.
"""
n_rows = len(percentiles) + 1 # +1 for MAE improvement curves
fig, axes = plt.subplots(n_rows, 1, figsize=(7, 4 * n_rows), sharex=True)
diff --git a/codes/benchmark/bench_utils.py b/codes/benchmark/bench_utils.py
index ae89945a..8b953764 100644
--- a/codes/benchmark/bench_utils.py
+++ b/codes/benchmark/bench_utils.py
@@ -192,10 +192,10 @@ def get_required_models_list(surrogate: str, conf: dict) -> list:
required_models.append(f"{surrogate.lower()}_main.pth")
# Gradients does not require a separate model
- if conf["gradients"]:
+ if conf.get("gradients", False):
pass
- if conf["interpolation"]["enabled"]:
+ if conf.get("interpolation", {}).get("enabled", False):
intervals = conf["interpolation"]["intervals"]
required_models.extend(
[
@@ -204,19 +204,19 @@ def get_required_models_list(surrogate: str, conf: dict) -> list:
]
)
- if conf["extrapolation"]["enabled"]:
+ if conf.get("extrapolation", {}).get("enabled", False):
cutoffs = conf["extrapolation"]["cutoffs"]
required_models.extend(
[f"{surrogate.lower()}_extrapolation_{cutoff}.pth" for cutoff in cutoffs]
)
- if conf["sparse"]["enabled"]:
+ if conf.get("sparse", {}).get("enabled", False):
factors = conf["sparse"]["factors"]
required_models.extend(
[f"{surrogate.lower()}_sparse_{factor}.pth" for factor in factors]
)
- if conf["uncertainty"]["enabled"]:
+ if conf.get("uncertainty", {}).get("enabled", False):
n_models = conf["uncertainty"]["ensemble_size"]
required_models.extend(
[f"{surrogate.lower()}_UQ_{i + 1}.pth" for i in range(n_models - 1)]
@@ -261,30 +261,27 @@ def count_trainable_parameters(model: torch.nn.Module) -> int:
return sum(p.numel() for p in model.parameters() if p.requires_grad)
-def measure_memory_footprint(model: torch.nn.Module, inputs: tuple) -> dict:
+def measure_memory_footprint(
+ model: torch.nn.Module, inputs: tuple, device: torch.device
+) -> tuple[dict, torch.nn.Module]:
"""
- Measure the memory footprint of a model during forward and backward passes using
- peak memory tracking and explicit synchronization.
+ Measure peak GPU memory usage for forward/backward passes.
Args:
- model (torch.nn.Module): The PyTorch model.
- inputs (tuple): The input data for the model.
+ model (torch.nn.Module): Model to profile.
+ inputs (tuple): Sample batch used for the forward pass.
Returns:
- dict: A dictionary containing measured memory usages for:
- - model_memory: Additional memory used when moving the model to GPU.
- - forward_memory: Peak additional memory during the forward pass with gradients.
- - backward_memory: Peak additional memory during the backward pass.
- - forward_memory_nograd: Peak additional memory during the forward pass without gradients.
- model: The model (possibly moved back to the original device).
+ tuple[dict, torch.nn.Module]: Dictionary with ``model_memory``, ``forward_memory``,
+ ``backward_memory``, and ``forward_memory_nograd`` plus the model (moved back to its
+ original device).
"""
# Determine the target device
- device = model.device if hasattr(model, "device") else torch.device("cuda:0")
+ if not torch.cuda.is_available() or device.type != "cuda":
+ raise RuntimeError("CUDA device required for memory profiling.")
- # Move the model to CPU first (simulate baseline)
model.to("cpu")
- # --- Model loading measurement ---
torch.cuda.synchronize(device)
torch.cuda.reset_peak_memory_stats(device)
before_load = torch.cuda.memory_allocated(device)
@@ -413,25 +410,25 @@ def clean_metrics(metrics: dict, conf: dict) -> dict:
write_metrics["accuracy"].pop("relative_errors", None)
write_metrics["accuracy"].pop("absolute_errors_log", None)
- if conf["iterative"]:
+ if conf.get("iterative", False):
write_metrics["iterative"].pop("absolute_errors", None)
write_metrics["iterative"].pop("absolute_errors_log", None)
- if conf["gradients"]:
+ if conf.get("gradients", False):
write_metrics["gradients"].pop("gradients", None)
write_metrics["gradients"].pop("max_counts", None)
write_metrics["gradients"].pop("max_gradient", None)
write_metrics["gradients"].pop("max_error", None)
write_metrics["gradients"].pop("errors_log", None)
- if conf["interpolation"]["enabled"]:
+ if conf.get("interpolation", {}).get("enabled", False):
write_metrics["interpolation"].pop("model_errors", None)
write_metrics["interpolation"].pop("intervals", None)
- if conf["extrapolation"]["enabled"]:
+ if conf.get("extrapolation", {}).get("enabled", False):
write_metrics["extrapolation"].pop("model_errors", None)
write_metrics["extrapolation"].pop("cutoffs", None)
- if conf["sparse"]["enabled"]:
+ if conf.get("sparse", {}).get("enabled", False):
write_metrics["sparse"].pop("model_errors", None)
write_metrics["sparse"].pop("n_train_samples", None)
- if conf["uncertainty"]["enabled"]:
+ if conf.get("uncertainty", {}).get("enabled", False):
write_metrics["UQ"].pop("pred_uncertainty_log", None)
write_metrics["UQ"].pop("max_counts", None)
write_metrics["UQ"].pop("axis_max", None)
@@ -651,7 +648,7 @@ def make_comparison_csv(metrics: dict, config: dict) -> None:
row.append(value)
writer.writerow(row)
- if config["verbose"]:
+ if config.get("verbose", False):
print(f"Comparison CSV file saved at {csv_file_path}")
diff --git a/codes/surrogates/AbstractSurrogate/abstract_config.py b/codes/surrogates/AbstractSurrogate/abstract_config.py
index b36c5bdb..17ab671f 100644
--- a/codes/surrogates/AbstractSurrogate/abstract_config.py
+++ b/codes/surrogates/AbstractSurrogate/abstract_config.py
@@ -21,9 +21,9 @@ class AbstractSurrogateBaseConfig:
- "poly": Use polynomial decay scheduler.
poly_power (float): Power for polynomial decay scheduler (used only if scheduler == "poly").
eta_min (float): Multiplier for minimum learning rate for cosine annealing scheduler (used only if scheduler == "cosine").
- activation (nn.Module): Activation function used in the model.
- loss_function (nn.Module): Loss function used for training.
- loss_kwargs (float): Additional arguments for the loss function (used only if loss_function == nn.SmoothL1Loss()).
+ activation (nn.Module | None): Activation module used in the model.
+ loss_function (nn.Module | None): Loss function used for training.
+ beta (float): Beta parameter forwarded to SmoothL1Loss.
"""
learning_rate: float = 3e-4
@@ -33,10 +33,16 @@ class AbstractSurrogateBaseConfig:
scheduler: str = "cosine" # Options: "schedulefree", "cosine", "poly"
poly_power: float = 0.9 # Used only if scheduler == "poly"
eta_min: float = 1e-1 # Used only if scheduler == "cosine"
- activation: nn.Module = nn.ReLU()
- loss_function: nn.Module = nn.MSELoss() # Options: nn.MSELoss(), nn.SmoothL1Loss()
+ activation: nn.Module | None = None
+ loss_function: nn.Module | None = None # Options: nn.MSELoss(), nn.SmoothL1Loss()
beta: float = 0.0 # Used only if loss_function == nn.SmoothL1Loss()
+ def __post_init__(self) -> None:
+ if self.activation is None:
+ self.activation = nn.ReLU()
+ if self.loss_function is None:
+ self.loss_function = nn.MSELoss()
+
@property
def loss(self) -> nn.Module:
"""
@@ -46,5 +52,5 @@ def loss(self) -> nn.Module:
Otherwise, it returns the loss function as is.
"""
if isinstance(self.loss_function, nn.SmoothL1Loss):
- return self.loss_function(beta=self.beta)
- return self.loss_function()
+ return nn.SmoothL1Loss(beta=self.beta)
+ return self.loss_function
diff --git a/codes/surrogates/AbstractSurrogate/abstract_surrogate.py b/codes/surrogates/AbstractSurrogate/abstract_surrogate.py
index 4b2a735b..c8893ef9 100644
--- a/codes/surrogates/AbstractSurrogate/abstract_surrogate.py
+++ b/codes/surrogates/AbstractSurrogate/abstract_surrogate.py
@@ -483,7 +483,7 @@ def denormalize(
return data.to(dtype=data_type)
if isinstance(data, np.ndarray):
return data.astype(data_type)
-
+
return data
def denormalize_old(self, data: Tensor) -> Tensor:
diff --git a/codes/surrogates/DeepONet/__init__.py b/codes/surrogates/DeepONet/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/codes/surrogates/DeepONet/don_utils.py b/codes/surrogates/DeepONet/don_utils.py
index baecab4c..6db0ff22 100644
--- a/codes/surrogates/DeepONet/don_utils.py
+++ b/codes/surrogates/DeepONet/don_utils.py
@@ -9,12 +9,11 @@
from torch import nn
from torch.utils.data import Dataset, IterableDataset
-# TODO complete type hints
-
def custom_collate_fn(batch):
"""
Custom collate function to ensure tensors are returned in the correct shape.
+
Args:
batch: A list of tuples from the dataset, where each tuple contains
(branch_input, trunk_input, targets).
diff --git a/codes/surrogates/FCNN/__init__.py b/codes/surrogates/FCNN/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/codes/surrogates/FCNN/fcnn.py b/codes/surrogates/FCNN/fcnn.py
index 888616e5..143a19b6 100644
--- a/codes/surrogates/FCNN/fcnn.py
+++ b/codes/surrogates/FCNN/fcnn.py
@@ -38,15 +38,16 @@ def __len__(self):
def fc_collate_fn(batch):
"""
Custom collate function to ensure tensors are returned in the correct shape.
+
Args:
- batch: A list of tuples (input_batch, target_batch)
- where each item is already a precomputed batch of shape:
- input_batch -> [batch_size, n_quantities+1]
- target_batch -> [batch_size, n_quantities]
+ batch (list[tuple[torch.Tensor, torch.Tensor]]): List of precomputed
+ batches. Each tuple contains an ``input_batch`` with shape
+ ``[batch_size, n_quantities + 1]`` and ``target_batch`` with shape
+ ``[batch_size, n_quantities]``.
+
Returns:
- A tuple of tensors with the final shapes:
- - inputs: [batch_size, n_quantities+1]
- - targets: [batch_size, n_quantities]
+ tuple[torch.Tensor, torch.Tensor]: Inputs and targets with the same shapes
+ as described above.
"""
# 'batch' is a list of length=1 if DataLoader has batch_size=1,
# and each element is (input_tensor, target_tensor).
@@ -59,6 +60,7 @@ def fc_collate_fn(batch):
class FCPrebatchedDataset(Dataset):
"""
Dataset for pre-batched data specifically for the FullyConnected model.
+
Args:
inputs_batches (list[Tensor]): List of precomputed input batches.
targets_batches (list[Tensor]): List of precomputed target batches.
diff --git a/codes/surrogates/LatentNeuralODE/__init__.py b/codes/surrogates/LatentNeuralODE/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/codes/surrogates/LatentPolynomial/__init__.py b/codes/surrogates/LatentPolynomial/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/codes/train/__init__.py b/codes/train/__init__.py
index e714f51a..74c2141b 100644
--- a/codes/train/__init__.py
+++ b/codes/train/__init__.py
@@ -1,10 +1,10 @@
from .train_fcts import (
+ DummyLock,
+ create_task_list_for_surrogate,
parallel_training,
sequential_training,
train_and_save_model,
- create_task_list_for_surrogate,
worker,
- DummyLock,
)
__all__ = [
diff --git a/codes/train/train_fcts.py b/codes/train/train_fcts.py
index b81bf091..1f6a858a 100644
--- a/codes/train/train_fcts.py
+++ b/codes/train/train_fcts.py
@@ -73,16 +73,21 @@ def train_and_save_model(
) = check_and_load_data(
config["dataset"]["name"],
verbose=config.get("verbose", False),
- log=config["dataset"]["log10_transform"],
- log_params=config["dataset"].get("log10_transform_params", False),
- normalisation_mode=config["dataset"]["normalise"],
- tolerance=config["dataset"]["tolerance"],
+ log=config["dataset"].get("log10_transform", True),
+ log_params=config["dataset"].get("log10_transform_params", True),
+ normalisation_mode=config["dataset"].get("normalise", "minmax"),
+ tolerance=config["dataset"].get("tolerance", None),
per_species=config["dataset"].get("normalise_per_species", False),
)
# Get the appropriate data subset
(train_data, test_data), (train_params, test_params), timesteps = get_data_subset(
- (train_data, test_data), timesteps, mode, metric, (train_params, test_params)
+ (train_data, test_data),
+ timesteps,
+ mode,
+ metric,
+ (train_params, test_params),
+ config["dataset"].get("subset_factor", 1),
)
_, n_timesteps, n_quantities = train_data.shape
@@ -156,7 +161,7 @@ def create_task_list_for_surrogate(config, surr_name: str) -> list:
list: A list of training tasks for the surrogate model.
"""
tasks = []
- seed = config["seed"]
+ seed = config.get("seed", 42)
surr_idx = config["surrogates"].index(surr_name)
id = config["training_id"]
epochs = (
@@ -167,29 +172,34 @@ def create_task_list_for_surrogate(config, surr_name: str) -> list:
tasks.append((surr_name, "main", "", id, seed, epochs))
- if config["interpolation"]["enabled"]:
+ interpolation_conf = config.get("interpolation", {})
+ if interpolation_conf.get("enabled", False):
mode = "interpolation"
- for interval in config["interpolation"]["intervals"]:
+ for interval in interpolation_conf["intervals"]:
tasks.append((surr_name, mode, interval, id, seed + interval, epochs))
- if config["extrapolation"]["enabled"]:
+ extrapolation_conf = config.get("extrapolation", {})
+ if extrapolation_conf.get("enabled", False):
mode = "extrapolation"
- for cutoff in config["extrapolation"]["cutoffs"]:
+ for cutoff in extrapolation_conf["cutoffs"]:
tasks.append((surr_name, mode, cutoff, id, seed + cutoff, epochs))
- if config["sparse"]["enabled"]:
- for factor in config["sparse"]["factors"]:
+ sparse_conf = config.get("sparse", {})
+ if sparse_conf.get("enabled", False):
+ for factor in sparse_conf["factors"]:
tasks.append((surr_name, "sparse", factor, id, seed + factor, epochs))
- if config["uncertainty"]["enabled"]:
- n_models = config["uncertainty"]["ensemble_size"]
+ uncertainty_conf = config.get("uncertainty", {})
+ if uncertainty_conf.get("enabled", False):
+ n_models = uncertainty_conf["ensemble_size"]
for i in range(n_models - 1):
tasks.append((surr_name, "UQ", i + 1, id, seed + i, epochs))
- if config["batch_scaling"]["enabled"]:
+ batch_scaling_conf = config.get("batch_scaling", {})
+ if batch_scaling_conf.get("enabled", False):
mode = "batchsize"
- for bf in config["batch_scaling"]["sizes"]:
- bf_index = config["batch_scaling"]["sizes"].index(bf)
+ for bf in batch_scaling_conf["sizes"]:
+ bf_index = batch_scaling_conf["sizes"].index(bf)
bf = batch_factor_to_float(bf)
tasks.append((surr_name, mode, float(bf), id, seed + bf_index, epochs))
@@ -249,6 +259,17 @@ def worker(
def parallel_training(tasks, device_list, task_list_filepath: str):
+ """
+ Execute the queued training tasks across multiple devices using worker threads.
+
+ Args:
+ tasks (list[tuple]): Output of :func:`create_task_list_for_surrogate`.
+ device_list (list[str]): Devices allocated to training (e.g. ["cuda:0", "cuda:1"]).
+ task_list_filepath (str): Path to the persisted JSON task list that tracks progress.
+
+ Returns:
+ float: Elapsed wall-clock time reported by the shared progress bar.
+ """
task_queue = Queue()
for task in tasks:
task_queue.put(task)
@@ -299,6 +320,17 @@ def parallel_training(tasks, device_list, task_list_filepath: str):
def sequential_training(tasks, device_list, task_list_filepath: str):
+ """
+ Run all training tasks sequentially on a single device.
+
+ Args:
+ tasks (list[tuple]): Task specification tuples generated from the config.
+ device_list (list[str]): Contains exactly one element (typically \"cpu\" or a single CUDA id).
+ task_list_filepath (str): Path to the JSON file used to resume interrupted runs.
+
+ Returns:
+ float: Total elapsed time once all tasks finish.
+ """
overall_progress_bar = get_progress_bar(tasks)
errors_encountered = False
device = device_list[0]
diff --git a/codes/tune/__init__.py b/codes/tune/__init__.py
index 09126b96..840278fd 100644
--- a/codes/tune/__init__.py
+++ b/codes/tune/__init__.py
@@ -24,6 +24,7 @@
initialize_optuna_database,
)
from .tune_utils import (
+ apply_tuning_defaults,
build_study_names,
copy_config,
delete_studies_if_requested,
@@ -44,6 +45,7 @@
"plot_test_losses",
"load_model_test_losses",
"build_study_names",
+ "apply_tuning_defaults",
"copy_config",
"delete_studies_if_requested",
"prepare_workspace",
diff --git a/codes/tune/optuna_fcts.py b/codes/tune/optuna_fcts.py
index 917b91c1..f371b468 100644
--- a/codes/tune/optuna_fcts.py
+++ b/codes/tune/optuna_fcts.py
@@ -244,6 +244,7 @@ def training_run(
download_data(config["dataset"]["name"], verbose=False)
+ dataset_cfg = config["dataset"]
(
(train_data, test_data, _),
(train_params, test_params, _),
@@ -252,20 +253,20 @@ def training_run(
data_info,
_,
) = check_and_load_data(
- config["dataset"]["name"],
+ dataset_cfg["name"],
verbose=False,
- log=config["dataset"]["log10_transform"],
- log_params=config.get("log10_transform_params", False),
- normalisation_mode=config["dataset"]["normalise"],
- tolerance=config["dataset"]["tolerance"],
- per_species=config["dataset"].get("normalise_per_species", False),
+ log=dataset_cfg.get("log10_transform", True),
+ log_params=dataset_cfg.get("log10_transform_params", True),
+ normalisation_mode=dataset_cfg.get("normalise", "minmax"),
+ tolerance=dataset_cfg.get("tolerance", None),
+ per_species=dataset_cfg.get("normalise_per_species", False),
)
- subset_factor = config["dataset"].get("subset_factor", 1)
+ subset_factor = dataset_cfg.get("subset_factor", 1)
train_data = train_data[::subset_factor]
train_params = train_params[::subset_factor] if train_params is not None else None
- set_random_seeds(config["seed"], device=device)
+ set_random_seeds(config.get("seed", 42), device=device)
surr_name = config["surrogate"]["name"]
# Load base (best) config from disk as you already do
@@ -316,12 +317,13 @@ def training_run(
epochs=config["epochs"],
position=slot_id + 2,
description=description,
- multi_objective=config["multi_objective"],
+ multi_objective=config.get("multi_objective", False),
)
preds, targets = model.predict(test_loader, leave_log=True)
p99_dex = torch.quantile(
- (preds - targets).abs().flatten(), float(config["target_percentile"])
+ (preds - targets).abs().flatten(),
+ float(config.get("target_percentile", 0.99)),
).item()
p99_dex = min(p99_dex, config.get("loss_cap", 20))
@@ -333,7 +335,7 @@ def training_run(
model_name = f"{surr_name.lower()}_{trial.number}"
model.save(model_name=model_name, base_dir="", training_id=savepath)
- if config["multi_objective"]:
+ if config.get("multi_objective", False):
with _inference_time_lock:
inference_times = measure_inference_time(model, test_loader)
return p99_dex, np.mean(inference_times)
diff --git a/codes/tune/tune_utils.py b/codes/tune/tune_utils.py
index df9ea1c5..dc0bfcf0 100644
--- a/codes/tune/tune_utils.py
+++ b/codes/tune/tune_utils.py
@@ -1,8 +1,13 @@
+import copy
import shutil
+import sys
+from datetime import datetime
from pathlib import Path
import optuna
+from codes.utils.utils import read_yaml_config
+
def yes_no(prompt: str, default: bool = False) -> bool:
"""Simple Y/N prompt. default=False -> [y/N], True -> [Y/n]."""
