Version: 1.9.10

Dagstermill

This library provides an integration with papermill to allow you to run Jupyter notebooks with Dagster.

Related Guides:

Using Jupyter notebooks with Papermill and Dagster

dagstermill.define_dagstermill_asset

Creates a Dagster asset for a Jupyter notebook.

Parameters:

name (str) – The name for the asset
notebook_path (str) – Path to the backing notebook
key_prefix (Optional[Union[str, Sequence[str]]]) – If provided, the asset’s key is the
ins (Optional[Mapping[str, AssetIn]]) – A dictionary that maps input names to information
deps (Optional[Sequence[Union[AssetsDefinition, SourceAsset, AssetKey, str]]]) – The assets
config_schema (Optional[ConfigSchema) – The configuration schema for the asset’s underlying
metadata (Optional[Dict[str, Any]]) – A dict of metadata entries for the asset.
required_resource_keys (Optional[Set[str]]) – Set of resource handles required by the notebook.
description (Optional[str]) – Description of the asset to display in the Dagster UI.
partitions_def (Optional[PartitionsDefinition]) – Defines the set of partition keys that
op_tags (Optional[Dict[str, Any]]) – A dictionary of tags for the op that computes the asset.
group_name (Optional[str]) – A string name used to organize multiple assets into groups. If not provided,
resource_defs (Optional[Mapping[str, ResourceDefinition]]) – (Experimental) A mapping of resource keys to resource definitions. These resources
io_manager_key (Optional[str]) – A string key for the IO manager used to store the output notebook.
retry_policy (Optional[RetryPolicy]) – The retry policy for the op that computes the asset.
save_notebook_on_failure (bool) – If True and the notebook fails during execution, the failed notebook will be
asset_tags (Optional[Dict[str, Any]]) – A dictionary of tags to apply to the asset.
non_argument_deps (Optional[Union[Set[AssetKey], Set[str]]]) – Deprecated, use deps instead. Set of asset keys that are

Examples:

from dagstermill import define_dagstermill_asset
from dagster import asset, AssetIn, AssetKey
from sklearn import datasets
import pandas as pd
import numpy as np

@asset
def iris_dataset():
    sk_iris = datasets.load_iris()
    return pd.DataFrame(
        data=np.c_[sk_iris["data"], sk_iris["target"]],
        columns=sk_iris["feature_names"] + ["target"],
    )

iris_kmeans_notebook = define_dagstermill_asset(
    name="iris_kmeans_notebook",
    notebook_path="/path/to/iris_kmeans.ipynb",
    ins={
        "iris": AssetIn(key=AssetKey("iris_dataset"))
    }
)

dagstermill.define_dagstermill_op

Wrap a Jupyter notebook in a op.

Parameters:

name (str) – The name of the op.
notebook_path (str) – Path to the backing notebook.
ins (Optional[Mapping[str, In]]) – The op’s inputs.
outs (Optional[Mapping[str, Out]]) – The op’s outputs. Your notebook should
required_resource_keys (Optional[Set[str]]) – The string names of any required resources.
output_notebook_name – (Optional[str]): If set, will be used as the name of an injected output
asset_key_prefix (Optional[Union[List[str], str]]) – If set, will be used to prefix the
description (Optional[str]) – If set, description used for op.
tags (Optional[Dict[str, str]]) – If set, additional tags used to annotate op.
io_manager_key (Optional[str]) – If using output_notebook_name, you can additionally provide
save_notebook_on_failure (bool) – If True and the notebook fails during execution, the failed notebook will be

Returns: OpDefinition

class dagstermill.ConfigurableLocalOutputNotebookIOManager: Built-in IO Manager for handling output notebook.

dagstermill.get_context

Get a dagstermill execution context for interactive exploration and development.

Parameters:

op_config (Optional[Any]) – If specified, this value will be made available on the
resource_defs (Optional[Mapping[str, ResourceDefinition]]) – Specifies resources to provide to context.
logger_defs (Optional[Mapping[str, LoggerDefinition]]) – Specifies loggers to provide to context.
run_config (Optional[dict]) – The config dict with which to construct