Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Add mocked notebook runner #740

Draft
wants to merge 6 commits into
base: main
Choose a base branch
from
Draft
Show file tree
Hide file tree
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
24 changes: 24 additions & 0 deletions environment.yml
Original file line number Diff line number Diff line change
@@ -0,0 +1,24 @@
name: pymc-examples
channels:
- conda-forge
dependencies:
- python=3.11
- pymc
- pymc-bart
- nutpie
# spatial notebooks
- geopandas
- folium
- libpysal
- rasterio
- pip:
- pymc-experimental
- preliz
- bambi
- jax
- papermill
- joblib
- jupyter
- seaborn
- watermark
- lifelines
74 changes: 74 additions & 0 deletions scripts/run_notebooks/injected.py
Original file line number Diff line number Diff line change
@@ -0,0 +1,74 @@
"""Injected code to the top of each notebook to mock long running code."""

import os
import numpy as np
import pymc as pm
import xarray as xr


def mock_sample(*args, **kwargs):
if len(args) > 0:
draws = args[0]
else:
draws = kwargs.get("draws", 1000)
random_seed = kwargs.get("random_seed", None)
rng = np.random.default_rng(random_seed)
model = kwargs.get("model", None)
chains = kwargs.get("chains", os.cpu_count())
idata = pm.sample_prior_predictive(
model=model,
random_seed=random_seed,
samples=draws,
)
n_chains = chains
expanded_chains = xr.DataArray(
np.ones(n_chains),
coords={"chain": np.arange(n_chains)},
)
idata.add_groups(
posterior=(idata.prior.mean("chain") * expanded_chains).transpose("chain", "draw", ...)
)
idata.posterior.attrs["sampling_time"] = 1.0

if "prior" in idata:
del idata.prior
if "prior_predictive" in idata:
del idata.prior_predictive

# Create mock sample stats with diverging data
if "sample_stats" not in idata:
n_chains = chains
n_draws = draws
sample_stats = xr.Dataset(
{
"diverging": xr.DataArray(
np.zeros((n_chains, n_draws), dtype=int),
dims=("chain", "draw"),
),
"energy": xr.DataArray(
rng.normal(loc=150, scale=2.5, size=(n_chains, n_draws)),
dims=("chain", "draw"),
),
"tree_depth": xr.DataArray(
rng.choice([1, 2, 3], p=[0.01, 0.86, 0.13], size=(n_chains, n_draws)),
dims=("chain", "draw"),
),
"acceptance_rate": xr.DataArray(
rng.beta(0.5, 0.5, size=(n_chains, n_draws)),
dims=("chain", "draw"),
),
# Different sampler
"accept": xr.DataArray(
rng.choice([0, 1], size=(n_chains, n_draws)),
dims=("chain", "draw"),
),
}
)
idata.add_groups(sample_stats=sample_stats)

return idata


pm.sample = mock_sample
pm.HalfFlat = pm.HalfNormal
pm.Flat = pm.Normal
219 changes: 219 additions & 0 deletions scripts/run_notebooks/runner.py
Original file line number Diff line number Diff line change
@@ -0,0 +1,219 @@
"""CLI to notebook or directory of notebooks.

Arguments
---------
--notebooks: Specific notebook or directory of notebooks to run.
--mock: Run notebooks with mock code. Default is True. If --no-mock is provided,
notebooks will run without mock code.

Examples
--------
Run all notebooks in a directory with mock code:

.. code-block:: bash

python scripts/run_notebooks/runner.py --notebooks notebooks/ --mock

Run a single notebook without mocked code:

.. code-block:: bash

python scripts/run_notebooks/runner.py --notebooks notebooks/notebook.ipynb --no-mock

Run all the notebook is two different directories with mocked code (default):

.. code-block:: bash

python scripts/run_notebooks/runner.py --notebooks notebooks/ notebooks2/

"""

from argparse import ArgumentParser

from rich.console import Console
from dataclasses import dataclass
import logging
from pathlib import Path
from tempfile import NamedTemporaryFile
from typing import TypedDict
from uuid import uuid4

import papermill
from joblib import Parallel, delayed
from nbformat.notebooknode import NotebookNode
from papermill.iorw import load_notebook_node, write_ipynb

KERNEL_NAME: str = "python3"