@@ -29,27 +34,86 @@ def build_study_names(config: dict, main_study_name: str) -> list[str]:
return [main_study_name]
-def prepare_workspace(master_cfg_path: Path, config: dict) -> None:
+def _prompt_config_decision(stored: Path, incoming: Path) -> str:
+ prompt = (
+ f"A stored config exists at '{stored}'.\n"
+ f"The provided config '{incoming}' differs.\n"
+ "Use stored version [U], overwrite with new version [O], or abort [A]? "
+ )
+ while True:
+ ans = input(prompt).strip().lower()
+ if ans in ("u", "use"):
+ return "use"
+ if ans in ("o", "overwrite"):
+ return "overwrite"
+ if ans in ("a", "abort"):
+ return "abort"
+ print("Please answer with U, O, or A.")
+
+
+def prepare_workspace(master_cfg_path: Path, config: dict) -> dict:
tuning_id = config["tuning_id"]
run_dir = Path("tuned") / tuning_id
- run_dir_exists = run_dir.exists()
-
- if run_dir_exists:
- overwrite = yes_no(f"Folder '{run_dir}' exists. Overwrite?", default=False)
- if overwrite:
- shutil.rmtree(run_dir)
- run_dir.mkdir(parents=True, exist_ok=True)
- copy_config(master_cfg_path, run_dir)
- config["_overwrite_run"] = True
- else:
- config["_overwrite_run"] = False
- # ensure config is present for reproducibility (copy if missing)
- if not (run_dir / "optuna_config.yaml").exists():
+ run_dir.mkdir(parents=True, exist_ok=True)
+ dst_cfg = run_dir / "optuna_config.yaml"
+
+ config["_overwrite_run"] = False
+
+ if dst_cfg.exists():
+ stored_config = read_yaml_config(dst_cfg)
+ if stored_config != config:
+ decision = _prompt_config_decision(dst_cfg, master_cfg_path)
+ if decision == "use":
+ print(
+ f"Using stored config for tuning_id '{tuning_id}'. "
+ "The provided config was ignored."
+ )
+ return stored_config
+ if decision == "overwrite":
+ backup = dst_cfg.with_name(
+ f"optuna_config.yaml.bak-{datetime.now():%Y%m%d-%H%M%S}"
+ )
+ shutil.copy2(dst_cfg, backup)
copy_config(master_cfg_path, run_dir)
- else:
- run_dir.mkdir(parents=True, exist_ok=True)
- copy_config(master_cfg_path, run_dir)
- config["_overwrite_run"] = True # brand new
+ config["_overwrite_run"] = True
+ return config
+ print("Aborting tuning run.")
+ sys.exit(1)
+ return stored_config
+
+ copy_config(master_cfg_path, run_dir)
+ config["_overwrite_run"] = True
+ return config
+
+
+def apply_tuning_defaults(config: dict) -> dict:
+ cfg = copy.deepcopy(config)
+
+ dataset = cfg.get("dataset", {})
+ if "name" not in dataset:
+ raise ValueError("Tuning config must specify dataset.name")
+ dataset.setdefault("log10_transform", True)
+ dataset.setdefault("log10_transform_params", True)
+ dataset.setdefault("normalise", "minmax")
+ dataset.setdefault("normalise_per_species", False)
+ dataset.setdefault("tolerance", None)
+ dataset.setdefault("subset_factor", 1)
+ dataset.setdefault("log_timesteps", False)
+ cfg["dataset"] = dataset
+
+ cfg.setdefault("devices", ["cpu"])
+ cfg.setdefault("seed", 42)
+ cfg.setdefault("multi_objective", False)
+ cfg.setdefault("target_percentile", 0.99)
+ cfg.setdefault("loss_cap", 20)
+ cfg.setdefault("time_pruning", True)
+ cfg.setdefault("population_size", 50)
+ cfg.setdefault("optuna_logging", False)
+ cfg.setdefault("use_optimal_params", True)
+ cfg.setdefault("prune", True)
+ cfg.setdefault("trials", 50)
+
+ return cfg
def delete_studies_if_requested(config: dict, main_study_name: str, db_url: str):
diff --git a/codes/utils/data_utils.py b/codes/utils/data_utils.py
index c35e9873..b60fb5d8 100644
--- a/codes/utils/data_utils.py
+++ b/codes/utils/data_utils.py
@@ -700,18 +700,40 @@ def download_data(dataset_name: str, path: str | None = None, verbose: bool = Tr
def print_data_info(data_path):
with h5py.File(data_path, "r") as f:
- if f.attrs.get("n_quantities", None) is None:
- print(f"Dataset '{data_path}' does not contain the required attributes.")
- return
print("Dataset Info:")
- n_quantities = 10 # f.attrs["n_quantities"]
- train_samples = f.attrs["n_train_samples"]
- test_samples = f.attrs["n_test_samples"]
- val_samples = f.attrs["n_val_samples"]
- labels = f.attrs.get("labels", None)
+
+ def _attr_or_default(name, default=None):
+ return f.attrs[name] if name in f.attrs else default
+
+ # infer n_quantities if missing
+ n_quantities = _attr_or_default("n_quantities")
+ if n_quantities is None:
+ if "train" in f:
+ n_quantities = f["train"].shape[-1]
+ else:
+ n_quantities = "unknown"
+
+ train_samples = _attr_or_default(
+ "n_train_samples", f["train"].shape[0] if "train" in f else 0
+ )
+ test_samples = _attr_or_default(
+ "n_test_samples", f["test"].shape[0] if "test" in f else 0
+ )
+ val_samples = _attr_or_default(
+ "n_val_samples", f["val"].shape[0] if "val" in f else 0
+ )
+ labels = _attr_or_default("labels")
+
total_samples = train_samples + test_samples + val_samples
- if "n_parameters" in f.attrs:
- n_parameters = f.attrs["n_parameters"]
+
+ n_parameters = _attr_or_default("n_parameters")
+ if n_parameters is None:
+ if "train_params" in f:
+ n_parameters = f["train_params"].shape[-1]
+ else:
+ n_parameters = 0
+
+ if n_parameters:
print(
f" - Dataset comprises {n_quantities} quantities and {n_parameters} parameters."
)
diff --git a/config.yaml b/config.yaml
deleted file mode 100644
index 4d10e240..00000000
--- a/config.yaml
+++ /dev/null
@@ -1,42 +0,0 @@
-# Global settings for the benchmark
-training_id: "_cloud_finetuned"
-surrogates: ["MultiONet", "FullyConnected", "LatentNeuralODE", "LatentPoly"]
-batch_size: [35838, 35838, 717, 717] # [65536, 65536, 512, 512]
-epochs: [8000, 8000, 8000, 8000] # [4096, 4096, 4096, 4096]
-dataset:
- name: cloud
- log10_transform: True
- normalise: minmax
- subset_factor: 1
- tolerance: 1e-25
- normalise_per_species: True
- log_timesteps: True
-devices: ["cuda:4", "cuda:2", "cuda:3", "cuda:5", "cuda:6", "cuda:7", "cuda:8", "cuda:9"]
-seed: 42
-verbose: False
-checkpoint: True
-
-# Models to train
-interpolation:
- enabled: True
- intervals: [2, 3, 4, 5, 6, 7, 8, 9, 10]
-extrapolation:
- enabled: True
- cutoffs: [50, 60, 70, 80, 90]
-sparse:
- enabled: True
- factors: [2, 4, 8, 16, 32]
-batch_scaling:
- enabled: True
- sizes: [1/16, 1/8, 1/4, 1/2]
-uncertainty:
- enabled: True
- ensemble_size: 5 # Number of models for deep ensemble
-
-# Evaluations during benchmark
-iterative: True
-losses: True
-gradients: True
-timing: True
-compute: True
-compare: True # Whether to compare the surrogates
diff --git a/config_full.yaml b/config_full.yaml
deleted file mode 100644
index b578f982..00000000
--- a/config_full.yaml
+++ /dev/null
@@ -1,44 +0,0 @@
-# Global settings for the benchmark
-training_id: "optimizer_test"
-surrogates: ["MultiONet", "FullyConnected", "LatentNeuralODE", "LatentPoly"]
-batch_size: [65536, 65536, 512, 512]
-epochs: [200, 200, 110, 200] # [20000, 7500, 20000, 15000]
-dataset:
- name: "cloud"
- log10_transform: True
- log10_transform_params: False
- normalise: "minmax" # "minmax" # "standardise", "minmax", "disable"
- normalise_per_species: True
- use_optimal_params: True
- tolerance: 1e-25
- subset_factor: 1
- log_timesteps: True
-devices: ["cuda:1", "cuda:1", "cuda:2", "cuda:3"] # ["cuda:0", "cuda:1", "cuda:2", "cuda:3", "cuda:4", "cuda:5", "cuda:6", "cuda:7", "cuda:8"]
-seed: 42
-verbose: False
-relative_error_threshold: 1e-10
-checkpointing: True
-
-# Models to train
-interpolation:
- enabled: True
- intervals: [2, 3, 4, 5, 6, 7, 8, 9, 10]
-extrapolation:
- enabled: True
- cutoffs: [50, 60, 70, 80, 90]
-sparse:
- enabled: True
- factors: [2, 4, 8, 16, 32]
-batch_scaling:
- enabled: True
- sizes: [1/16, 1/8, 1/4, 1/2]
-uncertainty:
- enabled: True
- ensemble_size: 5 # Number of models for deep ensemble
-
-# Evaluations during benchmark
-losses: True
-gradients: True
-timing: True
-compute: True
-compare: True # Whether to compare the surrogates
diff --git a/configs/train_eval/config_full.yaml b/configs/train_eval/config_full.yaml
new file mode 100644
index 00000000..7f482db1
--- /dev/null
+++ b/configs/train_eval/config_full.yaml
@@ -0,0 +1,54 @@
+# Full configuration template for run_training.py and run_eval.py
+
+# Global settings
+training_id: "example_run"
+surrogates: ["MultiONet"]
+devices: ["cpu"]
+seed: 42
+verbose: False
+
+# Training settings
+batch_size: [65536]
+epochs: [200]
+checkpoint: False
+
+# Dataset settings
+dataset:
+ name: "osu2008"
+ log10_transform: True
+ log10_transform_params: False
+ normalise: "minmax" # "standardise", "minmax", "disable"
+ normalise_per_species: False
+ tolerance: 1e-25
+ log_timesteps: False
+ use_optimal_params: True
+ subset_factor: 1
+
+# Models to train
+interpolation:
+ enabled: False
+ intervals: [2, 3, 4, 5, 6, 7, 8, 9, 10]
+extrapolation:
+ enabled: False
+ cutoffs: [50, 60, 70, 80, 90]
+sparse:
+ enabled: False
+ factors: [2, 4, 8, 16, 32]
+batch_scaling:
+ enabled: False
+ sizes: ["1/16", "1/8", "1/4", "1/2"]
+uncertainty:
+ enabled: False
+ ensemble_size: 5 # Number of models for deep ensemble
+
+# Evaluations during benchmark
+losses: True
+iterative: False
+gradients: True
+timing: True
+compute: True
+compare: True
+
+# Metric options
+relative_error_threshold: 1e-10
+error_percentile: 99
diff --git a/configs/train_eval/config_minimal.yaml b/configs/train_eval/config_minimal.yaml
new file mode 100644
index 00000000..7e1b641d
--- /dev/null
+++ b/configs/train_eval/config_minimal.yaml
@@ -0,0 +1,9 @@
+# Global settings for the benchmark
+training_id: "example_config_minimal"
+surrogates: ["FullyConnected", "LatentPoly"]
+batch_size: [65536, 512]
+epochs: [20, 20]
+dataset:
+ name: "lotka_volterra"
+ tolerance: 1e-15
+devices: ["cpu"]
diff --git a/configs/train_eval/config_minimal_evals.yaml b/configs/train_eval/config_minimal_evals.yaml
new file mode 100644
index 00000000..be6cea1b
--- /dev/null
+++ b/configs/train_eval/config_minimal_evals.yaml
@@ -0,0 +1,34 @@
+# Global settings for the benchmark
+training_id: "example_config_minimal"
+surrogates: ["FullyConnected", "LatentPoly"]
+batch_size: [65536, 512]
+epochs: [20, 20]
+dataset:
+ name: "lotka_volterra"
+ tolerance: 1e-15
+devices: ["cpu"]
+
+# # Models to train
+# interpolation:
+# enabled: False
+# intervals: [2, 3, 4, 5, 6, 7, 8, 9, 10]
+# extrapolation:
+# enabled: False
+# cutoffs: [50, 60, 70, 80, 90]
+# sparse:
+# enabled: False
+# factors: [2, 4, 8, 16, 32]
+# batch_scaling:
+# enabled: False
+# sizes: [1/16, 1/8, 1/4, 1/2]
+# uncertainty:
+# enabled: False
+# ensemble_size: 5 # Number of models for deep ensemble
+
+# # Evaluations during benchmark
+losses: True
+gradients: True
+timing: True
+compute: True
+compare: True # Whether to compare the surrogates
+iterative: True
diff --git a/configs/tuning/sqlite_quickstart.yaml b/configs/tuning/sqlite_quickstart.yaml
new file mode 100644
index 00000000..a9273bab
--- /dev/null
+++ b/configs/tuning/sqlite_quickstart.yaml
@@ -0,0 +1,34 @@
+seed: 123
+tuning_id: "sqlite_quickstart"
+
+storage:
+ backend: "sqlite"
+ path: "tuned/sqlite_quickstart/sqlite_quickstart.db"
+
+dataset:
+ name: "osu2008"
+ log10_transform: true
+ normalise: minmax
+
+devices: ["cuda:2", "cuda:3"]
+prune: false
+multi_objective: false
+
+surrogates:
+ - name: "MultiONet"
+ batch_size: 1024
+ epochs: 128
+ trials: 5
+ optuna_params:
+ hidden_size:
+ type: int
+ low: 32
+ high: 128
+ learning_rate:
+ type: float
+ low: 1.0e-4
+ high: 5.0e-3
+ log: true
+ activation:
+ type: categorical
+ choices: ["ReLU", "GELU"]
diff --git a/datasets/_data_analysis/__init__.py b/datasets/_data_analysis/__init__.py
index aecd8292..48bcafe0 100644
--- a/datasets/_data_analysis/__init__.py
+++ b/datasets/_data_analysis/__init__.py
@@ -4,5 +4,4 @@
"get_data_subset",
"create_dataset",
"normalize_data",
- "download_data"
]
diff --git a/datasets/data_sources.yaml b/datasets/data_sources.yaml
index 698a53b2..89974cb9 100644
--- a/datasets/data_sources.yaml
+++ b/datasets/data_sources.yaml
@@ -1,17 +1,16 @@
-osu2008: "https://zenodo.org/records/13749089/files/data.hdf5?download=1"
-simple_reaction: "https://zenodo.org/records/14712934/files/data.hdf5?download=1"
-simple_ode: "https://zenodo.org/records/14878373/files/data.hdf5?download=1"
-lotka_volterra: "https://zenodo.org/records/14703867/files/data.hdf5?download=1"
-branca24: "https://zenodo.org/records/13624794/files/data.hdf5?download=1"
-simple_primordial: "https://zenodo.org/records/13754361/files/data.hdf5?download=1"
-branca_norad: "https://zenodo.org/records/14041124/files/data.hdf5?download=1"
-branca_large_kyr: "https://zenodo.org/records/14204650/files/data.hdf5?download=1"
-branca_large_myr: "https://zenodo.org/records/14243267/files/data.hdf5?download=1"
-coupled_oscillators: "https://zenodo.org/records/14717175/files/data.hdf5?download=1"
+osu2008: https://zenodo.org/records/13749089/files/data.hdf5?download=1
+simple_reaction: https://zenodo.org/records/14712934/files/data.hdf5?download=1
+simple_ode: https://zenodo.org/records/14878373/files/data.hdf5?download=1
+lotka_volterra: https://zenodo.org/records/14703867/files/data.hdf5?download=1
+branca24: https://zenodo.org/records/13624794/files/data.hdf5?download=1
+simple_primordial: https://zenodo.org/records/13754361/files/data.hdf5?download=1
+branca_norad: https://zenodo.org/records/14041124/files/data.hdf5?download=1
+branca_large_kyr: https://zenodo.org/records/14204650/files/data.hdf5?download=1
+branca_large_myr: https://zenodo.org/records/14243267/files/data.hdf5?download=1
+coupled_oscillators: https://zenodo.org/records/14717175/files/data.hdf5?download=1
lv_parametric: https://zenodo.org/records/15412214/files/data.hdf5?download=1
lv_parametric_no_params: https://zenodo.org/records/15412297/files/data.hdf5?download=1
cloud: https://zenodo.org/records/18018572/files/codes_cloud_data.hdf5?download=1
cloud_parametric: https://zenodo.org/records/18018572/files/codes_cloud_parametric_data.hdf5?download=1
primordial: https://zenodo.org/records/18018572/files/codes_primordial_data.hdf5?download=1
-primordial_parametric: https://zenodo.org/records/18018572/files/codes_primordial_parametric_data.hdf5?download=1
-
+primordial_parametric: https://zenodo.org/records/18018572/files/codes_primordial_parametric_data.hdf5?download=1
\ No newline at end of file
diff --git a/docs/conf.py b/docs/conf.py
deleted file mode 100644
index e3c98018..00000000
--- a/docs/conf.py
+++ /dev/null
@@ -1,67 +0,0 @@
-# Configuration file for the Sphinx documentation builder.
-
-# -- Project information -----------------------------------------------------
-
-project = "CODES"
-copyright = "2024, Robin Janssen, Immanuel Sulzer"
-author = "Robin Janssen, Immanuel Sulzer"
-
-# -- General configuration ---------------------------------------------------
-
-extensions = [
- "sphinx.ext.autodoc",
- "sphinx.ext.napoleon",
- "sphinx.ext.autosummary",
- "sphinx_autodoc_typehints",
- "sphinx_book_theme",
-]
-autosummary_generate = True
-autosummary_generate_overwrite = False
-autosummary_output_dir = "_autosummary"
-
-templates_path = ["_templates"]
-exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
-
-# -- Options for HTML output -------------------------------------------------
-
-html_theme = "sphinx_book_theme"
-
-html_static_path = ["_static"]
-html_favicon = "_static/favicon-96x96.png"
-
-# Adjusted HTML theme options
-html_theme_options = {
- "icon_links": [
- {
- "name": "CODES GitHub",
- "url": "https://github.com/robin-janssen/CODES-Benchmark",
- "icon": "fa-brands fa-square-github",
- "type": "fontawesome",
- },
- {
- "name": "CODES Docs",
- "url": "https://codes-docs.web.app",
- "icon": "_static/favicon-96x96.png", # Make sure this is the correct path to the favicon
- "type": "local",
- },
- {
- "name": "CODES Paper",
- "url": "https://arxiv.org/abs/2410.20886",
- "icon": "fa-solid fa-file-alt",
- "type": "fontawesome",
- },
- # {
- # "name": "Exemplary Badge",
- # "url": "https://img.shields.io/badge/Exemplary-Yes-brightgreen",
- # "icon": "https://img.shields.io/badge/Exemplary-Yes-brightgreen",
- # "type": "url",
- # },
- # { # Link to the CODES paper once its on arxiv!
- # "name": "CODES paper",
- # "url": "https://arxiv.org/abs/2106.04420",
- # "icon": "fa-file-pdf",
- # "type": "fontawesome",
- # }
- ],
- "icon_links_label": "Quick Links",
-}
diff --git a/docs/index.rst b/docs/index.rst
deleted file mode 100644
index 6c77edaf..00000000
--- a/docs/index.rst
+++ /dev/null
@@ -1,24 +0,0 @@
-CODES API Documentation
-=======================
-
-Welcome to the CODES API documentation.
