Kedro Vineyard Plugin#
The Kedro vineyard plugin contains components (e.g., DataSet and Runner)
to share intermediate data among nodes in Kedro pipelines using vineyard.
Kedro on Vineyard#
Vineyard works as the DataSet provider for kedro workers to allow transferring
large-scale data objects between tasks that cannot be efficiently serialized and
is not suitable for pickle, without involving external storage systems like
AWS S3 (or Minio as an alternative). The Kedro vineyard plugin handles object migration
as well when the required inputs are not located where the task is scheduled to execute.
Requirements#
The following packages are needed to run Kedro on vineyard,
kedro >= 0.18
vineyard >= 0.14.5
Configuration#
Install required packages:
pip3 install vineyard-kedro
Configure Vineyard locally
The vineyard server can be easier launched locally with the following command:
python3 -m vineyard --socket=/tmp/vineyard.sock
See also our documentation about Launching Vineyard.
Configure the environment variable to tell Kedro vineyard plugin how to connect to the vineyardd server:
export VINEYARD_IPC_SOCKET=/tmp/vineyard.sock
Usage#
After installing the dependencies and preparing the vineyard server, you can execute the Kedro workflows as usual and benefits from vineyard for intermediate data sharing.
We take the Iris example as an example,
$ kedro new --starter=pandas-iris
The nodes in this pipeline look like
def split_data(
data: pd.DataFrame, parameters: Dict[str, Any]
) -> Tuple[pd.DataFrame, pd.DataFrame, pd.Series, pd.Series]:
data_train = data.sample(
frac=parameters["train_fraction"], random_state=parameters["random_state"]
)
data_test = data.drop(data_train.index)
X_train = data_train.drop(columns=parameters["target_column"])
X_test = data_test.drop(columns=parameters["target_column"])
y_train = data_train[parameters["target_column"]]
y_test = data_test[parameters["target_column"]]
return X_train, X_test, y_train, y_test
def make_predictions(
X_train: pd.DataFrame, X_test: pd.DataFrame, y_train: pd.Series
) -> pd.Series:
X_train_numpy = X_train.to_numpy()
X_test_numpy = X_test.to_numpy()
squared_distances = np.sum(
(X_train_numpy[:, None, :] - X_test_numpy[None, :, :]) ** 2, axis=-1
)
nearest_neighbour = squared_distances.argmin(axis=0)
y_pred = y_train.iloc[nearest_neighbour]
y_pred.index = X_test.index
return y_pred
You can see that the intermediate data between split_data and make_predictions is some pandas
dataframes and series.
Try running the pipeline without vineyard,
$ cd iris
$ kedro run
[05/25/23 11:38:56] INFO Kedro project iris session.py:355
[05/25/23 11:38:57] INFO Loading data from 'example_iris_data' (CSVDataSet)... data_catalog.py:343
INFO Loading data from 'parameters' (MemoryDataSet)... data_catalog.py:343
INFO Running node: split: split_data([example_iris_data,parameters]) -> [X_train,X_test,y_train,y_test] node.py:329
INFO Saving data to 'X_train' (MemoryDataSet)... data_catalog.py:382
INFO Saving data to 'X_test' (MemoryDataSet)... data_catalog.py:382
INFO Saving data to 'y_train' (MemoryDataSet)... data_catalog.py:382
INFO Saving data to 'y_test' (MemoryDataSet)... data_catalog.py:382
INFO Completed 1 out of 3 tasks sequential_runner.py:85
INFO Loading data from 'X_train' (MemoryDataSet)... data_catalog.py:343
INFO Loading data from 'X_test' (MemoryDataSet)... data_catalog.py:343
INFO Loading data from 'y_train' (MemoryDataSet)... data_catalog.py:343
INFO Running node: make_predictions: make_predictions([X_train,X_test,y_train]) -> [y_pred] node.py:329
...
You can see that the intermediate data is shared with memory. When kedro is deploy to a cluster, e.g.,
to argo workflow, the MemoryDataSet is not applicable anymore and you will need to setup the
AWS S3 or Minio service and sharing those intermediate data as CSV files.
