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 [source]

beta

This API is currently in beta, and may have breaking changes in minor version releases, with behavior changes in patch releases.

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 concatenation of the key_prefix and the asset’s name, which defaults to the name of the decorated function. Each item in key_prefix must be a valid name in dagster (ie only contains letters, numbers, and _) and may not contain python reserved keywords.
ins (Optional[Mapping[str, AssetIn]]) – A dictionary that maps input names to information about the input.
deps (Optional[Sequence[Union[AssetsDefinition, SourceAsset, AssetKey, str]]]) – The assets that are upstream dependencies, but do not pass an input value to the notebook.
config_schema (Optional[ConfigSchema) – The configuration schema for the asset’s underlying op. If set, Dagster will check that config provided for the op matches this schema and fail if it does not. If not set, Dagster will accept any config provided for the op.
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 compose the asset.
op_tags (Optional[Dict[str, Any]]) – A dictionary of tags for the op that computes the asset. Frameworks may expect and require certain metadata to be attached to a op. Values that are not strings will be json encoded and must meet the criteria that json.loads(json.dumps(value)) == value.
group_name (Optional[str]) – A string name used to organize multiple assets into groups. If not provided, the name “default” is used.
resource_defs (Optional[Mapping[str, ResourceDefinition]]) – beta (Beta) A mapping of resource keys to resource definitions. These resources will be initialized during execution, and can be accessed from the context within the notebook.
io_manager_key (Optional[str]) – A string key for the IO manager used to store the output notebook. If not provided, the default key output_notebook_io_manager will be used.
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 written to the Dagster storage directory. The location of the file will be printed in the Dagster logs. Defaults to False.
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 upstream dependencies, but do not pass an input to the asset.

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 [source]

beta

This API is currently in beta, and may have breaking changes in minor version releases, with behavior changes in patch releases.

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 call yield_result() to yield each of these outputs.
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 of type of BufferedIOBase that is the file object of the executed notebook (in addition to the AssetMaterialization that is always created). It allows the downstream ops to access the executed notebook via a file object.
asset_key_prefix (Optional[Union[List[str], str]]) – If set, will be used to prefix the asset keys for materialized notebooks.
description (Optional[str]) – If set, description used for op.
tags (Optional[Dict[str, str]]) – If set, additional tags used to annotate op. Dagster uses the tag keys notebook_path and kind, which cannot be overwritten by the user.
io_manager_key (Optional[str]) – If using output_notebook_name, you can additionally provide a string key for the IO manager used to store the output notebook. If not provided, the default key output_notebook_io_manager will be used.
save_notebook_on_failure (bool) – If True and the notebook fails during execution, the failed notebook will be written to the Dagster storage directory. The location of the file will be printed in the Dagster logs. Defaults to False.

Returns: OpDefinition

class dagstermill.ConfigurableLocalOutputNotebookIOManager [source]: beta
This API is currently in beta, and may have breaking changes in minor version releases, with behavior changes in patch releases.
Built-in IO Manager for handling output notebook.

dagstermill.get_context [source]

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 context as its op_config property.
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 the context.