-
-.. autosummary::
- :toctree: docs/source/
- :recursive:
-
- codes.benchmark
- codes.surrogates
- codes.train
- codes.tune
- codes.utils
-
-
-.. .. toctree::
-.. :maxdepth: 2
-.. :caption: API Reference:
-
-.. codes.benchmark
-.. codes.surrogates
-.. codes.train
-.. codes.utils
\ No newline at end of file
diff --git a/docs/source/_static/Extrapolation.png b/docs/source/_static/Extrapolation.png
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diff --git a/docs/source/_static/LatentNeuralODE.png b/docs/source/_static/LatentNeuralODE.png
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index 00000000..5d45f05a
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index 00000000..b4e1fca8
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diff --git a/docs/source/_static/MultiONet.png b/docs/source/_static/MultiONet.png
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index 00000000..e68bcfd5
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diff --git a/docs/source/_static/ScalingPlots.png b/docs/source/_static/ScalingPlots.png
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index 00000000..2c9c710c
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diff --git a/docs/source/_static/Sparse.png b/docs/source/_static/Sparse.png
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diff --git a/docs/source/_static/UQ.jpg b/docs/source/_static/UQ.jpg
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index 00000000..1b9768f2
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diff --git a/docs/_static/book-solid-white.svg b/docs/source/_static/book-solid-white.svg
similarity index 100%
rename from docs/_static/book-solid-white.svg
rename to docs/source/_static/book-solid-white.svg
diff --git a/docs/_static/book-solid.svg b/docs/source/_static/book-solid.svg
similarity index 100%
rename from docs/_static/book-solid.svg
rename to docs/source/_static/book-solid.svg
diff --git a/docs/_static/favicon-96x96.png b/docs/source/_static/favicon-96x96.png
similarity index 100%
rename from docs/_static/favicon-96x96.png
rename to docs/source/_static/favicon-96x96.png
diff --git a/docs/_static/file-alt-solid-white.svg b/docs/source/_static/file-alt-solid-white.svg
similarity index 100%
rename from docs/_static/file-alt-solid-white.svg
rename to docs/source/_static/file-alt-solid-white.svg
diff --git a/docs/_static/file-alt-solid.svg b/docs/source/_static/file-alt-solid.svg
similarity index 100%
rename from docs/_static/file-alt-solid.svg
rename to docs/source/_static/file-alt-solid.svg
diff --git a/docs/source/_static/logo.png b/docs/source/_static/logo.png
new file mode 100644
index 00000000..648e1e58
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diff --git a/docs/source/_static/osutrajectories.jpg b/docs/source/_static/osutrajectories.jpg
new file mode 100644
index 00000000..8be9114d
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diff --git a/docs/source/_templates/napoleon/attrs.rst_t b/docs/source/_templates/napoleon/attrs.rst_t
new file mode 100644
index 00000000..cd6d85d7
--- /dev/null
+++ b/docs/source/_templates/napoleon/attrs.rst_t
@@ -0,0 +1 @@
+{{ parameters(block) }}
diff --git a/docs/source/_templates/napoleon/params.rst_t b/docs/source/_templates/napoleon/params.rst_t
new file mode 100644
index 00000000..cd6d85d7
--- /dev/null
+++ b/docs/source/_templates/napoleon/params.rst_t
@@ -0,0 +1 @@
+{{ parameters(block) }}
diff --git a/docs/source/_templates/napoleon/raises.rst_t b/docs/source/_templates/napoleon/raises.rst_t
new file mode 100644
index 00000000..cd6d85d7
--- /dev/null
+++ b/docs/source/_templates/napoleon/raises.rst_t
@@ -0,0 +1 @@
+{{ parameters(block) }}
diff --git a/docs/source/api-reference.md b/docs/source/api-reference.md
new file mode 100644
index 00000000..0e0acdcb
--- /dev/null
+++ b/docs/source/api-reference.md
@@ -0,0 +1,11 @@
+# API Reference Overview
+
+The API docs are auto-generated from the `codes` package using Sphinx autodoc. Use the links below to jump directly into each area:
+
+- {doc}`codes.benchmark` — command-line entry points, benchmarking utilities, plotting helpers, and dataset utilities.
+- {doc}`codes.surrogates` — shared base classes plus module pages for every surrogate family (MultiONet, FCNN, LatentNeuralODE, LatentPoly, and helpers). Each surrogate package exposes its config dataclass, training loop, and evaluation helpers.
+- {doc}`codes.train` — task orchestration, queueing logic, and progress reporting for the training CLI.
+- {doc}`codes.tune` — Optuna integration, objective definitions, database utilities, and plotting for study analysis.
+- {doc}`codes.utils` — shared utilities for loading datasets, normalization, checkpointing, and miscellaneous helpers reused throughout the framework.
+
+Each page mirrors the Python package tree and lists modules in alphabetical order. Collapse/expand sections in the sidebar to navigate classes, functions, and dataclasses quickly. When extending the framework, start from `codes.surrogates` to see how existing models integrate with `AbstractSurrogateModel`, or browse `codes.benchmark` to discover which helpers power the CLI scripts.
diff --git a/docs/codes.benchmark.rst b/docs/source/codes.benchmark.rst
similarity index 100%
rename from docs/codes.benchmark.rst
rename to docs/source/codes.benchmark.rst
index 4641a6ea..ecc1d385 100644
--- a/docs/codes.benchmark.rst
+++ b/docs/source/codes.benchmark.rst
@@ -9,29 +9,29 @@ codes.benchmark.bench\_fcts module
.. automodule:: codes.benchmark.bench_fcts
:members:
- :undoc-members:
:show-inheritance:
+ :undoc-members:
codes.benchmark.bench\_plots module
-----------------------------------
.. automodule:: codes.benchmark.bench_plots
:members:
- :undoc-members:
:show-inheritance:
+ :undoc-members:
codes.benchmark.bench\_utils module
-----------------------------------
.. automodule:: codes.benchmark.bench_utils
:members:
- :undoc-members:
:show-inheritance:
+ :undoc-members:
Module contents
---------------
.. automodule:: codes.benchmark
:members:
- :undoc-members:
:show-inheritance:
+ :undoc-members:
diff --git a/docs/codes.rst b/docs/source/codes.rst
similarity index 59%
rename from docs/codes.rst
rename to docs/source/codes.rst
index 464fa946..508b43bb 100644
--- a/docs/codes.rst
+++ b/docs/source/codes.rst
@@ -1,22 +1,15 @@
+:orphan:
+
codes package
=============
Subpackages
-----------
-.. toctree::
- :maxdepth: 4
-
- codes.benchmark
- codes.surrogates
- codes.train
- codes.tune
- codes.utils
-
Module contents
---------------
.. automodule:: codes
:members:
- :undoc-members:
:show-inheritance:
+ :undoc-members:
diff --git a/docs/source/codes.surrogates.AbstractSurrogate.rst b/docs/source/codes.surrogates.AbstractSurrogate.rst
new file mode 100644
index 00000000..2212ac6e
--- /dev/null
+++ b/docs/source/codes.surrogates.AbstractSurrogate.rst
@@ -0,0 +1,29 @@
+codes.surrogates.AbstractSurrogate package
+==========================================
+
+Submodules
+----------
+
+codes.surrogates.AbstractSurrogate.abstract\_config module
+----------------------------------------------------------
+
+.. automodule:: codes.surrogates.AbstractSurrogate.abstract_config
+ :members:
+ :show-inheritance:
+ :undoc-members:
+
+codes.surrogates.AbstractSurrogate.abstract\_surrogate module
+-------------------------------------------------------------
+
+.. automodule:: codes.surrogates.AbstractSurrogate.abstract_surrogate
+ :members:
+ :show-inheritance:
+ :undoc-members:
+
+Module contents
+---------------
+
+.. automodule:: codes.surrogates.AbstractSurrogate
+ :members:
+ :show-inheritance:
+ :undoc-members:
diff --git a/docs/source/codes.surrogates.DeepONet.rst b/docs/source/codes.surrogates.DeepONet.rst
new file mode 100644
index 00000000..3669553d
--- /dev/null
+++ b/docs/source/codes.surrogates.DeepONet.rst
@@ -0,0 +1,45 @@
+codes.surrogates.DeepONet package
+=================================
+
+Submodules
+----------
+
+codes.surrogates.DeepONet.deeponet module
+-----------------------------------------
+
+.. automodule:: codes.surrogates.DeepONet.deeponet
+ :members:
+ :show-inheritance:
+ :undoc-members:
+
+codes.surrogates.DeepONet.deeponet\_config module
+-------------------------------------------------
+
+.. automodule:: codes.surrogates.DeepONet.deeponet_config
+ :members:
+ :show-inheritance:
+ :undoc-members:
+
+codes.surrogates.DeepONet.don\_utils module
+-------------------------------------------
+
+.. automodule:: codes.surrogates.DeepONet.don_utils
+ :members:
+ :show-inheritance:
+ :undoc-members:
+
+codes.surrogates.DeepONet.train\_utils module
+---------------------------------------------
+
+.. automodule:: codes.surrogates.DeepONet.train_utils
+ :members:
+ :show-inheritance:
+ :undoc-members:
+
+Module contents
+---------------
+
+.. automodule:: codes.surrogates.DeepONet
+ :members:
+ :show-inheritance:
+ :undoc-members:
diff --git a/docs/source/codes.surrogates.FCNN.rst b/docs/source/codes.surrogates.FCNN.rst
new file mode 100644
index 00000000..ff8dea6d
--- /dev/null
+++ b/docs/source/codes.surrogates.FCNN.rst
@@ -0,0 +1,29 @@
+codes.surrogates.FCNN package
+=============================
+
+Submodules
+----------
+
+codes.surrogates.FCNN.fcnn module
+---------------------------------
+
+.. automodule:: codes.surrogates.FCNN.fcnn
+ :members:
+ :show-inheritance:
+ :undoc-members:
+
+codes.surrogates.FCNN.fcnn\_config module
+-----------------------------------------
+
+.. automodule:: codes.surrogates.FCNN.fcnn_config
+ :members:
+ :show-inheritance:
+ :undoc-members:
+
+Module contents
+---------------
+
+.. automodule:: codes.surrogates.FCNN
+ :members:
+ :show-inheritance:
+ :undoc-members:
diff --git a/docs/source/codes.surrogates.LatentNeuralODE.rst b/docs/source/codes.surrogates.LatentNeuralODE.rst
new file mode 100644
index 00000000..a8560655
--- /dev/null
+++ b/docs/source/codes.surrogates.LatentNeuralODE.rst
@@ -0,0 +1,37 @@
+codes.surrogates.LatentNeuralODE package
+========================================
+
+Submodules
+----------
+
+codes.surrogates.LatentNeuralODE.latent\_neural\_ode module
+-----------------------------------------------------------
+
+.. automodule:: codes.surrogates.LatentNeuralODE.latent_neural_ode
+ :members:
+ :show-inheritance:
+ :undoc-members:
+
+codes.surrogates.LatentNeuralODE.latent\_neural\_ode\_config module
+-------------------------------------------------------------------
+
+.. automodule:: codes.surrogates.LatentNeuralODE.latent_neural_ode_config
+ :members:
+ :show-inheritance:
+ :undoc-members:
+
+codes.surrogates.LatentNeuralODE.utilities module
+-------------------------------------------------
+
+.. automodule:: codes.surrogates.LatentNeuralODE.utilities
+ :members:
+ :show-inheritance:
+ :undoc-members:
+
+Module contents
+---------------
+
+.. automodule:: codes.surrogates.LatentNeuralODE
+ :members:
+ :show-inheritance:
+ :undoc-members:
diff --git a/docs/source/codes.surrogates.LatentPolynomial.rst b/docs/source/codes.surrogates.LatentPolynomial.rst
new file mode 100644
index 00000000..02d32f2e
--- /dev/null
+++ b/docs/source/codes.surrogates.LatentPolynomial.rst
@@ -0,0 +1,29 @@
+codes.surrogates.LatentPolynomial package
+=========================================
+
+Submodules
+----------
+
+codes.surrogates.LatentPolynomial.latent\_poly module
+-----------------------------------------------------
+
+.. automodule:: codes.surrogates.LatentPolynomial.latent_poly
+ :members:
+ :show-inheritance:
+ :undoc-members:
+
+codes.surrogates.LatentPolynomial.latent\_poly\_config module
+-------------------------------------------------------------
+
+.. automodule:: codes.surrogates.LatentPolynomial.latent_poly_config
+ :members:
+ :show-inheritance:
+ :undoc-members:
+
+Module contents
+---------------
+
+.. automodule:: codes.surrogates.LatentPolynomial
+ :members:
+ :show-inheritance:
+ :undoc-members:
diff --git a/docs/codes.surrogates.rst b/docs/source/codes.surrogates.rst
similarity index 56%
rename from docs/codes.surrogates.rst
rename to docs/source/codes.surrogates.rst
index 3e6504ef..c1af9b1a 100644
--- a/docs/codes.surrogates.rst
+++ b/docs/source/codes.surrogates.rst
@@ -1,6 +1,16 @@
codes.surrogates package
========================
+.. toctree::
+ :maxdepth: 2
+ :hidden:
+
+ codes.surrogates.AbstractSurrogate
+ codes.surrogates.DeepONet
+ codes.surrogates.FCNN
+ codes.surrogates.LatentNeuralODE
+ codes.surrogates.LatentPolynomial
+
Submodules
----------
@@ -9,21 +19,14 @@ codes.surrogates.surrogate\_classes module
.. automodule:: codes.surrogates.surrogate_classes
:members:
- :undoc-members:
:show-inheritance:
-
-codes.surrogates.surrogates module
-----------------------------------
-
-.. automodule:: codes.surrogates.surrogates
- :members:
:undoc-members:
- :show-inheritance:
Module contents
---------------
.. automodule:: codes.surrogates
:members:
- :undoc-members:
:show-inheritance:
+ :undoc-members:
+ :exclude-members: AbstractSurrogate, DeepONet, FCNN, LatentNeuralODE, LatentPolynomial
diff --git a/docs/codes.train.rst b/docs/source/codes.train.rst
similarity index 100%
rename from docs/codes.train.rst
rename to docs/source/codes.train.rst
index 524ca983..377e3882 100644
--- a/docs/codes.train.rst
+++ b/docs/source/codes.train.rst
@@ -9,13 +9,13 @@ codes.train.train\_fcts module
.. automodule:: codes.train.train_fcts
:members:
- :undoc-members:
:show-inheritance:
+ :undoc-members:
Module contents
---------------
.. automodule:: codes.train
:members:
- :undoc-members:
:show-inheritance:
+ :undoc-members:
diff --git a/docs/source/codes.tune.rst b/docs/source/codes.tune.rst
new file mode 100644
index 00000000..0d2b0f5a
--- /dev/null
+++ b/docs/source/codes.tune.rst
@@ -0,0 +1,53 @@
+codes.tune package
+==================
+
+Submodules
+----------
+
+codes.tune.evaluate\_study module
+---------------------------------
+
+.. automodule:: codes.tune.evaluate_study
+ :members:
+ :show-inheritance:
+ :undoc-members:
+
+codes.tune.evaluate\_tuning module
+----------------------------------
+
+.. automodule:: codes.tune.evaluate_tuning
+ :members:
+ :show-inheritance:
+ :undoc-members:
+
+codes.tune.optuna\_fcts module
+------------------------------
+
+.. automodule:: codes.tune.optuna_fcts
+ :members:
+ :show-inheritance:
+ :undoc-members:
+
+codes.tune.postgres\_fcts module
+--------------------------------
+
+.. automodule:: codes.tune.postgres_fcts
+ :members:
+ :show-inheritance:
+ :undoc-members:
+
+codes.tune.tune\_utils module
+-----------------------------
+
+.. automodule:: codes.tune.tune_utils
+ :members:
+ :show-inheritance:
+ :undoc-members:
+
+Module contents
+---------------
+
+.. automodule:: codes.tune
+ :members:
+ :show-inheritance:
+ :undoc-members:
diff --git a/docs/codes.utils.rst b/docs/source/codes.utils.rst
similarity index 100%
rename from docs/codes.utils.rst
rename to docs/source/codes.utils.rst
index af2c2bb8..9365e939 100644
--- a/docs/codes.utils.rst
+++ b/docs/source/codes.utils.rst
@@ -9,21 +9,21 @@ codes.utils.data\_utils module
.. automodule:: codes.utils.data_utils
:members:
- :undoc-members:
:show-inheritance:
+ :undoc-members:
codes.utils.utils module
------------------------
.. automodule:: codes.utils.utils
:members:
- :undoc-members:
:show-inheritance:
+ :undoc-members:
Module contents
---------------
.. automodule:: codes.utils
:members:
- :undoc-members:
:show-inheritance:
+ :undoc-members:
diff --git a/docs/source/conf.py b/docs/source/conf.py
new file mode 100644
index 00000000..a47fd8df
--- /dev/null
+++ b/docs/source/conf.py
@@ -0,0 +1,145 @@
+from __future__ import annotations
+
+import warnings
+from pathlib import Path
+from urllib.parse import urlparse
+
+import yaml
+from sphinx.deprecation import RemovedInSphinx10Warning
+
+# Configuration file for the Sphinx documentation builder.
+
+# -- Project information -----------------------------------------------------
+
+project = "CODES"
+copyright = "2026, Robin Janssen"
+author = "Robin Janssen"
+
+# -- General configuration ---------------------------------------------------
+
+extensions = [
+ "sphinx.ext.autodoc",
+ "sphinx.ext.napoleon",
+ "sphinx.ext.autosummary",
+ "sphinx_autodoc_typehints",
+ "sphinx_book_theme",
+ "myst_nb",
+]
+autosummary_generate = True
+autosummary_generate_overwrite = False
+autosummary_output_dir = "_autosummary"
+
+# Render ``Attributes`` sections using the compact parameter style
+napoleon_custom_sections = [
+ ("Attributes", "params_style"),
+]
+napoleon_use_ivar = False
+
+# Support RST, Markdown, and notebooks as sources
+source_suffix = {
+ ".rst": "restructuredtext",
+ ".md": "myst-nb",
+ ".ipynb": "myst-nb",
+}
+
+# Allow Markdown/MyST and notebook content while keeping execution optional
+myst_enable_extensions = [
+ "colon_fence",
+ "deflist",
+ "linkify",
+]
+nb_execution_mode = "off"
+
+templates_path = ["_templates"]
+exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
+
+# Mock optional heavy dependencies for autodoc
+autodoc_mock_imports = [
+ "optuna",
+ "optuna.visualization",
+ "optuna.visualization.matplotlib",
+ "plotly",
+ "plotly.graph_objects",
+]
+
+# -- Options for HTML output -------------------------------------------------
+
+html_theme = "sphinx_book_theme"
+
+html_static_path = ["_static"]
+html_favicon = "_static/favicon-96x96.png"
+html_logo = "_static/logo.png"
+
+# Adjusted HTML theme options
+html_theme_options = {
+ "home_page_in_toc": True,
+ "show_navbar_depth": 2,
+ "show_toc_level": 2,
+ "icon_links": [
+ {
+ "name": "CODES GitHub",
+ "url": "https://github.com/robin-janssen/CODES-Benchmark",
+ "icon": "fa-brands fa-square-github",
+ "type": "fontawesome",
+ },
+ {
+ "name": "CODES Paper",
+ "url": "https://arxiv.org/abs/2410.20886",
+ "icon": "fa-solid fa-file-alt",
+ "type": "fontawesome",
+ },
+ ],
+ "icon_links_label": "Quick Links",
+}
+
+# Keep the default sidebar stack from sphinx-book-theme (empty dict -> theme defaults)
+html_sidebars = {}
+
+# Suppress noisy myst-nb warnings about Sphinx 10 API changes until upstream updates
+warnings.filterwarnings(
+ "ignore",
+ message=".*myst_nb\\.sphinx_\\.Parser\\.env.*",
+ category=RemovedInSphinx10Warning,
+)
+
+# -- Helpers ------------------------------------------------------------------
+
+PROJECT_ROOT = Path(__file__).resolve().parents[2]
+DATA_SOURCES = PROJECT_ROOT / "datasets" / "data_sources.yaml"
+GENERATED_DATASETS_TABLE = (
+ Path(__file__).resolve().parent / "reference" / "_datasets_table.rst"
+)
+
+
+def _clean_download_url(url: str) -> str:
+ """Strip the /files/... portion and query params so we link to the dataset landing page."""
