Feature transformation with Amazon SageMaker Processing and Dask

Typically a machine learning (ML) process consists of few steps. First, gathering data with various ETL jobs, then pre-processing the data, featurizing the dataset by incorporating standard techniques or prior knowledge, and finally training an ML model using an algorithm.

Often, distributed data processing frameworks such as Dask are used to pre-process data sets in order to prepare them for training. In this notebook we’ll use Amazon SageMaker Processing, and leverage the power of Dask in a managed SageMaker environment to run our preprocessing workload.

What is Dask Distributed?

Dask.distributed: is a lightweight and open source library for distributed computing in Python. It is also a centrally managed, distributed, dynamic task scheduler. It is also a centrally managed, distributed, dynamic task scheduler. Dask has three main components:

dask-scheduler process: coordinates the actions of several workers. The scheduler is asynchronous and event-driven, simultaneously responding to requests for computation from multiple clients and tracking the progress of multiple workers.

dask-worker processes: Which are spread across multiple machines and the concurrent requests of several clients.

dask-client process: which is is the primary entry point for users of dask.distributed


source: https://docs.dask.org/en/latest/


  1. Objective

  2. Setup

  3. Using Amazon SageMaker Processing to execute a Dask Job

  4. Downloading dataset and uploading to S3

  5. Build a Dask container for running the preprocessing job

  6. Run the preprocessing job using Amazon SageMaker Processing

    1. Inspect the preprocessed dataset


Let’s start by specifying: * The S3 bucket and prefixes that you use for training and model data. Use the default bucket specified by the Amazon SageMaker session. * The IAM role ARN used to give processing and training access to the dataset.

[ ]:
from time import gmtime, strftime
import sagemaker

sagemaker_session = sagemaker.Session()
role = sagemaker.get_execution_role()
bucket = sagemaker_session.default_bucket()
timestamp_prefix = strftime("%Y-%m-%d-%H-%M-%S", gmtime())

prefix = "sagemaker/dask-preprocess-demo"
input_prefix = prefix + "/input/raw/census"
input_preprocessed_prefix = prefix + "/input/preprocessed/census"
model_prefix = prefix + "/model"

Using Amazon SageMaker Processing to execute a Dask job

Downloading dataset and uploading to Amazon Simple Storage Service (Amazon S3)

The dataset used here is the Census-Income KDD Dataset. The first step are to select features, clean the data, and turn the data into features that the training algorithm can use to train a binary classification model which can then be used to predict whether rows representing census responders have an income greater or less than $50,000. In this example, we will use Dask distributed to preprocess and transform the data to make it ready for the training process. In the next section, you download from the bucket below then upload to your own bucket so that Amazon SageMaker can access the dataset.

[ ]:
import boto3
import pandas as pd

s3 = boto3.client("s3")
region = sagemaker_session.boto_region_name
input_data = "s3://sagemaker-sample-data-{}/processing/census/census-income.csv".format(region)
!aws s3 cp $input_data .

# Uploading the training data to S3
sagemaker_session.upload_data(path="census-income.csv", bucket=bucket, key_prefix=input_prefix)

Build a dask container for running the preprocessing job

An example Dask container is included in the ./container directory of this example. The container handles the bootstrapping of Dask Scheduler and mapping each instance to a Dask Worke. At a high level the container provides:

  • A set of default worker/scheduler configurations

  • A bootstrapping script for configuring and starting up scheduler/worker nodes

  • Starting dask cluster from all the workers including the scheduler node

After the container build and push process is complete, use the Amazon SageMaker Python SDK to submit a managed, distributed dask application that performs our dataset preprocessing.

Build the example Dask container.

[ ]:
%cd container
!docker build -t sagemaker-dask-example .
%cd ../

Create an Amazon Elastic Container Registry (Amazon ECR) repository for the Dask container and push the image.

[ ]:
import boto3

account_id = boto3.client("sts").get_caller_identity().get("Account")
region = boto3.session.Session().region_name

ecr_repository = "sagemaker-dask-example"
tag = ":latest"
uri_suffix = "amazonaws.com"
if region in ["cn-north-1", "cn-northwest-1"]:
    uri_suffix = "amazonaws.com.cn"
dask_repository_uri = "{}.dkr.ecr.{}.{}/{}".format(
    account_id, region, uri_suffix, ecr_repository + tag

# Create ECR repository and push docker image
!$(aws ecr get-login --region $region --registry-ids $account_id --no-include-email)
!aws ecr create-repository --repository-name $ecr_repository
!docker tag {ecr_repository + tag} $dask_repository_uri
!docker push $dask_repository_uri

Run the preprocessing job using Amazon SageMaker Processing on Dask Cluster

Next, use the Amazon SageMaker Python SDK to submit a processing job. Use the the custom Dask container that was just built, and a Scikit Learn script for preprocessing in the job configuration.