HERE = Path(__file__).parent
INJECTED_CODE_FILE = HERE / "injected.py"
INJECTED_CODE = INJECTED_CODE_FILE.read_text()


def setup_logging() -> None:
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
)


def generate_random_id() -> str:
return str(uuid4())


def inject_pymc_sample_mock_code(cells: list) -> None:
cells.insert(
0,
NotebookNode(
id=f"code-injection-{generate_random_id()}",
execution_count=sum(map(ord, "Mock pm.sample")),
cell_type="code",
metadata={"tags": []},
outputs=[],
source=INJECTED_CODE,
),
)


def mock_run(notebook_path: Path, i: int, total: int) -> None:
nb = load_notebook_node(str(notebook_path))
inject_pymc_sample_mock_code(nb.cells)
with NamedTemporaryFile(suffix=".ipynb") as f:
write_ipynb(nb, f.name)
desc = f"({i} / {total}) Mocked {notebook_path.name}"
papermill.execute_notebook(
input_path=f.name,
output_path=None,
progress_bar=dict(desc=desc),
kernel_name=KERNEL_NAME,
cwd=notebook_path.parent,
)


def actual_run(notebook_path: Path, i: int, total: int) -> None:
papermill.execute_notebook(
input_path=notebook_path,
output_path=None,
kernel_name=KERNEL_NAME,
progress_bar={"desc": f"({i} / {total}) Running {notebook_path.name}"},
cwd=notebook_path.parent,
)


@dataclass
class NotebookSuccess:
notebook_path: Path


@dataclass
class NotebookFailure:
notebook_path: Path
error: str


def run_notebook(
notebook_path: Path,
i: int,
total: int,
mock: bool = True,
) -> NotebookFailure | NotebookSuccess:
logging.info(f"Running notebook: {notebook_path.name}")
run = mock_run if mock else actual_run

try:
run(notebook_path, i=i, total=total)
except Exception as e:
logging.error(f"{e.__class__.__name__} encountered running notebook: {str(notebook_path)}")
return NotebookFailure(notebook_path=notebook_path, error=str(e))
else:
return NotebookSuccess(notebook_path=notebook_path)


class RunParams(TypedDict):
notebook_path: Path
mock: bool
i: int
total: int


def run_parameters(notebook_paths: list[Path], mock: bool = True) -> list[RunParams]:
def to_mock(notebook_path: Path, i: int) -> RunParams:
return RunParams(
notebook_path=notebook_path,
mock=mock,
i=i,
total=len(notebook_paths),
)

return [to_mock(notebook_path, i=i) for i, notebook_path in enumerate(notebook_paths, start=1)]


def main(notebooks_to_run: list[Path], mock: bool = True) -> None:
console = Console()
setup_logging()
logging.info("Starting notebook runner")
logging.info(f"Running {len(notebooks_to_run)} notebook(s).")
results = Parallel(n_jobs=-1)(
delayed(run_notebook)(**run_params)
for run_params in run_parameters(notebooks_to_run, mock=mock)
)
errors: list[NotebookFailure] = list(filter(lambda x: isinstance(x, NotebookFailure), results))
successes: list[NotebookSuccess] = list(
filter(lambda x: isinstance(x, NotebookSuccess), results)
)

if not errors:
logging.info("All notebooks ran successfully!")
return

for error in errors:
console.rule(f"[bold red]Error running {error.notebook_path}[/bold red]")
console.print(error.error)

for success in successes:
console.print(f"[bold green]Success running {success.notebook_path}[/bold green]")

logging.error(f"{len(errors)} / {len(notebooks_to_run)} notebooks failed")


def parse_args():
parser = ArgumentParser()
parser.add_argument(
"--notebooks",
nargs="+",
help="List of notebooks to run. If not provided, all notebooks will be run.",
)
mock_group = parser.add_mutually_exclusive_group()
mock_group.add_argument(
"--mock",
action="store_true",
help="Run notebooks with mock code",
dest="mock",
)
mock_group.add_argument(
"--no-mock",
action="store_false",
help="Run notebooks without mock code",
dest="mock",
)
parser.set_defaults(mock=True)
args = parser.parse_args()

notebooks_to_run = []
notebooks = args.notebooks
notebooks = [Path(notebook) for notebook in notebooks]
for notebook in notebooks:
if notebook.is_dir():
notebooks_to_run.extend(notebook.glob("*.ipynb"))
notebooks_to_run.extend(notebook.glob("*/*.ipynb"))
else:
notebooks_to_run.append(notebook)

args.notebooks = notebooks_to_run

return args


if __name__ == "__main__":
args = parse_args()
main(args.notebooks, mock=args.mock)
Loading