+ parsed = urlparse(url)
+ cleaned_path = parsed.path.split("/files/")[0]
+ return f"{parsed.scheme}://{parsed.netloc}{cleaned_path}"
+
+
+def _write_dataset_table() -> None:
+ if not DATA_SOURCES.exists():
+ return
+ data = yaml.safe_load(DATA_SOURCES.read_text())
+ lines = [
+ ".. list-table::",
+ " :header-rows: 1",
+ "",
+ " * - Dataset",
+ " - Source",
+ ]
+ for name, url in sorted(data.items()):
+ cleaned = _clean_download_url(url)
+ lines.append(f" * - ``{name}``")
+ lines.append(f" - `{cleaned} <{cleaned}>`_")
+ content = "\n".join(lines) + "\n"
+ GENERATED_DATASETS_TABLE.write_text(content)
+
+
+def _on_builder_inited(app): # noqa: D401
+ _write_dataset_table()
+
+
+def setup(app):
+ app.connect("builder-inited", _on_builder_inited)
diff --git a/docs/source/getting-started.md b/docs/source/getting-started.md
new file mode 100644
index 00000000..904d4606
--- /dev/null
+++ b/docs/source/getting-started.md
@@ -0,0 +1,79 @@
+# Getting Started
+
+CODES Benchmark helps you compare surrogate models for coupled ODE systems by running consistent training, evaluation, and reporting pipelines. This page summarizes the minimum you need to install the project, configure a run, and validate that everything is wired correctly.
+
+## Prerequisites
+
+- Python 3.10 with `pip` (we recommend `uv` for faster installs, but plain pip works)
+- (Optional) CUDA-capable GPU if you want to reproduce the default configurations
+- Enough disk space to store downloaded datasets and the checkpoints created under `trained/` and `results/`
+
+## Installation
+
+We recommend [`uv`](https://docs.astral.sh/uv/) because the repository already ships with `pyproject.toml` + `uv.lock`. Cloning and syncing is enough—`uv run` will automatically create/update the environment the first time you execute a command.
+
+**uv workflow (recommended)**
+
+```bash
+git clone https://github.com/robin-janssen/CODES-Benchmark.git
+cd CODES-Benchmark
+uv sync # creates .venv using uv.lock
+uv run python -c "import codes; print('CODES ready!')" # optional smoke test
+```
+
+After this, you can prefix any command with `uv run` (for example `uv run python run_training.py --config config.yaml`) and uv will ensure dependencies are in place.
+
+**pip / virtualenv fallback**
+
+```bash
+git clone https://github.com/robin-janssen/CODES-Benchmark.git
+cd CODES-Benchmark
+python -m venv .venv && source .venv/bin/activate
+pip install -e .
+pip install -r requirements.txt
+```
+
+Both approaches expose the `codes` package locally so scripts such as `run_training.py` and `run_eval.py` can import it without extra path hacks. Installing the pinned requirements ensures feature parity with CI and the documentation build.
+
+## Configure a run
+
+Benchmarks usually follow the pattern: **hyperparameter tuning → full training → evaluation/reporting**. Before diving into the advanced knobs, start from the minimal configuration we ship in `config.yaml`:
+
+```yaml
+training_id: "my_first_benchmark"
+surrogates: ["MultiONet"]
+batch_size: [65536]
+epochs: [200]
+dataset:
+ name: "osu2008"
+devices: ["cuda:0"] # or ["cpu"] if you lack a GPU
+```
+
+1. Copy `config.yaml` (or `config_full.yaml` for inspiration) to a new name inside the repo.
+2. Update the surrogate list, dataset, devices, and optional study switches such as `interpolation`, `extrapolation`, `sparse`, `batch_scaling`, or `uncertainty`.
+3. Keep the file under version control so you can trace results.
+
+See the [configuration reference](reference/configuration.md) for a complete list of keys, defaults, and tips.
+
+## Run your first benchmark
+
+Use the minimal configuration above (or a copy of it) to perform a smoke test:
+
+1. **Train** the requested surrogate (creates `trained/`):
+ ```bash
+ python run_training.py --config path/to/your_config.yaml
+ ```
+2. **Evaluate / benchmark** everything that was trained:
+ ```bash
+ python run_eval.py --config path/to/your_config.yaml
+ ```
+3. Inspect the generated tables under `results/` and the plots inside `plots/`.
+
+Every CLI script honours the `--config` flag and logs progress to the console.
+
+## Where to go next
+
+- [Running benchmarks](guides/running-benchmarks/index.md): deep-dive into the workflow, multi-device execution, and troubleshooting.
+- [Configuration reference](reference/configuration.md): every knob explained.
+- [Dataset catalog](reference/datasets.md): discover the bundled datasets and download URLs.
+- [Tutorials](tutorials/index.md): notebooks that demonstrate data loading, custom analysis, and plotting pipelines.
diff --git a/docs/source/guides/extending-benchmark.md b/docs/source/guides/extending-benchmark.md
new file mode 100644
index 00000000..a28cd832
--- /dev/null
+++ b/docs/source/guides/extending-benchmark.md
@@ -0,0 +1,58 @@
+# Extending The Benchmark
+
+Plugging in new datasets or surrogates should not require rewriting orchestration code. This guide captures the supported extension points and the conventions that keep everything interoperable.
+
+## Add a dataset
+
+Use `datasets/_data_generation/make_new_dataset.py` as a working example. It shows how to generate synthetic trajectories, parameters, and timesteps before calling `codes.create_dataset`.
+
+1. **Shape your arrays**
+ - `data`: `(n_samples, n_timesteps, n_quantities)` — the raw trajectories.
+ - `params` (optional): `(n_samples, n_parameters)` — per-trajectory parameters.
+ - `timesteps`: `(n_timesteps,)` — shared timeline for every trajectory.
+ - `labels` (optional): list of quantity names with length `n_quantities`.
+2. **Call `create_dataset`**
+ ```python
+ from codes import create_dataset
+
+ create_dataset(
+ "my_new_dataset",
+ data=full_dataset,
+ params=full_params,
+ timesteps=timesteps,
+ labels=labels,
+ split=(0.7, 0.1, 0.2), # train/test/val ratios
+ )
+ ```
+ `create_dataset` writes `datasets/my_new_dataset/data.hdf5` with the following groups: `train`, `test`, `val`, optional `*_params`, and `timesteps`. The helper also ensures consistent shuffling and folder creation.
+3. **Register the download link** (optional) in `datasets/data_sources.yaml` so `scripts/download_datasets.py` knows where to fetch the data. The docs pull this file automatically, so your dataset will appear in the catalog without extra work.
+4. **Reference the dataset in configs** via `dataset.name`. Log transforms / normalization flags in the config can stay unchanged unless your data needs special treatment.
+
+Once the folder exists, all CLI entry points (`run_training.py`, `run_eval.py`, `run_tuning.py`) will automatically pick it up based on `dataset.name`.
+
+## Add a surrogate model
+
+Every surrogate must inherit from `codes.surrogates.AbstractSurrogate.AbstractSurrogateModel`. This class wires together data preparation, training, logging, checkpointing, and evaluation.
+
+1. **Implement the class** under `codes/surrogates//.py`.
+ - `forward(self, inputs) -> tuple[Tensor, Tensor]`: receives exactly what `prepare_data` emits (for example `(branch_input, trunk_input, targets)` in MultiONet). Return `(predictions, targets)` in that order so the shared training/validation utilities can compute metrics without model-specific branching.
+ - `prepare_data(self, datasets, metadata, …) -> tuple[DataLoader, DataLoader]`: build the train/validation dataloaders from the benchmark datasets. This is where you slice parameter sets, apply custom transforms, or pack tuples that your `forward` expects.
+ - `fit(self, train_loader, val_loader, …)`: implements the training loop. `AbstractSurrogateModel.train_and_save_model` sets up everything (optimizers, schedulers, loggers) before calling `fit`, so you can focus on iterating over the loaders and calling `self.validate`.
+2. **Reuse shared helpers**
+ - `self.setup_progress_bar(...)` draws status updates without clashing with the multi-process trainer.
+ - `self.predict(loader, ...)` is the canonical way to produce predictions/targets during validation and evaluation—this ensures consistent batching, buffer pre-allocation, and shape handling for every surrogate. Test your model with it to guarantee compatibility.
+ - `self.validate(...)` bundles metric computation, Optuna pruning hooks, checkpointing, and logging. Call it from `fit` (typically once per epoch) after computing validation losses via `self.predict`.
+ - Other protected helpers (`save`, `load`, `denormalise`, optimizer/scheduler factories) already encode CODES conventions; override only when a model’s requirements truly differ.
+3. **Register the surrogate** by appending `AbstractSurrogateModel.register("MySurrogateName")` at the end of the file that defines the class. Use the string you want users to reference in `config.yaml`. Without this hook the CLI cannot instantiate your model.
+4. **Expose configuration**
+ - Create a companion config dataclass (e.g., `deeponet_config.py`’s `MultiONetBaseConfig`). Inherit from `AbstractSurrogateBaseConfig` whenever possible so shared hyperparameters (learning rate, optimizer, scheduler, loss, activation) remain documented and gain sensible defaults.
+ - Keep model-specific knobs inside that dataclass rather than the global config; users set them via the `surrogate_configs` section without touching other surrogates. The main benchmark config should stay model-agnostic unless you intentionally expose a cross-surrogate toggle.
+5. **Checkpointing + evaluation**
+ - `AbstractSurrogateModel` already serializes weights, optimizer state, and scheduler state, and `run_eval.py` expects that layout. If you modify the format, verify that evaluation still loads checkpoints without custom flags.
+ - `predict` and the evaluation pipeline assume consistent output shapes, so avoid ad-hoc reshaping in downstream scripts—handle it in `forward` or `prepare_data`. This uniform path is what keeps the benchmark fair across models.
+
+Before submitting a PR or relying on the surrogate in large runs, train it with the minimal config and confirm that `run_eval.py` + `predict` behave as expected.
+
+## Customize benchmark modes
+
+New benchmark modes (beyond interpolation/extrapolation/sparse/batch-scaling/uncertainty/iterative) follow the same pattern: place an object with `enabled: bool` and the parameters you want to sweep under the top-level config. Detailed documentation for additional modes is coming soon—refer to [Running Benchmarks](running-benchmarks/index.md) for the currently supported modalities and CLI flags.
diff --git a/docs/source/guides/running-benchmarks/accuracy-metrics.md b/docs/source/guides/running-benchmarks/accuracy-metrics.md
new file mode 100644
index 00000000..ce90c428
--- /dev/null
+++ b/docs/source/guides/running-benchmarks/accuracy-metrics.md
@@ -0,0 +1,23 @@
+# Accuracy Metrics
+
+Chemical-abundance trajectories span many orders of magnitude, which makes standard absolute or relative error metrics misleading. CODES therefore measures accuracy in log space everywhere—during tuning, training, and evaluation.
+
+## Log-space absolute error
+
+- **mLAE** (mean log absolute error): the mean of `|log10(pred) - log10(target)|` across all species, timesteps, and trajectories.
+- **LAE99`**: the 99th percentile of the same distribution. This captures worst-case behaviour without being dominated by a handful of outliers.
+
+Because the models are trained on log-transformed abundances, these values directly express errors in orders of magnitude (dex).
+
+## Why not relative error?
+
+- Relative errors explode when dividing by very small abundances and become asymmetric (large overestimations vs. capped underestimations).
+- Adding thresholds to stabilize the denominator implicitly weights species without a clear physical justification.
+
+Log-space metrics avoid these pitfalls: they treat over- and under-predictions symmetrically and remain finite even for tiny abundances.
+
+## Where metrics appear
+
+- **Tuning** — single-objective studies minimize LAE99; dual-objective studies minimize both LAE99 and inference time.
+- **Training** — loss functions (e.g., Smooth L1, MSE) operate on log-transformed outputs, so optimization aligns with the evaluation metrics.
+- **Evaluation** — core reports always include mLAE and LAE99, alongside timing/compute metrics. Optional diagnostics (loss curves, gradient correlations) are derived from the same log-space signals.
diff --git a/docs/source/guides/running-benchmarks/architectures.md b/docs/source/guides/running-benchmarks/architectures.md
new file mode 100644
index 00000000..3c197ce1
--- /dev/null
+++ b/docs/source/guides/running-benchmarks/architectures.md
@@ -0,0 +1,73 @@
+# Baseline Architectures
+
+CODES ships with four surrogate families. Each lives under `codes/surrogates/` with a dedicated config dataclass (`*_config.py`). This page summarizes their design so you know which model to pick or extend.
+
+All four share the same `AbstractSurrogateModel` interface: they receive `(initial state, desired time, optional parameters)` and emit the predicted state at that time. Training data are sampled at predefined timesteps, but each architecture treats time as a continuous input so you can query any point within the training horizon. Timesteps are defined per dataset but otherwise flexible, they can be equispaced, logarithmic, or irregular. They are always normalized to `[0, 1]` before feeding into the models.
+
+## MultiONet (DeepONet variant)
+
+- **Files**: `codes/surrogates/DeepONet/deeponet.py`, config `deeponet_config.py`.
+- **Idea**: Operator network with a **branch net** (ingests state + fixed parameters) and a **trunk net** (ingests timesteps). Their outputs form an inner product that predicts the next state.
+- **Key knobs**: `hidden_size`, number of branch/trunk layers, `output_factor`, activation function, loss choice (`MSE` or `SmoothL1`), and optimizer/scheduler inherited from `AbstractSurrogateBaseConfig`. Optional parameter routing lets you feed physical parameters either into the branch (state) or trunk (time) network.
+- **When to use**: Strong baseline for irregular sampling and parametric tasks because branch/trunk inputs can be customized via `prepare_data`.
+
+```{figure} ../../_static/MultiONet.png
+---
+align: center
+alt: MultiONet schematic
+---
+MultiONet splits inputs across branch (state) and trunk (time) networks before combining them.
+```
+
+## FullyConnected (FCNN)
+
+- **Files**: `codes/surrogates/FCNN/fcnn.py`, config `fcnn_config.py`.
+- **Idea**: Classic multilayer perceptron that flattens the trajectory context and predicts the next timestep. Parameters are simply concatenated to the inputs, keeping the architecture bias-free.
+- **Key knobs**: `num_hidden_layers`, `hidden_size`, activation, dropout, and optimizer settings from the base config.
+- **When to use**: Fast baseline for small- to medium-scale datasets; useful sanity check before moving to heavier models.
+
+```{figure} ../../_static/FullyConnected.png
+---
+align: center
+alt: FullyConnected schematic
+---
+FCNN consumes the initial state, desired time, and optional parameters with a single fully connected stack.
+```
+
+## LatentNeuralODE
+
+- **Files**: `codes/surrogates/LatentNeuralODE/latent_neural_ode.py`, config `latent_neural_ode_config.py`.
+- **Idea**: Encodes sequences into a latent space, integrates dynamics via a neural ODE block, then decodes back to the observable state.
+- **Key knobs**: Encoder/decoder depth/width, latent dimensionality, ODE solver depth (`ode_layers`) and width, activation choice, KL weighting, and rollout horizon. Parameters can enter via the encoder (concatenated with abundances) or as inputs to the gradient network, influencing latent dynamics rather than the encoding itself.
+- **When to use**: Long-horizon forecasting or scenarios where latent dynamics better capture system behavior than direct mapping.
+
+```{figure} ../../_static/LatentNeuralODE.png
+---
+align: center
+alt: LatentNeuralODE schematic
+---
+LatentNeuralODE encodes trajectories, integrates the latent dynamics, and decodes back to observables.
+```
+
+## LatentPolynomial (LatentPoly)
+
+- **Files**: `codes/surrogates/LatentPolynomial/latent_poly.py`, config `latent_poly_config.py`.
+- **Idea**: Encodes trajectories into a latent state, fits polynomial dynamics in that space, and decodes to observations. Trades expressiveness for analytic structure.
+- **Key knobs**: Polynomial degree, latent feature count, encoder/decoder layer counts and widths, activation, and regularization.
+- **When to use**: Systems known to follow low-order polynomial dynamics or when interpretability of the latent dynamics matters. Parameters can optionally drive a dedicated “polynomial network” that predicts the coefficients used during latent evolution.
+
+```{figure} ../../_static/LatentPoly.png
+---
+align: center
+alt: LatentPoly schematic
+---
+LatentPoly learns parameter-aware polynomial coefficients that evolve the latent state.
+```
+
+## Working with configs
+
+- Each surrogate has a config dataclass inheriting from `AbstractSurrogateBaseConfig`. Shared fields include learning rate, optimizer (`adamw`/`sgd`), scheduler (`cosine`, `poly`, `schedulefree`), activation, and loss definition.
+- Dataset-specific defaults live in `datasets//surrogates_config.py`. When `dataset.use_optimal_params: true`, CODES loads the corresponding dataclass and merges it with any overrides from `config.yaml`.
+- During tuning you reference config fields by name inside `optuna_params` so the sampled values get injected before training. Optional architectural variants (e.g., parameter routing choices) are exposed as categorical hyperparameters so studies can discover the best scheme per dataset.
+
+If you introduce a new surrogate, follow the same pattern: `codes/surrogates//`, config dataclass, and an entry in the docs so downstream users know how to configure it.
diff --git a/docs/source/guides/running-benchmarks/evaluation.md b/docs/source/guides/running-benchmarks/evaluation.md
new file mode 100644
index 00000000..85ae9bce
--- /dev/null
+++ b/docs/source/guides/running-benchmarks/evaluation.md
@@ -0,0 +1,38 @@
+# Evaluation & Reporting
+
+`run_eval.py` replays your configuration against the saved checkpoints, executes whichever evaluation suites you enabled, and collates the results under `results//` and `plots//`. All accuracy metrics are reported in log-space (see :doc:`accuracy-metrics`) so the evaluations align with how models were trained and tuned.
+
+## Launching evaluations
+
+```bash
+python run_eval.py --config config.yaml
+```
+
+- **Config fidelity** — `check_benchmark` compares the provided config to `trained//config.yaml`. Dataset settings, modalities, and surrogate names must match those used during training so that required checkpoints exist. You may disable modalities (e.g., skip an interpolation analysis even if it was trained) by toggling the evaluation switches, but you cannot evaluate a modality that lacks trained checkpoints.
+- **Devices** — the same `devices` list is reused for evaluation. Override `CUDA_VISIBLE_DEVICES` if you want to force CPU/GPU placement without editing the config.
+- **Per-surrogate loop** — for every entry in `surrogates`, `run_eval.py` loads the registered class, rehydrates the checkpoints, and calls `run_benchmark`. Missing classes or checkpoints are flagged early via `check_surrogate`.
+
+## What gets produced
+
+- `results///` — YAML and CSV files capturing numerical metrics (log-space MAE, LAE99, inference time, compute footprint, etc.).
+- `plots//` — visual artifacts: error heatmaps, loss curves, catastrophic detection plots, uncertainty charts, and more depending on the enabled evaluation switches.
+- `results//all_metrics.csv` + `metrics_table.csv` — flattened tables for spreadsheet analysis and the optional `compare: true` stage.
+
+## Evaluation switches recap
+
+| Switch | Effect |
+| --- | --- |
+| `losses` | Saves epoch-wise train/test loss plots. |
+| `iterative` | Runs multi-step rollouts to measure drift over time, defaulting to brackets of 10 steps. |
+| `gradients` | Correlates gradient norms with prediction errors. |
+| `timing` | Measures inference latency (multiple passes). |
+| `compute` | Records parameter counts and memory usage. |
+| `compare` | Builds cross-surrogate comparison tables/plots (requires ≥2 surrogates). |
+
+Leave switches off to skip expensive analyses and plotting functions. But compared to the training, eval is very lightweight: it only loads models and runs inference on the test set, and hence usually takes a few minutes at most.
+
+## Troubleshooting
+
+- **Config mismatch** — `check_benchmark` errors usually mean `training_id` or modality toggles differ between training and evaluation. Point the evaluator at the stored config (`trained//config.yaml`) or reconcile the differences manually.
+- **Missing checkpoints** — make sure the corresponding modality ran successfully. The evaluator cannot invent models that were never trained. You want to check the folder with the models at `trained//`. If the training was complete, you will find a `completed.txt` file there, otherwise inspect `task_list.json` for failed or pending tasks.
+- **Large runs** — evaluations are read-heavy. Run them on fast storage and keep the dataset cached locally if possible.
diff --git a/docs/source/guides/running-benchmarks/index.md b/docs/source/guides/running-benchmarks/index.md
new file mode 100644
index 00000000..15b23448
--- /dev/null
+++ b/docs/source/guides/running-benchmarks/index.md
@@ -0,0 +1,25 @@
+# Running Benchmarks
+
+This hub explains how the benchmark orchestrator works end to end. A typical run looks like this:
+
+1. **Hyperparameter tuning** — Optuna samples candidate configs per surrogate and stores the trials in a database. You decide which trials become new defaults (single- or multi-objective).