X_train:
type: pandas.CSVDataSet
filepath: s3://testing/data/02_intermediate/X_train.csv
credentials: minio
X_test:
type: pandas.CSVDataSet
filepath: s3://testing/data/02_intermediate/X_test.csv
credentials: minio
y_train:
type: pandas.CSVDataSet
filepath: s3://testing/data/02_intermediate/y_train.csv
credentials: minio
It might be inefficient for pickling pandas dataframes when data become larger. With the kedro vineyard plugin, you can run the pipeline with vineyard as the intermediate data medium by
$ kedro run --runner vineyard.contrib.kedro.runner.SequentialRunner
[05/25/23 11:45:34] INFO Kedro project iris session.py:355
INFO Loading data from 'example_iris_data' (CSVDataSet)... data_catalog.py:343
INFO Loading data from 'parameters' (MemoryDataSet)... data_catalog.py:343
INFO Running node: split: split_data([example_iris_data,parameters]) -> [X_train,X_test,y_train,y_test] node.py:329
INFO Saving data to 'X_train' (VineyardDataSet)... data_catalog.py:382
INFO Saving data to 'X_test' (VineyardDataSet)... data_catalog.py:382
INFO Saving data to 'y_train' (VineyardDataSet)... data_catalog.py:382
INFO Saving data to 'y_test' (VineyardDataSet)... data_catalog.py:382
INFO Loading data from 'X_train' (VineyardDataSet)... data_catalog.py:343
INFO Loading data from 'X_test' (VineyardDataSet)... data_catalog.py:343
INFO Loading data from 'y_train' (VineyardDataSet)... data_catalog.py:343
INFO Running node: make_predictions: make_predictions([X_train,X_test,y_train]) -> [y_pred] node.py:329
...
Without any modification to your pipeline code, you can see that the intermediate data is shared
with vineyard using the VineyardDataSet and no longer suffers from the overhead of (de)serialization
and the I/O cost between external AWS S3 or Minio services.
Like kedro catalog create, the Kedro vineyard plugin provides a command-line interface to generate
the catalog configuration for given pipeline, which will rewrite the unspecified intermediate data
to VineyardDataSet, e.g.,
$ kedro vineyard catalog create -p __default__
You will get
X_test:
ds_name: X_test
type: vineyard.contrib.kedro.io.dataset.VineyardDataSet
X_train:
ds_name: X_train
type: vineyard.contrib.kedro.io.dataset.VineyardDataSet
y_pred:
ds_name: y_pred
type: vineyard.contrib.kedro.io.dataset.VineyardDataSet
y_test:
ds_name: y_test
type: vineyard.contrib.kedro.io.dataset.VineyardDataSet
y_train:
ds_name: y_train
type: vineyard.contrib.kedro.io.dataset.VineyardDataSet
Deploy to Kubernetes#
When the pipeline scales to Kubernetes, the interaction with the Kedro vineyard plugin is still simple and non-intrusive. The plugin provides tools to prepare the docker image and generate Argo workflow specification file for the Kedro pipeline. Next, we’ll demonstrate how to deploy pipelines to Kubernetes while leverage Vineyard for efficient intermediate sharing between tasks step-by-step.
Prepare the vineyard cluster (see also Deploy on Kubernetes):
# export your kubeconfig path here $ export KUBECONFIG=/path/to/your/kubeconfig # install the vineyard operator $ go run k8s/cmd/main.go deploy vineyard-cluster --create-namespace
Install the argo server:
# install the argo server $ kubectl create namespace argo $ kubectl apply -n argo -f https://github.com/argoproj/argo-workflows/releases/download/v3.4.8/install.yaml
Generate the iris demo project from the official template:
$ kedro new --starter=pandas-iris
Build the Docker image for this iris demo project:
# walk to the iris demo root directory $ cd iris $ kedro vineyard docker build
A Docker image named
iriswill be built successfully. The docker image need to be pushed to your image registry, or loaded to the kind/minikube cluster, to be available in Kubernetes.$ docker images | grep iris iris latest 3c92da8241c6 About a minute ago 690MB
Next, generate the Argo workflow YAML file from the iris demo project:
$ kedro vineyard argo generate -i iris # check the generated Argo workflow YAML file, you can see the Argo workflow YAML file named `iris.yaml` # is generated successfully. $ ls -l argo-iris.yml -rw-rw-r-- 1 root root 3685 Jun 12 23:55 argo-iris.yml
Finally, submit the Argo workflow to Kubernetes:
$ argo submit -n argo argo-iris.yml
You can interact with the Argo workflow using the
argocommand-line tool, e.g.,$ argo list workflows -n argo NAME STATUS AGE DURATION PRIORITY MESSAGE iris-sg6qf Succeeded 18m 30s 0
We have prepared a benchmark to evaluate the performance gain brought by vineyard for data sharing when data scales, for more details, please refer to this report.