Examples

Every notebook under examples/ in the repo demonstrates a specific Strata capability. Each example is also rendered into the docs site automatically, click through to read the cell sources and any cached outputs without cloning the repo.

To run examples locally, point the server's storage root at the repo's examples/ directory and start in personal mode:

STRATA_NOTEBOOK_STORAGE_DIR=$PWD/examples \
STRATA_DEPLOYMENT_MODE=personal \
  uv run strata-server

Every example then appears on the Strata home page. No copying required, open the notebook, click Run, edit, watch the cascade.

(If you'd rather keep examples separate from your own work, copy the directory you want under your existing storage root instead: cp -R examples/iris_classification /tmp/strata-notebooks/.)

Walkthroughs, start here

Notebook	What you'll see
iris_classification	End-to-end ML in seven cells, load → split → train → evaluate → plot. The canonical "multi-cell DAG with caching" demo.
pandas_basics	Core DataFrame operations and rich display outputs.
titanic_ml	Feature engineering plus comparison of two classifiers, closer to a real ML workflow.

Variant cells

Notebook	What you'll see
model_variants	Three classifier variants (logistic regression, random forest, gradient boosting) sharing one DAG slot. Switch tabs, re-cascade downstream; the others stay cached.

Prompt cells and AI

Notebook	What you'll see
arxiv_classifier	AI-powered paper classification, prompt cells + distributed workers in one pipeline.
review_triage	Structured-output prompt cells with @output_schema + the validate-and-retry loop.
news_alpha_trader	Secret manager + AI pricing lookup + multi-cell DAG.

SQL cells

Notebook	What you'll see
sql_orders_report	Named connections, bind parameters from Python upstream, schema-aware caching.

Loop cells

Notebook	What you'll see
loop_hill_climb	# @loop with carry state and an early-termination predicate.

Library cells

Notebook	What you'll see
library_cells	Cross-cell def / class sharing via the synthetic-module slicing path.

Mounts

Notebook	What you'll see
s3_mount	# @mount annotation makes an S3 prefix available as a local pathlib.Path inside the cell.

Markdown rendering

Notebook	What you'll see
markdown_showcase	Every markdown rendering path, headings, lists, tables, code blocks, security guards, dynamic Markdown(...) output.