+2. **Training** — `run_training.py` reads your benchmark config, schedules “main” runs + optional modalities, and produces checkpoints under `trained//`.
+3. **Evaluation** — `run_eval.py` reloads those checkpoints, applies whichever evaluation suites you enabled, and writes structured metrics/plots.
+
+Two supporting sections describe the **baseline architectures** that ship with CODES and the **modalities** that expand each training run.
+
+```{admonition} TL;DR
+Follow the links below chronologically when learning the system; jump directly to modalities or architectures when you need implementation specifics.
+```
+
+```{toctree}
+:maxdepth: 1
+
+tuning
+training
+evaluation
+modalities
+architectures
+accuracy-metrics
+uncertainty
+```
diff --git a/docs/source/guides/running-benchmarks/modalities.md b/docs/source/guides/running-benchmarks/modalities.md
new file mode 100644
index 00000000..d27bfbb9
--- /dev/null
+++ b/docs/source/guides/running-benchmarks/modalities.md
@@ -0,0 +1,71 @@
+# Modalities
+
+Modalities extend the “main” training run by stressing models under additional data regimes. Every modality toggled in `config.yaml` multiplies the number of checkpoints produced. This page explains what each block does and how it impacts training/evaluation time.
+
+## Overview
+
+| Modality | Configuration Keys | What it does | Training impact | Evaluation impact |
+| --- | --- | --- | --- | --- |
+| Interpolation | `interpolation.enabled`, `interpolation.intervals` | Removes segments of the time axis and asks the surrogate to fill them in. | One extra training per interval per surrogate. | Enables interpolation metrics/plots when `losses`/`iterative`/`compare` reference them. |
+| Extrapolation | `extrapolation.enabled`, `extrapolation.cutoffs` | Trains models on truncated trajectories and evaluates beyond the cutoff. | One extra training per cutoff per surrogate. | Produces extrapolation error charts during evaluation. |
+| Sparse data | `sparse.enabled`, `sparse.factors` | Down-samples observations before training to test data efficiency. | One extra training per sparsity factor. | Evaluation compares sparse-vs-dense performance. |
+| Batch scaling | `batch_scaling.enabled`, `batch_scaling.sizes` | Sweeps different batch-size multipliers (floats or fractions). | One extra training per batch size (metric equals the literal batch value). | Timing/compute comparisons capture throughput scaling. |
+| Uncertainty (Deep Ensembles) | `uncertainty.enabled`, `uncertainty.ensemble_size` | Trains multiple seeds of the same configuration to estimate epistemic uncertainty. | Adds `(ensemble_size - 1)` trainings per surrogate (the “main” model counts as the first member). | Unlocks uncertainty plots (e.g., catastrophic detection curves). |
+
+All modalities share the same dataset preprocessing as the main run. Seeds are offset to keep ensembles diverse but deterministic.
+
+```{figure} ../../_static/ScalingPlots.png
+---
+align: center
+alt: Scaling plots across modalities
+---
+Scaling curves show how the MAE evolves for different modality settings and surrogate architectures. Use them to gauge how much extra training time is required to reach diminishing returns.
+```
+
+## Practical guidance
+
+- **Start narrow** — toggle one modality at a time when prototyping (interpolation is a good first candidate). Modalities multiply training duration rapidly.
+- **Resume friendly** — because each modality corresponds to independent tasks, you can stop the training script mid-run and resume later; completed modalities stay finished.
+- **Evaluation switches are separate** — Modalities that were trained are evaluated automatically, but the extra diagnostics are guarded by evaluation switches (`losses`, `timing`, `compute`, …). Enable those in your config if you want the corresponding plots/tables.
+
+## Interpolation
+
+Interpolation studies remove every _n_-th timestep, forcing the surrogate to reconstruct the skipped samples. This probes how well the model captures short-term continuity.
+
+```{figure} ../../_static/Interpolation.png
+---
+align: center
+alt: Interpolation modality example
+---
+Interpolation MAE over time for several interval widths. Wider gaps create bigger spikes but also highlight which surrogates remain stable.
+```
+
+## Extrapolation
+
+Extrapolation truncates trajectories earlier than usual, then evaluates them beyond the cutoff. It measures long-horizon drift and whether error grows gracefully or catastrophically.
+
+```{figure} ../../_static/Extrapolation.png
+---
+align: center
+alt: Extrapolation modality example
+---
+Extrapolation runs reveal how quickly MAE explodes once the model leaves the training time window.
+```
+
+## Sparse data
+
+Sparse training reduces the number of observations before fitting, emulating limited-data scenarios. Use this to judge how robust an architecture is when only every `k`-th observation is available.
+
+```{figure} ../../_static/Sparse.png
+---
+align: center
+alt: Sparse modality example
+---
+Down-sampling trajectories shows how MAE changes with fewer observations; FCNN tends to degrade earlier than the latent models.
+```
+
+## Batch scaling
+
+Batch scaling sweeps different batch sizes and records how accuracy/timing behave. This is useful to identify sweet spots for throughput without impacting convergence too heavily. Combine the results with the `timing` evaluation to compare throughput across surrogates.
+
+See the :doc:`configuration reference ` for the exact YAML schema and defaults.
diff --git a/docs/source/guides/running-benchmarks/training.md b/docs/source/guides/running-benchmarks/training.md
new file mode 100644
index 00000000..a69e7e70
--- /dev/null
+++ b/docs/source/guides/running-benchmarks/training.md
@@ -0,0 +1,32 @@
+# Training Surrogates
+
+`run_training.py` converts your benchmark config into a concrete list of training jobs, executes them sequentially or in parallel, and saves everything under `trained//`. All surrogates share the same pipeline: they learn trajectories represented at fixed timesteps, but the models learn to predict the state at any continuous time within the training horizon given an initial condition (and optional physical parameters).
+
+## From config to task list
+
+1. **Ordered surrogates** — the entries under `surrogates`, `batch_size`, and `epochs` must align. The `i`‑th surrogate uses `batch_size[i]` and `epochs[i]`.
+2. **Main run + modalities** — for each surrogate, CODES always schedules a `"main"` task. Modalities (`interpolation`, `extrapolation`, `sparse`, `batch_scaling`, `uncertainty`) add additional tasks: one per value you list, per surrogate. For example, enabling interpolation with 4 intervals on 2 surrogates yields `2 (surrogates) × 4 (intervals)` extra trainings. Modalities act as stress tests (scaling curves) rather than new architectures.
+3. **Task persistence** — tasks are written to `trained//task_list.json`. Completed tasks are removed from this file. If you interrupt and re-run `run_training.py`, remaining tasks resume automatically.
+4. **Config snapshot** — the script copies your `config.yaml` into `trained//config.yaml`. Evaluation uses this snapshot to verify compatibility.
+
+## Execution model
+
+```bash
+python run_training.py --config config.yaml
+```
+
+- **Devices** — `run_training.py` reads `config.devices`. If you pass a single entry (e.g., `["cuda:0"]` or `"cpu"`), tasks run sequentially. Multiple entries trigger a thread per device; each worker pops tasks from a queue and calls `train_and_save_model`.
+- **Determinism** — seeds are derived from the base `seed` plus the modality value so ensemble members differ but remain reproducible.
+- **Data loading** — the script downloads/preprocesses the requested dataset once per run and reuses logarithmic/normalization settings from the config.
+- **Checkpointing** — setting `checkpoint: true` stores intermediate checkpoints via the shared `validate` logic. Otherwise only the final model (per task) is saved as `__.pth` under `trained//`.
+- **Failure handling** — failed tasks stay in `task_list.json`. Fix the root cause and rerun to continue. When all tasks finish successfully, the task list is deleted and `completed.txt` is written.
+
+## Modalities vs. evaluation switches
+
+Modalities directly increase the number of models to train. See :doc:`modalities` for the available studies and their impact. Evaluation switches (`losses`, `gradients`, `timing`, `compute`, `compare`, `iterative`) do **not** create extra training tasks—they only control which analyses run later (details in :doc:`evaluation`). Plan your training duration around the modalities you enable.
+
+## Practical tips
+
+- Profiling new configs? Lower `epochs`/`batch_size` temporarily, or set `dataset.subset_factor` to work on fewer trajectories.
+- Keep `training_id` unique per experiment so you can compare checkpoints across branches.
+- If you plan to resume frequently, commit the generated `trained//config.yaml` alongside your original config for traceability.
diff --git a/docs/source/guides/running-benchmarks/tuning.md b/docs/source/guides/running-benchmarks/tuning.md
new file mode 100644
index 00000000..eae6847a
--- /dev/null
+++ b/docs/source/guides/running-benchmarks/tuning.md
@@ -0,0 +1,160 @@
+# Hyperparameter Tuning
+
+Optuna powers the tuning stage. Each study runs inside `run_tuning.py`, which manages device pools, Optuna storage, and all surrogate-specific settings.
+
+## Workspace layout
+
+1. Create a config file under `configs/tuning/` (for example `configs/tuning/sqlite_quickstart.yaml`). Each config must contain a unique `tuning_id`.
+2. Launch the tuner and pass the config path:
+ ```bash
+ python run_tuning.py --config configs/tuning/sqlite_quickstart.yaml
+ ```
+3. CODES copies the config into `tuned//optuna_config.yaml` for reproducibility. On subsequent runs it compares the stored copy against the newly provided file and asks whether you want to reuse the stored config, overwrite it (the old one is backed up), or abort.
+
+`tuning_id` becomes the prefix for all Optuna studies (e.g., `primordial_MultiONet`). The script also creates `tuned//models/` to store intermediate checkpoints when you enable pruning.
+
+## Config anatomy
+
+Below is a condensed version of `configs/tuning/sqlite_quickstart.yaml` using SQLite storage for zero setup. Adjust datasets/surrogates as needed.
+
+```yaml
+seed: 42
+tuning_id: "primordial"
+dataset:
+ name: primordial
+ log10_transform: true
+ normalise: minmax
+devices: ["cuda:0"]
+prune: true
+multi_objective: true
+population_size: 50
+
+storage:
+ backend: "sqlite"
+ path: "tuned/primordial/primordial.db"
+
+surrogates:
+ - name: MultiONet
+ batch_size: 4096
+ epochs: 8192
+ trials: 120
+ optuna_params:
+ activation:
+ type: categorical
+ choices: ["ReLU", "LeakyReLU", "Tanh", "GELU", "Softplus"]
+ hidden_size:
+ type: int
+ low: 10
+ high: 500
+ learning_rate:
+ type: float
+ low: 1.0e-6
+ high: 1.0e-3
+ log: true
+ output_factor:
+ type: int
+ low: 1
+ high: 200
+```
+
+### Key sections
+
+- **dataset** — mirrors the training config and ensures Optuna downloads/configures the same data pipeline.
+- **devices** — every entry becomes a worker slot. `run_tuning.py` keeps a queue (`queue.Queue`) of device tokens and runs Optuna with `n_jobs = len(devices)`. SQLite storage warns about concurrent writers; you can list multiple devices, but heavy parallelism works best with Postgres.
+- **storage** — choose between `sqlite` (single-file DB, no external services) and `postgres` (scales to many workers). If omitted, CODES defaults to Postgres for backward compatibility and expects `postgres_config` to be present.
+- **postgres_config** — only required when `storage.backend: "postgres"`. Supports
+ - `mode: local`: launches/validates a local PostgreSQL instance (binaries in `database_folder`).
+ - `mode: remote`: connects to an existing server (set `host`, `port`, `user`, and optionally `password` or rely on `PGPASSWORD`).
+- **surrogates** — per-architecture specs. Each entry sets:
+ - `batch_size` / `epochs`: used to build the objective.
+ - `trials`: maximum valid trials for that surrogate.
+ - `optuna_params`: the search space. The keys correspond to attributes on the surrogate’s config dataclass; Optuna writes the sampled values into that config before training.
+
+You can add `global_optuna_params` for common parameters, enable `fine: true` for automatic “around-the-best” refinement, or toggle between single-objective (`direction="minimize"`) and dual-objective (`directions=["minimize","minimize"]`) mode. In multi-objective runs we typically optimize log-space accuracy (LAE$_{99}$) and inference time, but you can choose any pair of metrics.
+
+## What `run_tuning.py` does
+
+1. Loads the YAML and copies it into `tuned//` (via `prepare_workspace`).
+2. Initializes the Optuna database (SQLite file or Postgres), prompting if a study with the same `tuning_id` already exists.
+3. Downloads the dataset once per run.
+ 4. Iterates over the `surrogates` list. For each surrogate it:
+ - Builds a study name (`_`).
+ - Selects the sampler/pruner (`TPESampler` + Hyperband or NSGA-II with no pruning).
+ - Creates a device queue and `objective_fn = create_objective(...)`.
+ - Calls `study.optimize()` with `n_jobs=len(devices)`. Each Optuna worker pulls a device token, trains a model with the sampled hyperparameters, and returns the objective(s).
+ - Tracks ETA via `tqdm`. The helper `MaxValidTrialsCallback` stops once enough successful trials finished (OOM and time-pruned trials are ignored).
+
+You can resume a study by rerunning the same command; Optuna reuses the storage and continues sampling until `n_trials` valid runs exist. Trial budgets are usually sized heuristically (e.g., `~15 ×` the number of tuned hyperparameters), but you can override per surrogate via the `trials` field.
+
+## Capturing the best hyperparameters
+
+CODES does not auto-promote trial settings. Use Optuna’s tooling to inspect studies:
+
+- Python REPL / script:
+ ```python
+ import optuna
+ study = optuna.create_study(
+ study_name="primordial_MultiONet",
+ storage="postgresql+psycopg2://optuna_user@localhost:5432/primordial",
+ direction="minimize",
+ load_if_exists=True,
+ )
+ print(study.best_trial.params)
+ ```
+- [Optuna Dashboard](https://optuna.github.io/optuna-dashboard/) or `optuna.visualization`.
+
+Dual-objective runs produce Pareto fronts like the ones shown in the paper excerpt. You can manually pick a “knee point” trade-off (accuracy vs. latency) or script your own selection rule. Whatever you choose, feed the accepted settings back into `config.yaml` under `surrogate_configs` or store dataset-specific defaults in `datasets//surrogates_config.py` (dataclasses). Those defaults load automatically when `dataset.use_optimal_params` is `true`; setting `use_optimal_params: false` switches back to plain config-defined hyperparameters.
+
+## Advanced options
+
+### Postgres storage
+
+For large-scale or multi-GPU sweeps, switch the storage block to Postgres:
+
+```yaml
+storage:
+ backend: "postgres"
+
+postgres_config:
+ mode: "local" # or "remote"
+ host: "localhost"
+ port: 5432
+ user: "optuna_user"
+ database_folder: "/path/to/postgres/"
+```
+
+If the `storage` section is omitted entirely, CODES assumes `backend: "postgres"` to remain backward compatible. Postgres handles concurrent writers gracefully, so `devices` can list many GPUs without hitting `database is locked` errors.
+
+### Fine-tuning stage
+
+Setting `fine: true` tells CODES to derive a narrow search space around the best-known configuration for each surrogate (taken from previous runs or dataset defaults):
+
+```yaml
+fine: true
+```
+
+The first run (with `fine: false`) explores the full search space and establishes a good baseline. The optional fine stage then:
+
+1. Builds tight bounds around every tunable scalar (log-space ±factor) using `build_fine_optuna_params`.
+2. Overrides the trial budget to `max(10 × N, 10)` where `N` is the number of fine-tunable parameters.
+3. Prints the refined ranges per surrogate and stores them in `tuned//fine_summary.yaml`.
+
+This two-step process saves compute by spending most trials in promising regions rather than re-sampling the entire space.
+
+### Conditional parameter sampling
+
+Some hyperparameters only matter when a parent switch takes a specific value (e.g., `momentum` is relevant only for SGD, `poly_power` only for the polynomial scheduler). The tuner encodes these relationships in `make_optuna_params`: it samples parent switches first and then conditionally samples child parameters. This prevents Optuna from proposing incompatible combinations (such as a momentum value while using Adam) that would otherwise waste trials or require manual filtering. You can still expose child parameters directly if desired; the helper samples them once the relevant switch is active.
+
+### Time-based pruning
+
+To avoid exceptionally slow trials hogging resources, CODES automatically sets a runtime threshold after the initial warm-up period. Once enough successful trials complete, it computes `mean + std` of their durations and prunes future trials whose wall-clock time exceeds that threshold. In multi-objective mode, this applies to both accuracy/time objectives—the accuracy value is capped while the runtime objective records the observed duration.
+
+Disable this behaviour by adding:
+
+```yaml
+time_pruning: false
+```
+
+to your tuning config. When disabled, all trials run to completion unless Optuna’s other pruners intervene.
+
+Remember that tuning explores unconstrained space—double-check the resulting configs before launching expensive training sweeps.
diff --git a/docs/source/guides/running-benchmarks/uncertainty.md b/docs/source/guides/running-benchmarks/uncertainty.md
new file mode 100644
index 00000000..5f081cf6
--- /dev/null
+++ b/docs/source/guides/running-benchmarks/uncertainty.md
@@ -0,0 +1,50 @@
+# Uncertainty Quantification
+
+Surrogates occasionally make large errors even on familiar datasets. CODES provides optional deep ensembles (modalities → `uncertainty`) so you can quantify confidence and fall back to the numerical solver when needed.
+
+## Deep Ensemble basics
+
+- Enable by setting `uncertainty.enabled: true` and `ensemble_size: M` in your training config.
+- Training seeds are offset per ensemble member so each model explores a different region of the loss landscape.
+- During evaluation, predictions are averaged across members; the standard deviation across members becomes the **predicted uncertainty**.
+
+## What it buys you
+
+1. **Potential accuracy gains** — averaging multiple members can reduce variance and improve mean performance, especially on noisy datasets.
+2. **Reliability signals** — high predicted uncertainty correlates with large actual errors, enabling selective trust or fallback strategies.
+
+## Costs
+
+- Training time scales linearly with `M` because each ensemble member is a separate task (see the modalities guide).
+- Inference time also scales with `M` when you request uncertainty-aware predictions, though most CODES surrogates have lightweight forward passes so the overhead is modest.
+
+## Evaluation hooks
+
+- Enable the `uncertainty` modality plus `losses`/`compare` evaluation switches to generate catastrophic-detection plots, uncertainty-vs-error correlations, and ensemble statistics.
+- Use the outputs to design thresholds (e.g., “re-run the numerical solver when predicted uncertainty exceeds X dex”).
+
+```{figure} ../../_static/UQ.jpg
+---
+align: center
+alt: Catastrophic detection curves
+---
+Deep Ensembles can flag catastrophic errors: the higher the percentile of the predicted uncertainty you filter out, the lower the residual MAE.
+```
+
+```{figure} ../../_static/UQHeat.jpg
+---
+align: center
+alt: Uncertainty vs error heat map
+---
+Heatmaps show how predicted uncertainty correlates with true error, giving you confidence that the ensemble is well calibrated.
+```
+
+```{figure} ../../_static/UQExample.jpg
+---
+align: center
+alt: Ensemble prediction spread
+---
+Ensemble members provide a distribution over trajectories; the mean prediction (solid) and spread (shaded) highlight regions where the solver may need to fall back to ground truth.
+```
+
+Deep ensembles are currently the default UQ mechanism, but the interface leaves room for future methods (e.g., MC Dropout, SWAG). Whatever approach you adopt, test it in the evaluation stage to ensure uncertainty signals align with actual errors.
diff --git a/docs/source/index.rst b/docs/source/index.rst
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--- /dev/null
+++ b/docs/source/index.rst
@@ -0,0 +1,37 @@
+CODES Benchmark
+===============
+
+**CODES** is an end-to-end benchmark for coupled ODE surrogates. It ships with curated datasets, reproducible tuning/training/evaluation scripts, and a comprehensive API so you can extend the stack with new models.
+
+.. note::
+
+ A typical workflow follows **Tune → Train → Evaluate**. Use the links below to jump straight into the relevant guide, or follow the quickstart links in *Getting Started* to run a toy experiment on your machine.
+
+* **Guided Quickstart** — :doc:`getting-started` walks you through cloning, installing, and running a smoke-test benchmark.
+* **Benchmark Workflow** — :doc:`guides/running-benchmarks/index` explains how tuning feeds training and how evaluations consolidate metrics.
+* **Extend the Stack** — :doc:`guides/extending-benchmark` shows how to add datasets or surrogates without rewriting orchestration glue.