[ ]:
%%writefile preprocess.py
from __future__ import print_function, unicode_literals
import argparse
import json
import logging
import os
import sys
import time
import warnings
import boto3
import numpy as np
import pandas as pd
from tornado import gen
import dask.dataframe as dd
import joblib
from dask.distributed import Client
from sklearn.compose import make_column_transformer
from sklearn.exceptions import DataConversionWarning
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import (

warnings.filterwarnings(action="ignore", category=DataConversionWarning)
attempts_counter = 3
attempts = 0

def upload_objects(bucket, prefix, local_path):
        bucket_name = bucket  # s3 bucket name
        root_path = local_path  # local folder for upload

        s3_bucket = s3_client.Bucket(bucket_name)

        for path, subdirs, files in os.walk(root_path):
            for file in files:
                s3_bucket.upload_file(os.path.join(path, file), "{}/output/{}".format(prefix, file))
    except Exception as err:

def print_shape(df):
    negative_examples, positive_examples = np.bincount(df["income"])
        "Data shape: {}, {} positive examples, {} negative examples".format(
            df.shape, positive_examples, negative_examples

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--train-test-split-ratio", type=float, default=0.3)
    args, _ = parser.parse_known_args()

    # Get processor scrip arguments
    args_iter = iter(sys.argv[1:])
    script_args = dict(zip(args_iter, args_iter))
    scheduler_ip = sys.argv[-1]

    # S3 client
    s3_region = script_args["s3_region"]
    s3_client = boto3.resource("s3", s3_region)
    print(f"Using the {s3_region} region")

    # Start the Dask cluster client
        client = Client("tcp://{ip}:8786".format(ip=scheduler_ip))
        logging.info("Printing cluster information: {}".format(client))
    except Exception as err:

    columns = [
        "major industry code",
        "class of worker",
        "num persons worked for employer",
        "capital gains",
        "capital losses",
        "dividends from stocks",
    class_labels = [" - 50000.", " 50000+."]
    input_data_path = "s3://{}".format(

    # Creating the necessary paths to save the output files
    if not os.path.exists("/opt/ml/processing/train"):

    if not os.path.exists("/opt/ml/processing/test"):

    print("Reading input data from {}".format(input_data_path))
    df = pd.read_csv(input_data_path)
    df = pd.DataFrame(data=df, columns=columns)
    df.replace(class_labels, [0, 1], inplace=True)

    negative_examples, positive_examples = np.bincount(df["income"])
        "Data after cleaning: {}, {} positive examples, {} negative examples".format(
            df.shape, positive_examples, negative_examples

    split_ratio = args.train_test_split_ratio
    print("Splitting data into train and test sets with ratio {}".format(split_ratio))
    X_train, X_test, y_train, y_test = train_test_split(
        df.drop("income", axis=1), df["income"], test_size=split_ratio, random_state=0

    preprocess = make_column_transformer(
            KBinsDiscretizer(encode="onehot-dense", n_bins=2),
            ["age", "num persons worked for employer"],
            ["capital gains", "capital losses", "dividends from stocks"],
            ["education", "major industry code", "class of worker"],

    print("Running preprocessing and feature engineering transformations in Dask")
    with joblib.parallel_backend("dask"):
        train_features = preprocess.fit_transform(X_train)
        test_features = preprocess.transform(X_test)

    print("Train data shape after preprocessing: {}".format(train_features.shape))
    print("Test data shape after preprocessing: {}".format(test_features.shape))

    train_features_output_path = os.path.join("/opt/ml/processing/train", "train_features.csv")
    train_labels_output_path = os.path.join("/opt/ml/processing/train", "train_labels.csv")

    test_features_output_path = os.path.join("/opt/ml/processing/test", "test_features.csv")
    test_labels_output_path = os.path.join("/opt/ml/processing/test", "test_labels.csv")

    print("Saving training features to {}".format(train_features_output_path))
    pd.DataFrame(train_features).to_csv(train_features_output_path, header=False, index=False)

    print("Saving test features to {}".format(test_features_output_path))
    pd.DataFrame(test_features).to_csv(test_features_output_path, header=False, index=False)

    print("Saving training labels to {}".format(train_labels_output_path))
    y_train.to_csv(train_labels_output_path, header=False, index=False)

    print("Saving test labels to {}".format(test_labels_output_path))
    y_test.to_csv(test_labels_output_path, header=False, index=False)

    # wait for the file creation
    while attempts < attempts_counter:
        if os.path.exists(train_features_output_path) and os.path.isfile(
                # Calculate the processed dataset baseline statistics on the Dask cluster
                dask_df = dd.read_csv(train_features_output_path)
                dask_df = client.persist(dask_df)
                baseline = dask_df.describe().compute()

    if attempts == attempts_counter:
        raise Exception("Output file {} couldn't be found".format(train_features_output_path))


Run a processing job using the Docker image and preprocessing script you just created. When invoking the dask_processor.run() function, pass the Amazon S3 input and output paths as arguments that are required by our preprocessing script to determine input and output location in Amazon S3. Here, you also specify the number of instances and instance type that will be used for the distributed Spark job.

[ ]:
from sagemaker.processing import ProcessingInput, ScriptProcessor

dask_processor = ScriptProcessor(


Take a look at a few rows of the transformed dataset to make sure the preprocessing was successful.

[ ]:
print("Top 5 rows from s3://{}/{}/train/".format(bucket, input_preprocessed_prefix))
!aws s3 cp --quiet s3://$bucket/$input_preprocessed_prefix/output/train_features.csv - | head -n5

Now, you can use the output files of the transformation process as input to a training job and train a regression model.