Ray or Dask? A Practical Guide for Data Scientists

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As data scientists, we handle large datasets or complex models that require a significant amount of time to run. To save time and achieve results faster, we utilize tools that execute tasks simultaneously or across multiple machines. Two popular Python libraries for this are Ray and Dask. Both help speed up data processing and model training, but they are used for different types of tasks.

In this article, we will explain what Ray and Dask are and when to choose each one.

# What Are Dask and Ray?

 
Dask is a library used for handling large amounts of data. It is designed to work in a way that feels familiar to users of pandas, NumPy, or scikit-learn. Dask breaks data and tasks into smaller parts and runs them in parallel. This makes it perfect for data scientists who want to scale up their data analysis without learning many new concepts.

Ray is a more general tool that helps you build and run distributed applications. It is particularly strong in machine learning and AI tasks.

Ray also has extra libraries built on top of it, like:

• Ray Tune for tuning hyperparameters in machine learning
• Ray Train for training models on multiple GPUs
• Ray Serve for deploying models as web services

Ray is great if you want to build scalable machine learning pipelines or deploy AI applications that need to run complex tasks in parallel.

# Feature Comparison

 
A structured comparison of Dask and Ray based on core attributes:
 

# When to Use What?

 
Choose Dask if you:

• Use Pandas/NumPy and want scalability
• Process tabular or array-like data
• Perform batch ETL or feature engineering
• Need dataframe or array abstractions with lazy execution

Choose Ray if you:

• Need to run many independent Python functions in parallel
• Want to build machine learning pipelines, serve models, or manage long-running tasks
• Need microservice-like scaling with stateful tasks

# Ecosystem Tools

 
Both libraries offer or support a range of tools to cover the data science lifecycle, but with different emphasis:

# Real-World Scenarios

// Large-Scale Data Cleaning and Feature Engineering

Use Dask.

Why? Dask integrates smoothly with pandas and NumPy. Many data teams already use these tools. If your dataset is too large to fit in memory, Dask can split it into smaller parts and process these parts in parallel. This helps with tasks like cleaning data and creating new features.

Example:

import dask.dataframe as dd
import numpy as np

df = dd.read_csv('s3://data/large-dataset-*.csv')
df = df[df['amount'] > 100]
df['log_amount'] = df['amount'].map_partitions(np.log)
df.to_parquet('s3://processed/output/')

This code reads multiple large CSV files from an S3 bucket using Dask in parallel. It filters rows where the amount column is greater than 100, applies a log transformation, and saves the result as Parquet files.

// Parallel Hyperparameter Tuning for Machine Learning Models

Use Ray.

Why? Ray Tune is great for trying different settings when training machine learning models. It integrates with tools like PyTorch and XGBoost, and it can stop bad runs early to save time.

Example:

from ray import tune
from ray.tune.schedulers import ASHAScheduler

def train_fn(config):
    # Model training logic here
    ...

tune.run(
    train_fn,
    config={"lr": tune.grid_search([0.01, 0.001, 0.0001])},
    scheduler=ASHAScheduler(metric="accuracy", mode="max")
)

This code defines a training function and uses Ray Tune to test different learning rates in parallel. It automatically schedules and evaluates the best configuration using the ASHA scheduler.

// Distributed Array Computations

Use Dask.

Why? Dask arrays are helpful when working with large sets of numbers. It splits the array into blocks and processes them in parallel.

Example:

import dask.array as da

x = da.random.random((10000, 10000), chunks=(1000, 1000))
y = x.mean(axis=0).compute()

This code creates a large random array divided into chunks that can be processed in parallel. It then calculates the mean of each column using Dask’s parallel computing power.

// Building an End-to-End Machine Learning Service

Use Ray.

Why? Ray is designed not just for model training but also for serving and lifecycle management. With Ray Serve, you can deploy models in production, run preprocessing logic in parallel, and even scale stateful actors.

Example:

from ray import serve

@serve.deployment
class ModelDeployment:
    def __init__(self):
        self.model = load_model()

    def __call__(self, request_body):
        data = request_body
        return self.model.predict([data])[0]

serve.run(ModelDeployment.bind())

This code defines a class to load a machine learning model and serve it through an API using Ray Serve. The class receives a request, makes a prediction using the model, and returns the result.

# Final Recommendations

# Conclusion

 
Ray and Dask are both tools that help data scientists handle large amounts of data and run programs faster. Ray is good for tasks that need a lot of flexibility, like machine learning projects. Dask is useful if you want to work with big datasets using tools similar to Pandas or NumPy.

Which one you choose depends on what your project needs and the type of data you have. It’s a good idea to try both on small examples to see which one fits your work better.
 
 

Jayita Gulati is a machine learning enthusiast and technical writer driven by her passion for building machine learning models. She holds a Master’s degree in Computer Science from the University of Liverpool.