+* **API Reference** — :doc:`api-reference` explains how the generated package docs are organized and links to each module.
+
+Looking for a bird’s-eye view first? Start with the **User Guide**. Already configuring experiments or integrating your own model? Skip ahead to the **API Reference**. Either way, the sidebar mirrors the sections below so you are one click away from the next step.
+
+.. toctree::
+ :maxdepth: 2
+ :caption: User Guide
+
+ Getting Started
+ Running Benchmarks
+ Extending The Benchmark
+ Configuration Reference
+ Dataset Catalog
+ Tutorials
+
+.. toctree::
+ :maxdepth: 1
+ :caption: API Reference
+
+ API Reference Overview
+ codes.benchmark package
+ codes.surrogates package
+ codes.train package
+ codes.tune package
+ codes.utils package
diff --git a/docs/source/reference/_datasets_table.rst b/docs/source/reference/_datasets_table.rst
new file mode 100644
index 00000000..a00fe29a
--- /dev/null
+++ b/docs/source/reference/_datasets_table.rst
@@ -0,0 +1,37 @@
+.. list-table::
+ :header-rows: 1
+
+ * - Dataset
+ - Source
+ * - ``branca24``
+ - `https://zenodo.org/records/13624794 `_
+ * - ``branca_large_kyr``
+ - `https://zenodo.org/records/14204650 `_
+ * - ``branca_large_myr``
+ - `https://zenodo.org/records/14243267 `_
+ * - ``branca_norad``
+ - `https://zenodo.org/records/14041124 `_
+ * - ``cloud``
+ - `https://zenodo.org/records/18018572 `_
+ * - ``cloud_parametric``
+ - `https://zenodo.org/records/18018572 `_
+ * - ``coupled_oscillators``
+ - `https://zenodo.org/records/14717175 `_
+ * - ``lotka_volterra``
+ - `https://zenodo.org/records/14703867 `_
+ * - ``lv_parametric``
+ - `https://zenodo.org/records/15412214 `_
+ * - ``lv_parametric_no_params``
+ - `https://zenodo.org/records/15412297 `_
+ * - ``osu2008``
+ - `https://zenodo.org/records/13749089 `_
+ * - ``primordial``
+ - `https://zenodo.org/records/18018572 `_
+ * - ``primordial_parametric``
+ - `https://zenodo.org/records/18018572 `_
+ * - ``simple_ode``
+ - `https://zenodo.org/records/14878373 `_
+ * - ``simple_primordial``
+ - `https://zenodo.org/records/13754361 `_
+ * - ``simple_reaction``
+ - `https://zenodo.org/records/14712934 `_
diff --git a/docs/source/reference/configuration.md b/docs/source/reference/configuration.md
new file mode 100644
index 00000000..8e3c7db3
--- /dev/null
+++ b/docs/source/reference/configuration.md
@@ -0,0 +1,127 @@
+# Configuration Reference
+
+All orchestration happens through YAML configuration files. This page documents every configuration option used by `run_training.py` and `run_eval.py`, including which keys are required and which ones have defaults.
+
+## How defaults work
+
+- Missing keys fall back to the defaults listed below via `.get(...)`.
+- Modality blocks (`interpolation`, `extrapolation`, `sparse`, `batch_scaling`, `uncertainty`) are **disabled** if the entire block is omitted.
+- Evaluation switches (`losses`, `iterative`, `gradients`, `timing`, `compute`, `compare`) default to **False** if omitted.
+- Required keys must be provided or the run will fail.
+
+## Top-level keys
+
+| Key | Required | Default | Used by | Notes |
+| --- | --- | --- | --- | --- |
+| `training_id` | Yes | None | Training, Eval | Folder name under `trained/`, `results/`, and `plots/`. |
+| `surrogates` | Yes | None | Training, Eval | Ordered list of surrogate class names. |
+| `batch_size` | Yes | None | Training, Eval | Int or list aligned with `surrogates`. |
+| `epochs` | Yes | None | Training, Eval | Int or list aligned with `surrogates`. |
+| `devices` | Yes | None | Training, Eval | List of device strings (`cpu`, `cuda:0`, `mps`, ...). |
+| `seed` | No | `42` | Training | Random seed for training. |
+| `verbose` | No | `false` | Training, Eval | Extra data-loading logs. |
+| `checkpoint` | No | `false` | Training | Enables best-checkpoint saving per model. |
+
+## Dataset block
+
+| Key | Required | Default | Used by | Notes |
+| --- | --- | --- | --- | --- |
+| `dataset.name` | Yes | None | Training, Eval | Folder inside `datasets/`. |
+| `dataset.log10_transform` | No | `true` | Training, Eval | Log10 transform the data. |
+| `dataset.log10_transform_params` | No | `true` | Training, Eval | Log10 transform the parameters (if present). |
+| `dataset.normalise` | No | `"minmax"` | Training, Eval | `"minmax"`, `"standardise"`, or `"disable"`. |
+| `dataset.normalise_per_species` | No | `false` | Training, Eval | Normalize each species independently. |
+| `dataset.tolerance` | No | `None` | Training, Eval | Lower bound before log transform (`None` means no lower bound). |
+| `dataset.subset_factor` | No | `1` | Training | Down-samples data (smoke tests). |
+| `dataset.log_timesteps` | No | `false` | Eval | Used for plotting/log-time axes. |
+| `dataset.use_optimal_params` | No | `true` | Training, Eval | Load surrogate-specific defaults from dataset configs. |
+
+## Modality blocks (optional)
+
+All modality blocks are disabled if omitted. If `enabled: true`, the corresponding list/value is required.
+
+| Block | Required | Default | Keys when enabled |
+| --- | --- | --- | --- |
+| `interpolation` | No | disabled | `intervals` (list of ints) |
+| `extrapolation` | No | disabled | `cutoffs` (list of ints) |
+| `sparse` | No | disabled | `factors` (list of ints) |
+| `batch_scaling` | No | disabled | `sizes` (list of factors, e.g. `["1/2", "1/4"]`) |
+| `uncertainty` | No | disabled | `ensemble_size` (int) |
+
+## Evaluation switches
+
+All switches default to `false` if omitted.
+
+| Key | Default | Notes |
+| --- | --- | --- |
+| `losses` | `false` | Plots training and test losses. |
+| `iterative` | `false` | Iterative roll-out evaluation. |
+| `gradients` | `false` | Gradient vs error analysis. |
+| `timing` | `false` | Inference timing benchmarks. |
+| `compute` | `false` | Memory/parameter count benchmarks. |
+| `compare` | `false` | Cross-surrogate comparison plots/tables. |
+
+## Metric options
+
+| Key | Default | Notes |
+| --- | --- | --- |
+| `relative_error_threshold` | `0.0` | Denominator floor for relative error. |
+| `error_percentile` | `99` | Percentile used in error summaries. |
+
+## Full example config (defaults)
+
+This example includes every key with the default behavior applied. Required values are filled with common placeholders.
+
+```yaml
+# Required
+training_id: "example_run"
+surrogates: ["MultiONet"]
+batch_size: [65536]
+epochs: [200]
+devices: ["cpu"]
+
+# Optional (defaults)
+seed: 42
+verbose: false
+checkpoint: false
+
+dataset:
+ name: "osu2008"
+ log10_transform: true
+ log10_transform_params: true
+ normalise: "minmax"
+ normalise_per_species: false
+ tolerance: null
+ subset_factor: 1
+ log_timesteps: false
+ use_optimal_params: true
+
+# Modalities (disabled unless enabled)
+interpolation:
+ enabled: false
+ intervals: [2, 3, 4]
+extrapolation:
+ enabled: false
+ cutoffs: [50, 60, 70]
+sparse:
+ enabled: false
+ factors: [2, 4, 8]
+batch_scaling:
+ enabled: false
+ sizes: ["1/16", "1/8", "1/4", "1/2"]
+uncertainty:
+ enabled: false
+ ensemble_size: 5
+
+# Evaluation switches (default false)
+losses: false
+iterative: false
+gradients: false
+timing: false
+compute: false
+compare: false
+
+# Metric options
+relative_error_threshold: 0.0
+error_percentile: 99
+```
diff --git a/docs/source/reference/datasets.md b/docs/source/reference/datasets.md
new file mode 100644
index 00000000..7182f817
--- /dev/null
+++ b/docs/source/reference/datasets.md
@@ -0,0 +1,36 @@
+# Dataset Catalog
+
+The repository ships with multiple HDF5 datasets stored under `datasets//`. Each folder contains `data.hdf5` (train/test/val splits), optional parameter sets, and metadata such as timesteps. Datasets are typically produced via the `codes.create_dataset` helper described in :doc:`guides/extending-benchmark`, which ensures a consistent layout:
+
+- `train` / `test` / `val`: arrays with shape `(n_samples, n_timesteps, n_quantities)`.
+- Optional `*_params`: per-trajectory parameters (e.g., radiation field, metallicity).
+- `timesteps`: explicit timeline (logarithmic or linear).
+- Attributes such as `n_quantities`, `n_parameters`, and train/test/val counts for quick inspection (older datasets may infer these values on the fly).
+
+When you add new datasets, follow the same convention so the CLI can auto-discover everything.
+
+```{eval-rst}
+.. include:: _datasets_table.rst
+```
+
+## Visualisations
+
+For each dataset we usually publish a quick set of plots (trajectories, gradients, distributions, random example). Generate or refresh them via:
+
+```bash
+python datasets/_data_analysis/analyse_all_datasets.py
+```
+
+The script iterates over the list called `datasets` inside `datasets/_data_analysis/analyse_all_datasets.py`. To visualise a new dataset, append its identifier there and add an entry to the dictionary defined in `datasets/_data_analysis/dataset_dict.py`. Each entry specifies whether to plot on a log scale, how many quantities per subplot (`app`), and the plotting tolerance (controls axis limits). The script then produces PNGs under each dataset folder.
+
+```{figure} ../_static/osutrajectories.jpg
+---
+align: center
+alt: osu dataset trajectories
+---
+Example visualisation (osu2008) showing the log-scale trajectories generated by the helper script.
+```
+
+## Download helper
+
+You rarely need to download anything manually: the training, tuning, and evaluation CLIs call `download_data` on demand, which uses the URLs defined in `datasets/data_sources.yaml`. The first time you reference a dataset, its `data.hdf5` is fetched and cached under `datasets//`. Keep `data_sources.yaml` up to date if you mirror the data in your own storage.
diff --git a/docs/source/tutorials/benchmark_quickstart.ipynb b/docs/source/tutorials/benchmark_quickstart.ipynb
new file mode 100644
index 00000000..095a4419
--- /dev/null
+++ b/docs/source/tutorials/benchmark_quickstart.ipynb
@@ -0,0 +1,937 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "cd00f531",
+ "metadata": {},
+ "source": [
+ "# Benchmark Quickstart\n",
+ "\n",
+ "This notebook mirrors the CLI quickstart flow in an executable format. Use it to \n",
+ "validate your environment and to inspect the outputs of a short benchmark run."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f09152d0",
+ "metadata": {},
+ "source": [
+ "## 1. Running the CLI from this notebook\n",
+ "\n",
+ "Because this notebook lives inside the repo, we can invoke the CLI scripts directly. The helper below ensures the kernel's working directory is the repository root so imports and scripts resolve correctly."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "f7ed4d06",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Working directory set to /export/home/rjanssen/CODES-Benchmark\n",
+ "Using config file at /export/home/rjanssen/CODES-Benchmark/configs/train_eval/config_minimal.yaml\n",
+ "Configuration:\n",
+ "training_id: example_config_minimal\n",
+ "surrogates:\n",
+ "- FullyConnected\n",
+ "- LatentPoly\n",
+ "batch_size:\n",
+ "- 65536\n",
+ "- 512\n",
+ "epochs:\n",
+ "- 20\n",
+ "- 20\n",
+ "dataset:\n",
+ " name: lotka_volterra\n",
+ " tolerance: 1e-15\n",
+ "devices:\n",
+ "- cpu\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "import os\n",
+ "import pathlib\n",
+ "import sys\n",
+ "import yaml\n",
+ "\n",
+ "def find_repo_root(start: pathlib.Path) -> pathlib.Path:\n",
+ " for path in (start, *start.parents):\n",
+ " if (path / 'codes').is_dir() and (path / 'docs').is_dir():\n",
+ " return path\n",
+ " raise RuntimeError('Could not locate repo root; start the kernel from within the project directory.')\n",
+ "\n",
+ "repo_root = find_repo_root(pathlib.Path.cwd().resolve())\n",
+ "os.chdir(repo_root)\n",
+ "if str(repo_root) not in sys.path:\n",
+ " sys.path.insert(0, str(repo_root))\n",
+ "print(f\"Working directory set to {repo_root}\")\n",
+ "\n",
+ "config_path = repo_root / \"configs\" / \"train_eval\" / \"config_minimal.yaml\"\n",
+ "print(f\"Using config file at {config_path}\")\n",
+ "config = yaml.safe_load(config_path.read_text())\n",
+ "print(\"Configuration:\")\n",
+ "print(yaml.dump(config, sort_keys=False))\n",
+ "training_id = config[\"training_id\"]\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ab2f2f06",
+ "metadata": {},
+ "source": [
+ "## 2. Trigger training\n",
+ "\n",
+ "Uncomment the next cell when you want to actually run training from inside the \n",
+ "notebook. Keeping it as a string prevents accidental long-running jobs when the \n",
+ "notebook is rendered on the documentation site."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "84243b0d",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "8 0\n",
+ "\n",
+ "--------------------------------------------------------------------------------\n",
+ "| Starting training |\n",
+ "--------------------------------------------------------------------------------\n",
+ "\n",
+ "Training models sequentially on device cpu\n",
+ "Overall Progress : 0%| | 0/2 models trained [elapsed\n",
+ "FullyConnected main (cpu) : 0%| | 0/20 [00:00//`.\n",
+ "As you can see, each surrogate has its own subdirectory containing the model (.pth), as well as a YAML file, which contains all model attributes relevant to restore the model later.\n",
+ "\n",
+ "Results from evaluation are stored under `results///`.\n",
+ "For each surrogate, a YAML summary file is created, which contains various metrics computed during evaluation.\n",
+ "\n",
+ "Plots generated during evaluation are stored under `plots///`.\n",
+ "In this very basic setting, four plots were created for each surrogate we trained. \n",
+ "\n",
+ "Next, let us take a look at the contents of the results yaml files and the generated plots!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "873c98eb",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "- fullyconnected_metrics.yaml\n",
+ "Contents of fullyconnected_metrics.yaml:\n",
+ "n_params: 0\n",
+ "accuracy:\n",
+ " root_mean_squared_error_log: 11.999552726745605\n",
+ " median_absolute_error_log: 8.740435600280762\n",
+ " mean_absolute_error_log: 11.029707908630371\n",
+ " percentile_absolute_error_log: 21.89702796936035\n",
+ " root_mean_squared_error_real: 251635024.0\n",
+ " median_absolute_error_real: 18.237300872802734\n",
+ " mean_absolute_error_real: 115018664.0\n",
+ " percentile_absolute_error_real: 617406528.0\n",
+ " median_relative_error: 1.0\n",
+ " mean_relative_error: 2023090816.0\n",
+ " percentile_relative_error: 11246115840.0\n",
+ " error_percentile: 99\n",
+ " main_model_training_time: 35.57754993438721\n",
+ " main_model_epochs: 20\n",
+ "\n",
+ "- latentpoly_metrics.yaml\n",
+ "Contents of latentpoly_metrics.yaml:\n",
+ "n_params: 0\n",
+ "accuracy:\n",
+ " root_mean_squared_error_log: 2.213813543319702\n",
+ " median_absolute_error_log: 1.4329252243041992\n",
+ " mean_absolute_error_log: 1.7928467988967896\n",
+ " percentile_absolute_error_log: 6.6917595863342285\n",
+ " root_mean_squared_error_real: 185.49114990234375\n",
+ " median_absolute_error_real: 204.94384765625\n",
+ " mean_absolute_error_real: 169.51358032226562\n",
+ " percentile_absolute_error_real: 215.49717712402344\n",
+ " median_relative_error: 26.082883834838867\n",
+ " mean_relative_error: 24715276288.0\n",
+ " percentile_relative_error: 4917673.0\n",
+ " error_percentile: 99\n",
+ " main_model_training_time: 125.99766206741333\n",
+ " main_model_epochs: 20\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "results_root = repo_root / \"results\" / training_id\n",
+ "# List all files in results_root\n",
+ "for item in results_root.iterdir():\n",
+ " print(f\"- {item.name}\")\n",
+ " with (results_root / item.name).open() as f:\n",
+ " content = f.read()\n",
+ " print(f\"Contents of {item.name}:\")\n",
+ " print(content)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "175478bc",
+ "metadata": {},
+ "source": [
+ "These metrics may be a bit hard to read in raw YAML format. But luckily, we can look at the generated plots as well!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "f63d2fea",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Displaying plots from /export/home/rjanssen/CODES-Benchmark/plots/example_config_minimal:\n",
+ "Loading plot /export/home/rjanssen/CODES-Benchmark/plots/example_config_minimal/FullyConnected/accuracy_delta_dex_per_quantity.jpg for surrogate FullyConnected\n",
+ "Loading plot /export/home/rjanssen/CODES-Benchmark/plots/example_config_minimal/FullyConnected/accuracy_rel_error_per_quantity.jpg for surrogate FullyConnected\n",
+ "Loading plot /export/home/rjanssen/CODES-Benchmark/plots/example_config_minimal/FullyConnected/accuracy_delta_dex_time.jpg for surrogate FullyConnected\n",
+ "Loading plot /export/home/rjanssen/CODES-Benchmark/plots/example_config_minimal/FullyConnected/accuracy_rel_errors_time.jpg for surrogate FullyConnected\n",
+ "Loading plot /export/home/rjanssen/CODES-Benchmark/plots/example_config_minimal/LatentPoly/accuracy_delta_dex_per_quantity.jpg for surrogate LatentPoly\n",
+ "Loading plot /export/home/rjanssen/CODES-Benchmark/plots/example_config_minimal/LatentPoly/accuracy_rel_error_per_quantity.jpg for surrogate LatentPoly\n",
+ "Loading plot /export/home/rjanssen/CODES-Benchmark/plots/example_config_minimal/LatentPoly/accuracy_rel_errors_time.jpg for surrogate LatentPoly\n",
+ "Loading plot /export/home/rjanssen/CODES-Benchmark/plots/example_config_minimal/LatentPoly/accuracy_delta_dex_time.jpg for surrogate LatentPoly\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "# Create figure with 4x2 subplots\n",
+ "plots_dir = repo_root / \"plots\" / training_id\n",
+ "print(f\"Displaying plots from {plots_dir}:\")\n",
+ "fig, axs = plt.subplots(4, 2, figsize=(12, 15))\n",
+ "row = 0\n",
+ "col = 0\n",
+ "for item in plots_dir.iterdir():\n",
+ " for plot_file in item.iterdir():\n",
+ " print(f\"Loading plot {plot_file} for surrogate {item.name}\")\n",
+ " img = plt.imread(plot_file)\n",
+ " axs[row, col].imshow(img)\n",
+ " axs[row, col].axis('off')\n",
+ " row += 1\n",
+ " if row >= 4:\n",
+ " row = 0\n",
+ " col += 1\n",
+ " \n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "870ec5bc",
+ "metadata": {},
+ "source": [
+ "## 5. Additional Evals & Comparing Surrogates\n",
+ "\n",
+ "These plots and metrics already provide some insights into the performance of the trained surrogates.\n",
+ "But since CODES is a benchmark, the crucial part is to compare different surrogates against each other.\n",
+ "\n",
+ "Additionally, we can run some more evaluations even with the two models we trained above (one per surrogate). To do this, let us rerun `run_eval.py` with modified configuration!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "46e4ffc6",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Updated Configuration for Additional Evaluations:\n",
+ "training_id: example_config_minimal\n",
+ "surrogates:\n",
+ "- FullyConnected\n",
+ "- LatentPoly\n",
+ "batch_size:\n",
+ "- 65536\n",
+ "- 512\n",
+ "epochs:\n",
+ "- 20\n",
+ "- 20\n",
+ "dataset:\n",
+ " name: lotka_volterra\n",
+ " tolerance: 1e-15\n",
+ "devices:\n",
+ "- cpu\n",
+ "losses: true\n",
+ "gradients: true\n",
+ "timing: true\n",
+ "compute: true\n",
+ "compare: true\n",
+ "iterative: true\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "config.update({\n",
+ " \"losses\": True,\n",
