Context
PR #885 integrates myst-nb notebook rendering infrastructure. Notebooks currently render as static code (no outputs) because execution exceeds the 120s timeout.
Parent: #883
Goal
Optimize tutorials so they execute within 120s, then flip nb_execution_mode from "off" to "cache" so rendered docs show actual outputs.
Tasks
Notebooks that can be made fast
Notebooks that cannot be made fast
Rendering quality
Infra
Constraints
- Each notebook must complete execution within 120 seconds
- No interactive HTML widgets in output (PNG plots only)
create_synthetic_forecasting_dataset is the preferred data source for tutorials
- Backtesting benchmark stays in
examples/benchmarks/ — NOT in docs toctree
Related
Context
PR #885 integrates myst-nb notebook rendering infrastructure. Notebooks currently render as static code (no outputs) because execution exceeds the 120s timeout.
Parent: #883
Goal
Optimize tutorials so they execute within 120s, then flip
nb_execution_modefrom"off"to"cache"so rendered docs show actual outputs.Tasks
Notebooks that can be made fast
forecasting_with_workflow_presets.py: Reduce dataset to ~14 days, use minimal model params (n_estimators=10)hyperparameter_tuning_with_optuna.py: Usecreate_synthetic_forecasting_dataset(length=timedelta(days=14)), reduce to 3 Optuna trialsNotebooks that cannot be made fast
backtesting_openstef_with_beam.py: Move toexamples/benchmarks/(not rendered in docs). Create a simpler "backtesting quickstart" tutorial that demonstrates the API with 1-2 horizons and a tiny datasetRendering quality
%matplotlib inlineormatplotlib.use("agg")) — avoid interactive HTML plots (plotly/bokeh) which produce large outputs and render poorly in static docsInfra
nb_execution_mode = "cache"indocs/source/conf.py.jupyter_cache/to GitHub Actions cache for faster CI buildspoe docsbuilds with outputs in < 3 minutes totalConstraints
create_synthetic_forecasting_datasetis the preferred data source for tutorialsexamples/benchmarks/— NOT in docs toctreeRelated