def test_nb_op(name, path, **kwargs):
output_defs = kwargs.pop("output_defs",
[OutputDefinition(is_required=False)])
return dagstermill.define_dagstermill_op(
name=name,
notebook_path=path,
output_notebook_name="notebook",
output_defs=output_defs,
**kwargs,
)
import dagstermill as dm
from dagster import job
from dagster.utils import script_relative_path
k_means_iris = dm.define_dagstermill_op(
"k_means_iris",
script_relative_path("iris-kmeans.ipynb"),
output_notebook_name="iris_kmeans_output",
)
@job(resource_defs={
"output_notebook_io_manager": dm.local_output_notebook_io_manager,
})
def iris_classify():
k_means_iris()
import dagstermill as dm
from dagster import Field, InputDefinition, Int, job
from dagster.utils import script_relative_path
from docs_snippets.legacy.data_science.download_file import download_file
k_means_iris = dm.define_dagstermill_op(
"k_means_iris",
script_relative_path("iris-kmeans_2.ipynb"),
output_notebook_name="iris_kmeans_output",
input_defs=[
InputDefinition("path",
str,
description="Local path to the Iris dataset")
],
config_schema=Field(Int,
default_value=3,
is_required=False,
description="The number of clusters to find"),
)
@job(resource_defs={
"output_notebook_io_manager": dm.local_output_notebook_io_manager,
})
def iris_classify():
k_means_iris(download_file())
import dagstermill as dm
from dagster import InputDefinition, job
from dagster.utils import script_relative_path
from docs_snippets.legacy.data_science.download_file import download_file
k_means_iris = dm.define_dagstermill_op(
"k_means_iris",
script_relative_path("iris-kmeans_2.ipynb"),
output_notebook_name="iris_kmeans_output",
input_defs=[
InputDefinition("path",
str,
description="Local path to the Iris dataset")
],
)
@job(resource_defs={
"output_notebook_io_manager": dm.local_output_notebook_io_manager,
})
def iris_classify():
k_means_iris(download_file())