+ " \"gradients\": True,\n",
+ " \"timing\": True,\n",
+ " \"compute\": True,\n",
+ " \"compare\": True,\n",
+ " \"iterative\": True,\n",
+ "})\n",
+ "# Replace the saved config file with the updated one\n",
+ "saved_config_path = repo_root / \"trained\" / training_id / \"config.yaml\"\n",
+ "yaml.dump(config, open(saved_config_path, 'w'), sort_keys=False)\n",
+ "\n",
+ "# Check contents of updated config file\n",
+ "print(\"Updated Configuration for Additional Evaluations:\")\n",
+ "config_new = yaml.safe_load(saved_config_path.read_text())\n",
+ "print(yaml.dump(config_new, sort_keys=False))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3c95bb85",
+ "metadata": {},
+ "source": [
+ "Note that we modified the config file which was copied over to the `trained//` directory during training!This is simply because I wanted to avoid creating yet another config file in this quickstart notebook.\n",
+ "\n",
+ "Now, let us evaluate the trained surrogates with these additional eval toggles enabled:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "34806152",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Checking benchmark configuration...\n",
+ "Configuration check passed successfully.\n",
+ "\n",
+ "--------------------------------------------------------------------------------\n",
+ "| Running benchmark for FullyConnected |\n",
+ "--------------------------------------------------------------------------------\n",
+ "\n",
+ "All required models for surrogate FullyConnected are present.\n",
+ "Loss plots...\n",
+ "Running accuracy benchmark...\n",
+ "Running iterative training benchmark...\n",
+ "Running gradients benchmark...\n",
+ "Running timing benchmark...\n",
+ "Running compute benchmark...\n",
+ "Skipping GPU memory profiling for compute evaluation (requested device is not CUDA).\n",
+ "\n",
+ "--------------------------------------------------------------------------------\n",
+ "| Running benchmark for LatentPoly |\n",
+ "--------------------------------------------------------------------------------\n",
+ "\n",
+ "All required models for surrogate LatentPoly are present.\n",
+ "Loss plots...\n",
+ "Running accuracy benchmark...\n",
+ "Running iterative training benchmark...\n",
+ "Running gradients benchmark...\n",
+ "Running timing benchmark...\n",
+ "Running compute benchmark...\n",
+ "Skipping GPU memory profiling for compute evaluation (requested device is not CUDA).\n",
+ "\n",
+ "--------------------------------------------------------------------------------\n",
+ "| Comparing models |\n",
+ "--------------------------------------------------------------------------------\n",
+ "\n",
+ "Making comparative plots... \n",
+ "\n",
+ "Figure(700x1000)\n",
+ "The results are in! Here is a summary of the benchmark metrics:\n",
+ "\n",
+ "┌───────────────────────┬────────────────────────┬──────────────────────┐\n",
+ "│ Metric │ FullyConnected │ LatentPoly │\n",
+ "├───────────────────────┼────────────────────────┼──────────────────────┤\n",
+ "│ RMSE │ 2.52e+08 │ * 185 * │\n",
+ "├───────────────────────┼────────────────────────┼──────────────────────┤\n",
+ "│ MAE │ 1.15e+08 │ * 17 * │\n",
+ "├───────────────────────┼────────────────────────┼──────────────────────┤\n",
+ "│ Median AE │ * 18.2 * │ 205 │\n",
+ "├───────────────────────┼────────────────────────┼──────────────────────┤\n",
+ "│ 99th Perc. AE │ 6.17e+08 │ * 215 * │\n",
+ "├───────────────────────┼────────────────────────┼──────────────────────┤\n",
+ "│ RMSE (log) │ 12 dex │ * 2.21 dex * │\n",
+ "├───────────────────────┼────────────────────────┼──────────────────────┤\n",
+ "│ MAE (log) │ 11 dex │ * 1.79 dex * │\n",
+ "├───────────────────────┼────────────────────────┼──────────────────────┤\n",
+ "│ Median AE (log) │ 8.74 dex │ * 1.43 dex * │\n",
+ "├───────────────────────┼────────────────────────┼──────────────────────┤\n",
+ "│ 99th Perc. AE (log) │ 21.9 dex │ * 6.69 dex * │\n",
+ "├───────────────────────┼────────────────────────┼──────────────────────┤\n",
+ "│ MRE │ * 2.02e+11 % * │ 2.47e+12 % │\n",
+ "├───────────────────────┼────────────────────────┼──────────────────────┤\n",
+ "│ Median RE │ * 1 % * │ 2608 % │\n",
+ "├───────────────────────┼────────────────────────┼──────────────────────┤\n",
+ "│ 99th Percentile RE │ 1.12e+12 % │ * 4.92e+08 % * │\n",
+ "├───────────────────────┼────────────────────────┼──────────────────────┤\n",
+ "│ Epochs │ 20 │ 20 │\n",
+ "├───────────────────────┼────────────────────────┼──────────────────────┤\n",
+ "│ Train Time (hh:mm:ss) │ * 00:00:35 * │ 00:02:05 │\n",
+ "├───────────────────────┼────────────────────────┼──────────────────────┤\n",
+ "│ Inference Times │ * 97.58 ms ± 6.16 ms * │ 220.90 ms ± 13.85 ms │\n",
+ "├───────────────────────┼────────────────────────┼──────────────────────┤\n",
+ "│ Iterative MAE (log) │ 10.9 dex │ * 1.79 dex * │\n",
+ "├───────────────────────┼────────────────────────┼──────────────────────┤\n",
+ "│ Gradient-Error PCC │ 0.102 │ -0.411 │\n",
+ "├───────────────────────┼────────────────────────┼──────────────────────┤\n",
+ "│ # Trainable Params │ 52620 │ * 4810 * │\n",
+ "└───────────────────────┴────────────────────────┴──────────────────────┘\n",
+ "\n",
+ "CLI table metrics saved to results/example_config_minimal/metrics_table.csv\n",
+ "\n",
+ "--------------------------------------------------------------------------------\n",
+ "| Evaluation completed. |\n",
+ "--------------------------------------------------------------------------------\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "!\"{sys.executable}\" run_eval.py --config {saved_config_path}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cdc71ec9",
+ "metadata": {},
+ "source": [
+ "Nice! Now we have some more comprehensive output. As you can see, we get a direct comparison in the CLI with some of the most important metrics across all surrogates. Best values are highlighted with an asterisk (*).\n",
+ "\n",
+ "Below, we can again inspect the contents of the results directory to see what additional files were created during this extended evaluation."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "2416c668",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[01;34mtrained/example_config_minimal\u001b[0m\n",
+ "├── \u001b[01;34mFullyConnected\u001b[0m\n",
+ "│ ├── \u001b[00mfullyconnected_main.pth\u001b[0m\n",
+ "│ └── \u001b[00mfullyconnected_main.yaml\u001b[0m\n",
+ "├── \u001b[01;34mLatentPoly\u001b[0m\n",
+ "│ ├── \u001b[00mlatentpoly_main.pth\u001b[0m\n",
+ "│ └── \u001b[00mlatentpoly_main.yaml\u001b[0m\n",
+ "├── \u001b[00mcompleted.txt\u001b[0m\n",
+ "└── \u001b[00mconfig.yaml\u001b[0m\n",
+ "\n",
+ "3 directories, 6 files\n",
+ "\u001b[01;34mresults/example_config_minimal\u001b[0m\n",
+ "├── \u001b[00mall_metrics.csv\u001b[0m\n",
+ "├── \u001b[00mfullyconnected_metrics.yaml\u001b[0m\n",
+ "├── \u001b[00mlatentpoly_metrics.yaml\u001b[0m\n",
+ "├── \u001b[00mmetrics_table.csv\u001b[0m\n",
+ "└── \u001b[00mmetrics_table.txt\u001b[0m\n",
+ "\n",
+ "1 directory, 5 files\n",
+ "\u001b[01;34mplots/example_config_minimal\u001b[0m\n",
+ "├── \u001b[01;34mFullyConnected\u001b[0m\n",
+ "│ ├── \u001b[01;35maccuracy_delta_dex_per_quantity.jpg\u001b[0m\n",
+ "│ ├── \u001b[01;35maccuracy_delta_dex_time.jpg\u001b[0m\n",
+ "│ ├── \u001b[01;35maccuracy_rel_error_per_quantity.jpg\u001b[0m\n",
+ "│ ├── \u001b[01;35maccuracy_rel_errors_time.jpg\u001b[0m\n",
+ "│ ├── \u001b[01;35mexample_preds_iterative.jpg\u001b[0m\n",
+ "│ ├── \u001b[01;35mgradient_error_heatmap.jpg\u001b[0m\n",
+ "│ └── \u001b[01;35mlosses_main.jpg\u001b[0m\n",
+ "├── \u001b[01;34mLatentPoly\u001b[0m\n",
+ "│ ├── \u001b[01;35maccuracy_delta_dex_per_quantity.jpg\u001b[0m\n",
+ "│ ├── \u001b[01;35maccuracy_delta_dex_time.jpg\u001b[0m\n",
+ "│ ├── \u001b[01;35maccuracy_rel_error_per_quantity.jpg\u001b[0m\n",
+ "│ ├── \u001b[01;35maccuracy_rel_errors_time.jpg\u001b[0m\n",
+ "│ ├── \u001b[01;35mexample_preds_iterative.jpg\u001b[0m\n",
+ "│ ├── \u001b[01;35mgradient_error_heatmap.jpg\u001b[0m\n",
+ "│ └── \u001b[01;35mlosses_main.jpg\u001b[0m\n",
+ "├── \u001b[01;35maccuracy_delta_dex_time.jpg\u001b[0m\n",
+ "├── \u001b[01;35maccuracy_error_dist_deltadex.jpg\u001b[0m\n",
+ "├── \u001b[01;35maccuracy_error_dist_relative.jpg\u001b[0m\n",
+ "├── \u001b[01;35maccuracy_rel_errors_time_models.jpg\u001b[0m\n",
+ "├── \u001b[01;35mgradients_heatmaps.jpg\u001b[0m\n",
+ "├── \u001b[01;35miterative_delta_dex_time.jpg\u001b[0m\n",
+ "├── \u001b[01;35miterative_error_dist_deltadex.jpg\u001b[0m\n",
+ "├── \u001b[01;35mlosses_main_model.jpg\u001b[0m\n",
+ "├── \u001b[01;35mlosses_main_model_duration.jpg\u001b[0m\n",
+ "├── \u001b[01;35mlosses_main_model_equal.jpg\u001b[0m\n",
+ "└── \u001b[01;35mtiming_inference.jpg\u001b[0m\n",
+ "\n",
+ "3 directories, 25 files\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Use tree visualization if available\n",
+ "import shutil\n",
+ "tree_cmd = shutil.which(\"tree\")\n",
+ "if tree_cmd:\n",
+ " !\"{tree_cmd}\" -L 3 trained/{training_id}\n",
+ " !\"{tree_cmd}\" -L 3 results/{training_id}\n",
+ " !\"{tree_cmd}\" -L 3 plots/{training_id}\n",
+ "else:\n",
+ " print(\"The 'tree' command is not available. Please install it to visualize the directory structure.\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7bd132a3",
+ "metadata": {},
+ "source": [
+ "Nothing changed in the trained model directories, but the results directories now contain additional summary files with more metrics computed during this extended evaluation, and explicit comparisons across surrogates, since we enabled the `compare` toggle in the config.\n",
+ "\n",
+ "Let us look at some of the additional plots we generated during this extended evaluation. We will start with results for one of the surrogates (`FullyConnected`), and then look at comparison plots across all surrogates."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "04b6de7a",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Displaying new plots from /export/home/rjanssen/CODES-Benchmark/plots/example_config_minimal:\n",
+ "Showing plot losses_main.jpg\n",
+ "Showing plot example_preds_iterative.jpg\n",
+ "Showing plot gradient_error_heatmap.jpg\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "new_plot_identifiers = [\"losses_main.jpg\", \"example_preds_iterative.jpg\", \"gradient_error_heatmap.jpg\"]\n",
+ "\n",
+ "print(f\"Displaying new plots from {plots_dir}:\")\n",
+ "for i, plot_id in enumerate(new_plot_identifiers):\n",
+ " plot_file = None\n",
+ " for item in plots_dir.iterdir():\n",
+ " candidate = item / plot_id\n",
+ " if candidate.exists():\n",
+ " plot_file = candidate\n",
+ " break\n",
+ " if plot_file is not None:\n",
+ " print(f\"Showing plot {plot_file.name}\")\n",
+ " img = plt.imread(plot_file)\n",
+ " plt.figure(figsize=(6, 4))\n",
+ " plt.imshow(img)\n",
+ " plt.axis('off')\n",
+ " else:\n",
+ " print(f\"Plot {plot_id} not found in {plots_dir}\")\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4c702c00",
+ "metadata": {},
+ "source": [
+ "Of course, the plots look a bit weird because we did not really train for long enough. But you get the idea!\n",
+ "\n",
+ "Now, let us look at some comparison plots across the two surrogates we trained."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "1ca0b131",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Displaying comparison plots from /export/home/rjanssen/CODES-Benchmark/plots/example_config_minimal:\n",
+ "Showing comparison plot accuracy_rel_errors_time_models.jpg\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Showing comparison plot accuracy_error_dist_deltadex.jpg\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Showing comparison plot losses_main_model.jpg\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Showing comparison plot iterative_delta_dex_time.jpg\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Showing comparison plot timing_inference.jpg\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "comparison_plot_identifiers = [\"accuracy_rel_errors_time_models.jpg\", \"accuracy_error_dist_deltadex.jpg\", \"losses_main_model.jpg\", \"iterative_delta_dex_time.jpg\", \"timing_inference.jpg\"]\n",
+ "\n",
+ "print(f\"Displaying comparison plots from {plots_dir}:\")\n",
+ "# This time, the plots live directly in plots_dir\n",
+ "for i, plot_id in enumerate(comparison_plot_identifiers):\n",
+ " plot_file = plots_dir / plot_id\n",
+ " if plot_file.exists():\n",
+ " print(f\"Showing comparison plot {plot_file.name}\")\n",
+ " img = plt.imread(plot_file)\n",
+ " plt.figure(figsize=(6, 5))\n",
+ " plt.imshow(img)\n",
+ " plt.axis('off')\n",
+ " plt.show()\n",
+ "\n",
+ " else:\n",
+ " print(f\"Comparison plot {plot_id} not found in {plots_dir}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4020dec4",
+ "metadata": {},
+ "source": [
+ "The above plots yield some insights into how the two surrogates compare against each other. Of course, for lack of proper training, the only plot that really shows us something meaningful is the timing plot, where we can see that both surrogates are roughly on par for this simple dataset.*\n",
+ "\n",
+ "## End of Benchmark Quickstart - Congratulations!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3f243c41",
+ "metadata": {},
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "codes_new",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/source/tutorials/index.md b/docs/source/tutorials/index.md
new file mode 100644
index 00000000..25842679
--- /dev/null
+++ b/docs/source/tutorials/index.md
@@ -0,0 +1,24 @@
+# Tutorials
+
+Interactive tutorials live in this folder so they can be rendered directly on the documentation site via `myst-nb`. Use them when you want a guided, executable tour of the benchmark.
+
+## Available tutorials
+
+| Notebook | Description |
+| --- | --- |
+| [Benchmark Quickstart](benchmark_quickstart.ipynb) | Walks through loading a configuration file, running a short training job, and visualizing KPI tables. |
+
+To execute a notebook locally:
+
+```bash
+uv pip install --group dev # installs docs + notebook deps
+jupyter lab docs/source/tutorials/benchmark_quickstart.ipynb
+```
+
+When building the docs (`sphinx-build`), notebooks are parsed but not executed. You can enable execution later by setting `nb_execution_mode = "auto"` in `docs/source/conf.py`.
+
+```{toctree}
+:maxdepth: 1
+
+benchmark_quickstart
+```
diff --git a/pyproject.toml b/pyproject.toml
index 2f23064f..e05efbaf 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -41,6 +41,8 @@ dev = [
"pre-commit>=4.2.0",
"pytest>=8.4.1",
"pytest-cov>=6.2.1",
+ "myst-nb>=1.1.0",
+ "linkify-it-py>=2.0.2",
"sphinx>=8.1.3",
"sphinx-autodoc-typehints>=3.0.1",
"sphinx-book-theme>=1.1.4",
diff --git a/requirements.txt b/requirements.txt
index ae21ec12..d11fcdbd 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,82 +1,118 @@
accessible-pygments==0.0.5
alabaster==1.0.0
-alembic==1.16.4
-babel==2.17.0
-beautifulsoup4==4.13.4
-black==25.1.0
+alembic==1.18.4
+asttokens==3.0.1
+attrs==25.4.0
+babel==2.18.0
+beautifulsoup4==4.14.3
+black==26.1.0
bottle==0.13.4
-certifi==2025.7.14
-cfgv==3.4.0
-charset-normalizer==3.4.2
-click==8.2.1
+certifi==2026.1.4
+cfgv==3.5.0
+charset-normalizer==3.4.4
+click==8.3.1
-e file:///home/runner/work/CODES-Benchmark/CODES-Benchmark
-colorlog==6.9.0
+colorlog==6.10.1
+comm==0.2.3
contourpy==1.3.2
-coverage==7.10.1
+coverage==7.13.4
+cuda-bindings==12.9.4
+cuda-pathfinder==1.3.4
cycler==0.12.1
+debugpy==1.8.20
+decorator==5.2.1
distlib==0.4.0
docutils==0.21.2
-exceptiongroup==1.3.0
-filelock==3.18.0
-fonttools==4.59.0
-fsspec==2025.7.0
-greenlet==3.2.3
-h5py==3.14.0
-identify==2.6.12
-idna==3.10
+exceptiongroup==1.3.1
+executing==2.2.1
+fastjsonschema==2.21.2
+filelock==3.21.0
+fonttools==4.61.1
+fsspec==2026.2.0
+greenlet==3.3.1
+h5py==3.15.1
+identify==2.6.16
+idna==3.11
imagesize==1.4.1
-iniconfig==2.1.0
-isort==6.0.1
+importlib-metadata==8.7.1
+iniconfig==2.3.0
+ipykernel==7.2.0
+ipython==8.38.0
+isort==7.0.0
+jedi==0.19.2
jinja2==3.1.6
-joblib==1.5.1
-kiwisolver==1.4.8
+jsonschema==4.26.0
+jsonschema-specifications==2025.9.1
+jupyter-cache==1.0.1
+jupyter-client==8.8.0
+jupyter-core==5.9.1
+kiwisolver==1.4.9
+linkify-it-py==2.0.3
mako==1.3.10
-markupsafe==3.0.2
-matplotlib==3.10.3
+markdown-it-py==3.0.0
+markupsafe==3.0.3
+matplotlib==3.10.8
+matplotlib-inline==0.2.1
+mdit-py-plugins==0.5.0
+mdurl==0.1.2
mpmath==1.3.0
mypy-extensions==1.1.0
+myst-nb==1.3.0
+myst-parser==4.0.1
+nbclient==0.10.4
+nbformat==5.10.4
+nest-asyncio==1.6.0
networkx==3.4.2
-nodeenv==1.9.1
+nodeenv==1.10.0
numpy==2.2.6
-nvidia-cublas-cu12==12.6.4.1
-nvidia-cuda-cupti-cu12==12.6.80
-nvidia-cuda-nvrtc-cu12==12.6.77
-nvidia-cuda-runtime-cu12==12.6.77
-nvidia-cudnn-cu12==9.5.1.17
-nvidia-cufft-cu12==11.3.0.4
-nvidia-cufile-cu12==1.11.1.6
-nvidia-curand-cu12==10.3.7.77
-nvidia-cusolver-cu12==11.7.1.2
-nvidia-cusparse-cu12==12.5.4.2
-nvidia-cusparselt-cu12==0.6.3
-nvidia-nccl-cu12==2.26.2
-nvidia-nvjitlink-cu12==12.6.85
-nvidia-nvtx-cu12==12.6.77
-optuna==4.4.0
-optuna-dashboard==0.19.0
-packaging==25.0
-pathspec==0.12.1
-pillow==11.3.0
-platformdirs==4.3.8
+nvidia-cublas-cu12==12.8.4.1
+nvidia-cuda-cupti-cu12==12.8.90
+nvidia-cuda-nvrtc-cu12==12.8.93
+nvidia-cuda-runtime-cu12==12.8.90
+nvidia-cudnn-cu12==9.10.2.21
+nvidia-cufft-cu12==11.3.3.83
+nvidia-cufile-cu12==1.13.1.3
+nvidia-curand-cu12==10.3.9.90
+nvidia-cusolver-cu12==11.7.3.90
+nvidia-cusparse-cu12==12.5.8.93
+nvidia-cusparselt-cu12==0.7.1
+nvidia-nccl-cu12==2.27.5
+nvidia-nvjitlink-cu12==12.8.93
+nvidia-nvshmem-cu12==3.4.5
+nvidia-nvtx-cu12==12.8.90
+optuna==4.7.0
+optuna-dashboard==0.20.0
+packaging==26.0
+parso==0.8.6
+pathspec==1.0.4
+pexpect==4.9.0
+pillow==12.1.1
+platformdirs==4.6.0
pluggy==1.6.0
pockets==0.9.1
-pre-commit==4.2.0
-psycopg2-binary==2.9.10
+pre-commit==4.5.1
+prompt-toolkit==3.0.52
+psutil==7.2.2
+psycopg2-binary==2.9.11
+ptyprocess==0.7.0
+pure-eval==0.2.3
pydata-sphinx-theme==0.15.4
pygments==2.19.2
-pyparsing==3.2.3
-pytest==8.4.1
-pytest-cov==6.2.1
+pyparsing==3.3.2
+pytest==9.0.2
+pytest-cov==7.0.0
python-dateutil==2.9.0.post0
-pyyaml==6.0.2
-requests==2.32.4
+pytokens==0.4.1
+pyyaml==6.0.3
+pyzmq==27.1.0
+referencing==0.37.0
+requests==2.32.5
+rpds-py==0.30.0
schedulefree==1.4.1
-scikit-learn==1.7.1
scipy==1.15.3
-setuptools==80.9.0
six==1.17.0
snowballstemmer==3.0.1
-soupsieve==2.7
+soupsieve==2.8.3
sphinx==8.1.3
sphinx-autodoc-typehints==3.0.1
sphinx-book-theme==1.1.4
@@ -87,17 +123,22 @@ sphinxcontrib-jsmath==1.0.1
sphinxcontrib-napoleon==0.7
sphinxcontrib-qthelp==2.0.0
sphinxcontrib-serializinghtml==2.0.0
-sqlalchemy==2.0.41
+sqlalchemy==2.0.46
+stack-data==0.6.3
sympy==1.14.0
tabulate==0.9.0
-threadpoolctl==3.6.0
-tomli==2.2.1
-torch==2.7.1
+tomli==2.4.0
+torch==2.10.0
torchode==1.0.0
torchtyping==0.1.5
-tqdm==4.67.1
-triton==3.3.1
+tornado==6.5.4
+tqdm==4.67.3
+traitlets==5.14.3
+triton==3.6.0
typeguard==2.13.3
-typing-extensions==4.14.1
-urllib3==2.5.0
-virtualenv==20.32.0
+typing-extensions==4.15.0
+uc-micro-py==1.0.3
+urllib3==2.6.3
+virtualenv==20.36.1
+wcwidth==0.6.0
+zipp==3.23.0
diff --git a/run_eval.py b/run_eval.py
index 698e7135..a5f7b375 100644
--- a/run_eval.py
+++ b/run_eval.py
@@ -39,13 +39,15 @@ def main(args):
print(f"Surrogate {surrogate_name} not recognized. Skipping.")
