Creates a Dagster asset for a Jupyter notebook.
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.
non_argument_deps (Optional[Union[Set[AssetKey], Set[str]]]) – Set of asset keys that are upstream dependencies, but do not pass an input to the asset.
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 Dagit.
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]]) – (Experimental) 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.
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"))
}
)
Wrap a Jupyter notebook in a op.
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.
Built-in IO Manager that handles output notebooks.
Get a dagstermill execution context for interactive exploration and development.
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.
Yield a dagster event directly from notebook code.
When called interactively or in development, returns its input.
dagster_event (Union[dagster.AssetMaterialization, dagster.ExpectationResult, dagster.TypeCheck, dagster.Failure, dagster.RetryRequested]) – An event to yield back to Dagster.
Yield a result directly from notebook code.
When called interactively or in development, returns its input.
value (Any) – The value to yield.
output_name (Optional[str]) – The name of the result to yield (default: 'result').
Dagstermill-specific execution context.
Do not initialize directly: use dagstermill.get_context().
The job definition for the context.
This will be a dagstermill-specific shim.
The logging tags for the context.
dict
A dynamically-created type whose properties allow access to op-specific config.
collections.namedtuple
The op definition for the context.
In interactive contexts, this may be a dagstermill-specific shim, depending whether an
op definition was passed to dagstermill.get_context.
The job run for the context.
The run_config for the context.
dict
The run_id for the context.
str