# Compare models
- if config["compare"]:
+ if config.get("compare", False):
if len(surrogates) < 2:
nice_print("At least two surrogate models are required to compare.")
else:
nice_print("Comparing models")
compare_models(all_metrics, config)
+ nice_print("Evaluation completed.")
+
if __name__ == "__main__":
parser = ArgumentParser()
diff --git a/run_training.py b/run_training.py
index 220c48fb..b4324c76 100644
--- a/run_training.py
+++ b/run_training.py
@@ -34,6 +34,12 @@ def main(args):
"""
torch.use_deterministic_algorithms(True)
config = load_and_save_config(config_path=args.config, save=False)
+
+ if torch.cuda.is_available() and config.get("verbose", False):
+ nice_print(
+ f"CUDA devices detected: count={torch.cuda.device_count()}, current={torch.cuda.current_device()}"
+ )
+
download_data(config["dataset"]["name"], verbose=config.get("verbose", False))
task_list_filepath, copy_config = check_training_status(config)
if copy_config:
diff --git a/run_tuning.py b/run_tuning.py
index e4834a59..8872b8e8 100644
--- a/run_tuning.py
+++ b/run_tuning.py
@@ -14,6 +14,7 @@
from codes.tune import (
MaxValidTrialsCallback,
_count_valid_trials,
+ apply_tuning_defaults,
build_fine_optuna_params,
create_objective,
initialize_optuna_database,
@@ -24,11 +25,40 @@
from codes.utils import download_data, nice_print
-def run_single_study(config: dict, study_name: str, db_url: str):
+def resolve_storage_backend(config: dict, tuning_id: str) -> tuple[str, bool]:
+ """
+ Return (storage_url, is_sqlite).
+ Defaults to Postgres for backward compatibility.
+ """
+ storage_cfg = config.get("storage", {})
+ backend = storage_cfg.get("backend", "postgres").lower()
+
+ if backend == "sqlite":
+ sqlite_path = storage_cfg.get("path")
+ if not sqlite_path:
+ raise ValueError(
+ "SQLite storage requires `storage.path` in optuna_config.yaml."
+ )
+ sqlite_path = Path(sqlite_path).expanduser().resolve()
+ sqlite_path.parent.mkdir(parents=True, exist_ok=True)
+ return f"sqlite:///{sqlite_path}", True
+
+ if backend == "postgres":
+ storage_url = initialize_optuna_database(config, study_folder_name=tuning_id)
+ return storage_url, False
+
+ raise ValueError(
+ f"Unknown storage backend '{backend}'. Use 'sqlite' or 'postgres'."
+ )
+
+
+def run_single_study(config: dict, study_name: str, db_url: str, sqlite_backend: bool):
if not config.get("optuna_logging", False):
optuna.logging.set_verbosity(optuna.logging.WARNING)
- if config["multi_objective"]:
+ multi_objective = config.get("multi_objective", False)
+
+ if multi_objective:
sampler = optuna.samplers.NSGAIISampler(
seed=config["seed"], population_size=config["population_size"]
)
@@ -69,12 +99,19 @@ def run_single_study(config: dict, study_name: str, db_url: str):
return
device_queue = queue.Queue()
- for slot_id, dev in enumerate(config["devices"]):
+ devices = config.get("devices", ["cpu"])
+ if sqlite_backend and len(devices) > 1:
+ print(
+ "⚠️ SQLite storage does not handle concurrent writers well. "
+ "Continuing with multiple devices may trigger 'database is locked' errors."
+ )
+
+ for slot_id, dev in enumerate(devices):
device_queue.put((dev, slot_id))
objective_fn = create_objective(config, study_name, device_queue)
n_trials = config["n_trials"]
- n_jobs = len(config["devices"])
+ n_jobs = len(devices)
warmup_target = max(10, int(n_trials * 0.10))
all_durations: list[float] = []
@@ -100,9 +137,11 @@ def trial_complete_callback(study_: optuna.Study, trial_: optuna.trial.FrozenTri
)
# try to set threshold (no-op if not enough data or already set)
- maybe_set_runtime_threshold(study_, warmup_target, include_pruned=True)
+ if config.get("time_pruning", True):
+ maybe_set_runtime_threshold(study_, warmup_target, include_pruned=True)
- download_data(config["dataset"]["name"])
+ dataset_cfg = config["dataset"]
+ download_data(dataset_cfg["name"])
with tqdm(
total=n_trials,
@@ -111,19 +150,30 @@ def trial_complete_callback(study_: optuna.Study, trial_: optuna.trial.FrozenTri
leave=True,
bar_format="{l_bar}{bar}| {n_fmt}/{total_fmt} [Elapsed: {elapsed}{postfix}]",
) as trial_pbar:
- study.optimize(
- objective_fn,
- n_trials=n_trials * 2 if config["multi_objective"] else n_trials,
- n_jobs=n_jobs,
- callbacks=[
- MaxValidTrialsCallback(n_trials),
- trial_complete_callback,
- ],
- )
-
-
-def run_all_studies(config: dict, main_study_name: str, db_url: str):
+ try:
+ study.optimize(
+ objective_fn,
+ n_trials=n_trials,
+ n_jobs=n_jobs,
+ callbacks=[
+ MaxValidTrialsCallback(n_trials),
+ trial_complete_callback,
+ ],
+ )
+ except Exception as exc: # pragma: no cover - guidance-oriented handling
+ if sqlite_backend and "database is locked" in str(exc).lower():
+ raise RuntimeError(
+ "SQLite storage encountered a concurrency lock. "
+ "Please rerun with fewer devices or switch to Postgres."
+ ) from exc
+ raise
+
+
+def run_all_studies(
+ config: dict, main_study_name: str, db_url: str, sqlite_backend: bool
+):
surrogates = config["surrogates"]
+ dataset_cfg = config["dataset"]
global_params = (
{} if config.get("fine", False) else config.get("global_optuna_params", {})
)
@@ -141,6 +191,7 @@ def run_all_studies(config: dict, main_study_name: str, db_url: str):
for surr in surrogates:
arch_name = surr["name"]
+ n_trials_override = None
if config.get("fine", False):
# ignore manual search spaces
surr["optuna_params"] = {}
@@ -186,30 +237,37 @@ def run_all_studies(config: dict, main_study_name: str, db_url: str):
study_name = f"{main_study_name}_{arch_name.lower()}"
arch_pbar.set_postfix({"study": study_name})
- trials = surr.get("trials", config.get("trials", None))
+ trials = surr.get("trials", config.get("trials"))
sub_config = {
"batch_size": surr["batch_size"],
- "dataset": config["dataset"],
- "devices": config["devices"],
+ "dataset": dataset_cfg.copy(),
+ "devices": list(config.get("devices", ["cpu"])),
"epochs": surr["epochs"],
"n_trials": trials if not n_trials_override else n_trials_override,
- "seed": config["seed"],
+ "seed": config.get("seed", 42),
"surrogate": {"name": arch_name},
"optuna_params": surr.get("optuna_params", {}),
"prune": config.get("prune", True),
"optuna_logging": config.get("optuna_logging", False),
- "use_optimal_params": config.get("use_optimal_params", False),
+ "use_optimal_params": config.get("use_optimal_params", True),
"multi_objective": config.get("multi_objective", False),
"population_size": config.get("population_size", 50),
- "target_percentile": config.get("target_percentile", 0.95),
+ "target_percentile": config.get("target_percentile", 0.99),
"fine": config.get("fine", False), # pass through
"loss_cap": config.get("loss_cap", 20),
+ "time_pruning": config.get("time_pruning", True),
}
+ if sub_config["n_trials"] is None:
+ raise ValueError(
+ f"No trial count specified for surrogate '{arch_name}'. "
+ "Add 'trials' either globally or per surrogate."
+ )
+
if config.get("fine", False):
sub_config["fine_space"] = fine_space
- run_single_study(sub_config, study_name, db_url)
+ run_single_study(sub_config, study_name, db_url, sqlite_backend)
arch_pbar.update(1)
arch_pbar.set_postfix({"done": study_name})
@@ -228,8 +286,8 @@ def parse_arguments():
parser.add_argument(
"--config",
type=str,
- default="codes/tune/optuna_config.yaml",
- help="Path to master optuna_config.yaml",
+ default="configs/tuning/sqlite_quickstart.yaml",
+ help="Path to tuning config YAML.",
)
return parser.parse_args()
@@ -244,20 +302,18 @@ def main():
sys.exit(1)
config = load_yaml_config(str(master_cfg_path))
- prepare_workspace(master_cfg_path, config)
+ config = prepare_workspace(master_cfg_path, config)
+ config = apply_tuning_defaults(config)
tuning_id = config["tuning_id"]
# Initialize DB (remote/local)
- db_url = initialize_optuna_database(config, study_folder_name=tuning_id)
-
- # # If overwriting, delete Optuna studies
- # delete_studies_if_requested(config, tuning_id, db_url)
+ db_url, sqlite_backend = resolve_storage_backend(config, tuning_id)
# Run
if "surrogates" in config:
- run_all_studies(config, tuning_id, db_url)
+ run_all_studies(config, tuning_id, db_url, sqlite_backend)
else:
- run_single_study(config, tuning_id, db_url)
+ run_single_study(config, tuning_id, db_url, sqlite_backend)
nice_print("Optuna tuning completed!")
diff --git a/test/test_bench_main.py b/test/test_bench_main.py
index 12df0fd1..5e3e2d1c 100644
--- a/test/test_bench_main.py
+++ b/test/test_bench_main.py
@@ -147,13 +147,47 @@ def test_time_inference(monkeypatch):
assert result["mean_inference_time_per_prediction"] == pytest.approx(3.0 / 2)
-def test_evaluate_compute(monkeypatch):
- # patch memory footprint and parameter count
+def test_evaluate_compute_cpu_path(monkeypatch):
+ # Force non-CUDA execution
+ monkeypatch.setattr(torch.cuda, "is_available", lambda: False)
+
+ # measure_memory_footprint should not be called
+ called = {"mem": False}
+
+ def fake_measure(model, inputs, device):
+ called["mem"] = True
+ return {}, model
+
+ monkeypatch.setattr(bf, "measure_memory_footprint", fake_measure)
+ monkeypatch.setattr(bf, "count_trainable_parameters", lambda m: 12345)
+
+ class DummyLoader:
+ def __iter__(self):
+ yield ("inp",)
+
+ loader = DummyLoader()
+ model = FakeModel()
+ surr = "SurrB"
+ conf = {"training_id": "TID"}
+ out = bf.evaluate_compute(model, surr, test_loader=loader, conf=conf)
+ assert model.load_calls == [("TID", surr, f"{surr.lower()}_main")]
+ assert out["num_trainable_parameters"] == 12345
+ assert out["memory_footprint"] == {}
+ assert not called["mem"]
+
+
+@pytest.mark.skipif(
+ not torch.cuda.is_available(), reason="CUDA unavailable for memory profiling test."
+)
+def test_evaluate_compute_cuda_path(monkeypatch):
fake_mem = {"model_memory": 100, "forward_memory_nograd": 50}
- monkeypatch.setattr(bf, "measure_memory_footprint", lambda m, inp: (fake_mem, m))
+
+ def fake_measure(model, inputs, device):
+ return fake_mem, model
+
+ monkeypatch.setattr(bf, "measure_memory_footprint", fake_measure)
monkeypatch.setattr(bf, "count_trainable_parameters", lambda m: 12345)
- # test_loader yields one tuple of inputs
class DummyLoader:
def __iter__(self):
yield ("inp",)
@@ -163,7 +197,6 @@ def __iter__(self):
surr = "SurrB"
conf = {"training_id": "TID"}
out = bf.evaluate_compute(model, surr, test_loader=loader, conf=conf)
- # load main was invoked
assert model.load_calls == [("TID", surr, f"{surr.lower()}_main")]
assert out["num_trainable_parameters"] == 12345
assert out["memory_footprint"] is fake_mem
diff --git a/test/test_training_pipeline.py b/test/test_training_pipeline.py
index 41686ae1..43992f83 100644
--- a/test/test_training_pipeline.py
+++ b/test/test_training_pipeline.py
@@ -1,20 +1,20 @@
import threading
from queue import Queue
-import pytest
from unittest.mock import Mock, patch
+import pytest
+
# import *from* the module that actually defines them
from codes.train.train_fcts import (
DummyLock,
create_task_list_for_surrogate,
- train_and_save_model,
- worker,
parallel_training,
sequential_training,
+ train_and_save_model,
+ worker,
)
from codes.utils import load_task_list, save_task_list
-
# — fixtures —
diff --git a/test/test_utils.py b/test/test_utils.py
index 857eddc2..b499aec4 100644
--- a/test/test_utils.py
+++ b/test/test_utils.py
@@ -1,26 +1,27 @@
import os
-import time
import random
-import yaml
-import torch
+import time
+
import numpy as np
import pytest
+import torch
+import yaml
from codes.utils import (
- read_yaml_config,
- time_execution,
+ batch_factor_to_float,
+ check_training_status,
create_model_dir,
+ determine_batch_size,
get_progress_bar,
load_and_save_config,
- set_random_seeds,
- nice_print,
+ load_task_list,
make_description,
- worker_init_fn,
+ nice_print,
+ read_yaml_config,
save_task_list,
- load_task_list,
- check_training_status,
- determine_batch_size,
- batch_factor_to_float,
+ set_random_seeds,
+ time_execution,
+ worker_init_fn,
)
diff --git a/uv.lock b/uv.lock
index 8971c9d6..571474ba 100644
--- a/uv.lock
+++ b/uv.lock
@@ -43,6 +43,33 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/c2/62/96b5217b742805236614f05904541000f55422a6060a90d7fd4ce26c172d/alembic-1.16.4-py3-none-any.whl", hash = "sha256:b05e51e8e82efc1abd14ba2af6392897e145930c3e0a2faf2b0da2f7f7fd660d", size = 247026, upload-time = "2025-07-10T16:17:21.845Z" },
]
+[[package]]
+name = "appnope"
+version = "0.1.4"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/35/5d/752690df9ef5b76e169e68d6a129fa6d08a7100ca7f754c89495db3c6019/appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee", size = 4170, upload-time = "2024-02-06T09:43:11.258Z" }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/81/29/5ecc3a15d5a33e31b26c11426c45c501e439cb865d0bff96315d86443b78/appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c", size = 4321, upload-time = "2024-02-06T09:43:09.663Z" },
+]
+
+[[package]]
+name = "asttokens"
+version = "3.0.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/be/a5/8e3f9b6771b0b408517c82d97aed8f2036509bc247d46114925e32fe33f0/asttokens-3.0.1.tar.gz", hash = "sha256:71a4ee5de0bde6a31d64f6b13f2293ac190344478f081c3d1bccfcf5eacb0cb7", size = 62308, upload-time = "2025-11-15T16:43:48.578Z" }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/d2/39/e7eaf1799466a4aef85b6a4fe7bd175ad2b1c6345066aa33f1f58d4b18d0/asttokens-3.0.1-py3-none-any.whl", hash = "sha256:15a3ebc0f43c2d0a50eeafea25e19046c68398e487b9f1f5b517f7c0f40f976a", size = 27047, upload-time = "2025-11-15T16:43:16.109Z" },
+]
+
+[[package]]
+name = "attrs"
+version = "25.4.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/6b/5c/685e6633917e101e5dcb62b9dd76946cbb57c26e133bae9e0cd36033c0a9/attrs-25.4.0.tar.gz", hash = "sha256:16d5969b87f0859ef33a48b35d55ac1be6e42ae49d5e853b597db70c35c57e11", size = 934251, upload-time = "2025-10-06T13:54:44.725Z" }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/3a/2a/7cc015f5b9f5db42b7d48157e23356022889fc354a2813c15934b7cb5c0e/attrs-25.4.0-py3-none-any.whl", hash = "sha256:adcf7e2a1fb3b36ac48d97835bb6d8ade15b8dcce26aba8bf1d14847b57a3373", size = 67615, upload-time = "2025-10-06T13:54:43.17Z" },
+]
+
[[package]]
name = "babel"
version = "2.17.0"
@@ -117,6 +144,88 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/4f/52/34c6cf5bb9285074dc3531c437b3919e825d976fde097a7a73f79e726d03/certifi-2025.7.14-py3-none-any.whl", hash = "sha256:6b31f564a415d79ee77df69d757bb49a5bb53bd9f756cbbe24394ffd6fc1f4b2", size = 162722, upload-time = "2025-07-14T03:29:26.863Z" },
]
+[[package]]
+name = "cffi"
+version = "2.0.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "pycparser", marker = "implementation_name != 'PyPy'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/eb/56/b1ba7935a17738ae8453301356628e8147c79dbb825bcbc73dc7401f9846/cffi-2.0.0.tar.gz", hash = "sha256:44d1b5909021139fe36001ae048dbdde8214afa20200eda0f64c068cac5d5529", size = 523588, upload-time = "2025-09-08T23:24:04.541Z" }
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